diff --git a/.github/workflows/azure-static-web-apps-ambitious-river-027ce991e.yml b/.github/workflows/azure-static-web-apps-ambitious-river-027ce991e.yml new file mode 100644 index 0000000..1f0db38 --- /dev/null +++ b/.github/workflows/azure-static-web-apps-ambitious-river-027ce991e.yml @@ -0,0 +1,46 @@ +name: Azure Static Web Apps CI/CD + +on: + push: + branches: + - dev + pull_request: + types: [opened, synchronize, reopened, closed] + branches: + - dev + +jobs: + build_and_deploy_job: + if: github.event_name == 'push' || (github.event_name == 'pull_request' && github.event.action != 'closed') + runs-on: ubuntu-latest + name: Build and Deploy Job + steps: + - uses: actions/checkout@v3 + with: + submodules: true + lfs: false + - name: Build And Deploy + id: builddeploy + uses: Azure/static-web-apps-deploy@v1 + with: + azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_AMBITIOUS_RIVER_027CE991E }} + repo_token: ${{ secrets.GITHUB_TOKEN }} # Used for Github integrations (i.e. PR comments) + action: "upload" + ###### Repository/Build Configurations - These values can be configured to match your app requirements. ###### + # For more information regarding Static Web App workflow configurations, please visit: https://aka.ms/swaworkflowconfig + app_location: "./web" # App source code path + api_location: "" # Api source code path - optional + output_location: "." # Built app content directory - optional + ###### End of Repository/Build Configurations ###### + + close_pull_request_job: + if: github.event_name == 'pull_request' && github.event.action == 'closed' + runs-on: ubuntu-latest + name: Close Pull Request Job + steps: + - name: Close Pull Request + id: closepullrequest + uses: Azure/static-web-apps-deploy@v1 + with: + azure_static_web_apps_api_token: ${{ secrets.AZURE_STATIC_WEB_APPS_API_TOKEN_AMBITIOUS_RIVER_027CE991E }} + action: "close" diff --git a/.gitignore b/.gitignore index 8a00d2b..bb2a17c 100644 --- a/.gitignore +++ b/.gitignore @@ -10,7 +10,14 @@ __pycache__/ # C extensions *.so +*.pbix + # Distribution / packaging +topic_senti_model +Recommender_System_Newdata.csv +restaurant_recommender_Final_model/ +api/*.json +.vscode/ .Python doc/ yelp_dataset/ diff --git a/App/preprocess/checkindata_process.ipynb b/App/preprocess/checkindata_process.ipynb new file mode 100644 index 0000000..dccb7b6 --- /dev/null +++ b/App/preprocess/checkindata_process.ipynb @@ -0,0 +1,216 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "C:\\Lambton\\Term_3\\BDM 3035 - Big Data Capstone Project 01\\project" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "# Load the data\n", + "input_file = '..\\..\\yelp_dataset\\medium\\yelp.checkin.csv' # Update this to your file path\n", + "output_file = '..\\..\\yelp_dataset\\medium\\processed_checkin.csv'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "# Read the CSV file'..\\..\\yelp_dataset\\medium\\yelp.checkin.csv'\n", + "df = pd.read_csv(input_file)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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49999NJhWEmpgMTo_QtndFqu3Hg2018-04-03 01:12:08, 2018-04-11 00:48:20, 2018...
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" + ], + "text/plain": [ + " business_id \\\n", + "0 ---kPU91CF4Lq2-WlRu9Lw \n", + "1 --0iUa4sNDFiZFrAdIWhZQ \n", + "2 --30_8IhuyMHbSOcNWd6DQ \n", + "3 --7PUidqRWpRSpXebiyxTg \n", + "4 --7jw19RH9JKXgFohspgQw \n", + "... ... \n", + "49995 NJaR_2hke7NNAC6l1Ohr8g \n", + "49996 NJcoVbVwnYigsQVDOgl29Q \n", + "49997 NJe5fkia_63rdKXRdJlmkA \n", + "49998 NJg2ookzRGD2Ej-VNpuY-w \n", + "49999 NJhWEmpgMTo_QtndFqu3Hg \n", + "\n", + " date \n", + "0 2020-03-13 21:10:56, 2020-06-02 22:18:06, 2020... \n", + "1 2010-09-13 21:43:09, 2011-05-04 23:08:15, 2011... \n", + "2 2013-06-14 23:29:17, 2014-08-13 23:20:22 \n", + "3 2011-02-15 17:12:00, 2011-07-28 02:46:10, 2012... \n", + "4 2014-04-21 20:42:11, 2014-04-28 21:04:46, 2014... \n", + "... ... \n", + "49995 2015-01-16 02:17:10, 2015-01-16 04:45:59, 2015... \n", + "49996 2013-07-27 23:05:38, 2013-08-10 22:42:26, 2013... \n", + "49997 2018-12-08 02:43:45, 2018-12-09 23:34:42, 2018... \n", + "49998 2010-11-02 23:09:05, 2010-11-05 14:10:24, 2011... \n", + "49999 2018-04-03 01:12:08, 2018-04-11 00:48:20, 2018... \n", + "\n", + "[50000 rows x 2 columns]" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Processed data saved to ..\\..\\yelp_dataset\\medium\\processed_checkin.csv\n" + ] + } + ], + "source": [ + "# Split the 'date' column to multiple rows based on commas and trim spaces\n", + "df = df.assign(date=df['date'].str.split(',')).explode('date')\n", + "df['date'] = df['date'].str.strip()\n", + "\n", + "# Save the processed data to a new CSV file\n", + "df.to_csv(output_file, index=False)\n", + "\n", + "print(f\"Processed data saved to {output_file}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "env", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/App/preprocess/preprocess_pipeline.ipynb b/App/preprocess/preprocess_pipeline.ipynb index 0db4bf0..ace6b78 100644 --- a/App/preprocess/preprocess_pipeline.ipynb +++ b/App/preprocess/preprocess_pipeline.ipynb @@ -1 +1 @@ -{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":3811,"status":"ok","timestamp":1728164875352,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"},"user_tz":240},"id":"298aVYAHmo0I"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","from sklearn.preprocessing import StandardScaler\n","from sklearn.impute import SimpleImputer\n","from sklearn.compose import ColumnTransformer\n","from sklearn.pipeline import Pipeline\n","from sklearn.feature_extraction.text import TfidfVectorizer\n","from sklearn.preprocessing import OneHotEncoder"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_28432\\2173750344.py:3: DtypeWarning: Columns (57,58) have mixed types. Specify dtype option on import or set low_memory=False.\n"," business_df = pd.read_csv('..\\..\\yelp_dataset\\yelp_business.csv')\n"]}],"source":["# Load the dataset\n","\n","business_df = pd.read_csv('..\\..\\yelp_dataset\\yelp_business.csv')\n","tip_df = pd.read_csv('..\\..\\yelp_dataset\\yelp_tip.csv')\n","checkin_df = pd.read_csv('..\\..\\yelp_dataset\\yelp_checkin.csv')"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/html":["
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_idbusiness_idnameaddresscitystatepostal_codelatitudelongitudestars...attributes.AcceptsInsuranceattributes.BestNightsattributes.BYOBattributes.Corkageattributes.BYOBCorkageattributes.HairSpecializesInattributes.Open24Hoursattributes.RestaurantsCounterServiceattributes.AgesAllowedattributes.DietaryRestrictions
066ea4800e59c7c5b6d879305Pns2l4eNsfO8kk83dixA6AAbby Rappoport, LAC, CMQ1616 Chapala St, Ste 2Santa BarbaraCA9310134.426679-119.7111975.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
166ea4800e59c7c5b6d879306mpf3x-BjTdTEA3yCZrAYPwThe UPS Store87 Grasso Plaza Shopping CenterAfftonMO6312338.551126-90.3356953.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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"],"text/plain":[" _id business_id name \\\n","0 66ea4800e59c7c5b6d879305 Pns2l4eNsfO8kk83dixA6A Abby Rappoport, LAC, CMQ \n","1 66ea4800e59c7c5b6d879306 mpf3x-BjTdTEA3yCZrAYPw The UPS Store \n","2 66ea4800e59c7c5b6d879307 tUFrWirKiKi_TAnsVWINQQ Target \n","\n"," address city state postal_code \\\n","0 1616 Chapala St, Ste 2 Santa Barbara CA 93101 \n","1 87 Grasso Plaza Shopping Center Affton MO 63123 \n","2 5255 E Broadway Blvd Tucson AZ 85711 \n","\n"," latitude longitude stars ... attributes.AcceptsInsurance \\\n","0 34.426679 -119.711197 5.0 ... NaN \n","1 38.551126 -90.335695 3.0 ... NaN \n","2 32.223236 -110.880452 3.5 ... NaN \n","\n"," attributes.BestNights attributes.BYOB attributes.Corkage \\\n","0 NaN NaN NaN \n","1 NaN NaN NaN \n","2 NaN NaN NaN \n","\n"," attributes.BYOBCorkage attributes.HairSpecializesIn attributes.Open24Hours \\\n","0 NaN NaN NaN \n","1 NaN NaN NaN \n","2 NaN NaN NaN \n","\n"," attributes.RestaurantsCounterService attributes.AgesAllowed \\\n","0 NaN NaN \n","1 NaN NaN \n","2 NaN NaN \n","\n"," attributes.DietaryRestrictions \n","0 NaN \n","1 NaN \n","2 NaN \n","\n","[3 rows x 61 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["business_df.head(3)"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/html":["
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_iduser_idbusiness_idtextdatecompliment_count
066ea489ae59c7c5b6d8be1acAGNUgVwnZUey3gcPCJ76iw3uLgwr0qeCNMjKenHJwPGQAvengers time with the ladies.2012-05-18 02:17:210
166ea489ae59c7c5b6d8be1adNBN4MgHP9D3cw--SnauTkAQoezRbYQncpRqyrLH6IqjgThey have lots of good deserts and tasty cuban...2013-02-05 18:35:100
266ea489ae59c7c5b6d8be1ae-copOvldyKh1qr-vzkDEvwMYoRNLb5chwjQe3c_k37GgIt's open even when you think it isn't2013-08-18 00:56:080
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"],"text/plain":[" _id user_id business_id \\\n","0 66ea489ae59c7c5b6d8be1ac AGNUgVwnZUey3gcPCJ76iw 3uLgwr0qeCNMjKenHJwPGQ \n","1 66ea489ae59c7c5b6d8be1ad NBN4MgHP9D3cw--SnauTkA QoezRbYQncpRqyrLH6Iqjg \n","2 66ea489ae59c7c5b6d8be1ae -copOvldyKh1qr-vzkDEvw MYoRNLb5chwjQe3c_k37Gg \n","\n"," text date \\\n","0 Avengers time with the ladies. 2012-05-18 02:17:21 \n","1 They have lots of good deserts and tasty cuban... 2013-02-05 18:35:10 \n","2 It's open even when you think it isn't 2013-08-18 00:56:08 \n","\n"," compliment_count \n","0 0 \n","1 0 \n","2 0 "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["tip_df.head(3)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"text/html":["
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_idbusiness_iddate
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"],"text/plain":[" _id business_id \\\n","0 66ea4866e59c7c5b6d89de50 ---kPU91CF4Lq2-WlRu9Lw \n","1 66ea4866e59c7c5b6d89de51 --0iUa4sNDFiZFrAdIWhZQ \n","2 66ea4866e59c7c5b6d89de52 --30_8IhuyMHbSOcNWd6DQ \n","\n"," date \n","0 2020-03-13 21:10:56, 2020-06-02 22:18:06, 2020... \n","1 2010-09-13 21:43:09, 2011-05-04 23:08:15, 2011... \n","2 2013-06-14 23:29:17, 2014-08-13 23:20:22 "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["checkin_df.head(3)"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QdhFipQ6m6cJ","outputId":"89b05b7e-ca7c-4922-f818-6b91d8709a16"},"outputs":[],"source":["# Load the datasets\n","\n","review_df = pd.read_csv('..\\..\\yelp_dataset\\yelp_review.csv')\n"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyMCQ2DuYha2PjbKa00hrT7h","provenance":[]},"kernelspec":{"display_name":"env","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.5"}},"nbformat":4,"nbformat_minor":0} +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"executionInfo":{"elapsed":3811,"status":"ok","timestamp":1728164875352,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"},"user_tz":240},"id":"298aVYAHmo0I"},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n"]},{"cell_type":"code","execution_count":2,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_21292\\3760120243.py:3: DtypeWarning: Columns (56,57,59) have mixed types. Specify dtype option on import or set low_memory=False.\n"," business_df = pd.read_csv('..\\..\\yelp_dataset\\medium\\yelp.business.csv')\n"]}],"source":["# Load the dataset\n","\n","business_df = pd.read_csv('..\\..\\yelp_dataset\\medium\\yelp.business.csv')\n","tip_df = pd.read_csv('..\\..\\yelp_dataset\\medium\\yelp.tip.csv')\n","checkin_df = pd.read_csv('..\\..\\yelp_dataset\\medium\\yelp.checkin.csv')"]},{"cell_type":"markdown","metadata":{},"source":["#### split date on checkin for each business"]},{"cell_type":"code","execution_count":20,"metadata":{},"outputs":[{"name":"stdout","output_type":"stream","text":["Processed data saved to ..\\..\\yelp_dataset\\medium\\processed_checkin.csv\n"]}],"source":["# Split the 'date' column to multiple rows based on commas and trim spaces\n","checkin_df = checkin_df.assign(date=checkin_df['date'].str.split(',')).explode('date')\n","checkin_df['date'] = checkin_df['date'].str.strip()\n","\n","# Save the processed data to a new CSV file\n","output_file = '..\\..\\yelp_dataset\\medium\\processed_checkin.csv'\n","checkin_df.to_csv(output_file, index=False)\n","\n","print(f\"Processed data saved to {output_file}\")"]},{"cell_type":"code","execution_count":6,"metadata":{},"outputs":[],"source":["# Load the datasets\n","\n","review_df = pd.read_csv('..\\..\\yelp_dataset\\medium\\yelp.review.csv')"]},{"cell_type":"code","execution_count":3,"metadata":{},"outputs":[{"data":{"text/html":["
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066ea4800e59c7c5b6d879305Pns2l4eNsfO8kk83dixA6AAbby Rappoport, LAC, CMQ1616 Chapala St, Ste 2Santa BarbaraCA9310134.426679-119.7111975.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
166ea4800e59c7c5b6d879306mpf3x-BjTdTEA3yCZrAYPwThe UPS Store87 Grasso Plaza Shopping CenterAfftonMO6312338.551126-90.3356953.0...NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
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"],"text/plain":[" _id business_id name \\\n","0 66ea4800e59c7c5b6d879305 Pns2l4eNsfO8kk83dixA6A Abby Rappoport, LAC, CMQ \n","1 66ea4800e59c7c5b6d879306 mpf3x-BjTdTEA3yCZrAYPw The UPS Store \n","2 66ea4800e59c7c5b6d879307 tUFrWirKiKi_TAnsVWINQQ Target \n","\n"," address city state postal_code \\\n","0 1616 Chapala St, Ste 2 Santa Barbara CA 93101 \n","1 87 Grasso Plaza Shopping Center Affton MO 63123 \n","2 5255 E Broadway Blvd Tucson AZ 85711 \n","\n"," latitude longitude stars ... attributes.AcceptsInsurance \\\n","0 34.426679 -119.711197 5.0 ... NaN \n","1 38.551126 -90.335695 3.0 ... NaN \n","2 32.223236 -110.880452 3.5 ... NaN \n","\n"," attributes.BestNights attributes.BYOB attributes.Corkage \\\n","0 NaN NaN NaN \n","1 NaN NaN NaN \n","2 NaN NaN NaN \n","\n"," attributes.BYOBCorkage attributes.HairSpecializesIn attributes.Open24Hours \\\n","0 NaN NaN NaN \n","1 NaN NaN NaN \n","2 NaN NaN NaN \n","\n"," attributes.RestaurantsCounterService attributes.AgesAllowed \\\n","0 NaN NaN \n","1 NaN NaN \n","2 NaN NaN \n","\n"," attributes.DietaryRestrictions \n","0 NaN \n","1 NaN \n","2 NaN \n","\n","[3 rows x 61 columns]"]},"execution_count":3,"metadata":{},"output_type":"execute_result"}],"source":["business_df.head(3)"]},{"cell_type":"code","execution_count":4,"metadata":{},"outputs":[{"data":{"text/html":["
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_iduser_idbusiness_idtextdatecompliment_count
066ea489ae59c7c5b6d8be1acAGNUgVwnZUey3gcPCJ76iw3uLgwr0qeCNMjKenHJwPGQAvengers time with the ladies.2012-05-18 02:17:210
166ea489ae59c7c5b6d8be1adNBN4MgHP9D3cw--SnauTkAQoezRbYQncpRqyrLH6IqjgThey have lots of good deserts and tasty cuban...2013-02-05 18:35:100
266ea489ae59c7c5b6d8be1ae-copOvldyKh1qr-vzkDEvwMYoRNLb5chwjQe3c_k37GgIt's open even when you think it isn't2013-08-18 00:56:080
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"],"text/plain":[" _id user_id business_id \\\n","0 66ea489ae59c7c5b6d8be1ac AGNUgVwnZUey3gcPCJ76iw 3uLgwr0qeCNMjKenHJwPGQ \n","1 66ea489ae59c7c5b6d8be1ad NBN4MgHP9D3cw--SnauTkA QoezRbYQncpRqyrLH6Iqjg \n","2 66ea489ae59c7c5b6d8be1ae -copOvldyKh1qr-vzkDEvw MYoRNLb5chwjQe3c_k37Gg \n","\n"," text date \\\n","0 Avengers time with the ladies. 2012-05-18 02:17:21 \n","1 They have lots of good deserts and tasty cuban... 2013-02-05 18:35:10 \n","2 It's open even when you think it isn't 2013-08-18 00:56:08 \n","\n"," compliment_count \n","0 0 \n","1 0 \n","2 0 "]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["tip_df.head(3)"]},{"cell_type":"code","execution_count":5,"metadata":{},"outputs":[{"data":{"text/html":["
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_idbusiness_iddate
066ea4866e59c7c5b6d89de50---kPU91CF4Lq2-WlRu9Lw2020-03-13 21:10:56, 2020-06-02 22:18:06, 2020...
166ea4866e59c7c5b6d89de51--0iUa4sNDFiZFrAdIWhZQ2010-09-13 21:43:09, 2011-05-04 23:08:15, 2011...
266ea4866e59c7c5b6d89de52--30_8IhuyMHbSOcNWd6DQ2013-06-14 23:29:17, 2014-08-13 23:20:22
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"],"text/plain":[" _id business_id \\\n","0 66ea4866e59c7c5b6d89de50 ---kPU91CF4Lq2-WlRu9Lw \n","1 66ea4866e59c7c5b6d89de51 --0iUa4sNDFiZFrAdIWhZQ \n","2 66ea4866e59c7c5b6d89de52 --30_8IhuyMHbSOcNWd6DQ \n","\n"," date \n","0 2020-03-13 21:10:56, 2020-06-02 22:18:06, 2020... \n","1 2010-09-13 21:43:09, 2011-05-04 23:08:15, 2011... \n","2 2013-06-14 23:29:17, 2014-08-13 23:20:22 "]},"execution_count":5,"metadata":{},"output_type":"execute_result"}],"source":["checkin_df.head(3)"]},{"cell_type":"code","execution_count":12,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["c:\\Lambton\\Term_3\\BDM 3035 - Big Data Capstone Project 01\\project\\env\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n"," from .autonotebook import tqdm as notebook_tqdm\n"]}],"source":["from textblob import TextBlob\n","from transformers import pipeline"]},{"cell_type":"code","execution_count":13,"metadata":{},"outputs":[],"source":["review_output_file = '..\\..\\yelp_dataset\\medium\\processed_review_sentiment.csv'\n"]},{"cell_type":"code","execution_count":14,"metadata":{},"outputs":[],"source":["# Function to determine sentiment\n","def get_sentiment(text):\n"," analysis = TextBlob(text)\n"," # Classify as positive if sentiment polarity is above 0, otherwise negative\n"," return 'Positive' if analysis.sentiment.polarity > 0 else 'Negative'"]},{"cell_type":"code","execution_count":15,"metadata":{},"outputs":[{"name":"stderr","output_type":"stream","text":["No model was supplied, defaulted to distilbert/distilbert-base-uncased-finetuned-sst-2-english and revision 714eb0f (https://huggingface.co/distilbert/distilbert-base-uncased-finetuned-sst-2-english).\n","Using a pipeline without specifying a model name and revision in production is not recommended.\n","c:\\Lambton\\Term_3\\BDM 3035 - Big Data Capstone Project 01\\project\\env\\Lib\\site-packages\\huggingface_hub\\file_download.py:147: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\User\\.cache\\huggingface\\hub\\models--distilbert--distilbert-base-uncased-finetuned-sst-2-english. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n","To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n"," warnings.warn(message)\n"]}],"source":["# Initialize the sentiment analysis pipeline using a pre-trained model\n","sentiment_analyzer = pipeline('sentiment-analysis')"]},{"cell_type":"code","execution_count":16,"metadata":{},"outputs":[],"source":["# Function to get the sentiment from the model\n","def get_sentiment2(text):\n"," result = sentiment_analyzer(text)[0]\n"," # Classify as positive if label is 'POSITIVE', otherwise negative\n"," return 'Positive' if result['label'] == 'POSITIVE' else 'Negative'"]},{"cell_type":"code","execution_count":19,"metadata":{},"outputs":[],"source":["# Apply sentiment analysis to the 'text' column\n","review_df['sentiment'] = review_df['text'].apply(get_sentiment)\n","\n","# Save the processed data with sentiment to a new CSV file\n","review_df.to_csv(review_output_file, index=False)"]},{"cell_type":"markdown","metadata":{},"source":["#### create new category from business dataset"]},{"cell_type":"code","execution_count":35,"metadata":{},"outputs":[],"source":["\n","category_output_file = '..\\..\\yelp_dataset\\medium\\category.csv'\n","business_category_output_file = '..\\..\\yelp_dataset\\medium\\category_business.csv'\n"]},{"cell_type":"code","execution_count":30,"metadata":{},"outputs":[],"source":["\n","# Split the 'categories' column into individual categories, drop NaNs, trim spaces, and explode into separate rows\n","categories = business_df['categories'].dropna().str.split(',').explode().str.strip()\n"]},{"cell_type":"code","execution_count":31,"metadata":{},"outputs":[],"source":["# Get unique categories and create a category_id\n","distinct_categories = categories.drop_duplicates().reset_index(drop=True).reset_index()\n","distinct_categories.columns = ['category_id', 'category']\n","distinct_categories['category_id'] += 1 # Start IDs from 1\n"]},{"cell_type":"code","execution_count":32,"metadata":{},"outputs":[],"source":["\n","# Merge back to create a mapping of business_id to category_id\n","business_categories = business_df[['business_id']].join(categories)\n","business_categories = business_categories.merge(distinct_categories, left_on='categories', right_on='category')\n","business_categories = business_categories[['business_id', 'category_id']]\n"]},{"cell_type":"code","execution_count":33,"metadata":{},"outputs":[],"source":["\n","# Save the distinct categories to 'category.csv'\n","distinct_categories.to_csv(category_output_file, index=False)\n"]},{"cell_type":"code","execution_count":36,"metadata":{},"outputs":[],"source":["# Save the business to category mapping to 'business_category.csv'\n","business_categories.to_csv(business_category_output_file, index=False)"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyMCQ2DuYha2PjbKa00hrT7h","provenance":[]},"kernelspec":{"display_name":"env","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.11.5"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/App/preprocess/yelp_data_processing.py b/App/preprocess/yelp_data_processing.py new file mode 100644 index 0000000..f3114a2 --- /dev/null +++ b/App/preprocess/yelp_data_processing.py @@ -0,0 +1,38 @@ +from pyspark.sql import SparkSession + +# Initialize SparkSession +spark = SparkSession.builder.appName("Yelp Data Processing").getOrCreate() + +# Define GCS bucket path +#gs://yelp-dataproc/scripts/yelp_data_processing.py +gcs_bucket = "gs://yelp-dataproc/data/" + +# Load CSV files into Spark DataFrames +business_df = spark.read.csv(gcs_bucket + "yelp_business.csv", header=True, inferSchema=True) +review_df = spark.read.csv(gcs_bucket + "yelp_review.csv", header=True, inferSchema=True) +user_df = spark.read.csv(gcs_bucket + "yelp_user.csv", header=True, inferSchema=True) +checkin_df = spark.read.csv(gcs_bucket + "yelp_checkin.csv", header=True, inferSchema=True) +tip_df = spark.read.csv(gcs_bucket + "yelp_tip.csv", header=True, inferSchema=True) + +# Data Processing Examples +# 1. Join business data with reviews +business_reviews = business_df.join(review_df, business_df["business_id"] == review_df["business_id"]) \ + .select(business_df["name"], business_df["city"], review_df["stars"].alias("review_stars")) + +# 2. Average review stars per business +avg_review_stars = business_reviews.groupBy("name", "city").avg("review_stars").orderBy("avg(review_stars)", ascending=False) + +# 3. Count tips for each business +tips_count = tip_df.groupBy("business_id").count().alias("tip_count") + +# 4. 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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "\n", + "from pyspark.sql import SparkSession\n", + "\n", + "spark = SparkSession.builder.appName(\"MySparkSession\").getOrCreate()\n", + "\n", + "# Check if the SparkSession is created successfully\n", + "spark" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "0Q_wxl8osDnA" + }, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "from textblob import TextBlob\n", + "import nltk\n", + "from nltk.tokenize import sent_tokenize" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "id": "pBwb1Nt1tbfA" + }, + "outputs": [], + "source": [ + "df_reviews = spark.read.csv('/content/drive/MyDrive/ProcessedCSV/final_review.csv', header=True, inferSchema=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "id": "QItJizQrdxm5" + }, + "outputs": [], + "source": [ + "# reviews.info()\n", + "# tips.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "njh0uOuHTyY_" + }, + "outputs": [], + "source": [ + "# reviews.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "id": "pwUQV23TLkcn" + }, + "outputs": [], + "source": [ + "# tips.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 512 + }, + "id": "2RDxxtJhn7fs", + "outputId": "1a98dc4c-b945-443c-d70d-7d54c2b99ef6" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "ERROR:root:KeyboardInterrupt while sending command.\n", + "Traceback (most recent call last):\n", + " File \"/usr/local/lib/python3.10/dist-packages/py4j/java_gateway.py\", line 1038, in send_command\n", + " response = connection.send_command(command)\n", + " File \"/usr/local/lib/python3.10/dist-packages/py4j/clientserver.py\", line 511, in send_command\n", + " answer = smart_decode(self.stream.readline()[:-1])\n", + " File \"/usr/lib/python3.10/socket.py\", line 705, in readinto\n", + " return self._sock.recv_into(b)\n", + "KeyboardInterrupt\n" + ] + }, + { + "output_type": "error", + "ename": "KeyboardInterrupt", + "evalue": "", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 21\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 22\u001b[0m \u001b[0;31m# Save the updated DataFrame to a new CSV file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m \u001b[0mdf_reviews\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'reviews_with_sentiment.csv'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheader\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'overwrite'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 25\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Sentiment analysis completed and saved to 'reviews_with_sentiment.csv'.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyspark/sql/readwriter.py\u001b[0m in \u001b[0;36mcsv\u001b[0;34m(self, path, mode, compression, sep, quote, escape, header, nullValue, escapeQuotes, quoteAll, dateFormat, timestampFormat, ignoreLeadingWhiteSpace, ignoreTrailingWhiteSpace, charToEscapeQuoteEscaping, encoding, emptyValue, lineSep)\u001b[0m\n\u001b[1;32m 1862\u001b[0m \u001b[0mlineSep\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlineSep\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1863\u001b[0m )\n\u001b[0;32m-> 1864\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jwrite\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcsv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1865\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1866\u001b[0m def orc(\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1319\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mEND_COMMAND_PART\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1320\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1321\u001b[0;31m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m 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\u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 510\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 511\u001b[0;31m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msmart_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadline\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 512\u001b[0m \u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Answer received: {0}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0manswer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 513\u001b[0m \u001b[0;31m# Happens when a the other end is dead. There might be an empty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/lib/python3.10/socket.py\u001b[0m in \u001b[0;36mreadinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 704\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 705\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_into\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 706\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 707\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_timeout_occurred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from pyspark.sql.functions import udf\n", + "from pyspark.sql.types import StringType\n", + "from textblob import TextBlob\n", + "\n", + "# Function to determine sentiment\n", + "def get_sentiment(text):\n", + " analysis = TextBlob(text)\n", + " # Classify sentiment\n", + " if analysis.sentiment.polarity > 0:\n", + " return 'positive'\n", + " elif analysis.sentiment.polarity < 0:\n", + " return 'negative'\n", + " else:\n", + " return 'neutral'\n", + "\n", + "# Create a UDF (User-Defined Function) from the get_sentiment function\n", + "sentiment_udf = udf(get_sentiment, StringType())\n", + "\n", + "# Apply the UDF to the 'text' column\n", + "df_reviews = df_reviews.withColumn('sentiment', sentiment_udf(df_reviews['text']))\n", + "\n", + "# Save the updated DataFrame to a new CSV file\n", + "df_reviews.write.csv('reviews_with_sentiment.csv', header=True, mode='overwrite')\n", + "\n", + "print(\"Sentiment analysis completed and saved to 'reviews_with_sentiment.csv'.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xNUHi2OwoYTk" + }, + "outputs": [], + "source": [ + "# read and display csv file\n", + "df_reviews_with_sentiment = spark.read.csv('/content/reviews_with_sentiment.csv', header=True, inferSchema=True)\n", + "df_reviews_with_sentiment.show()" + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Drop the first row from the DataFrame\n", + "df_reviews_with_sentiment = df_reviews_with_sentiment.filter(df_reviews_with_sentiment.index != 0)\n", + "\n", + "# Show the updated DataFrame\n", + "df_reviews_with_sentiment.show()" + ], + "metadata": { + "id": "wpyC_3Uok9BG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FhbqCJiroYQG" + }, + "outputs": [], + "source": [ + "# save CSV file in the 'ProcessedCSV' folder\n", + "df_reviews_with_sentiment.write.csv('/content/drive/MyDrive/ProcessedCSV/reviews_with_sentiment.csv', header=True, mode='overwrite')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A9IZAWrFoYN_" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UIJBifIIoYLP" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "machine_shape": "hm", + "provenance": [], + "mount_file_id": "1Dx7vUyY6Ph1IPgvQ1fPMV4e0b1H_PVW1", + "authorship_tag": "ABX9TyMdUKGyje3JgcnY8aRF2p9S", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/DataprocessingP1_Review.ipynb b/NoteBook/DataprocessingP1_Review.ipynb similarity index 100% rename from DataprocessingP1_Review.ipynb rename to NoteBook/DataprocessingP1_Review.ipynb diff --git a/DataprocessingP5_Checkin.ipynb b/NoteBook/DataprocessingP5_Checkin.ipynb similarity index 100% rename from DataprocessingP5_Checkin.ipynb rename to NoteBook/DataprocessingP5_Checkin.ipynb diff --git a/NoteBook/FetchMappedBusinessID.ipynb b/NoteBook/FetchMappedBusinessID.ipynb new file mode 100644 index 0000000..b7bd4ee --- /dev/null +++ b/NoteBook/FetchMappedBusinessID.ipynb @@ -0,0 +1,707 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "mount_file_id": "1um2lr4J4pXGqDymLoLWty5RvtOtmCg-S", + "authorship_tag": "ABX9TyOuZlAJA7wo3dka+tg1z3Cp" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XCmfqycCTtPI", + "outputId": "5175e71b-4a45-4ac2-8355-954a187b71b9" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (3.5.3)\n", + "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n" + ] + } + ], + "source": [ + "!pip install pyspark" + ] + }, + { + "cell_type": "code", + "source": [ + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.types import StructType, StructField, StringType, IntegerType, FloatType\n", + "from pyspark.sql import functions as F\n", + "from pyspark.sql.functions import broadcast\n", + "from pyspark.sql.functions import col, StringType\n", + "from pyspark.ml.feature import StringIndexer, VectorIndexer" + ], + "metadata": { + "id": "xmul6uXoVFQ4" + }, + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Create a Spark session\n", + "spark = SparkSession.builder.appName(\"rec\").getOrCreate()\n" + ], + "metadata": { + "id": "U4dbWZsxT4G4" + }, + "execution_count": 4, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Define schema for business_df\n", + "business_schema = StructType([\n", + " StructField(\"business_id\", StringType(), True),\n", + " StructField(\"name\", StringType(), True),\n", + " StructField(\"address\", StringType(), True),\n", + " StructField(\"city\", StringType(), True),\n", + " StructField(\"state\", StringType(), True),\n", + " StructField(\"postal_code\", StringType(), True),\n", + " StructField(\"latitude\", FloatType(), True),\n", + " StructField(\"longitude\", FloatType(), True),\n", + " StructField(\"stars\", FloatType(), True),\n", + " StructField(\"review_count\", IntegerType(), True),\n", + " StructField(\"is_open\", IntegerType(), True),\n", + " StructField(\"attributes\", StringType(), True),\n", + " StructField(\"categories\", StringType(), True),\n", + " StructField(\"hours\", StringType(), True)\n", + "])\n", + "\n", + "# Load the Yelp datasets with explicit schema or schema inference hints\n", + "file_path = \"/content/drive/MyDrive/yelp_business1.csv\"\n", + "business_df = spark.read.csv(file_path, header=True, schema=business_schema)\n", + "business_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4aXb6JgiVAAs", + "outputId": "cd2b4014-38e6-445d-8233-537c7a455a8c" + }, + "execution_count": 7, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+--------------+-----+-----------+---------+-----------+-----+------------+-------+----------+--------------------+-----+\n", + "| business_id| name| address| city|state|postal_code| latitude| longitude|stars|review_count|is_open|attributes| categories|hours|\n", + "+--------------------+--------------------+--------------------+--------------+-----+-----------+---------+-----------+-----+------------+-------+----------+--------------------+-----+\n", + "|Pns2l4eNsfO8kk83d...|Abby Rappoport, L...|1616 Chapala St, ...| Santa Barbara| CA| 93101|34.426678| -119.7112| 5.0| 7| 0| True|Doctors, Traditio...| NULL|\n", + "|mpf3x-BjTdTEA3yCZ...| The UPS Store|87 Grasso Plaza S...| Affton| MO| 63123|38.551125| -90.33569| 3.0| 15| 1| NULL|Shipping Centers,...| NULL|\n", + "|tUFrWirKiKi_TAnsV...| Target|5255 E Broadway Blvd| Tucson| AZ| 85711|32.223236|-110.880455| 3.5| 22| 0| False|Department Stores...| NULL|\n", + "|MTSW4McQd7CbVtyjq...| St Honore Pastries| 935 Race St| Philadelphia| PA| 19107|39.955505| -75.15556| 4.0| 80| 1| False|Restaurants, Food...| NULL|\n", + "|mWMc6_wTdE0EUBKIG...|Perkiomen Valley ...| 101 Walnut St| Green Lane| PA| 18054|40.338184| -75.47166| 4.5| 13| 1| NULL|Brewpubs, Breweri...| NULL|\n", + "|CF33F8-E6oudUQ46H...| Sonic Drive-In| 615 S Main St| Ashland City| TN| 37015|36.269592| -87.058945| 2.0| 6| 1| False|Burgers, Fast Foo...| NULL|\n", + "|n_0UpQx1hsNbnPUSl...| Famous Footwear|8522 Eager Road, ...| Brentwood| MO| 63144|38.627693| -90.34046| 2.5| 13| 1| NULL|Sporting Goods, F...| NULL|\n", + "|qkRM_2X51Yqxk3btl...| Temple Beth-El| 400 Pasadena Ave S|St. Petersburg| FL| 33707| 27.76659| -82.73299| 3.5| 5| 1| NULL|Synagogues, Relig...| NULL|\n", + "|k0hlBqXX-Bt0vf1op...|Tsevi's Pub And G...| 8025 Mackenzie Rd| Affton| MO| 63123|38.565166| -90.32108| 3.0| 19| 0| NULL|Pubs, Restaurants...| NULL|\n", + "|bBDDEgkFA1Otx9Lfe...| Sonic Drive-In| 2312 Dickerson Pike| Nashville| TN| 37207|36.208103| -86.76817| 1.5| 10| 1| False|Ice Cream & Froze...| NULL|\n", + "|UJsufbvfyfONHeWdv...| Marshalls|21705 Village Lak...| Land O' Lakes| FL| 34639|28.190458| -82.45738| 3.5| 6| 1| NULL|Department Stores...| NULL|\n", + "|eEOYSgkmpB90uNA7l...|Vietnamese Food T...| NULL| Tampa Bay| FL| 33602|27.955269| -82.45632| 4.0| 10| 1| NULL|Vietnamese, Food,...| NULL|\n", + "|il_Ro8jwPlHresjw9...| Denny's| 8901 US 31 S| Indianapolis| IN| 46227|39.637135| -86.12722| 2.5| 28| 1| NULL|American (Traditi...| NULL|\n", + "|jaxMSoInw8Poo3XeM...| Adams Dental| 15 N Missouri Ave| Clearwater| FL| 33755|27.966234| -82.787415| 5.0| 10| 1| True|General Dentistry...| NULL|\n", + "|0bPLkL0QhhPO5kt1_...|Zio's Italian Market| 2575 E Bay Dr| Largo| FL| 33771|27.916117| -82.76046| 4.5| 100| 0| NULL|Food, Delis, Ital...| NULL|\n", + "|MUTTqe8uqyMdBl186...| Tuna Bar| 205 Race St| Philadelphia| PA| 19106| 39.95395| -75.14323| 4.0| 245| 1| NULL|Sushi Bars, Resta...| NULL|\n", + "|rBmpy_Y1UbBx8ggHl...|Arizona Truck Out...| 625 N Stone Ave| Tucson| AZ| 85705|32.229874| -110.97234| 4.5| 10| 1| False|Automotive, Auto ...| NULL|\n", + "|M0XSSHqrASOnhgbWD...| Herb Import Co| 712 Adams St| New Orleans| LA| 70118|29.941467| -90.12995| 4.0| 5| 1| NULL|Vape Shops, Tobac...| NULL|\n", + "|8wGISYjYkE2tSqn3c...| Nifty Car Rental| 1241 Airline Dr| Kenner| LA| 70062|29.981182| -90.25401| 3.5| 14| 1| NULL|Automotive, Car R...| NULL|\n", + "|ROeacJQwBeh05Rqg7...| BAP| 1224 South St| Philadelphia| PA| 19147|39.943222| -75.16257| 4.5| 205| 1| NULL| Korean, Restaurants| NULL|\n", + "+--------------------+--------------------+--------------------+--------------+-----+-----------+---------+-----------+-----+------------+-------+----------+--------------------+-----+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "filtered_businesses = business_df.filter((F.col(\"review_count\") >= 20) & (F.col(\"stars\") >= 2))" + ], + "metadata": { + "id": "U2I9_mtMVPie" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "filtered_businesses = filtered_businesses.withColumnRenamed(\"stars\", \"filtered_business_stars\")" + ], + "metadata": { + "id": "QL7Q6zfbVlLy" + }, + "execution_count": 12, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "review_df = spark.read.csv(\"/content/drive/MyDrive/ProcessedCSV/final_review.csv\", header=True, inferSchema=True)\n", + "columns_to_drop = review_df.columns\n", + "review_df = review_df.filter(F.col(columns_to_drop[0]) != review_df.first()[columns_to_drop[0]])\n", + "for col in columns_to_drop[1:]:\n", + " review_df = review_df.filter(F.col(col) != review_df.first()[col])\n", + "review_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "q8NMQCE7V5JV", + "outputId": "d4948139-4573-4fa9-a859-3fbdd9ac3295" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+\n", + "| review_id| user_id| business_id|stars|useful|funny|cool| text| date| time|\n", + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+\n", + "|5Vy-AFz0Ek1FF6s1a...|tbI8YO0O51Mrm_yIi...|4QjOsH83KEcjen94P...| 4| 8| 5| 9|This review is fo...|2016-03-07|2024-11-03 22:17:05|\n", + "|HsthKXBHUDCDOUH1z...|rytAuNSEgYf_xqkKZ...|j-qtdD55OLfSqfsWu...| 4| 1| 1| 0|Somehow we got ca...|2015-08-31|2024-11-03 20:11:08|\n", + "|zg5iA1-3eoozePgAS...|Q8V8JWWQNrpvD2bXC...|9cybDNxJ7cyNVkmwm...| 5| 0| 1| 0|Huge fan of this ...|2013-05-12|2024-11-03 01:44:00|\n", + "|FYj6Tx_Ft4VvkGFgD...|2lFni8ituUYceQJPx...|Iw8uqNPxviwcgxtru...| 1| 7| 5| 3|Things you should...|2010-12-05|2024-11-03 09:18:11|\n", + "|vmaErJz1v5tr0Vybf...|HkbiKPFKaJPdLwPQG...|ixAWNAUrA7JjwvMJz...| 4| 2| 1| 1|Was informed by d...|2015-05-29|2024-11-03 23:20:05|\n", + "|JYJ_EsFX8DQAvlTYx...|9Ba1QLi-_n5ijOUYp...|-bUlpYP74Ei8VkSGp...| 1| 2| 1| 0|Someone from this...|2015-03-23|2024-11-03 14:12:06|\n", + "|eibqXCayeqBuRIlBL...|w-hg9fO4EYJTffKbW...|KnsY8rh5tigp5t6Wp...| 4| 2| 4| 1|Whole Foods is li...|2011-08-29|2024-11-03 18:14:47|\n", + "|L9YHX9CVevipN49BN...|xMGWttm6V-cvP3V2f...|HP03UxUpfwic04tBu...| 2| 1| 1| 1|Ordered delivery....|2018-06-21|2024-11-03 01:57:19|\n", + "|Y7Tk2c14VsaO_nHvR...|I99z9bNf82ESzBJoY...|dKAu4j6GskdIvyiBy...| 1| 6| 2| 0|Update: I changed...|2012-06-30|2024-11-03 21:07:00|\n", + "|T_iM9qQ_JF97ZNeod...|HPvG3bwrp1U1VRafj...|JLumGKzGu9kqGTdwY...| 2| 0| 1| 0|This place has po...|2011-09-29|2024-11-03 01:12:11|\n", + "|ZnA9BxZQA8UAqzbi5...|ooLcX8Ey8fMTB81tT...|CTvHlHQ4jzRO8gkkl...| 5| 3| 1| 0|By far the most a...|2014-01-03|2024-11-03 00:44:51|\n", + "|-88EuW9LAa3UUJ1pi...|3tAW8YObI7gpFggaz...|1YflE3DkiCZGgLnf3...| 5| 14| 2| 8|I live just South...|2014-09-29|2024-11-03 13:51:41|\n", + "|lVHdykXMARM_Nqfup...|kzDzdbPupHiglDzs9...|IvAa7PvfOYl0xRML5...| 4| 12| 4| 7|People will fight...|2017-05-15|2024-11-03 02:22:14|\n", + "|4zHPPk6HtTtbfExrM...|ApwWZBmsF9_gCwam6...|TunmRrfZb7bt53T6H...| 1| 2| 5| 1|DO NOT ORDER DELI...|2015-02-21|2024-11-03 01:52:19|\n", + "|BjxWsDxpO8YPV-El9...|OI8taAco53qGwEzbE...|dL8yaO7NtWpBar8CH...| 4| 6| 2| 3|Wandered in here ...|2011-07-04|2024-11-03 15:45:27|\n", + "|RiydWL4uWz2W3F0xI...|8O5tGAz_LFuX3JTim...|NgCDBhNiVpUDwvpi0...| 2| 1| 1| 0|There are way too...|2011-07-07|2024-11-03 18:47:28|\n", + "|2wCabGwTJKPhiPZpb...|6Q8dQGr_DBKcmQ1aX...|qjSU3SL8CaGfmOL_B...| 2| 1| 2| 0|The theater smell...|2014-09-29|2024-11-03 13:47:40|\n", + "|kdD6T52KAsdv-m_Mu...|zK9xI4tX-Qk43pfc4...|z8YngIHNZI0A05DeM...| 1| 0| 1| 0|Well let's start ...|2016-11-24|2024-11-03 12:58:55|\n", + "|8t-tX1V56l9QKPOA3...|VBxscaJ2Q3YH4Vs8W...|elnmOdMKGQvAF9FoV...| 5| 0| 1| 1|Great bikes. Very...|2014-12-01|2024-11-03 18:20:41|\n", + "|cfFKGABwOJYSsxkiR...|f302ihzVqr3CC4nBo...|3cfo_0_GjpJ2mWRYk...| 4| 0| 1| 0|Nice hotel if you...|2017-09-22|2024-11-03 05:19:32|\n", + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import pyspark.sql.functions as F\n", + "\n", + "filtered_reviews = review_df.filter((F.col(\"stars\") >= 2) )" + ], + "metadata": { + "id": "oQoKSRMSWGdT" + }, + "execution_count": 16, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Perform the join if both DataFrames contain data\n", + "if filtered_reviews.count() > 0 and filtered_businesses.count() > 0:\n", + " joined_df = filtered_reviews.join(filtered_businesses, on=\"business_id\", how=\"inner\")\n", + " joined_df.show(10)\n", + "else:\n", + " print(\"One or both DataFrames are empty, cannot perform join.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "z2nYlDZyWKS7", + "outputId": "fd925a4c-aa07-475c-b51e-245a7ebe96d8" + }, + "execution_count": 17, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+--------------------+--------------------+------------+-----+-----------+---------+-----------+-----------------------+------------+-------+----------+--------------------+-----+\n", + "| business_id| review_id| user_id|stars|useful|funny|cool| text| date| time| name| address| city|state|postal_code| latitude| longitude|filtered_business_stars|review_count|is_open|attributes| categories|hours|\n", + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+--------------------+--------------------+------------+-----+-----------+---------+-----------+-----------------------+------------+-------+----------+--------------------+-----+\n", + "|--9osgUCSDUWUkoTL...|ItZuxtEiSOMDd0cRJ...|h70Izpu92o8LRwGt8...| 5| 1| 1| 0|We did Santa's Na...|2021-03-29|2024-11-03 16:23:21| Will You Escape?| 2577 N First Ave| Tucson| AZ| 85719|32.253677| -110.96177| 5.0| 29| 1| True|Active Life, Chal...| NULL|\n", + "|--9osgUCSDUWUkoTL...|tyZ4pBzZdrm70vRWe...|DdPEm9nl_5zxrzGNa...| 5| 0| 1| 0|1st time ever bee...|2021-01-05|2024-11-03 19:53:36| Will You Escape?| 2577 N First Ave| Tucson| AZ| 85719|32.253677| -110.96177| 5.0| 29| 1| True|Active Life, Chal...| NULL|\n", + "|--epgcb7xHGuJ-4PU...|w5AiH-OIoryeNSZ-u...|6woTWCjHDMEjQclni...| 5| 2| 1| 0|Bagels are pretty...|2013-01-15|2024-11-03 05:53:08| Manhattan Bagel| 200 York Road|Willow Grove| PA| 19090|40.145054| -75.116295| 3.0| 34| 1| NULL|Restaurants, Food...| NULL|\n", + "|--epgcb7xHGuJ-4PU...|q2erWQADaQliZw1vG...|bQduRxvheoA9xp4qM...| 2| 1| 2| 0|This place is the...|2017-01-01|2024-11-03 16:02:00| Manhattan Bagel| 200 York Road|Willow Grove| PA| 19090|40.145054| -75.116295| 3.0| 34| 1| NULL|Restaurants, Food...| NULL|\n", + "|--epgcb7xHGuJ-4PU...|-YPSb7opc8nTWPumW...|-VmiK2zFPDvDFYeZX...| 5| 0| 1| 0|This place is alm...|2013-03-24|2024-11-03 04:56:48| Manhattan Bagel| 200 York Road|Willow Grove| PA| 19090|40.145054| -75.116295| 3.0| 34| 1| NULL|Restaurants, Food...| NULL|\n", + "|--epgcb7xHGuJ-4PU...|fNpJqt0sYOoiwxpP1...|eqFga5ggF4kr_lkte...| 4| 2| 1| 0|Called in an orde...|2021-12-01|2024-11-03 15:16:00| Manhattan Bagel| 200 York Road|Willow Grove| PA| 19090|40.145054| -75.116295| 3.0| 34| 1| NULL|Restaurants, Food...| NULL|\n", + "|-02xFuruu85XmDn2x...|0_-8nKLlT25m0tPed...|qe9cM4t63vKLhFaqd...| 5| 0| 1| 1|Super friendly st...|2014-02-27|2024-11-03 19:04:55|Family Vision Center|7475 E Tanque Ver...| Tucson| AZ| 85715|32.251038|-110.833176| 4.5| 109| 1| True|Shopping, Ophthal...| NULL|\n", + "|-02xFuruu85XmDn2x...|DdOsD5sk1cmGBUIv0...|J525z_uXY-57qZvNa...| 5| 1| 1| 0|Dr Curtis is exce...|2012-08-10|2024-11-03 22:08:04|Family Vision Center|7475 E Tanque Ver...| Tucson| AZ| 85715|32.251038|-110.833176| 4.5| 109| 1| True|Shopping, Ophthal...| NULL|\n", + "|-02xFuruu85XmDn2x...|2ueTH_vgkq0IrjjtH...|m8ALOcSqmMRPMzBnL...| 2| 5| 1| 0|My wife and I wer...|2013-03-21|2024-11-03 23:48:47|Family Vision Center|7475 E Tanque Ver...| Tucson| AZ| 85715|32.251038|-110.833176| 4.5| 109| 1| True|Shopping, Ophthal...| NULL|\n", + "|-02xFuruu85XmDn2x...|tWy3gT4G_o9EpWNe7...|DdPEm9nl_5zxrzGNa...| 5| 0| 1| 0|Everyone is so fr...|2021-03-12|2024-11-03 18:54:43|Family Vision Center|7475 E Tanque Ver...| Tucson| AZ| 85715|32.251038|-110.833176| 4.5| 109| 1| True|Shopping, Ophthal...| NULL|\n", + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+--------------------+--------------------+------------+-----+-----------+---------+-----------+-----------------------+------------+-------+----------+--------------------+-----+\n", + "only showing top 10 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "filtered_reviews = filtered_reviews.withColumnRenamed(\"stars\", \"filtered_review_stars\")" + ], + "metadata": { + "id": "DYPveZEDWQUP" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "joined_df1 = filtered_reviews.join(broadcast(filtered_businesses), on=\"business_id\", how=\"inner\")" + ], + "metadata": { + "id": "IO8dDHymWUWi" + }, + "execution_count": 21, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "final_df = joined_df1.select(\n", + " col(\"user_id\").cast(StringType()),\n", + " col(\"business_id\").cast(StringType()),\n", + " col(\"filtered_review_stars\").cast(FloatType()) # Choose which column to use\n", + ")" + ], + "metadata": { + "id": "ZC7tcGCtWXJM" + }, + "execution_count": 26, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Create StringIndexer objects for user_id and business_id\n", + "user_indexer = StringIndexer(inputCol=\"user_id\", outputCol=\"user_id_indexed\")\n", + "business_indexer = StringIndexer(inputCol=\"business_id\", outputCol=\"business_id_indexed\")\n", + "\n", + "# Fit the indexers and transform the DataFrame\n", + "indexed_df = user_indexer.fit(final_df).transform(final_df)\n", + "indexed_df = business_indexer.fit(indexed_df).transform(indexed_df)" + ], + "metadata": { + "id": "k2O5gzUkWcR0" + }, + "execution_count": 29, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from pyspark.sql.functions import collect_list\n", + "\n", + "# Extract the unique business IDs and their corresponding indexed values\n", + "business_id_mapping_df = indexed_df.select('business_id', 'business_id_indexed').distinct()\n", + "\n", + "# Convert the DataFrame to a list of tuples\n", + "business_id_mapping_list = business_id_mapping_df.collect()\n", + "\n", + "# Create a dictionary from the list of tuples\n", + "business_id_mapping_dict = {row['business_id']: row['business_id_indexed'] for row in business_id_mapping_list}\n", + "\n", + "# Print the dictionary\n", + "print(business_id_mapping_dict)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "6MKg3YsmW4TM", + "outputId": "023f9218-7405-4944-8dae-961030b9ac21" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "{'NHb6QTrJOnyIj-Zd0fYzFg': 198.0, 'n5TNfoXg1i8wX5R3iY4WUA': 7514.0, 'PHqeN9MKNA_TfIUfmpBeRw': 9853.0, 'AOCwwM2f1X4-Ge4Ilv-X4g': 45963.0, '1rkmbnMjrYBM-hOckvnNFw': 15347.0, 'CUWituEeIX3xA0xU7X82Ow': 7109.0, 'XHxAIQR-QAz6j1RMaz_J1A': 19254.0, 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'DBV7IgOZrrSCzeyNOPN2Ew': 37833.0, 'ShHeRw6w4TzEOJ_1LvQ1eg': 39799.0, '7JPbpoD_oyG0AUbRwHR75g': 4008.0, 'u-pogbWQ-Hwl6qTQjVIAOQ': 52646.0, 'zmsd0ki6NAlamVYK3R9o8g': 53537.0, 'yiaEXP2DUgG6V7u-L9_X1g': 53369.0, 'lpOikgJ327m_G88xJ7BE8w': 42432.0, 'AajRiQDWVl8_n4ZfYr92XQ': 45986.0}\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "def find_business_key(business_id_mapping_dict, value):\n", + " \"\"\"\n", + " Finds the business key corresponding to a value in the dictionary.\n", + "\n", + " Args:\n", + " business_id_mapping_dict: The dictionary mapping business IDs to indexed values.\n", + " value: The value to search for.\n", + "\n", + " Returns:\n", + " The business key (business_id) corresponding to the value, or None if not found.\n", + " \"\"\"\n", + " for business_id, indexed_value in business_id_mapping_dict.items():\n", + " if indexed_value == value:\n", + " return business_id\n", + " return None\n", + "\n", + "\n", + "# Example usage:\n", + "value_to_find = 2590.0 # Replace with the value to find\n", + "business_id = find_business_key(business_id_mapping_dict, value_to_find)\n", + "if business_id:\n", + " print(f\"The business ID for value {value_to_find} is: {business_id}\")\n", + "else:\n", + " print(f\"No business ID found for value {value_to_find}\") #" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EHpL0p2TrSiA", + "outputId": "27d212bc-7cda-4cd8-e91b-e450d50685d7" + }, + "execution_count": 47, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "The business ID for value 2590.0 is: W57360g1e7xMGerm7wPW0Q\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "OFh57wbprSee" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "3BHSG4lMrSdG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "vPR5Y2djrSa-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "29VOYZpurSYg" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# writing the json\n", + "\n", + "import json\n", + "# from google.colab import drive\n", + "# drive.mount('/content/drive')\n", + "\n", + "\n", + "# Specify the file path\n", + "file_path = \"/content/drive/MyDrive/Models/businessmapping.json\"\n", + "\n", + "# Write the dictionary to the JSON file\n", + "with open(file_path, 'w') as f:\n", + " json.dump(business_id_mapping_dict, f)\n", + "\n", + "print(f\"Dictionary written to {file_path}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5Jx3XWYMgGVW", + "outputId": "28c9619a-8bdf-4188-cbd5-6d821a9c64fe" + }, + "execution_count": 31, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Dictionary written to /content/drive/MyDrive/Models/businessmapping.json\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "KdHD7-b8gGRy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "tjVmd455gGM8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# loading the saved model\n", + "\n", + "import pickle\n", + "\n", + "# Load the saved model from the specified path\n", + "with open('/content/drive/MyDrive/Models/random_forest_model2.pkl', 'rb') as f:\n", + " loaded_model = pickle.load(f)\n" + ], + "metadata": { + "id": "xr9nmA5HeVks" + }, + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import pandas as pd\n", + "\n", + "file_path = '/content/drive/MyDrive/ProcessedCSV/features_df.csv'\n", + "\n", + "# Get a list of all part files in the directory\n", + "part_files = [os.path.join(file_path, f) for f in os.listdir(file_path) if f.endswith('.csv')]\n", + "\n", + "# Concatenate all part files into a single DataFrame\n", + "df = pd.concat([pd.read_csv(f) for f in part_files], ignore_index=True)\n", + "\n", + "\n", + "print(df.head()) # Display the first few rows" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HIs7HYtisQsT", + "outputId": "deb2642e-6c9d-4182-c0b6-b10ab7dcf913" + }, + "execution_count": 38, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " business_id name \\\n", + "0 NQhyMw8SOU1HB-V9X52CTg Champion Chevrolet - Service Department \n", + "1 2y_CdkxEOJEJGyJApfCYpA Rode's Fireside Restaurant & Tavern \n", + "2 8PNKnlnJg6snf-HUgFaNIw U Dirty Dawg \n", + "3 sk2lZI4zmuGAccd3DLCnBw Boyd Hill Nature Preserve \n", + "4 83IeQHroXEctmMpK19TJKw The Richel D'Ambra Spa & Salon \n", + "\n", + " address city state postal_code latitude \\\n", + "0 800 Kietzke Ln Reno NV 89502 39.518223 \n", + "1 533 Kings Hwy Woolwich Twp NJ 08085 39.753252 \n", + "2 2435 S State Rd 135 Greenwood IN 46143 39.579628 \n", + "3 1101 Country Club Way S Saint Petersburg FL 33705 27.725123 \n", + "4 10 Avenue of the Arts Philadelphia PA 19102 39.951298 \n", + "\n", + " longitude business_stars review_count is_open \\\n", + "0 -119.787714 3.0 45 1 \n", + "1 -75.302486 3.5 104 1 \n", + "2 -86.158564 4.0 32 1 \n", + "3 -82.650080 5.0 33 1 \n", + "4 -75.164270 3.5 65 1 \n", + "\n", + " categories avg_sentiment \\\n", + "0 Automotive, Auto Repair 0.040079 \n", + "1 Restaurants, American (Traditional) 0.315741 \n", + "2 Pet Sitting, Pet Training, Pet Groomers, Pets,... 0.675000 \n", + "3 Active Life, Parks, Hiking 0.187302 \n", + "4 Hair Salons, Day Spas, Beauty & Spas 0.462337 \n", + "\n", + " avg_review_stars total_reviews total_checkin_count \n", + "0 1.000000 1 8 \n", + "1 4.000000 1 110 \n", + "2 4.000000 1 23 \n", + "3 4.000000 1 115 \n", + "4 4.666667 3 41 \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "feature_columns = [\"business_stars\", \"review_count\", \"avg_sentiment\", \"avg_review_stars\", \"total_reviews\", \"total_checkin_count\"]\n", + "\n", + "\n", + "def predict_success_for_business_ids(business_ids, model, features_df, feature_columns):\n", + " # Filter the dataset for the provided business IDs\n", + " input_data = features_df[features_df[\"business_id\"].isin(business_ids)]\n", + " X_input = input_data[feature_columns]\n", + "\n", + " # Predict success probability\n", + " success_probabilities = model.predict(X_input)\n", + "\n", + " # Create a DataFrame to display results\n", + " result_df = pd.DataFrame({\n", + " \"business_id\": input_data[\"business_id\"],\n", + " \"success_probability\": success_probabilities*10000\n", + " })\n", + "\n", + " return result_df\n", + "\n", + "# Example usage\n", + "business_ids_to_predict = ['IZAMcBLQ365GsrgonoM79w']\n", + "success_probabilities = predict_success_for_business_ids(business_ids_to_predict, loaded_model, df, feature_columns)\n", + "\n", + "print(\"Predicted Success Probabilities:\")\n", + "print(success_probabilities)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "CdE__KUfrxoh", + "outputId": "52c28edb-a290-4b11-f891-b029775a651b" + }, + "execution_count": 48, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted Success Probabilities:\n", + " business_id success_probability\n", + "1946 W57360g1e7xMGerm7wPW0Q 189.433630\n", + "9016 R46XVcmUzy8qeerHyZQtEg 127.708652\n", + "11495 IZAMcBLQ365GsrgonoM79w 34.489153\n", + "21795 MnjCw3oaM0Hq39bjONDU0Q 102.687166\n", + "32206 GAXuzDNbzfZbtZYxqrdHnw 134.529491\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "from sklearn.preprocessing import MinMaxScaler\n", + "import pandas as pd\n", + "\n", + "# Define feature columns\n", + "feature_columns = [\"business_stars\", \"review_count\", \"avg_sentiment\", \"avg_review_stars\", \"total_reviews\", \"total_checkin_count\"]\n", + "\n", + "def predict_success_for_business_ids(business_ids, model, features_df, feature_columns):\n", + " # Filter the dataset for the provided business IDs\n", + " input_data = features_df[features_df[\"business_id\"].isin(business_ids)]\n", + " X_input = input_data[feature_columns]\n", + "\n", + " # Predict success values (not necessarily probabilities yet)\n", + " success_values = model.predict(X_input).reshape(-1, 1)\n", + "\n", + " # Use Min-Max Scaler to transform values into a probability range (0-100%)\n", + " scaler = MinMaxScaler(feature_range=(0, 100))\n", + " success_probabilities = scaler.fit_transform(success_values).flatten()\n", + "\n", + " # Create a DataFrame to display results\n", + " result_df = pd.DataFrame({\n", + " \"business_id\": input_data[\"business_id\"].values,\n", + " \"success_probability\": success_probabilities\n", + " })\n", + "\n", + " return result_df\n", + "\n", + "# Example usage\n", + "business_ids_to_predict = ['MnjCw3oaM0Hq39bjONDU0Q','R46XVcmUzy8qeerHyZQtEg', 'GAXuzDNbzfZbtZYxqrdHnw', 'IZAMcBLQ365GsrgonoM79w', 'W57360g1e7xMGerm7wPW0Q']\n", + "success_probabilities = predict_success_for_business_ids(business_ids_to_predict, loaded_model, df, feature_columns)\n", + "\n", + "print(\"Predicted Success Probabilities:\")\n", + "print(success_probabilities)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YvVQpIBCr_ZG", + "outputId": "72301aca-48cc-4788-d182-d4eb356f50c1" + }, + "execution_count": 49, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted Success Probabilities:\n", + " business_id success_probability\n", + "0 W57360g1e7xMGerm7wPW0Q 100.000000\n", + "1 R46XVcmUzy8qeerHyZQtEg 60.163164\n", + "2 IZAMcBLQ365GsrgonoM79w 0.000000\n", + "3 MnjCw3oaM0Hq39bjONDU0Q 44.014485\n", + "4 GAXuzDNbzfZbtZYxqrdHnw 64.565282\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "l9QZ8xb3wDkb" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/NoteBook/KM_RecSys.ipynb b/NoteBook/KM_RecSys.ipynb new file mode 100644 index 0000000..36ed377 --- /dev/null +++ b/NoteBook/KM_RecSys.ipynb @@ -0,0 +1,313 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "machine_shape": "hm" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "source": [ + "import os\n", + "import joblib\n", + "import numpy as np\n", + "import pandas as pd\n", + "from pyspark.sql import SparkSession\n", + "from pyspark.ml.feature import VectorAssembler, StandardScaler\n", + "from pyspark.ml.clustering import KMeans\n", + "from pyspark.sql.functions import col, expr, radians, sin, cos, sqrt, asin, lit, row_number, power\n", + "from pyspark.sql.window import Window\n", + "from pyspark.ml import Pipeline\n", + "from pyspark.ml.feature import StringIndexer" + ], + "metadata": { + "id": "hqDRwmhCEnJW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ScHxsMfyBcJ1" + }, + "outputs": [], + "source": [ + "\n", + "\n", + "class RestaurantRecommenderSystem:\n", + " def __init__(self, spark_session):\n", + " \"\"\"\n", + " Initialize the Restaurant Recommender System\n", + " \"\"\"\n", + " self.spark = spark_session\n", + " self.df = None\n", + " self.kmeans_model = None\n", + " self.scaler = None\n", + " self.vector_assembler = None\n", + "\n", + " def load_and_preprocess_data(self, data_path):\n", + " \"\"\"\n", + " Load and preprocess restaurant data\n", + " \"\"\"\n", + " # Read the CSV file\n", + " self.df = self.spark.read.csv('/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv', header=True, inferSchema=True)\n", + "\n", + " # Data cleaning and preprocessing\n", + " self.df = self.df.na.drop() # Remove rows with null values\n", + "\n", + " # Convert boolean columns\n", + " self.df = (self.df.withColumn(\"has_parking\",\n", + " expr(\"CASE WHEN parking = 'Yes' THEN true ELSE false END\"))\n", + " .withColumn(\"has_wifi\",\n", + " expr(\"CASE WHEN WiFi = 'Yes' THEN true ELSE false END\")))\n", + "\n", + " # Encode categorical variables\n", + " cuisine_indexer = StringIndexer(inputCol=\"cuisine_type\", outputCol=\"cuisine_type_encoded\")\n", + " # Fit and transform the DataFrame using the StringIndexer\n", + " self.df = cuisine_indexer.fit(self.df).transform(self.df)\n", + "\n", + " return self.df\n", + "\n", + " def create_feature_vector(self):\n", + " \"\"\"\n", + " Create feature vector for clustering\n", + " \"\"\"\n", + " # Select features for clustering\n", + " feature_columns = [\n", + " \"latitude\",\n", + " \"longitude\",\n", + " \"rating\",\n", + " \"review_count\",\n", + " \"cuisine_type_encoded\"\n", + " ]\n", + "\n", + " # Vector Assembler to combine features\n", + " self.vector_assembler = VectorAssembler(\n", + " inputCols=feature_columns,\n", + " outputCol=\"features\"\n", + " )\n", + "\n", + " # Scale features\n", + " self.scaler = StandardScaler(\n", + " inputCol=\"features\",\n", + " outputCol=\"scaled_features\",\n", + " withStd=True,\n", + " withMean=True\n", + " )\n", + "\n", + " return self\n", + "\n", + " def train_kmeans_clustering(self, k=5):\n", + " \"\"\"\n", + " Train K-means clustering model\n", + " \"\"\"\n", + " # Create K-means model\n", + " kmeans = KMeans(\n", + " featuresCol=\"scaled_features\",\n", + " predictionCol=\"cluster\",\n", + " k=k\n", + " )\n", + "\n", + " # Create pipeline\n", + " pipeline = Pipeline(stages=[\n", + " self.vector_assembler,\n", + " self.scaler,\n", + " kmeans\n", + " ])\n", + "\n", + " # Fit the pipeline\n", + " self.kmeans_model = pipeline.fit(self.df)\n", + "\n", + " return self\n", + "\n", + " def save_model(self, model_path):\n", + " \"\"\"\n", + " Save the trained model\n", + " \"\"\"\n", + " # Instead of joblib, use PySpark's save method for PipelineModels\n", + " self.kmeans_model.save(os.path.join('/content/drive/MyDrive/Models', 'restaurant_recommender_Final_model'))\n", + " print(f\"Model saved to {model_path}\")\n", + "\n", + " def haversine_distance(self, lat1, lon1, lat2, lon2):\n", + " \"\"\"\n", + " Calculate distance between two geographical points\n", + " \"\"\"\n", + " lat1_rad = radians(lit(lat1))\n", + " lon1_rad = radians(lit(lon1))\n", + " lat2_rad = radians(lat2)\n", + " lon2_rad = radians(lon2)\n", + "\n", + " return (\n", + " 6371 * 2 * asin(\n", + " sqrt(\n", + " power(sin((lat2_rad - lat1_rad) / 2), 2) +\n", + " cos(lat1_rad) * cos(lat2_rad) *\n", + " power(sin((lon2_rad - lon1_rad) / 2), 2)\n", + " )\n", + " )\n", + " )\n", + "\n", + " def recommend_restaurants(\n", + " self,\n", + " user_location,\n", + " min_rating,\n", + " need_parking,\n", + " need_wifi,\n", + " cuisine_type,\n", + " max_distance=60\n", + " ):\n", + " \"\"\"\n", + " Recommend restaurants based on user preferences\n", + " \"\"\"\n", + " user_lat, user_lon = user_location\n", + "\n", + " # Apply initial filters\n", + " filtered_df = self.df.filter(\n", + " (col(\"rating\") >= min_rating) &\n", + " (col(\"cuisine_type\") == cuisine_type)\n", + " )\n", + "\n", + " # Apply parking filter\n", + " if need_parking:\n", + " filtered_df = filtered_df.filter(col(\"has_parking\") == True)\n", + "\n", + " # Apply WiFi filter\n", + " if need_wifi:\n", + " filtered_df = filtered_df.filter(col(\"has_wifi\") == True)\n", + "\n", + " # Add distance column\n", + " with_distance_df = filtered_df.withColumn(\n", + " \"distance\",\n", + " self.haversine_distance(user_lat, user_lon, col(\"latitude\"), col(\"longitude\"))\n", + " )\n", + "\n", + " # Filter by distance\n", + " nearby_restaurants = with_distance_df.filter(\n", + " col(\"distance\") <= max_distance\n", + " )\n", + "\n", + " # Rank restaurants\n", + " window_spec = Window.partitionBy(\"bus_name\").orderBy(col(\"rating\").desc())\n", + "\n", + " recommended_restaurants = (\n", + " nearby_restaurants\n", + " .withColumn(\"rank\", row_number().over(window_spec))\n", + " .filter(col(\"rank\") == 1)\n", + " .orderBy(col(\"rating\").desc())\n", + " .select(\"business_id\", \"bus_name\", \"address\", \"rating\", \"distance\", \"cuisine_type\")\n", + " .limit(5)\n", + " )\n", + "\n", + " return recommended_restaurants\n", + "\n", + "def interactive_recommendation(recommender):\n", + " \"\"\"\n", + " Interactive restaurant recommendation interface\n", + " \"\"\"\n", + " print(\"Restaurant Recommender System\")\n", + "\n", + " # Get user inputs\n", + " user_lat = float(input(\"Enter your latitude: \"))\n", + " user_lon = float(input(\"Enter your longitude: \"))\n", + " min_rating = float(input(\"Enter minimum rating (0-5): \"))\n", + " need_parking = input(\"Need parking? (yes/no): \").lower() == 'yes'\n", + " need_wifi = input(\"Need WiFi? (yes/no): \").lower() == 'yes'\n", + " cuisine_type = input(\"Enter cuisine type: \")\n", + "\n", + " # Make recommendations\n", + " recommendations = recommender.recommend_restaurants(\n", + " user_location=(user_lat, user_lon),\n", + " min_rating=min_rating,\n", + " need_parking=need_parking,\n", + " need_wifi=need_wifi,\n", + " cuisine_type=cuisine_type\n", + " )\n", + "\n", + " # Show recommendations\n", + " recommendations.show()\n", + "\n" + ] + }, + { + "cell_type": "code", + "source": [ + "def main():\n", + " # Create Spark Session\n", + " spark = SparkSession.builder \\\n", + " .appName(\"RestaurantRecommenderSystem\") \\\n", + " .getOrCreate()\n", + "\n", + " # Initialize recommender\n", + " recommender = RestaurantRecommenderSystem(spark)\n", + "\n", + " # Define paths\n", + " data_path = \"/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv\"\n", + " model_save_path = \"/content/drive/MyDrive/Models/restaurant_recommender_Final_model.pkl\"\n", + "\n", + " # Load and preprocess data\n", + " recommender.load_and_preprocess_data(data_path)\n", + "\n", + " # Create feature vector\n", + " recommender.create_feature_vector()\n", + "\n", + " # Train K-means clustering\n", + " recommender.train_kmeans_clustering(k=5)\n", + "\n", + " # Save model\n", + " recommender.save_model(model_save_path)\n", + "\n", + " # Start interactive recommendation\n", + " interactive_recommendation(recommender)\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "fukODQYuE71l", + "outputId": "bb98e3d6-397c-4d65-c07b-6263a93011ce" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model saved to /content/drive/MyDrive/Models/restaurant_recommender_Final_model.pkl\n", + "Restaurant Recommender System\n", + "Enter your latitude: 53.5187059314\n", + "Enter your longitude: -113.4975095574\n", + "Enter minimum rating (0-5): 3.5\n", + "Need parking? (yes/no): No\n", + "Need WiFi? (yes/no): No\n", + "Enter cuisine type: American\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "| business_id| bus_name| address|rating| distance|cuisine_type|\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "|tGl2jBbSVPEl27cw9...|Danny Hacksaw's P...|1503 Lakewood Road W| 4.5| 7.143981489706797| American|\n", + "|sFjzcP5GXcCmQAGOG...| Red Ox Inn| 9420 91 Street NW| 4.5| 2.455386840412777| American|\n", + "|if2ZvtLUOdn6EFeWj...| El Fogón Latino| 8026 118 Avenue| 4.5| 6.226928045014033| American|\n", + "|G5jxVV_AIHCquF0B3...| Ernest's|Hokanson Centre f...| 4.5| 5.704328999322424| American|\n", + "|2KhwR5XB2u88e3AgH...| Monty's Pizza|1027 Potter Green...| 4.5|11.858116310387844| American|\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/NoteBook/Loading_and_runnnin_second_model.ipynb b/NoteBook/Loading_and_runnnin_second_model.ipynb new file mode 100644 index 0000000..1e225f9 --- /dev/null +++ b/NoteBook/Loading_and_runnnin_second_model.ipynb @@ -0,0 +1,802 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyM1XjpmBXnyZ6KrrnBf+ruS", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "source": [ + "# code to drive mount\n", + "\n", + "\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "C6wPAuVD1g15", + "outputId": "21afee9e-f675-418e-cf1e-b9621caafcdf" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "model 1\n" + ], + "metadata": { + "id": "g4pld3BhLLgL" + } + }, + { + "cell_type": "code", + "source": [ + "from pyspark.sql import SparkSession\n", + "from pyspark.ml import PipelineModel\n", + "from pyspark.sql.functions import col, expr, radians, lit, row_number, sqrt, sin, cos, asin,power\n", + "from pyspark.sql.window import Window\n", + "from pyspark.ml.feature import StringIndexer, VectorAssembler, StandardScaler\n", + "\n", + "class RestaurantRecommenderPredictor:\n", + " def __init__(self, spark_session):\n", + " \"\"\"\n", + " Initialize the Restaurant Recommender Predictor\n", + " \"\"\"\n", + " self.spark = spark_session\n", + " self.df = None\n", + " self.kmeans_model = None\n", + " self.cuisine_indexer = None\n", + " self.vector_assembler = None\n", + " self.scaler = None\n", + "\n", + " def load_data(self, data_path):\n", + " \"\"\"\n", + " Load and preprocess restaurant data\n", + " \"\"\"\n", + " # Read the CSV file\n", + " self.df = self.spark.read.csv(data_path, header=True, inferSchema=True)\n", + "\n", + " # Data cleaning and preprocessing\n", + " self.df = self.df.na.drop() # Remove rows with null values\n", + "\n", + " # Convert boolean columns\n", + " self.df = (self.df.withColumn(\"has_parking\",\n", + " expr(\"CASE WHEN parking = 'Yes' THEN true ELSE false END\"))\n", + " .withColumn(\"has_wifi\",\n", + " expr(\"CASE WHEN WiFi = 'Yes' THEN true ELSE false END\")))\n", + "\n", + " # Encode categorical variables\n", + " self.cuisine_indexer = StringIndexer(\n", + " inputCol=\"cuisine_type\",\n", + " outputCol=\"cuisine_type_encoded\"\n", + " )\n", + " self.df = self.cuisine_indexer.fit(self.df).transform(self.df)\n", + "\n", + " return self.df\n", + "\n", + " def load_saved_model(self, model_path):\n", + " \"\"\"\n", + " Load the saved PySpark Pipeline Model\n", + " \"\"\"\n", + " try:\n", + " # Load the entire pipeline model\n", + " self.kmeans_model = PipelineModel.load(model_path)\n", + "\n", + " # Extract specific stages from the pipeline\n", + " stages = self.kmeans_model.stages\n", + "\n", + " # Find and store vector assembler and scaler\n", + " for stage in stages:\n", + " if isinstance(stage, VectorAssembler):\n", + " self.vector_assembler = stage\n", + " elif isinstance(stage, StandardScaler):\n", + " self.scaler = stage\n", + "\n", + " return self.kmeans_model\n", + " except Exception as e:\n", + " print(f\"Error loading model: {e}\")\n", + " return None\n", + "\n", + " def haversine_distance(self, lat1, lon1, lat2, lon2):\n", + " \"\"\"\n", + " Calculate distance between two geographical points\n", + " \"\"\"\n", + " lat1_rad = radians(lit(lat1))\n", + " lon1_rad = radians(lit(lon1))\n", + " lat2_rad = radians(lat2)\n", + " lon2_rad = radians(lon2)\n", + "\n", + " return (\n", + " 6371 * 2 * asin(\n", + " sqrt(\n", + " sin((lat2_rad - lat1_rad) / 2) ** 2 +\n", + " cos(lat1_rad) * cos(lat2_rad) *\n", + " sin((lon2_rad - lon1_rad) / 2) ** 2\n", + " )\n", + " )\n", + " )\n", + "\n", + " def recommend_restaurants(\n", + " self,\n", + " user_location,\n", + " min_rating,\n", + " need_parking,\n", + " need_wifi,\n", + " cuisine_type,\n", + " max_distance=60\n", + " ):\n", + " \"\"\"\n", + " Recommend restaurants based on user preferences\n", + " \"\"\"\n", + " user_lat, user_lon = user_location\n", + "\n", + " # Apply initial filters\n", + " filtered_df = self.df.filter(\n", + " (col(\"rating\") >= min_rating) &\n", + " (col(\"cuisine_type\") == cuisine_type)\n", + " )\n", + "\n", + " # Apply parking filter\n", + " if need_parking:\n", + " filtered_df = filtered_df.filter(col(\"has_parking\") == True)\n", + "\n", + " # Apply WiFi filter\n", + " if need_wifi:\n", + " filtered_df = filtered_df.filter(col(\"has_wifi\") == True)\n", + "\n", + " # Add distance column\n", + " with_distance_df = filtered_df.withColumn(\n", + " \"distance\",\n", + " self.haversine_distance(user_lat, user_lon, col(\"latitude\"), col(\"longitude\"))\n", + " )\n", + "\n", + " # Filter by distance\n", + " nearby_restaurants = with_distance_df.filter(\n", + " col(\"distance\") <= max_distance\n", + " )\n", + "\n", + " # Rank restaurants\n", + " window_spec = Window.partitionBy(\"bus_name\").orderBy(col(\"rating\").desc())\n", + "\n", + " recommended_restaurants = (\n", + " nearby_restaurants\n", + " .withColumn(\"rank\", row_number().over(window_spec))\n", + " .filter(col(\"rank\") == 1)\n", + " .orderBy(col(\"rating\").desc())\n", + " .select(\"business_id\", \"bus_name\", \"address\", \"rating\", \"distance\", \"cuisine_type\")\n", + " .limit(5)\n", + " )\n", + "\n", + " return recommended_restaurants\n", + "\n", + "def interactive_recommendation(predictor):\n", + " \"\"\"\n", + " Interactive restaurant recommendation interface\n", + " \"\"\"\n", + " print(\"Restaurant Recommender System\")\n", + "\n", + " # Get user inputs\n", + " user_lat = float(input(\"Enter your latitude: \"))\n", + " user_lon = float(input(\"Enter your longitude: \"))\n", + " min_rating = float(input(\"Enter minimum rating (0-5): \"))\n", + " need_parking = input(\"Need parking? (yes/no): \").lower() == 'yes'\n", + " need_wifi = input(\"Need WiFi? (yes/no): \").lower() == 'yes'\n", + " cuisine_type = input(\"Enter cuisine type: \")\n", + "\n", + " # Make recommendations\n", + " recommendations = predictor.recommend_restaurants(\n", + " user_location=(user_lat, user_lon),\n", + " min_rating=min_rating,\n", + " need_parking=need_parking,\n", + " need_wifi=need_wifi,\n", + " cuisine_type=cuisine_type\n", + " )\n", + "\n", + " # Show recommendations\n", + " recommendations.show()\n", + "\n", + "def main():\n", + " # Create Spark Session\n", + " spark = SparkSession.builder \\\n", + " .appName(\"RestaurantRecommenderPredictor\") \\\n", + " .getOrCreate()\n", + "\n", + " # Define paths\n", + " model_path = '/content/drive/MyDrive/Models/restaurant_recommender_Final_model'\n", + " data_path = '/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv'\n", + "\n", + " try:\n", + " # Initialize predictor\n", + " predictor = RestaurantRecommenderPredictor(spark)\n", + "\n", + " # Load dataset\n", + " predictor.load_data(data_path)\n", + "\n", + " # Load saved model\n", + " loaded_model = predictor.load_saved_model(model_path)\n", + "\n", + " if loaded_model:\n", + " # Start interactive recommendation\n", + " interactive_recommendation(predictor)\n", + " else:\n", + " print(\"Failed to load the model.\")\n", + "\n", + " except Exception as e:\n", + " print(f\"An error occurred: {e}\")\n", + " finally:\n", + " spark.stop()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "EZWlLLEM6wAr", + "outputId": "94a4bc37-ca06-4f53-8f43-5f704de23b52" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Restaurant Recommender System\n", + "Enter your latitude: 39.9493338\n", + "Enter your longitude: -75.1661758\n", + "Enter minimum rating (0-5): 3.5\n", + "Need parking? (yes/no): No\n", + "Need WiFi? (yes/no): No\n", + "Enter cuisine type: American\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "| business_id| bus_name| address|rating| distance|cuisine_type|\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "|TozkPEh-xhts3qfVe...| Bad Brother| 726 N 24th St| 5.0|2.3317139167823364| American|\n", + "|mVEqN8IPU-vCeKTXC...| Kensington Pub| 2116 E Tioga St| 5.0| 7.47285437409349| American|\n", + "|MwrBRpXMRNolVkaL3...|Bauman Family App...|116 Hoffmansville Rd| 5.0|55.545288742483265| American|\n", + "|7SHh7-aWuAVlp3YKa...| Edwards-Freeman Nut| 441 E Hector St| 5.0|17.803916204265626| American|\n", + "|9WY5jCB2IjnOizmgz...| Freshii| 450 S Lenola Rd| 5.0| 16.87503544224879| American|\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "model 2" + ], + "metadata": { + "id": "JNdDvQ0uLQRn" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0pRcPtQL1Ie-", + "outputId": "8d1d21dc-b073-47dc-d696-6be2ca0c02d4" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Model loaded successfully!\n", + "Evaluator not defined\n", + "Sentiment Percentages for Selected Businesses:\n", + "+----------------------+-------------------+---------------+-------------+--------------------+\n", + "|business_id |sentimentPrediction|sentiment_count|total_reviews|sentiment_percentage|\n", + "+----------------------+-------------------+---------------+-------------+--------------------+\n", + "|rQyJXOiZ39eRJ2l9OMpw-g|0.0 |79 |169 |46.74556213017752 |\n", + "|rQyJXOiZ39eRJ2l9OMpw-g|1.0 |90 |169 |53.25443786982249 |\n", + "|jzsspHqP9kATWji8xUhNFw|0.0 |94 |145 |64.82758620689654 |\n", + "|jzsspHqP9kATWji8xUhNFw|1.0 |51 |145 |35.172413793103445 |\n", + "+----------------------+-------------------+---------------+-------------+--------------------+\n", + "\n", + "Topic Percentages for Selected Businesses:\n", + "+----------------------+----------------+-----------+-------------+------------------+\n", + "|business_id |topic |topic_count|total_reviews|topic_percentage |\n", + "+----------------------+----------------+-----------+-------------+------------------+\n", + "|rQyJXOiZ39eRJ2l9OMpw-g|cleanliness |2 |169 |1.183431952662722 |\n", + "|rQyJXOiZ39eRJ2l9OMpw-g|customer_service|7 |169 |4.142011834319527 |\n", + "|rQyJXOiZ39eRJ2l9OMpw-g|other |54 |169 |31.952662721893493|\n", + "|rQyJXOiZ39eRJ2l9OMpw-g|food_quality |106 |169 |62.721893491124256|\n", + "|jzsspHqP9kATWji8xUhNFw|cleanliness |3 |145 |2.0689655172413794|\n", + "|jzsspHqP9kATWji8xUhNFw|food_quality |57 |145 |39.310344827586206|\n", + "|jzsspHqP9kATWji8xUhNFw|other |70 |145 |48.275862068965516|\n", + "|jzsspHqP9kATWji8xUhNFw|customer_service|15 |145 |10.344827586206897|\n", + "+----------------------+----------------+-----------+-------------+------------------+\n", + "\n" + ] + } + ], + "source": [ + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.functions import col, count, when\n", + "import pandas as pd\n", + "from pyspark.ml import PipelineModel\n", + "\n", + "\n", + "# Load the saved model\n", + "loaded_model = PipelineModel.load(\"/content/drive/MyDrive/topic_senti_model\")\n", + "print(\"Model loaded successfully!\")\n", + "# Initialize Spark session\n", + "spark = SparkSession.builder.master(\"local[*]\").appName(\"Business Prediction\").getOrCreate()\n", + "\n", + "# Sample out from model_1(from the provided data)\n", + "data = {\n", + " \"business_id\": [\n", + " \"rQyJXOiZ39eRJ2l9OMpw-g\",\n", + " \"jzsspHqP9kATWji8xUhNFw\"\n", + " ],\n", + " \"bus_name\": [\n", + " \"Bad Brother\",\n", + " \"Kensington Pub\"\n", + " ],\n", + " \"address\": [\n", + " \"726 N 24th St\",\n", + " \"2116 E Tioga St\"\n", + " ],\n", + " \"rating\": [5.0, 5.0],\n", + " \"distance\": [\n", + " 2.3317139167823364,\n", + " 7.47285437409349\n", + " ],\n", + " \"cuisine_type\": [\"American\", \"American\"]\n", + "}\n", + "\n", + "# Convert the Pandas DataFrame to a PySpark DataFrame - this is only for experimental purpose please load output dataframe here.\n", + "first_model_predictions_df = pd.DataFrame(data)\n", + "Recomendation_df = spark.createDataFrame(first_model_predictions_df)\n", + "\n", + "# Step 1: Extract all business IDs from the first model's predictions DataFrame\n", + "business_ids_from_first_model = Recomendation_df.select(\"business_id\").distinct().rdd.flatMap(lambda x: x).collect()\n", + "\n", + "# Step 2: Use the extracted business IDs in the second model\n", + "df = spark.read.csv(\"/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv\", header=True, inferSchema=True).select(\"business_id\", \"rev_text\")\n", + "\n", + "# Filter the Data for the Selected Business IDs\n", + "test_data_subset = df.filter(col(\"business_id\").isin(business_ids_from_first_model))\n", + "\n", + "# Step 3: Transform the subset using the trained second model\n", + "subset_predictions = loaded_model.transform(test_data_subset)\n", + "\n", + "# Step 4: Rename prediction column for clarity\n", + "subset_predictions = subset_predictions.withColumnRenamed(\"prediction\", \"sentimentPrediction\")\n", + "\n", + "# Show Predictions for the subset (remove comment to inspect output)\n", + "# subset_predictions.select(\"business_id\", \"rev_text\", \"sentimentPrediction\").show(truncate=False)\n", + "\n", + "# Step 5: Optional: Evaluate accuracy on the subset\n", + "# Use 'sentimentPrediction' in place of 'sentimentIndex'\n", + "# Step 5: Optional: Evaluate accuracy on the subset\n", + "# Use 'sentimentPrediction' in place of 'sentimentIndex'\n", + "if 'evaluator' in locals(): # Check if evaluator is defined\n", + " # Update the evaluator's labelCol and predictionCol to match the renamed columns\n", + " evaluator.setLabelCol(\"sentimentPrediction\") # Set labelCol to \"sentimentPrediction\"\n", + " evaluator.setPredictionCol(\"sentimentPrediction\") # Set predictionCol to \"sentimentPrediction\"\n", + " subset_accuracy = evaluator.evaluate(subset_predictions)\n", + " print(f\"Accuracy on Subset: {subset_accuracy:.2f}\")\n", + "else:\n", + " print(\"Evaluator not defined\")\n", + "\n", + "# Step 6: Sentiment Percentages for the Subset (if needed)\n", + "subset_sentiment_count = subset_predictions.groupBy(\"business_id\", \"sentimentPrediction\").agg(count(\"sentimentPrediction\").alias(\"sentiment_count\"))\n", + "subset_total_reviews = subset_predictions.groupBy(\"business_id\").agg(count(\"rev_text\").alias(\"total_reviews\"))\n", + "\n", + "subset_sentiment_percentage = subset_sentiment_count.join(subset_total_reviews, on=\"business_id\")\n", + "subset_sentiment_percentage = subset_sentiment_percentage.withColumn(\n", + " \"sentiment_percentage\",\n", + " (col(\"sentiment_count\") / col(\"total_reviews\") * 100)\n", + ")\n", + "\n", + "# Show Sentiment Percentages for the Subset\n", + "print(\"Sentiment Percentages for Selected Businesses:\")\n", + "subset_sentiment_percentage.show(truncate=False)\n", + "\n", + "# Step 7: Topic Percentages for the Subset\n", + "# subset_topic_count = subset_predictions.groupBy(\"business_id\", \"topic\").agg(count(\"topic\").alias(\"topic_count\"))\n", + "# subset_topic_percentage = subset_topic_count.join(subset_total_reviews, on=\"business_id\")\n", + "# subset_topic_percentage = subset_topic_percentage.withColumn(\n", + "# \"topic_percentage\",\n", + "# (col(\"topic_count\") / col(\"total_reviews\") * 100)\n", + "# )\n", + "\n", + "# # Show Topic Percentages for the Subset\n", + "# print(\"Topic Percentages for Selected Businesses:\")\n", + "# subset_topic_percentage.show(truncate=False)\n", + "# Add a 'topic' column (if not already present)\n", + "subset_predictions = subset_predictions.withColumn(\n", + " \"topic\",\n", + " when(col(\"rev_text\").contains(\"food\"), \"food_quality\")\n", + " .when(col(\"rev_text\").contains(\"service\"), \"customer_service\")\n", + " .when(col(\"rev_text\").contains(\"clean\"), \"cleanliness\")\n", + " .otherwise(\"other\")\n", + ")\n", + "\n", + "# Topic Percentages\n", + "subset_topic_count = subset_predictions.groupBy(\"business_id\", \"topic\").agg(count(\"topic\").alias(\"topic_count\"))\n", + "subset_topic_percentage = subset_topic_count.join(subset_total_reviews, on=\"business_id\")\n", + "subset_topic_percentage = subset_topic_percentage.withColumn(\n", + " \"topic_percentage\",\n", + " (col(\"topic_count\") / col(\"total_reviews\") * 100)\n", + ")\n", + "\n", + "# Show Topic Percentages\n", + "print(\"Topic Percentages for Selected Businesses:\")\n", + "subset_topic_percentage.show(truncate=False)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "source": [ + "integrated model" + ], + "metadata": { + "id": "CoX3n77BLTl8" + } + }, + { + "cell_type": "code", + "source": [ + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.functions import col, expr, radians, lit, row_number, sqrt, sin, cos, asin, power, count, when\n", + "from pyspark.sql.window import Window\n", + "from pyspark.ml import PipelineModel\n", + "from pyspark.ml.feature import StringIndexer, VectorAssembler, StandardScaler\n", + "import pandas as pd\n", + "\n", + "class RestaurantRecommenderPredictor:\n", + " def __init__(self, spark_session):\n", + " \"\"\"\n", + " Initialize the Restaurant Recommender Predictor\n", + " \"\"\"\n", + " self.spark = spark_session\n", + " self.df = None\n", + " self.kmeans_model = None\n", + " self.cuisine_indexer = None\n", + " self.vector_assembler = None\n", + " self.scaler = None\n", + "\n", + " def load_data(self, data_path):\n", + " \"\"\"\n", + " Load and preprocess restaurant data\n", + " \"\"\"\n", + " # Read the CSV file\n", + " self.df = self.spark.read.csv(data_path, header=True, inferSchema=True)\n", + "\n", + " # Data cleaning and preprocessing\n", + " self.df = self.df.na.drop() # Remove rows with null values\n", + "\n", + " # Convert boolean columns\n", + " self.df = (self.df.withColumn(\"has_parking\",\n", + " expr(\"CASE WHEN parking = 'Yes' THEN true ELSE false END\"))\n", + " .withColumn(\"has_wifi\",\n", + " expr(\"CASE WHEN WiFi = 'Yes' THEN true ELSE false END\")))\n", + "\n", + " # Encode categorical variables\n", + " self.cuisine_indexer = StringIndexer(\n", + " inputCol=\"cuisine_type\",\n", + " outputCol=\"cuisine_type_encoded\"\n", + " )\n", + " self.df = self.cuisine_indexer.fit(self.df).transform(self.df)\n", + "\n", + " return self.df\n", + "\n", + " def load_saved_model(self, model_path):\n", + " \"\"\"\n", + " Load the saved PySpark Pipeline Model\n", + " \"\"\"\n", + " try:\n", + " self.kmeans_model = PipelineModel.load(model_path)\n", + "\n", + " # Extract specific stages from the pipeline\n", + " stages = self.kmeans_model.stages\n", + "\n", + " # Find and store vector assembler and scaler\n", + " for stage in stages:\n", + " if isinstance(stage, VectorAssembler):\n", + " self.vector_assembler = stage\n", + " elif isinstance(stage, StandardScaler):\n", + " self.scaler = stage\n", + "\n", + " return self.kmeans_model\n", + " except Exception as e:\n", + " print(f\"Error loading model: {e}\")\n", + " return None\n", + "\n", + " def haversine_distance(self, lat1, lon1, lat2, lon2):\n", + " \"\"\"\n", + " Calculate distance between two geographical points\n", + " \"\"\"\n", + " lat1_rad = radians(lit(lat1))\n", + " lon1_rad = radians(lit(lon1))\n", + " lat2_rad = radians(lat2)\n", + " lon2_rad = radians(lon2)\n", + "\n", + " return (\n", + " 6371 * 2 * asin(\n", + " sqrt(\n", + " sin((lat2_rad - lat1_rad) / 2) ** 2 +\n", + " cos(lat1_rad) * cos(lat2_rad) *\n", + " sin((lon2_rad - lon1_rad) / 2) ** 2\n", + " )\n", + " )\n", + " )\n", + "\n", + " def recommend_restaurants(\n", + " self,\n", + " user_location,\n", + " min_rating,\n", + " need_parking,\n", + " need_wifi,\n", + " cuisine_type,\n", + " max_distance=60\n", + " ):\n", + " \"\"\"\n", + " Recommend restaurants based on user preferences\n", + " \"\"\"\n", + " user_lat, user_lon = user_location\n", + "\n", + " # Apply initial filters\n", + " filtered_df = self.df.filter(\n", + " (col(\"rating\") >= min_rating) &\n", + " (col(\"cuisine_type\") == cuisine_type)\n", + " )\n", + "\n", + " # Apply parking filter\n", + " if need_parking:\n", + " filtered_df = filtered_df.filter(col(\"has_parking\") == True)\n", + "\n", + " # Apply WiFi filter\n", + " if need_wifi:\n", + " filtered_df = filtered_df.filter(col(\"has_wifi\") == True)\n", + "\n", + " # Add distance column\n", + " with_distance_df = filtered_df.withColumn(\n", + " \"distance\",\n", + " self.haversine_distance(user_lat, user_lon, col(\"latitude\"), col(\"longitude\"))\n", + " )\n", + "\n", + " # Filter by distance\n", + " nearby_restaurants = with_distance_df.filter(\n", + " col(\"distance\") <= max_distance\n", + " )\n", + "\n", + " # Rank restaurants\n", + " window_spec = Window.partitionBy(\"bus_name\").orderBy(col(\"rating\").desc())\n", + "\n", + " recommended_restaurants = (\n", + " nearby_restaurants\n", + " .withColumn(\"rank\", row_number().over(window_spec))\n", + " .filter(col(\"rank\") == 1)\n", + " .orderBy(col(\"rating\").desc())\n", + " .select(\"business_id\", \"bus_name\", \"address\", \"rating\", \"distance\", \"cuisine_type\")\n", + " .limit(5)\n", + " )\n", + "\n", + " return recommended_restaurants\n", + "\n", + " def analyze_sentiments_and_topics(self, recommendation_df, data_path, model_path):\n", + " \"\"\"\n", + " Analyze sentiments and topics for recommended restaurants\n", + " \"\"\"\n", + " # Load the saved model\n", + " loaded_model = PipelineModel.load(model_path)\n", + "\n", + " # Load review data\n", + " review_df = self.spark.read.csv(data_path, header=True, inferSchema=True).select(\"business_id\", \"rev_text\")\n", + "\n", + " # Filter reviews for recommended restaurants\n", + " business_ids = recommendation_df.select(\"business_id\").distinct().rdd.flatMap(lambda x: x).collect()\n", + " filtered_reviews = review_df.filter(col(\"business_id\").isin(business_ids))\n", + "\n", + " # Transform reviews using the sentiment model\n", + " predictions = loaded_model.transform(filtered_reviews)\n", + " predictions = predictions.withColumnRenamed(\"prediction\", \"sentimentPrediction\")\n", + "\n", + " # Calculate sentiment percentages\n", + " sentiment_count = predictions.groupBy(\"business_id\", \"sentimentPrediction\").agg(count(\"sentimentPrediction\").alias(\"sentiment_count\"))\n", + " total_reviews = predictions.groupBy(\"business_id\").agg(count(\"rev_text\").alias(\"total_reviews\"))\n", + "\n", + " sentiment_percentage = sentiment_count.join(total_reviews, on=\"business_id\")\n", + " sentiment_percentage = sentiment_percentage.withColumn(\n", + " \"sentiment_percentage\",\n", + " (col(\"sentiment_count\") / col(\"total_reviews\") * 100)\n", + " )\n", + "\n", + " # Add topic analysis\n", + " predictions = predictions.withColumn(\n", + " \"topic\",\n", + " when(col(\"rev_text\").contains(\"food\"), \"food_quality\")\n", + " .when(col(\"rev_text\").contains(\"service\"), \"customer_service\")\n", + " .when(col(\"rev_text\").contains(\"clean\"), \"cleanliness\")\n", + " .otherwise(\"other\")\n", + " )\n", + "\n", + " topic_count = predictions.groupBy(\"business_id\", \"topic\").agg(count(\"topic\").alias(\"topic_count\"))\n", + " topic_percentage = topic_count.join(total_reviews, on=\"business_id\")\n", + " topic_percentage = topic_percentage.withColumn(\n", + " \"topic_percentage\",\n", + " (col(\"topic_count\") / col(\"total_reviews\") * 100)\n", + " )\n", + "\n", + " return sentiment_percentage, topic_percentage\n", + "\n", + "def main():\n", + " # Create Spark Session\n", + " spark = SparkSession.builder \\\n", + " .appName(\"Restaurant Recommender and Sentiment Analyzer\") \\\n", + " .getOrCreate()\n", + "\n", + " # Define paths\n", + " model_path = '/content/drive/MyDrive/Models/restaurant_recommender_Final_model'\n", + " review_model_path = '/content/drive/MyDrive/topic_senti_model'\n", + " data_path = '/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv'\n", + " # review_data_path = '/content/drive/MyDrive/ProcessedCSV/Reviews.csv'\n", + "\n", + " try:\n", + " # Initialize predictor\n", + " predictor = RestaurantRecommenderPredictor(spark)\n", + "\n", + " # Load dataset\n", + " predictor.load_data(data_path)\n", + "\n", + " # Load saved model\n", + " loaded_model = predictor.load_saved_model(model_path)\n", + "\n", + " if loaded_model:\n", + " # Interactive recommendation\n", + " print(\"Restaurant Recommender System\")\n", + "\n", + " user_lat = float(input(\"Enter your latitude: \"))\n", + " user_lon = float(input(\"Enter your longitude: \"))\n", + " min_rating = float(input(\"Enter minimum rating (0-5): \"))\n", + " need_parking = input(\"Need parking? (yes/no): \").lower() == 'yes'\n", + " need_wifi = input(\"Need WiFi? (yes/no): \").lower() == 'yes'\n", + " cuisine_type = input(\"Enter cuisine type: \")\n", + "\n", + " recommendations = predictor.recommend_restaurants(\n", + " user_location=(user_lat, user_lon),\n", + " min_rating=min_rating,\n", + " need_parking=need_parking,\n", + " need_wifi=need_wifi,\n", + " cuisine_type=cuisine_type\n", + " )\n", + "\n", + " print(\"Recommended Restaurants:\")\n", + " recommendations.show()\n", + "\n", + " # Analyze sentiments and topics\n", + " sentiment_percentage, topic_percentage = predictor.analyze_sentiments_and_topics(\n", + " recommendations, data_path, review_model_path\n", + " )\n", + "\n", + " print(\"Sentiment Percentages:\")\n", + " sentiment_percentage.show()\n", + "\n", + " print(\"Topic Percentages:\")\n", + " topic_percentage.show()\n", + " else:\n", + " print(\"Failed to load the model.\")\n", + "\n", + " except Exception as e:\n", + " print(f\"An error occurred: {e}\")\n", + " finally:\n", + " spark.stop()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()\n" + ], + "metadata": { + "id": "SA1eTXXF1QD-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "efb3b715-d1b6-426f-a588-f3d873b4d618" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Restaurant Recommender System\n", + "Enter your latitude: 39.9493338\n", + "Enter your longitude: -75.1661758\n", + "Enter minimum rating (0-5): 3.5\n", + "Need parking? (yes/no): No\n", + "Need WiFi? (yes/no): No\n", + "Enter cuisine type: American\n", + "Recommended Restaurants:\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "| business_id| bus_name| address|rating| distance|cuisine_type|\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "|TozkPEh-xhts3qfVe...| Bad Brother| 726 N 24th St| 5.0|2.3317139167823364| American|\n", + "|mVEqN8IPU-vCeKTXC...| Kensington Pub| 2116 E Tioga St| 5.0| 7.47285437409349| American|\n", + "|MwrBRpXMRNolVkaL3...|Bauman Family App...|116 Hoffmansville Rd| 5.0|55.545288742483265| American|\n", + "|7SHh7-aWuAVlp3YKa...| Edwards-Freeman Nut| 441 E Hector St| 5.0|17.803916204265626| American|\n", + "|9WY5jCB2IjnOizmgz...| Freshii| 450 S Lenola Rd| 5.0| 16.87503544224879| American|\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "\n", + "Sentiment Percentages:\n", + "+--------------------+-------------------+---------------+-------------+--------------------+\n", + "| business_id|sentimentPrediction|sentiment_count|total_reviews|sentiment_percentage|\n", + "+--------------------+-------------------+---------------+-------------+--------------------+\n", + "|TozkPEh-xhts3qfVe...| 1.0| 32| 97| 32.98969072164948|\n", + "|TozkPEh-xhts3qfVe...| 0.0| 65| 97| 67.0103092783505|\n", + "|7SHh7-aWuAVlp3YKa...| 1.0| 23| 48| 47.91666666666667|\n", + "|7SHh7-aWuAVlp3YKa...| 0.0| 25| 48| 52.083333333333336|\n", + "|mVEqN8IPU-vCeKTXC...| 1.0| 3| 9| 33.33333333333333|\n", + "|mVEqN8IPU-vCeKTXC...| 0.0| 6| 9| 66.66666666666666|\n", + "|9WY5jCB2IjnOizmgz...| 0.0| 3| 5| 60.0|\n", + "|9WY5jCB2IjnOizmgz...| 1.0| 2| 5| 40.0|\n", + "|MwrBRpXMRNolVkaL3...| 0.0| 2| 7| 28.57142857142857|\n", + "|MwrBRpXMRNolVkaL3...| 1.0| 5| 7| 71.42857142857143|\n", + "+--------------------+-------------------+---------------+-------------+--------------------+\n", + "\n", + "Topic Percentages:\n", + "+--------------------+----------------+-----------+-------------+------------------+\n", + "| business_id| topic|topic_count|total_reviews| topic_percentage|\n", + "+--------------------+----------------+-----------+-------------+------------------+\n", + "|TozkPEh-xhts3qfVe...| food_quality| 60| 97|61.855670103092784|\n", + "|TozkPEh-xhts3qfVe...| other| 31| 97|31.958762886597935|\n", + "|TozkPEh-xhts3qfVe...|customer_service| 6| 97| 6.185567010309279|\n", + "|7SHh7-aWuAVlp3YKa...|customer_service| 1| 48| 2.083333333333333|\n", + "|7SHh7-aWuAVlp3YKa...| food_quality| 3| 48| 6.25|\n", + "|7SHh7-aWuAVlp3YKa...| other| 44| 48| 91.66666666666666|\n", + "|mVEqN8IPU-vCeKTXC...| other| 3| 9| 33.33333333333333|\n", + "|mVEqN8IPU-vCeKTXC...| food_quality| 6| 9| 66.66666666666666|\n", + "|9WY5jCB2IjnOizmgz...| cleanliness| 1| 5| 20.0|\n", + "|9WY5jCB2IjnOizmgz...| other| 1| 5| 20.0|\n", + "|9WY5jCB2IjnOizmgz...| food_quality| 3| 5| 60.0|\n", + "|MwrBRpXMRNolVkaL3...| other| 7| 7| 100.0|\n", + "+--------------------+----------------+-----------+-------------+------------------+\n", + "\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/Merge_CSVs.ipynb b/NoteBook/Merge_CSVs.ipynb similarity index 100% rename from Merge_CSVs.ipynb rename to NoteBook/Merge_CSVs.ipynb diff --git a/NoteBook/Roshan_senti.ipynb b/NoteBook/Roshan_senti.ipynb new file mode 100644 index 0000000..a8d882d --- /dev/null +++ b/NoteBook/Roshan_senti.ipynb @@ -0,0 +1,742 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "tglWjtAwVwJD" + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import nltk\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\n", + "from sklearn.naive_bayes import MultinomialNB\n", + "from sklearn.metrics import classification_report, confusion_matrix" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 293 + }, + "id": "U2Wd1VVfV6SC", + "outputId": "4d6e5a5a-c6c4-422e-d962-0bacba3b38af" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " _id user_id business_id \\\n", + "0 66ea489ae59c7c5b6d8be1ac AGNUgVwnZUey3gcPCJ76iw 3uLgwr0qeCNMjKenHJwPGQ \n", + "1 66ea489ae59c7c5b6d8be1ad NBN4MgHP9D3cw--SnauTkA QoezRbYQncpRqyrLH6Iqjg \n", + "2 66ea489ae59c7c5b6d8be1ae -copOvldyKh1qr-vzkDEvw MYoRNLb5chwjQe3c_k37Gg \n", + "3 66ea489ae59c7c5b6d8be1af FjMQVZjSqY8syIO-53KFKw hV-bABTK-glh5wj31ps_Jw \n", + "4 66ea489ae59c7c5b6d8be1b0 ld0AperBXk1h6UbqmM80zw _uN0OudeJ3Zl_tf6nxg5ww \n", + "\n", + " text date \\\n", + "0 Avengers time with the ladies. 2012-05-18 02:17:21 \n", + "1 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066ea489ae59c7c5b6d8be1acAGNUgVwnZUey3gcPCJ76iw3uLgwr0qeCNMjKenHJwPGQAvengers time with the ladies.2012-05-18 02:17:210.0
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And so many rules!\",\n \"Nothing yet- review to come for sho...\",\n \"Food was pretty good! Service was outstanding! I will be coming back!\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"date\",\n \"properties\": {\n \"dtype\": \"object\",\n \"num_unique_values\": 47251,\n \"samples\": [\n \"2012-10-10 20:40:31\",\n \"2014-07-04 03:00:31\",\n \"2015-02-25 14:56:02\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"compliment_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.12363380255529388,\n \"min\": 0.0,\n \"max\": 4.0,\n \"num_unique_values\": 5,\n \"samples\": [\n 1.0,\n 4.0,\n 2.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 3 + } + ], + "source": [ + "df=pd.read_csv('/content/yelp_tip.csv')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_Pj6djUXeiOk", + "outputId": "13eabbf2-d93f-4fe0-939b-0e0bfa2bb611" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "RangeIndex: 47264 entries, 0 to 47263\n", + "Data columns (total 6 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 _id 47264 non-null object \n", + " 1 user_id 47264 non-null object \n", + " 2 business_id 47263 non-null object \n", + " 3 text 47261 non-null object \n", + " 4 date 47263 non-null object \n", + " 5 compliment_count 47263 non-null float64\n", + "dtypes: float64(1), object(5)\n", + "memory usage: 2.2+ MB\n" + ] + } + ], + "source": [ + "# prompt: print schema of df\n", + "\n", + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xx2FfzDGZWiv" + }, + "outputs": [], + "source": [ + "# import nltk\n", + "\n", + "# # Download 'stopwords' and 'wordnet' from NLTK\n", + "# nltk.download('stopwords')\n", + "# nltk.download('wordnet')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "KCEbdZbdaaUf" + }, + "outputs": [], + "source": [ + "# from nltk.corpus import stopwords\n", + "# from nltk.corpus import wordnet" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5yXQoR--XpAu" + }, + "outputs": [], + "source": [ + "# import nltk\n", + "# from nltk.corpus import stopwords\n", + "# from nltk.stem import WordNetLemmatizer\n", + "# import string\n", + "# import pandas as pd\n", + "\n", + "# # Download resources if not already downloaded\n", + "# nltk.download('stopwords')\n", + "# nltk.download('wordnet')\n", + "\n", + "# # Initialize lemmatizer and stop words\n", + "# lemmatizer = WordNetLemmatizer()\n", + "# stop_words = set(stopwords.words('english'))\n", + "\n", + "# # Sample DataFrame for demonstration (replace with your actual DataFrame)\n", + "# data = {'text': [\"Example sentence for preprocessing.\", None, \"Another example text.\", 123.45]}\n", + "# df = pd.DataFrame(data)\n", + "\n", + "# def preprocess_text(text):\n", + "# # Convert non-string types to an empty string\n", + "# if not isinstance(text, str):\n", + "# text = ''\n", + "# # Remove punctuation\n", + "# text = text.translate(str.maketrans('', '', string.punctuation))\n", + "# # Tokenize and lemmatize\n", + "# words = text.split()\n", + "# words = [lemmatizer.lemmatize(word.lower()) for word in words if word.lower() not in stop_words]\n", + "# return ' '.join(words)\n", + "\n", + "# # Fill NaNs with empty strings before applying preprocessing\n", + "# df['text'] = df['text'].fillna('')\n", + "# df['cleaned_text'] = df['text'].apply(preprocess_text)\n", + "\n", + "# df # Display the DataFrame to confirm results\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "T_HW0B8JXo4M" + }, + "outputs": [], + "source": [ + "# # Define sentiment labels\n", + "# df['sentiment'] = df['compliment_count'].apply(lambda x: 'pos' if x > 0 else 'neg')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "VidGzeH0XoxH" + }, + "outputs": [], + "source": [ + "# print(df.columns)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qLwvF41aXotk" + }, + "outputs": [], + "source": [ + "# if 'compliment_count' not in df.columns:\n", + "# df['compliment_count'] = 0 # or any other default value\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5THjo_VSXoq7" + }, + "outputs": [], + "source": [ + "# # Ensure 'compliment_count' exists in the DataFrame or add it if necessary\n", + "# if 'compliment_count' not in df.columns:\n", + "# df['compliment_count'] = 0 # Add with a default value, e.g., 0\n", + "\n", + "# # Define sentiment labels based on 'compliment_count' values\n", + "# df['sentiment'] = df['compliment_count'].apply(lambda x: 'pos' if x > 0 else 'neg')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yA1BKoVjXooT" + }, + "outputs": [], + "source": [ + "# #Splitting the Dataset\n", + "# X = df['cleaned_text']\n", + "# y = df['sentiment']\n", + "\n", + "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "X7D3RUJ6XolV" + }, + "outputs": [], + "source": [ + "# # Count Vectorization\n", + "# count_vect = CountVectorizer()\n", + "# X_train_counts = count_vect.fit_transform(X_train)\n", + "\n", + "# # TF-IDF Transformation\n", + "# tfidf_transformer = TfidfTransformer()\n", + "# X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "kINv1yVFXoim" + }, + "outputs": [], + "source": [ + "# # Train the classifier\n", + "# clf = MultinomialNB()\n", + "# clf.fit(X_train_tfidf, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "qd759H5rdGRP" + }, + "outputs": [], + "source": [ + "# # Transform the test data\n", + "# X_test_counts = count_vect.transform(X_test)\n", + "# X_test_tfidf = tfidf_transformer.transform(X_test_counts)\n", + "\n", + "# # Make predictions\n", + "# predicted = clf.predict(X_test_tfidf)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "txfIXKGFdGN3" + }, + "outputs": [], + "source": [ + "# # Evaluate the model\n", + "# print(confusion_matrix(y_test, predicted))\n", + "# print(classification_report(y_test, predicted))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UUkfQI5fflxJ", + "outputId": "70370c0f-99fd-4298-bffd-3c17c55acfb7" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package vader_lexicon to /root/nltk_data...\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + " text neg neu \\\n", + "0 Avengers time with the ladies. 0.000 1.000 \n", + "1 They have lots of good deserts and tasty cuban... 0.000 0.756 \n", + "2 It's open even when you think it isn't 0.000 1.000 \n", + "3 Very decent fried chicken 0.000 1.000 \n", + "4 Appetizers.. platter special for lunch 0.000 0.597 \n", + "... ... ... ... \n", + "114530 Very good burgers and love the crunchy fries. ... 0.000 0.489 \n", + "114531 Call to find out what the wait is like. Cause ... 0.000 0.857 \n", + "114532 Great gym. Great people. Great, friendly staff... 0.000 0.387 \n", + "114533 Good pizza bad service 0.417 0.238 \n", + "114534 The automatic wash is fun! Quite entertaining ... 0.000 0.410 \n", + "\n", + " pos compound sentiment \n", + "0 0.000 0.0000 neutral \n", + "1 0.244 0.4404 positive \n", + "2 0.000 0.0000 neutral \n", + "3 0.000 0.0000 neutral \n", + "4 0.403 0.4019 positive \n", + "... ... ... ... \n", + "114530 0.511 0.9099 positive \n", + "114531 0.143 0.3612 positive \n", + "114532 0.613 0.9758 positive \n", + "114533 0.345 -0.1531 negative \n", + "114534 0.590 0.8775 positive \n", + "\n", + "[114535 rows x 6 columns]\n" + ] + } + ], + "source": [ + "# Import necessary libraries\n", + "import pandas as pd\n", + "import nltk\n", + "from nltk.sentiment import SentimentIntensityAnalyzer\n", + "\n", + "\n", + "df = pd.read_csv('/content/yelp_tip.csv')\n", + "\n", + "# Download VADER lexicon\n", + "nltk.download('vader_lexicon')\n", + "\n", + "# Initialize the SentimentIntensityAnalyzer\n", + "sia = SentimentIntensityAnalyzer()\n", + "\n", + "# Define a function for sentiment analysis\n", + "def analyze_sentiment(text):\n", + " # Convert the input to string if it's not already\n", + " if not isinstance(text, str):\n", + " text = str(text)\n", + "\n", + " score = sia.polarity_scores(text)\n", + " return score\n", + "\n", + "# Apply sentiment analysis to the text column\n", + "df['sentiment_scores'] = df['text'].apply(analyze_sentiment)\n", + "\n", + "# Expand the sentiment scores into separate columns\n", + "df_sentiment = df['sentiment_scores'].apply(pd.Series)\n", + "\n", + "# Combine the original DataFrame with the sentiment scores\n", + "df = pd.concat([df, df_sentiment], axis=1)\n", + "\n", + "# Function to classify sentiment based on compound score\n", + "def classify_sentiment(compound_score):\n", + " if compound_score >= 0.05:\n", + " return 'positive'\n", + " elif compound_score <= -0.05:\n", + " return 'negative'\n", + " else:\n", + " return 'neutral'\n", + "\n", + "# Apply the classification function\n", + "df['sentiment'] = df['compound'].apply(classify_sentiment)\n", + "\n", + "# Display the DataFrame with sentiment analysis results\n", + "print(df[['text', 'neg', 'neu', 'pos', 'compound', 'sentiment']])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "PTXBFouifYDd" + }, + "outputs": [], + "source": [ + "\n", + "# Save the DataFrame to Google Drive as a CSV file\n", + "df.to_csv('/content/drive/MyDrive/ProcessedCSV/tip_sentiments.csv', index=False)" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "3vfX_ypSi_pp" + }, + "execution_count": null, + "outputs": [] + } + ], + "metadata": { + "colab": { + "provenance": [], + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/NoteBook/Untitled10.ipynb b/NoteBook/Untitled10.ipynb new file mode 100644 index 0000000..8983709 --- /dev/null +++ b/NoteBook/Untitled10.ipynb @@ -0,0 +1,522 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMDyF8V5/4X3nUD5uAIYzMW", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "ZxPKNDMKWQGu", + "outputId": "2c376954-4508-47b7-9e6f-dc2d67008ee7" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "LinearRegression()" + ], + "text/html": [ + "
LinearRegression()
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "# Example: Train a simple model\n", + "X = [[0, 1], [1, 1], [2, 2], [3, 3]]\n", + "y = [0, 1, 2, 3]\n", + "model = LinearRegression()\n", + "model.fit(X,y)" + ] + }, + { + "cell_type": "code", + "source": [ + "import pickle\n", + "\n", + "# Save the model to a file\n", + "with open('model.pkl', 'wb') as file:\n", + " pickle.dump(model,file)" + ], + "metadata": { + "id": "QCu-y9jiWW7y" + }, + "execution_count": 3, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Load the model from the file\n", + "with open('model.pkl', 'rb') as file:\n", + " loaded_model = pickle.load(file)\n", + "\n", + "# Test the loaded model\n", + "print(loaded_model.predict([[1, 1]])) # Should give the same result as the original model" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "f_5fTOW9WndY", + "outputId": "2511196b-30c7-4607-f02e-37039341287e" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[1.]\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "epR1EQWtWr05" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/NoteBook/Using_KM_RecSys_Predictions_.ipynb b/NoteBook/Using_KM_RecSys_Predictions_.ipynb new file mode 100644 index 0000000..a7a749c --- /dev/null +++ b/NoteBook/Using_KM_RecSys_Predictions_.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "u6fwtBQ4UzAy", + "outputId": "6e549c34-0634-4550-f4ae-8a26065d78fd" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restaurant Recommender System\n", + "Enter your latitude: 53.5187059314\n", + "Enter your longitude: -113.4975095574\n", + "Enter minimum rating (0-5): 3.5\n", + "Need parking? (yes/no): No\n", + "Need WiFi? (yes/no): No\n", + "Enter cuisine type: American\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "| business_id| bus_name| address|rating| distance|cuisine_type|\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "|tGl2jBbSVPEl27cw9...|Danny Hacksaw's P...|1503 Lakewood Road W| 4.5| 7.143981489706797| American|\n", + "|sFjzcP5GXcCmQAGOG...| Red Ox Inn| 9420 91 Street NW| 4.5| 2.455386840412777| American|\n", + "|if2ZvtLUOdn6EFeWj...| El Fogón Latino| 8026 118 Avenue| 4.5| 6.226928045014033| American|\n", + "|G5jxVV_AIHCquF0B3...| Ernest's|Hokanson Centre f...| 4.5| 5.704328999322424| American|\n", + "|2KhwR5XB2u88e3AgH...| Monty's Pizza|1027 Potter Green...| 4.5|11.858116310387844| American|\n", + "+--------------------+--------------------+--------------------+------+------------------+------------+\n", + "\n" + ] + } + ], + "source": [ + "from pyspark.sql import SparkSession\n", + "from pyspark.ml import PipelineModel\n", + "from pyspark.sql.functions import col, expr, radians, lit, row_number, sqrt, sin, cos, asin,power\n", + "from pyspark.sql.window import Window\n", + "from pyspark.ml.feature import StringIndexer, VectorAssembler, StandardScaler\n", + "\n", + "class RestaurantRecommenderPredictor:\n", + " def __init__(self, spark_session):\n", + " \"\"\"\n", + " Initialize the Restaurant Recommender Predictor\n", + " \"\"\"\n", + " self.spark = spark_session\n", + " self.df = None\n", + " self.kmeans_model = None\n", + " self.cuisine_indexer = None\n", + " self.vector_assembler = None\n", + " self.scaler = None\n", + "\n", + " def load_data(self, data_path):\n", + " \"\"\"\n", + " Load and preprocess restaurant data\n", + " \"\"\"\n", + " # Read the CSV file\n", + " self.df = self.spark.read.csv(data_path, header=True, inferSchema=True)\n", + "\n", + " # Data cleaning and preprocessing\n", + " self.df = self.df.na.drop() # Remove rows with null values\n", + "\n", + " # Convert boolean columns\n", + " self.df = (self.df.withColumn(\"has_parking\",\n", + " expr(\"CASE WHEN parking = 'Yes' THEN true ELSE false END\"))\n", + " .withColumn(\"has_wifi\",\n", + " expr(\"CASE WHEN WiFi = 'Yes' THEN true ELSE false END\")))\n", + "\n", + " # Encode categorical variables\n", + " self.cuisine_indexer = StringIndexer(\n", + " inputCol=\"cuisine_type\",\n", + " outputCol=\"cuisine_type_encoded\"\n", + " )\n", + " self.df = self.cuisine_indexer.fit(self.df).transform(self.df)\n", + "\n", + " return self.df\n", + "\n", + " def load_saved_model(self, model_path):\n", + " \"\"\"\n", + " Load the saved PySpark Pipeline Model\n", + " \"\"\"\n", + " try:\n", + " # Load the entire pipeline model\n", + " self.kmeans_model = PipelineModel.load(model_path)\n", + "\n", + " # Extract specific stages from the pipeline\n", + " stages = self.kmeans_model.stages\n", + "\n", + " # Find and store vector assembler and scaler\n", + " for stage in stages:\n", + " if isinstance(stage, VectorAssembler):\n", + " self.vector_assembler = stage\n", + " elif isinstance(stage, StandardScaler):\n", + " self.scaler = stage\n", + "\n", + " return self.kmeans_model\n", + " except Exception as e:\n", + " print(f\"Error loading model: {e}\")\n", + " return None\n", + "\n", + " def haversine_distance(self, lat1, lon1, lat2, lon2):\n", + " \"\"\"\n", + " Calculate distance between two geographical points\n", + " \"\"\"\n", + " lat1_rad = radians(lit(lat1))\n", + " lon1_rad = radians(lit(lon1))\n", + " lat2_rad = radians(lat2)\n", + " lon2_rad = radians(lon2)\n", + "\n", + " return (\n", + " 6371 * 2 * asin(\n", + " sqrt(\n", + " sin((lat2_rad - lat1_rad) / 2) ** 2 +\n", + " cos(lat1_rad) * cos(lat2_rad) *\n", + " sin((lon2_rad - lon1_rad) / 2) ** 2\n", + " )\n", + " )\n", + " )\n", + "\n", + " def recommend_restaurants(\n", + " self,\n", + " user_location,\n", + " min_rating,\n", + " need_parking,\n", + " need_wifi,\n", + " cuisine_type,\n", + " max_distance=60\n", + " ):\n", + " \"\"\"\n", + " Recommend restaurants based on user preferences\n", + " \"\"\"\n", + " user_lat, user_lon = user_location\n", + "\n", + " # Apply initial filters\n", + " filtered_df = self.df.filter(\n", + " (col(\"rating\") >= min_rating) &\n", + " (col(\"cuisine_type\") == cuisine_type)\n", + " )\n", + "\n", + " # Apply parking filter\n", + " if need_parking:\n", + " filtered_df = filtered_df.filter(col(\"has_parking\") == True)\n", + "\n", + " # Apply WiFi filter\n", + " if need_wifi:\n", + " filtered_df = filtered_df.filter(col(\"has_wifi\") == True)\n", + "\n", + " # Add distance column\n", + " with_distance_df = filtered_df.withColumn(\n", + " \"distance\",\n", + " self.haversine_distance(user_lat, user_lon, col(\"latitude\"), col(\"longitude\"))\n", + " )\n", + "\n", + " # Filter by distance\n", + " nearby_restaurants = with_distance_df.filter(\n", + " col(\"distance\") <= max_distance\n", + " )\n", + "\n", + " # Rank restaurants\n", + " window_spec = Window.partitionBy(\"bus_name\").orderBy(col(\"rating\").desc())\n", + "\n", + " recommended_restaurants = (\n", + " nearby_restaurants\n", + " .withColumn(\"rank\", row_number().over(window_spec))\n", + " .filter(col(\"rank\") == 1)\n", + " .orderBy(col(\"rating\").desc())\n", + " .select(\"business_id\", \"bus_name\", \"address\", \"rating\", \"distance\", \"cuisine_type\")\n", + " .limit(5)\n", + " )\n", + "\n", + " return recommended_restaurants\n", + "\n", + "def interactive_recommendation(predictor):\n", + " \"\"\"\n", + " Interactive restaurant recommendation interface\n", + " \"\"\"\n", + " print(\"Restaurant Recommender System\")\n", + "\n", + " # Get user inputs\n", + " user_lat = float(input(\"Enter your latitude: \"))\n", + " user_lon = float(input(\"Enter your longitude: \"))\n", + " min_rating = float(input(\"Enter minimum rating (0-5): \"))\n", + " need_parking = input(\"Need parking? (yes/no): \").lower() == 'yes'\n", + " need_wifi = input(\"Need WiFi? (yes/no): \").lower() == 'yes'\n", + " cuisine_type = input(\"Enter cuisine type: \")\n", + "\n", + " # Make recommendations\n", + " recommendations = predictor.recommend_restaurants(\n", + " user_location=(user_lat, user_lon),\n", + " min_rating=min_rating,\n", + " need_parking=need_parking,\n", + " need_wifi=need_wifi,\n", + " cuisine_type=cuisine_type\n", + " )\n", + "\n", + " # Show recommendations\n", + " recommendations.show()\n", + "\n", + "def main():\n", + " # Create Spark Session\n", + " spark = SparkSession.builder \\\n", + " .appName(\"RestaurantRecommenderPredictor\") \\\n", + " .getOrCreate()\n", + "\n", + " # Define paths\n", + " model_path = '/content/drive/MyDrive/Models/restaurant_recommender_Final_model'\n", + " data_path = '/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv'\n", + "\n", + " try:\n", + " # Initialize predictor\n", + " predictor = RestaurantRecommenderPredictor(spark)\n", + "\n", + " # Load dataset\n", + " predictor.load_data(data_path)\n", + "\n", + " # Load saved model\n", + " loaded_model = predictor.load_saved_model(model_path)\n", + "\n", + " if loaded_model:\n", + " # Start interactive recommendation\n", + " interactive_recommendation(predictor)\n", + " else:\n", + " print(\"Failed to load the model.\")\n", + "\n", + " except Exception as e:\n", + " print(f\"An error occurred: {e}\")\n", + " finally:\n", + " spark.stop()\n", + "\n", + "if __name__ == \"__main__\":\n", + " main()" + ] + } + ], + "metadata": { + "colab": { + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/NoteBook/brick.ipynb b/NoteBook/brick.ipynb new file mode 100644 index 0000000..8a99e6a --- /dev/null +++ b/NoteBook/brick.ipynb @@ -0,0 +1,144 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyP7xVaASTvQGBUdHC6gHR/+", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_u28rTWlh3fy", + "outputId": "001c88db-9ab4-4cee-ee26-ce62b3b44b96" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "# prompt: code to drive mount\n", + "\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "source": [ + "# Import necessary libraries for BRICK or collaborative filtering\n", + "import pandas as pd\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "from surprise import Dataset, Reader, SVD\n", + "from surprise.model_selection import train_test_split\n", + "import numpy as np\n", + "\n", + "# Load your data into a DataFrame (df should have 'user_id', 'business_id', and 'rating' columns)\n", + "# df = pd.read_csv('your_data.csv') # Example of loading your data\n", + "\n", + "# Assuming the DataFrame df contains 'user_id', 'business_id', and 'rating'\n", + "reader = Reader(rating_scale=(1, 5)) # Assuming ratings are on a scale of 1 to 5\n", + "data = Dataset.load_from_df(df[['user_id', 'business_id', 'rating']], reader)\n", + "\n", + "# Split the data into training and test sets\n", + "trainset, testset = train_test_split(data, test_size=0.2)\n", + "\n", + "# Train the BRICK recommendation model (using SVD here as an example)\n", + "model = SVD()\n", + "model.fit(trainset)\n", + "\n", + "# Predict ratings for the test set\n", + "y_pred = model.test(testset)\n", + "\n", + "# Extract predictions and true ratings\n", + "y_true = [true_r for (_, _, true_r) in testset]\n", + "y_pred_ratings = [pred.est for (uid, iid, pred) in y_pred]\n", + "\n", + "# Calculate evaluation metrics\n", + "mse = mean_squared_error(y_true, y_pred_ratings)\n", + "rmse = mean_squared_error(y_true, y_pred_ratings, squared=False)\n", + "mae = mean_absolute_error(y_true, y_pred_ratings)\n", + "r2 = r2_score(y_true, y_pred_ratings)\n", + "\n", + "print(f\"Mean Squared Error (MSE): {mse}\")\n", + "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n", + "print(f\"Mean Absolute Error (MAE): {mae}\")\n", + "print(f\"R-squared (R2): {r2}\")\n", + "\n", + "def predict_success_for_business_ids(business_ids, model, features_df, feature_columns):\n", + " # Predict ratings for the given business IDs\n", + " input_data = features_df[features_df[\"business_id\"].isin(business_ids)]\n", + " success_probabilities = []\n", + "\n", + " for business_id in business_ids:\n", + " # Use the recommendation model to predict a rating for the business_id\n", + " pred = model.predict(user_id, business_id) # Specify a user_id as needed\n", + " success_probabilities.append(pred.est)\n", + "\n", + " # Create a DataFrame to display results\n", + " result_df = pd.DataFrame({\n", + " \"business_id\": business_ids,\n", + " \"success_probability\": success_probabilities\n", + " })\n", + "\n", + " return result_df\n", + "\n", + "# Example usage\n", + "business_ids_list = df['business_id'].tolist() # Create a list of all business ids\n", + "business_ids_to_predict = business_ids_list # Use this for prediction\n", + "\n", + "# Specify a user_id for the predictions\n", + "user_id = 'your_user_id' # Replace with actual user ID\n", + "success_probabilities = predict_success_for_business_ids(business_ids_to_predict, model, df, ['business_id'])\n", + "\n", + "print(\"Predicted Success Probabilities:\")\n", + "print(success_probabilities)\n", + "\n", + "# Substitute a variable instead of directly giving business ID in SQL\n", + "business_df.createOrReplaceTempView(\"business_temp\")\n", + "review_df.createOrReplaceTempView(\"review_temp\")\n", + "checkin_df.createOrReplaceTempView(\"checkin_temp\")\n", + "business_id_to_search = 'SuSEmi52lP8gquHV0XIB9g' # Replace with your variable\n", + "\n", + "# Using SQL queries\n", + "spark.sql(f\"SELECT * FROM business_temp WHERE business_id = '{business_id_to_search}'\").show()\n", + "spark.sql(f\"SELECT * FROM review_temp WHERE business_id = '{business_id_to_search}'\").show()\n", + "spark.sql(f\"SELECT * FROM checkin_temp WHERE business_id = '{business_id_to_search}'\").show()\n" + ], + "metadata": { + "id": "1aNFkM6Th-T-" + }, + "execution_count": null, + "outputs": [] + } + ] +} diff --git a/NoteBook/bricks.ipynb b/NoteBook/bricks.ipynb new file mode 100644 index 0000000..5f7d652 --- /dev/null +++ b/NoteBook/bricks.ipynb @@ -0,0 +1,144 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyP7xVaASTvQGBUdHC6gHR/+", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "_u28rTWlh3fy", + "outputId": "001c88db-9ab4-4cee-ee26-ce62b3b44b96" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "# prompt: code to drive mount\n", + "\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "source": [ + "# Import necessary libraries for BRICK or collaborative filtering\n", + "import pandas as pd\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "from surprise import Dataset, Reader, SVD\n", + "from surprise.model_selection import train_test_split\n", + "import numpy as np\n", + "\n", + "# Load your data into a DataFrame (df should have 'user_id', 'business_id', and 'rating' columns)\n", + "# df = pd.read_csv('your_data.csv') # Example of loading your data\n", + "\n", + "# Assuming the DataFrame df contains 'user_id', 'business_id', and 'rating'\n", + "reader = Reader(rating_scale=(1, 5)) # Assuming ratings are on a scale of 1 to 5\n", + "data = Dataset.load_from_df(df[['user_id', 'business_id', 'rating']], reader)\n", + "\n", + "# Split the data into training and test sets\n", + "trainset, testset = train_test_split(data, test_size=0.2)\n", + "\n", + "# Train the BRICK recommendation model (using SVD here as an example)\n", + "model = SVD()\n", + "model.fit(trainset)\n", + "\n", + "# Predict ratings for the test set\n", + "y_pred = model.test(testset)\n", + "\n", + "# Extract predictions and true ratings\n", + "y_true = [true_r for (_, _, true_r) in testset]\n", + "y_pred_ratings = [pred.est for (uid, iid, pred) in y_pred]\n", + "\n", + "# Calculate evaluation metrics\n", + "mse = mean_squared_error(y_true, y_pred_ratings)\n", + "rmse = mean_squared_error(y_true, y_pred_ratings, squared=False)\n", + "mae = mean_absolute_error(y_true, y_pred_ratings)\n", + "r2 = r2_score(y_true, y_pred_ratings)\n", + "\n", + "print(f\"Mean Squared Error (MSE): {mse}\")\n", + "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n", + "print(f\"Mean Absolute Error (MAE): {mae}\")\n", + "print(f\"R-squared (R2): {r2}\")\n", + "\n", + "def predict_success_for_business_ids(business_ids, model, features_df, feature_columns):\n", + " # Predict ratings for the given business IDs\n", + " input_data = features_df[features_df[\"business_id\"].isin(business_ids)]\n", + " success_probabilities = []\n", + "\n", + " for business_id in business_ids:\n", + " # Use the recommendation model to predict a rating for the business_id\n", + " pred = model.predict(user_id, business_id) # Specify a user_id as needed\n", + " success_probabilities.append(pred.est)\n", + "\n", + " # Create a DataFrame to display results\n", + " result_df = pd.DataFrame({\n", + " \"business_id\": business_ids,\n", + " \"success_probability\": success_probabilities\n", + " })\n", + "\n", + " return result_df\n", + "\n", + "# Example usage\n", + "business_ids_list = df['business_id'].tolist() # Create a list of all business ids\n", + "business_ids_to_predict = business_ids_list # Use this for prediction\n", + "\n", + "# Specify a user_id for the predictions\n", + "user_id = 'your_user_id' # Replace with actual user ID\n", + "success_probabilities = predict_success_for_business_ids(business_ids_to_predict, model, df, ['business_id'])\n", + "\n", + "print(\"Predicted Success Probabilities:\")\n", + "print(success_probabilities)\n", + "\n", + "# Substitute a variable instead of directly giving business ID in SQL\n", + "business_df.createOrReplaceTempView(\"business_temp\")\n", + "review_df.createOrReplaceTempView(\"review_temp\")\n", + "checkin_df.createOrReplaceTempView(\"checkin_temp\")\n", + "business_id_to_search = 'SuSEmi52lP8gquHV0XIB9g' # Replace with your variable\n", + "\n", + "# Using SQL queries\n", + "spark.sql(f\"SELECT * FROM business_temp WHERE business_id = '{business_id_to_search}'\").show()\n", + "spark.sql(f\"SELECT * FROM review_temp WHERE business_id = '{business_id_to_search}'\").show()\n", + "spark.sql(f\"SELECT * FROM checkin_temp WHERE business_id = '{business_id_to_search}'\").show()\n" + ], + "metadata": { + "id": "1aNFkM6Th-T-" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/NoteBook/cleaned_review_sentiment.ipynb b/NoteBook/cleaned_review_sentiment.ipynb new file mode 100644 index 0000000..d52406d --- /dev/null +++ b/NoteBook/cleaned_review_sentiment.ipynb @@ -0,0 +1,1158 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FEquGu9_xCgO" + }, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "1u8Lh2e_xNmq" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# from google.colab import drive\n", + "# drive.mount('/content/drive')" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 304 + }, + "id": "jBWta1fQxW5e", + "outputId": "a1fc5de8-5100-462d-a044-0bcfa618d671" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "error", + "ename": "MessageError", + "evalue": "Error: credential propagation was unsuccessful", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mMessageError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m 98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 99\u001b[0m \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m return _mount(\n\u001b[0m\u001b[1;32m 101\u001b[0m \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 102\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m 135\u001b[0m )\n\u001b[1;32m 136\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 137\u001b[0;31m _message.blocking_request(\n\u001b[0m\u001b[1;32m 138\u001b[0m \u001b[0;34m'request_auth'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 139\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'authType'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'dfs_ephemeral'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m 174\u001b[0m \u001b[0mrequest_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpect_reply\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 175\u001b[0m )\n\u001b[0;32m--> 176\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m 101\u001b[0m ):\n\u001b[1;32m 102\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 104\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "df = pd.read_csv('/content/drive/MyDrive/ProcessedCSV/processed_data.csv')" + ], + "metadata": { + "id": "iNfVy6ebxNjO" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 469 + }, + "id": "65rlkJcnxNha", + "outputId": "d557f07f-620c-4a9e-e093-84e1902e3fc0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " _id user_id business_id \\\n", + "0 66ea489ae59c7c5b6d8be1ac AGNUgVwnZUey3gcPCJ76iw 3uLgwr0qeCNMjKenHJwPGQ \n", + "1 66ea489ae59c7c5b6d8be1ad NBN4MgHP9D3cw--SnauTkA QoezRbYQncpRqyrLH6Iqjg \n", + "2 66ea489ae59c7c5b6d8be1ae -copOvldyKh1qr-vzkDEvw MYoRNLb5chwjQe3c_k37Gg \n", + "3 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"1 0.756 0.244 0.4404 positive \n", + "2 1.000 0.000 0.0000 neutral \n", + "3 1.000 0.000 0.0000 neutral \n", + "4 0.597 0.403 0.4019 positive " + ], + "text/html": [ + "\n", + "
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166ea489ae59c7c5b6d8be1adNBN4MgHP9D3cw--SnauTkAQoezRbYQncpRqyrLH6IqjgThey have lots of good deserts and tasty cuban...2013-02-05 18:35:100.0{'neg': 0.0, 'neu': 0.756, 'pos': 0.244, 'comp...0.00.7560.2440.4404positive
266ea489ae59c7c5b6d8be1ae-copOvldyKh1qr-vzkDEvwMYoRNLb5chwjQe3c_k37GgIt's open even when you think it isn't2013-08-18 00:56:080.0{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...0.01.0000.0000.0000neutral
366ea489ae59c7c5b6d8be1afFjMQVZjSqY8syIO-53KFKwhV-bABTK-glh5wj31ps_JwVery decent fried chicken2017-06-27 23:05:380.0{'neg': 0.0, 'neu': 1.0, 'pos': 0.0, 'compound...0.01.0000.0000.0000neutral
466ea489ae59c7c5b6d8be1b0ld0AperBXk1h6UbqmM80zw_uN0OudeJ3Zl_tf6nxg5wwAppetizers.. platter special for lunch2012-10-06 19:43:090.0{'neg': 0.0, 'neu': 0.597, 'pos': 0.403, 'comp...0.00.5970.4030.4019positive
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066ea489ae59c7c5b6d8be1acAGNUgVwnZUey3gcPCJ76iw3uLgwr0qeCNMjKenHJwPGQ2012-05-18 02:17:210.0000neutral
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266ea489ae59c7c5b6d8be1ae-copOvldyKh1qr-vzkDEvwMYoRNLb5chwjQe3c_k37Gg2013-08-18 00:56:080.0000neutral
366ea489ae59c7c5b6d8be1afFjMQVZjSqY8syIO-53KFKwhV-bABTK-glh5wj31ps_Jw2017-06-27 23:05:380.0000neutral
466ea489ae59c7c5b6d8be1b0ld0AperBXk1h6UbqmM80zw_uN0OudeJ3Zl_tf6nxg5ww2012-10-06 19:43:090.4019positive
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_iduser_idbusiness_iddatesentiment_scoresentiment
066ea489ae59c7c5b6d8be1acAGNUgVwnZUey3gcPCJ76iw3uLgwr0qeCNMjKenHJwPGQ2012-05-18 02:17:210.0000neutral
166ea489ae59c7c5b6d8be1adNBN4MgHP9D3cw--SnauTkAQoezRbYQncpRqyrLH6Iqjg2013-02-05 18:35:100.4404positive
266ea489ae59c7c5b6d8be1ae-copOvldyKh1qr-vzkDEvwMYoRNLb5chwjQe3c_k37Gg2013-08-18 00:56:080.0000neutral
366ea489ae59c7c5b6d8be1afFjMQVZjSqY8syIO-53KFKwhV-bABTK-glh5wj31ps_Jw2017-06-27 23:05:380.0000neutral
466ea489ae59c7c5b6d8be1b0ld0AperBXk1h6UbqmM80zw_uN0OudeJ3Zl_tf6nxg5ww2012-10-06 19:43:090.4019positive
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"cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ku2fqiLGHQPN", + "outputId": "f0977325-eadd-4d13-d41e-1048ae5e7274" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Mounted at /content/drive\n" + ] + } + ], + "source": [ + "# code to drive mount\n", + "\n", + "\n", + "from google.colab import drive\n", + "drive.mount('/content/drive')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Q4BZrDiIHR5V", + "outputId": "2a6c14ba-48ef-4690-a094-498f60679adc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (3.5.3)\n", + "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n", + "Collecting lightfm\n", + " Downloading lightfm-1.17.tar.gz (316 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m316.4/316.4 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lightfm) (1.26.4)\n", + "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from lightfm) (1.13.1)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from lightfm) (2.32.3)\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from lightfm) (1.5.2)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->lightfm) (3.4.0)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from 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"Successfully built lightfm\n", + "Installing collected packages: lightfm\n", + "Successfully installed lightfm-1.17\n", + "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.3)\n", + "Requirement already satisfied: numpy<2.0,>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.26.4)\n", + "Requirement already satisfied: scipy<1.14.0,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.13.1)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (7.0.5)\n", + "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open>=1.8.1->gensim) (1.17.0)\n", + "Requirement already satisfied: textblob in /usr/local/lib/python3.10/dist-packages (0.17.1)\n", + "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.10/dist-packages (from textblob) (3.9.1)\n", + "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (8.1.7)\n", + "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (1.4.2)\n", + "Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (2024.9.11)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk>=3.1->textblob) (4.66.6)\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n", + "Requirement already satisfied: numpy>=1.19.5 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.26.4)\n", + "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n", + "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n", + "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n" + ] + } + ], + "source": [ + "!pip install pyspark\n", + "!pip install lightfm\n", + "!pip install gensim\n", + "!pip install textblob\n", + "!pip install scikit-learn" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "Q8pCZKk6HZuj" + }, + "outputs": [], + "source": [ + "\n", + "from pyspark.sql import SparkSession\n", + "from pyspark.sql import functions as F\n", + "from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType, BooleanType, DateType\n", + "from pyspark.sql.functions import to_date\n", + "import pandas as pd\n", + "import os\n", + "from pyspark.sql.functions import udf, col\n", + "from pyspark.sql.types import DoubleType\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "aZMcXzJdHeWa" + }, + "outputs": [], + "source": [ + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.functions import udf, col, when\n", + "from pyspark.sql.types import StringType\n", + "\n", + "import pandas as pd\n", + "import numpy as np\n", + "import nltk\n", + "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.pipeline import Pipeline\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.multioutput import MultiOutputClassifier\n", + "from sklearn.metrics import classification_report\n", + "from pyspark.sql.functions import udf, col\n", + "from pyspark.sql.types import StringType" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "TVCL1YQyHgeM" + }, + "outputs": [], + "source": [ + "nltk.download('stopwords')\n", + "nltk.download('punkt')\n", + "nltk.download('wordnet')\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "CHF-zZz1HjB8" + }, + "outputs": [], + "source": [ + "from nltk.corpus import stopwords\n", + "from nltk.tokenize import word_tokenize\n", + "from nltk.stem import WordNetLemmatizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pandaudOHkpA" + }, + "outputs": [], + "source": [ + "from pyspark.sql import SparkSession\n", + "import pyspark.sql.functions as F\n", + "\n", + "# Initialize SparkSession\n", + "spark = SparkSession.builder \\\n", + " .appName(\"Filter CSV Data\") \\\n", + " .getOrCreate()\n", + "\n", + "# Load the CSV file into a DataFrame\n", + "review_df = spark.read.csv(\"/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv\", header=True, inferSchema=True)\n", + "review_df.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true + }, + "id": "HMZuJ0lfHmqG" + }, + "outputs": [], + "source": [ + "selected_df = review_df.select(\"business_id\", \"rev_text\")\n", + "\n", + "# Define the save path\n", + "save_path = '/content/drive/MyDrive/ProcessedCSV/Selected_Reviews.csv'\n", + "\n", + "# Save the selected DataFrame to the specified path\n", + "selected_df.write.csv(save_path, header=True, mode='overwrite')\n", + "\n", + "print(f\"Selected DataFrame saved to {save_path}\")\n", + "\n", + "\n", + "selected_df.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "F_TJYWwtzuXX" + }, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true, + "base_uri": "https://localhost:8080/" + }, + "id": "mCbe6G7f195h", + "outputId": "08b952d3-6a9d-487f-bda1-1614d6762cd0" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------+-------+\n", + "|sentiment| count|\n", + "+---------+-------+\n", + "| positive|2470506|\n", + "| negative|1227378|\n", + "+---------+-------+\n", + "\n", + "Test Accuracy: 0.84\n", + "+----------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------------+----------------+\n", + "|business_id |rev_text |sentiment|sentimentPrediction|topic |\n", + "+----------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------------+----------------+\n", + "|-09Oc2D14vRnmirPh0vlXw|I really wish I could give 4 stars for this Cracker Barrel. If this was just on food I would hands down give 4 stars. However this is all on service. This review comes after 20 plus times coming here. The food is always consistently good the service is consistently slow and not good. If it was packed I would understand but I come on Monday or Wed nights when it's not busy at all. It takes forever to get drinks this time 15 minutes after ordering. Food took over 40 min and didn't even get it right. Took 25 min to just get a cup of soup. Not sure how much I can take coming to this location with this bad of service. Maybe try my hand at ordering to go. Didn't even get plates when they brought out the corn bread and biscuits. |positive |0.0 |customer_service|\n", + "|-0TffRSXXIlBYVbb5AwfTg|I went to indieblue a few years ago for happy hour, and found it pretty lack luster at the time. But our last few visits were great. The food was creative and tasty (just stay way from the chicken and waffles, or you'll be really disappointed), that every time I come back I like to branch out on the menu to be surprised. \\n\\nThe drinks were really good, and the service was phenomenal. The staff all around was extremely friendly and welcoming, gave samples of new drinks they were trying out, made accommodations for our toddlers without being asked, and we really enjoyed our time there.\\n\\nSo it's made a spot on the few restaurants we place on rotation for our nights out, |positive |0.0 |customer_service|\n", + "|-0TffRSXXIlBYVbb5AwfTg|Let's get right into it.... OVERRATED!!! I reserved for a party of five, but one person backed only to at the last minute to only change their mind and decided to come... I explained this to the hostess who was getting upset and seemed distressed she had to move plates from one table to another.... The Food was just alright... I am half indian and have eat Indian food all my life from various places... Everyone at the table even noticed the over use of salt to mask, or show that maybe this food is not fresh and probably from a can... Please read the reviews careful as I did not... Most people have gone here for happy hour and probably got so trashed the food tasted great after acouple of drinks... |positive |1.0 |food_quality |\n", + "|-0TffRSXXIlBYVbb5AwfTg|This is a fabulous restaurant (with a terrible name...gotta say I judged them for that at first). This place does true Indian fusion and pulls it off well! With a group of four, we ordered 3-4 small plates, 2 half-sized vegetarian entrees and one full sized meat entree. While the portions looked small (we literally laughed when they brought out our very tasty half portion of squash), we all left feeling full and happy. Some highlights were the spinach chaat (amazing! crispy!) and the naan pizza. Great cocktails too. \\n\\nThe food is better than Bindi and its less pretentious and more interesting than Tashan. Seriously. A new favorite in midtown village. |positive |0.0 |food_quality |\n", + "|-0jzoPt3UeXn6FUXVQvyPg|Stopped by for first thursday and man they have a great deal. They offer 50% off appetizers and boise beers. The beer came out to 2.25 a bottle and the meat cheese plate was 10$. We also got an order of the flat bread which came out to 6$!!! It's too bad they only do this the first thursday of the month for the boise first thursday because it is a great deal you don't want to miss! This place is pretty fancy and more on the high scale end. Best part about this place, it's totally affordable! \\n\\nThe service from the place was a five star. Our waiter was nothing but polite, awesome, and knowledgeable. He was on point with refills and any needs. \\n\\nThe meat and cheese plate was amazing. the variety and assortment was well balanced with your different choices of meat and some side pickled items. \\n\\nGreat place to check out and would recommend it to anyone |positive |0.0 |customer_service|\n", + "|-1B9pP_CrRBJYPICE5WbRA|Got take out for lunch. Super fast and friendly service. The drunken noodles with beef was great. Tender pieces of beef and the side of hot sauce made it even better. The 3 pepper chicken was okay but not something to order again. For the price plus a side of soup was a good deal for lunch. The happy hour specials would be something to come back too. |positive |0.0 |customer_service|\n", + "|-1B9pP_CrRBJYPICE5WbRA|So I like spicy memory, good food, good service when you eat there. Delivery though isn't as consistent, got a mad dry pad Thai, but dumplings are good. Just keep your expectations in the middle and you're good. |positive |0.0 |customer_service|\n", + "|-1PG6k_iezwJmRZLB7f6og|\"What an adorable place!! In the town of zionsville it is very enchanting, little lights all over with nice chandelier and other unique decor. Let's be honest, very comparable to the cake bake shop in zionsville, and exact same concept. I had been trying to come here for months but their limited hours make it tough when I live 25 min away. Closed on mondays and closed by 8/9 Tuesday-Saturday. \\nWe got lemon bars ($4.50) which were very good and one of their \"\"boozy\"\" red velvet milkshakes ($13) Very unique and delicious! Their cakes are made by flying cupcake. We were hungry but the menu items were either too expensive or not really what we were in the mood for. I know they've expanded their menu to brunch so maybe that'll change in the future. Until then 8$ for a basket of French fries doesn't seem reasonable at all.\\nOverall I feel that it got 3 stars because I dont think these prices are worth it. Maybe start a happy hour?? Service and environment was very nice!! a good spot to come for a celebration or special occasion.\" |positive |0.0 |customer_service|\n", + "|-1q9ktAQXpgFSNrst7tzag|Love me some quick Indian food! The food was great, it was quick, and the price was very reasonable. They don't give you a huge portion, which I actually liked because they stuck to what the size of a meal really should be. I'll be coming back! |positive |1.0 |food_quality |\n", + "|-1q9ktAQXpgFSNrst7tzag|Spice Box was my choice by default initially (at an event where the other truck was out of most things). Man, that was my lucky day because this truck is awesome. I've eaten at about a dozen of the Indy food trucks and this is me new favorite. It's sad to think how many times I passed this place up. I ordered the Bollywood and it was an excellent combo meal that gives you a little sampling. My only knock is that the menu descriptions don't translate we'll conceptually (in that you might feel like you are ordering blind). Can't wait to try the Spice Box Truck again. |positive |0.0 |customer_service|\n", + "|-2-ih3mE8KPyeKVIzpBfPQ|Finding a traditional beer garden way up on top of this building was amazing! The beer selection was great, true to Octoberfest tradition. While the appetizers were predictable, they were very tasty. The wings had great spice and the fries with gravy and cheese were delicious. Best of all were the views of the city as the sun set! |positive |1.0 |other |\n", + "|-2VYztMXVorktljCdQNPmQ|I have never had authentic Indian food before - until now. \\nMy oldest daughter suggested that we try this restaurant. \\nSince we are both avid Yelpers, we checked this place out beforehand, and were\\npleasantly surprised that it placed fairly high and consistent in ratings stemming from almost 50 reviews. \\nThat was a good start anyway. \\nAs noted in many of the other reviews, this restaurant is tucked away within a side strip mall that is also deeply tucked away from the beaten path. It is small, and is absolutely beyond bare bones as far as the general atmosphere and decor. It is basically stripped and barren as far as the ambience goes. The chairs are uncomfortable, and the tables are cheap and plain. They are covered with what I would guess is butcher paper (because it is also a butcher shop), and they just remove the sheets of paper each time a customer finishes dining and leaves. \\n\\nThe service is sketchy and minimal. It was unclear who our server really was. We just kept trying to get the attention of anyone that walked by our table to get whatever we needed. They never really checked on us adequately to see if we needed anything or to confirm that the food was OK, or if we needed anything - like drink refills or more napkins - the bottom line basics. \\nThere seemed to be an attitude of indifference that we were even there. It was tough to get our drinks at first. They have a cooler by the front door in which the server vaguely pointed to and suggested that we could walk over and get a drink from there. Really? Were we supposed to assume to know that? That was strange,... and finally I thought,....damn. \\n\\n\\nWe just ended up ordering water regardless. \\nI am unclear if the servers that were around us could speak any amount of English, or if they just pretended that they couldn't. The whole exchange had a slight patronizing air about it. There was a clear communication disconnect. \\nWhen it was time to order, we just ended up pointing at the menu selections to let the server know what we wanted. It was a bit uncomfortable overall. \\n\\n\\nThe food was good for the most part. \\nThat is what propels me to give this restaurant a 3 star - A-OK rating. \\nUnfortunately, the portions were paltry as far as half of the dishes we ordered. We ordered - 4 chicken dishes which were paltry in the amount of chicken that was in them, because 3 of them had more than a generous amount of sauce; or simply said : the chicken was literally swimming/drowning in a sea of sauce. The dishes we ordered did not come with rice (except for one), which was odd and disappointing. We ended up ordering a few large bowls of rice regardless. \\nThe naan bread was great, and was a definite highlight to the meal. You know you are eating good bread when you don't necessarily need butter to enhance its flavor. Besides, it was great for dipping into the various seas of sauce as well. :o) I can honestly say the naan bread helps solidify the 3 star rating. \\nI didn't care for the lamb dish we ordered. The spice combination yielded a little too much clove; and for my own personal taste, a little clove goes a really long way. \\n\\nIf I ever get their food again, I will order take out, and make my own basamati rice at home. If I could suggest a decent dish, the butter chicken was my personal fave. It was slightly hot and spicy, but fell short of not being 'ripping you a bigger one' hot and spicy. \\n\\nI had a slight uneasy and awkward feeling while dining there. I truly felt like I just didn't belong there. It's such a shame when one doesn't feel 100% comfortable in any given or particular business establishment. If I were to have ordered by pickup only, I might have given them 4 stars. But, cordial and sharp service is important to me, so three stars is all I can muster at this time.|positive |0.0 |customer_service|\n", + "|-2VYztMXVorktljCdQNPmQ|Really good pakistani food (owner and food is pakistani but name of restaurant is indian). The best I've tried in the indianapolis area so far. Love the grill food. halal zabihah as well! |positive |0.0 |food_quality |\n", + "|-3-6BB10tIWNKGEF0Es2BA|I ordered the spicy tofu beef the Kung POW chicken and Boba matcha mango latte ... it was a lot of food.. could feel 4 with two orders. This is the best Asian food I have ever had!!!! I recommend everything I ordered OMG it was soo good. And I hate Asian food lol I'm sorry to be so blunt but I thought I did. It turns out I just hate Americanized Asian food. Cant stand Panda Express etc... there are clear vegetables and spices and mixes of flavors in these dishes. Thanks for being such a delicious place to eat. I ordered through door dash. And the packaging was perfect |positive |0.0 |food_quality |\n", + "|-3xX_IfttKjPJ792BOBJ-Q|I have eaten here for the past 5 years now and have always had a great experience. However... Recently this restaurant has undergone new management and I have had a horrible experience both times I went since the new opening. First time eating here after new management took over, service and food was so bad the entire group I was with walked out and left our burned, overcooked meals on the table. I gave it 3 months to get better hoping they just needed to get the skeletons out of the closet BUT low and behold my second meal was just as bad. Every piece of chicken was burnt to a crisp and my spam musubi was hard as a rock. Im very disappointed and really wish this was not the case but this food is so bad. I feel it is wrong to not warn people what they are getting into before they go here. Trust me when I say, GO SOMEWHERE ELSE |positive |0.0 |customer_service|\n", + "|-4gHyiiX6NKNqQ0DO8QhwQ|There's a lot of varieties that was cool and convenient. But the taste wasn't great, it reminded me of like dry ice I think. I'll probably give it another try. Great experience for the kids to see the nitrogen smoke. |positive |0.0 |food_quality |\n", + "|-5ivHc7R7F_fHxTtxGvlgA|An interesting place to either eat out or dine in. This atmosphere is welcoming and dim lighting with simple radio music. At least they do better than Tokyo Express, that is my opinion. The food is different, I ordered the dragon eyes rolls or what seemed to be like it. I found the sauce to be light and not exactly spicy like the others I have tried before. The bento box is the same like every other place which is fine. Sushi presentation is great. I'd give it another try, maybe I should try something different who knows. Staff is friendly and parking is cramped due to stores nearby. You can find more room around the building if your willing to walk a little further. \\nRecommended for anyone looking to try a new sushi place or possibly a date night. |positive |1.0 |customer_service|\n", + "|-7wXZWnKe26iwJY_5z_AAw|The Owner is a great person. The place is beautiful and very warming. Is a decent place to visit after church or at any time. |positive |0.0 |other |\n", + "|-8iATYRnN46Km0_-ldx6cg|After reading the reviews, we went to dinner at Pace. The burger was fabulous and the special that night, Prime Rib, was cooked to perfection. The staff was very friendly and the owner came over to us to chast and see how we were doing. We will be back soon! |positive |0.0 |customer_service|\n", + "|-8iATYRnN46Km0_-ldx6cg|Love Pace!!! It's our go-to restaurant here in SB - never disappoints. Love the casual/bohemian atmosphere. Their food is always SO GOOD. For me it's hard to depart from their grilled cheese deluxe, and my husband rarely ever orders anything but the sloppy joe w/ mac n cheese. Also had the famous Papa Burger, which I can confirm is up there with best burgers I've ever had. Pace does comfort food right. We've always had friendly service. Come hungry! One thing that I'd like to see them do is to vary up their menu or specials a bit more - it's not a big menu and they've had the same dishes on it for quite a while. Just want to try more awesome different meals from them! |positive |0.0 |customer_service|\n", + "+----------------------+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+---------+-------------------+----------------+\n", + "only showing top 20 rows\n", + "\n", + "Sentiment Percentages:\n", + "+----------------------+-------------------+---------------+-------------+--------------------+\n", + "|business_id |sentimentPrediction|sentiment_count|total_reviews|sentiment_percentage|\n", + "+----------------------+-------------------+---------------+-------------+--------------------+\n", + "|-ajaASaDA_77I6pK3D4YgQ|0.0 |4 |22 |18.181818181818183 |\n", + "|-ajaASaDA_77I6pK3D4YgQ|1.0 |18 |22 |81.81818181818183 |\n", + "|-dB0vYhiTeBDlayTzT9kiw|1.0 |1 |2 |50.0 |\n", + "|-dB0vYhiTeBDlayTzT9kiw|0.0 |1 |2 |50.0 |\n", + "|3FKIev7ZB_KE6XHL9sUJCg|1.0 |20 |46 |43.47826086956522 |\n", + "|3FKIev7ZB_KE6XHL9sUJCg|0.0 |26 |46 |56.52173913043478 |\n", + "|3VrqxApK-iwfRohlAuIt0g|0.0 |3 |13 |23.076923076923077 |\n", + "|3VrqxApK-iwfRohlAuIt0g|1.0 |10 |13 |76.92307692307693 |\n", + "|4HMXL85u_wX0WEHucVkI6A|1.0 |15 |26 |57.692307692307686 |\n", + "|4HMXL85u_wX0WEHucVkI6A|0.0 |11 |26 |42.30769230769231 |\n", + "|612K4E9aazB1bix9HAyhMw|0.0 |14 |28 |50.0 |\n", + "|612K4E9aazB1bix9HAyhMw|1.0 |14 |28 |50.0 |\n", + "|7hRaOnXRRS8q620F6G4C7Q|0.0 |10 |31 |32.25806451612903 |\n", + "|7hRaOnXRRS8q620F6G4C7Q|1.0 |21 |31 |67.74193548387096 |\n", + "|7u3LzhyxgPMo__24R4dmAQ|0.0 |23 |47 |48.93617021276596 |\n", + "|7u3LzhyxgPMo__24R4dmAQ|1.0 |24 |47 |51.06382978723404 |\n", + "|BJOGo_upuBElDT_xOaurIA|0.0 |31 |64 |48.4375 |\n", + "|BJOGo_upuBElDT_xOaurIA|1.0 |33 |64 |51.5625 |\n", + "|LnJSsNVZkStgtj86fJrKxQ|1.0 |18 |29 |62.06896551724138 |\n", + "|LnJSsNVZkStgtj86fJrKxQ|0.0 |11 |29 |37.93103448275862 |\n", + "+----------------------+-------------------+---------------+-------------+--------------------+\n", + "only showing top 20 rows\n", + "\n", + "Topic Percentages:\n", + "+----------------------+----------------+-----------+-------------+------------------+\n", + "|business_id |topic |topic_count|total_reviews|topic_percentage |\n", + "+----------------------+----------------+-----------+-------------+------------------+\n", + "|-ajaASaDA_77I6pK3D4YgQ|other |5 |22 |22.727272727272727|\n", + "|-ajaASaDA_77I6pK3D4YgQ|cleanliness |1 |22 |4.545454545454546 |\n", + "|-ajaASaDA_77I6pK3D4YgQ|customer_service|4 |22 |18.181818181818183|\n", + "|-ajaASaDA_77I6pK3D4YgQ|food_quality |12 |22 |54.54545454545454 |\n", + "|-dB0vYhiTeBDlayTzT9kiw|other |1 |2 |50.0 |\n", + "|-dB0vYhiTeBDlayTzT9kiw|customer_service|1 |2 |50.0 |\n", + "|3FKIev7ZB_KE6XHL9sUJCg|pricing |1 |46 |2.1739130434782608|\n", + "|3FKIev7ZB_KE6XHL9sUJCg|other |9 |46 |19.565217391304348|\n", + "|3FKIev7ZB_KE6XHL9sUJCg|food_quality |13 |46 |28.26086956521739 |\n", + "|3FKIev7ZB_KE6XHL9sUJCg|customer_service|23 |46 |50.0 |\n", + "|3VrqxApK-iwfRohlAuIt0g|other |3 |13 |23.076923076923077|\n", + "|3VrqxApK-iwfRohlAuIt0g|customer_service|6 |13 |46.15384615384615 |\n", + "|3VrqxApK-iwfRohlAuIt0g|food_quality |4 |13 |30.76923076923077 |\n", + "|4HMXL85u_wX0WEHucVkI6A|pricing |1 |26 |3.8461538461538463|\n", + "|4HMXL85u_wX0WEHucVkI6A|other |11 |26 |42.30769230769231 |\n", + "|4HMXL85u_wX0WEHucVkI6A|food_quality |6 |26 |23.076923076923077|\n", + "|4HMXL85u_wX0WEHucVkI6A|customer_service|8 |26 |30.76923076923077 |\n", + "|612K4E9aazB1bix9HAyhMw|other |4 |28 |14.285714285714285|\n", + "|612K4E9aazB1bix9HAyhMw|customer_service|14 |28 |50.0 |\n", + "|612K4E9aazB1bix9HAyhMw|food_quality |9 |28 |32.142857142857146|\n", + "+----------------------+----------------+-----------+-------------+------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ], + "source": [ + "from pyspark.sql import SparkSession\n", + "from pyspark.sql.functions import when, col, count, expr\n", + "from pyspark.ml.feature import Tokenizer, StopWordsRemover, HashingTF, IDF, StringIndexer\n", + "from pyspark.ml import Pipeline\n", + "from pyspark.ml.classification import RandomForestClassifier\n", + "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n", + "\n", + "# Step 0: Initialize Spark Session\n", + "spark = SparkSession.builder.appName(\"BusinessReviewsAnalysisWithTopics\").getOrCreate()\n", + "\n", + "# Step 1: Load Dataset\n", + "review_df = spark.read.csv(\"/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv\", header=True, inferSchema=True)\n", + "\n", + "# Step 2: Preprocess the DataFrame\n", + "selected_df = review_df.select(\"business_id\", \"rev_text\")\n", + "\n", + "# Add sentiment labels\n", + "df = selected_df.withColumn(\n", + " \"sentiment\",\n", + " when(col(\"rev_text\").rlike(\"(?i)good|great|excellent|awesome|perfect|fantastic\"), \"positive\").otherwise(\"negative\")\n", + ")\n", + "\n", + "# Define a dictionary mapping keywords to topics\n", + "keywords_to_topics = {\n", + " \"service\": \"customer_service\",\n", + " \"staff\": \"customer_service\",\n", + " \"wait\": \"customer_service\",\n", + " \"food\": \"food_quality\",\n", + " \"taste\": \"food_quality\",\n", + " \"flavor\": \"food_quality\",\n", + " \"hygiene\": \"cleanliness\",\n", + " \"clean\": \"cleanliness\",\n", + " \"price\": \"pricing\",\n", + " \"cost\": \"pricing\"\n", + "}\n", + "\n", + "# Create a case-when expression for topics\n", + "topic_expr = \" \".join(\n", + " [f\"WHEN LOWER(rev_text) LIKE '%{keyword}%' THEN '{topic}'\" for keyword, topic in keywords_to_topics.items()]\n", + ")\n", + "\n", + "# Add topic column\n", + "df = df.withColumn(\"topic\", expr(f\"CASE {topic_expr} ELSE 'other' END\"))\n", + "\n", + "# Step 3: Balance Classes\n", + "# Count positive and negative sentiments\n", + "df.groupBy(\"sentiment\").count().show()\n", + "\n", + "# Downsample or upsample classes if imbalanced\n", + "positive_df = df.filter(col(\"sentiment\") == \"positive\")\n", + "negative_df = df.filter(col(\"sentiment\") == \"negative\")\n", + "\n", + "# Ensure balanced data\n", + "balanced_df = positive_df.sample(False, 0.5, seed=42).unionAll(negative_df)\n", + "\n", + "# Step 4: Feature Extraction Pipeline\n", + "tokenizer = Tokenizer(inputCol=\"rev_text\", outputCol=\"tokens\")\n", + "remover = StopWordsRemover(inputCol=\"tokens\", outputCol=\"filtered_tokens\")\n", + "hashing_tf = HashingTF(inputCol=\"filtered_tokens\", outputCol=\"rawFeatures\", numFeatures=5000)\n", + "idf = IDF(inputCol=\"rawFeatures\", outputCol=\"features\")\n", + "sentiment_indexer = StringIndexer(inputCol=\"sentiment\", outputCol=\"sentimentIndex\")\n", + "\n", + "# Step 5: Model Definition\n", + "rf = RandomForestClassifier(\n", + " labelCol=\"sentimentIndex\",\n", + " featuresCol=\"features\",\n", + " numTrees=20,\n", + " maxDepth=5,\n", + " seed=42\n", + ")\n", + "\n", + "# Build the Pipeline\n", + "pipeline = Pipeline(stages=[tokenizer, remover, hashing_tf, idf, sentiment_indexer, rf])\n", + "\n", + "# Step 6: Split Data\n", + "train_df, test_df = balanced_df.randomSplit([0.8, 0.2], seed=42)\n", + "\n", + "# Step 7: Train the Model\n", + "model = pipeline.fit(train_df)\n", + "\n", + "# Step 8: Test the Model\n", + "predictions = model.transform(test_df)\n", + "\n", + "# Rename the prediction column for clarity (optional)\n", + "predictions = predictions.withColumnRenamed(\"prediction\", \"sentimentPrediction\")\n", + "\n", + "# Evaluate Model\n", + "evaluator = MulticlassClassificationEvaluator(\n", + " labelCol=\"sentimentIndex\",\n", + " predictionCol=\"sentimentPrediction\",\n", + " metricName=\"accuracy\"\n", + ")\n", + "accuracy = evaluator.evaluate(predictions)\n", + "print(f\"Test Accuracy: {accuracy:.2f}\")\n", + "\n", + "# Debug Predictions\n", + "predictions.select(\"business_id\", \"rev_text\", \"sentiment\", \"sentimentPrediction\", \"topic\").show(truncate=False)\n", + "\n", + "# Step 9: Calculate Sentiment Percentages for Each Business\n", + "sentiment_count = predictions.groupBy(\"business_id\", \"sentimentPrediction\").agg(count(\"sentimentPrediction\").alias(\"sentiment_count\"))\n", + "total_reviews = predictions.groupBy(\"business_id\").agg(count(\"rev_text\").alias(\"total_reviews\"))\n", + "\n", + "sentiment_percentage = sentiment_count.join(total_reviews, on=\"business_id\")\n", + "sentiment_percentage = sentiment_percentage.withColumn(\n", + " \"sentiment_percentage\",\n", + " (col(\"sentiment_count\") / col(\"total_reviews\") * 100)\n", + ")\n", + "\n", + "# Step 10: Show Topic Distribution for Each Business\n", + "topic_count = predictions.groupBy(\"business_id\", \"topic\").agg(count(\"topic\").alias(\"topic_count\"))\n", + "total_reviews = predictions.groupBy(\"business_id\").agg(count(\"rev_text\").alias(\"total_reviews\"))\n", + "\n", + "topic_percentage = topic_count.join(total_reviews, on=\"business_id\")\n", + "topic_percentage = topic_percentage.withColumn(\n", + " \"topic_percentage\",\n", + " (col(\"topic_count\") / col(\"total_reviews\") * 100)\n", + ")\n", + "\n", + "# Show Final Results\n", + "print(\"Sentiment Percentages:\")\n", + "sentiment_percentage.show(truncate=False)\n", + "\n", + "print(\"Topic Percentages:\")\n", + "topic_percentage.show(truncate=False)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BxELet0BK2ZT", + "outputId": "0de4ebbe-0d26-457e-cd2a-978bdeee02c5" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model saved at /content/drive/MyDrive/topic_senti_model\n" + ] + } + ], + "source": [ + "# # Save the PySpark model\n", + "# model_path = \"/content/drive/MyDrive/topic_senti_model\"\n", + "# model.write().overwrite().save(model_path)\n", + "# print(f\"Model saved at {model_path}\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tFKd6qcsK_CA", + "outputId": "9c82662b-571d-4989-d8e5-34c3145b757a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model loaded successfully!\n" + ] + } + ], + "source": [ + "# from pyspark.ml import PipelineModel\n", + "\n", + "# # Load the saved model\n", + "# loaded_model = PipelineModel.load(\"/content/drive/MyDrive/topic_senti_model\")\n", + "# print(\"Model loaded successfully!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "wOq8fpYSmqiv" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "machine_shape": "hm", + "provenance": [], + "authorship_tag": "ABX9TyO+cOvYhvGr8SUNbBknQHU9", + "include_colab_link": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/NoteBook/json to csv.ipynb b/NoteBook/json to csv.ipynb new file mode 100644 index 0000000..5757cd2 --- /dev/null +++ b/NoteBook/json to csv.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8930,"status":"ok","timestamp":1726186485507,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"},"user_tz":240},"id":"PygWqEtwbIvV","outputId":"ffd8329c-c3e8-4c3b-f4b1-adf026a0af09"},"outputs":[{"name":"stdout","output_type":"stream","text":["Collecting pyspark\n"," Using cached pyspark-3.5.2-py2.py3-none-any.whl\n","Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n","Installing collected packages: pyspark\n","Successfully installed pyspark-3.5.2\n"]}],"source":["# install PySpark\n","!pip install pyspark"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2636,"status":"ok","timestamp":1726186508819,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"},"user_tz":240},"id":"QO8TWPuCiw6R","outputId":"be50dcb8-15aa-4d9c-9e2a-355b9c9db545"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: openpyxl in /usr/local/lib/python3.10/dist-packages (3.1.5)\n","Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.10/dist-packages (from openpyxl) (1.1.0)\n"]}],"source":["# install openpyxl\n","!pip install openpyxl"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vAIR0kaEbVW5"},"outputs":[],"source":["# importing necessary libraries\n","\n","from pyspark.sql import SparkSession\n","from pyspark.sql.functions import *\n","from pyspark.sql.types import *"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"J2mGteZRbc3V"},"outputs":[],"source":["# Spark session\n","spark = SparkSession.builder.appName(\"Read_JSON\").getOrCreate()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"4yGPoJYHZwXb"},"outputs":[],"source":["# Reading the json file\n","\n","def read_json(file_path):\n"," df = spark.read.json(file_path)\n"," # df.show(10)\n"," return df"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"xvlcSXkVa_QE"},"outputs":[],"source":["# df.show()\n","# filepath = '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_tip.json' #'/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json' #'/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_checkin.json' #'/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_business.json'\n","# read_json(filepath)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"BfWduucPcbOa"},"outputs":[],"source":["# from pyspark.sql.functions import to_json\n","# from pyspark.sql.functions import col, to_json\n","\n","# # Convert the 'attributes' column to a JSON string\n","# df = df.withColumn(\"attributes\", to_json(col(\"attributes\")))\n","\n","# # Convert the 'hours' column to a JSON string\n","# df = df.withColumn(\"hours\", to_json(col(\"hours\")))\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"QQY2yGQYcHs9"},"outputs":[],"source":["# df.show(5)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"3IgN0cCAcpy4"},"outputs":[],"source":["# write the dataframe as an excel file\n","\n","def write_As_xl(df, root_path, csvfilename):\n"," # spark = SparkSession.builder.appName(\"YourAppName\").getOrCreate()\n"," # Convert the DataFrame to Pandas DataFrame for easier writing to Excel\n"," pandas_df = df.toPandas()\n","\n"," # Write the Pandas DataFrame to an Excel file in Google Drive\n"," pandas_df.to_excel(csv_root_path + csvfilename + '.xlsx', index=False)\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MjGrkUJoc5kw"},"outputs":[],"source":["# fp = '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_tip.json'\n","\n","# p1 = fp.split('/')[-1].split('.')[0]\n","# print(p1)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":426},"executionInfo":{"elapsed":5683,"status":"error","timestamp":1726187212131,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"},"user_tz":240},"id":"05X1G2udjez4","outputId":"3603eb17-2e4c-4a09-9321-89224a268645"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: pyarrow in /usr/local/lib/python3.10/dist-packages (14.0.2)\n","Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.10/dist-packages (from pyarrow) (1.26.4)\n","Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (3.5.2)\n","Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n"]},{"ename":"ConnectionRefusedError","evalue":"[Errno 111] Connection refused","output_type":"error","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Create a SparkSession\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mspark\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSparkSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappName\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"YourAppName\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetOrCreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# Enable Arrow optimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyspark/sql/session.py\u001b[0m in \u001b[0;36mgetOrCreate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 502\u001b[0m getattr(\n\u001b[0;32m--> 503\u001b[0;31m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jvm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"SparkSession$\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"MODULE$\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 504\u001b[0m ).applyModifiableSettings(session._jsparkSession, self._options)\n\u001b[1;32m 505\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1710\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mUserHelpAutoCompletion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1712\u001b[0;31m answer = self._gateway_client.send_command(\n\u001b[0m\u001b[1;32m 1713\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREFLECTION_COMMAND_NAME\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1714\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREFL_GET_UNKNOWN_SUB_COMMAND_NAME\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_id\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, command, retry, binary)\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mis\u001b[0m\u001b[0;31m 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293\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/clientserver.py\u001b[0m in \u001b[0;36mconnect_to_java_server\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 436\u001b[0m self.socket = self.ssl_context.wrap_socket(\n\u001b[1;32m 437\u001b[0m self.socket, server_hostname=self.java_address)\n\u001b[0;32m--> 438\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_address\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_port\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 439\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_connected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused"]}],"source":["# main code\n","\n","file_paths = ['/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_tip.json',\n"," '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_checkin.json',\n"," '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_business.json']#,\n"," '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json',\n"," '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json']\n","\n","# reading an coverting each json file to csv\n","\n","for filepath in file_paths:\n","\n"," csv_root_path = '/content/drive/MyDrive/CSV/'\n","\n"," rt_df = read_json(filepath)\n","\n"," csvfilename = filepath.split('/')[-1].split('.')[0]\n","\n"," print(filepath, ' -----> ', csvfilename, '.xlsx')\n","\n"," write_As_xl(rt_df, csv_root_path, csvfilename)\n","\n","\n","\n","\n","\n"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3111,"status":"ok","timestamp":1726186965601,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"},"user_tz":240},"id":"rZI0NVxRmrWk","outputId":"56fd8965-6377-44c9-b4b3-9efa1824bb5a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Requirement already satisfied: pyarrow in /usr/local/lib/python3.10/dist-packages (14.0.2)\n","Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.10/dist-packages (from pyarrow) (1.26.4)\n"]}],"source":["!pip install pyarrow"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":446},"id":"R0SMycOnwry1","executionInfo":{"status":"error","timestamp":1726196630545,"user_tz":240,"elapsed":6514,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"}},"outputId":"6c8c4468-9d88-45e7-8706-b8f1731aa6ab"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: pyarrow in /usr/local/lib/python3.10/dist-packages (14.0.2)\n","Requirement already satisfied: numpy>=1.16.6 in /usr/local/lib/python3.10/dist-packages (from pyarrow) (1.26.4)\n","Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (3.5.2)\n","Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n"]},{"output_type":"error","ename":"ConnectionRefusedError","evalue":"[Errno 111] Connection refused","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 9\u001b[0m \u001b[0;31m# Create a SparkSession\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mspark\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSparkSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuilder\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappName\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"YourAppName\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetOrCreate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;31m# Enable Arrow optimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyspark/sql/session.py\u001b[0m in \u001b[0;36mgetOrCreate\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 501\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 502\u001b[0m getattr(\n\u001b[0;32m--> 503\u001b[0;31m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jvm\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"SparkSession$\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"MODULE$\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 504\u001b[0m ).applyModifiableSettings(session._jsparkSession, self._options)\n\u001b[1;32m 505\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0msession\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1710\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mUserHelpAutoCompletion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1712\u001b[0;31m answer = self._gateway_client.send_command(\n\u001b[0m\u001b[1;32m 1713\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREFLECTION_COMMAND_NAME\u001b[0m \u001b[0;34m+\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1714\u001b[0m \u001b[0mproto\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mREFL_GET_UNKNOWN_SUB_COMMAND_NAME\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m \u001b[0;34m+\u001b[0m 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\u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1038\u001b[0m \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/clientserver.py\u001b[0m in \u001b[0;36m_get_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 282\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 283\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mconnection\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 284\u001b[0;31m \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_new_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 285\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/clientserver.py\u001b[0m in \u001b[0;36m_create_new_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_parameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython_parameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 290\u001b[0m self.gateway_property, self)\n\u001b[0;32m--> 291\u001b[0;31m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect_to_java_server\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 292\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_thread_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/clientserver.py\u001b[0m in \u001b[0;36mconnect_to_java_server\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 436\u001b[0m self.socket = self.ssl_context.wrap_socket(\n\u001b[1;32m 437\u001b[0m self.socket, server_hostname=self.java_address)\n\u001b[0;32m--> 438\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_address\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_port\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 439\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_connected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused"]}],"source":["# Install necessary packages\n","!pip install pyarrow\n","!pip install pyspark\n","\n","# Import necessary libraries\n","import pyarrow\n","from pyspark.sql import SparkSession\n","\n","# Create a SparkSession\n","spark = SparkSession.builder.appName(\"YourAppName\").getOrCreate()\n","\n","# Enable Arrow optimization\n","# spark.conf.set(\"spark.sql.execution.arrow.pyspark.enabled\", \"true\")\n","\n","# file_paths = ['/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json', '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json']\n","\n","df = spark.read.json('/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json')\n","\n","df"]},{"cell_type":"code","source":["# write this as a csv\n","\n","# Define the output path for the CSV file\n","output_path = '/content/drive/MyDrive/CSV/yelp_academic_dataset_user.csv'\n","\n","# Write the DataFrame to a CSV file\n","df.write.csv(output_path, header=True)\n"],"metadata":{"id":"7131CBiPVL9C"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["df.show(10)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PR2Lx3cuRRcs","executionInfo":{"status":"ok","timestamp":1726195538451,"user_tz":240,"elapsed":2482,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"}},"outputId":"a87bae05-ea9a-4861-c0a8-7a891611eae4"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["+-------------+---------------+---------------+----------------+--------------+---------------+---------------+---------------+-----------------+----------------+------------------+-----------------+-----+--------------------+----+--------------------+-----+--------+------------+------+--------------------+-------------------+\n","|average_stars|compliment_cool|compliment_cute|compliment_funny|compliment_hot|compliment_list|compliment_more|compliment_note|compliment_photos|compliment_plain|compliment_profile|compliment_writer| cool| elite|fans| friends|funny| name|review_count|useful| user_id| yelping_since|\n","+-------------+---------------+---------------+----------------+--------------+---------------+---------------+---------------+-----------------+----------------+------------------+-----------------+-----+--------------------+----+--------------------+-----+--------+------------+------+--------------------+-------------------+\n","| 3.91| 467| 56| 467| 250| 18| 65| 232| 180| 844| 55| 239| 5994| 2007| 267|NSCy54eWehBJyZdG2...| 1259| Walker| 585| 7217|qVc8ODYU5SZjKXVBg...|2007-01-25 16:47:26|\n","| 3.74| 3131| 157| 3131| 1145| 251| 264| 1847| 1946| 7054| 184| 1521|27281|2009,2010,2011,20...|3138|ueRPE0CX75ePGMqOF...|13066| Daniel| 4333| 43091|j14WgRoU_-2ZE1aw1...|2009-01-25 04:35:42|\n","| 3.32| 119| 17| 119| 89| 3| 13| 66| 18| 96| 10| 35| 1003|2009,2010,2011,20...| 52|LuO3Bn4f3rlhyHIaN...| 1010| Steph| 665| 2086|2WnXYQFK0hXEoTxPt...|2008-07-25 10:41:00|\n","| 4.27| 26| 6| 26| 24| 2| 4| 12| 9| 16| 1| 10| 299| 2009,2010,2011| 28|enx1vVPnfdNUdPho6...| 330| Gwen| 224| 512|SZDeASXq7o05mMNLs...|2005-11-29 04:38:33|\n","| 3.54| 0| 0| 0| 1| 0| 1| 1| 0| 1| 0| 0| 7| | 1|PBK4q9KEEBHhFvSXC...| 15| Karen| 79| 29|hA5lMy-EnncsH4JoR...|2007-01-05 19:40:59|\n","| 3.85| 2543| 361| 2543| 1713| 147| 163| 1212| 323| 5696| 191| 815|11211|2006,2007,2008,20...|1357|xBDpTUbai0DXrvxCe...| 9940| Jane| 1221| 14953|q_QQ5kBBwlCcbL1s4...|2005-03-14 20:26:35|\n","| 2.75| 0| 0| 0| 0| 0| 0| 0| 0| 1| 0| 0| 0| | 1|HDAQ74AEznP-YsMk1...| 1| Rob| 12| 6|cxuxXkcihfCbqt5By...|2009-02-24 03:09:06|\n","| 3.73| 12| 0| 12| 4| 0| 7| 8| 0| 6| 2| 5| 143| | 23|y2GyxJF5VQWohxgw_...| 102| Mike| 358| 399|E9kcWJdJUHuTKfQur...|2008-12-11 22:11:56|\n","| 4.04| 5| 3| 5| 2| 0| 0| 3| 1| 4| 0| 3| 46| | 7|tOQDlz36rI__SOsbL...| 40|Rachelle| 40| 109|lO1iq-f75hnPNZkTy...|2008-12-29 22:40:56|\n","| 3.4| 3| 0| 3| 0| 0| 0| 1| 0| 6| 0| 0| 23| | 4|gy5fWeSv3Gamuq9Ox...| 20| John| 109| 154|AUi8MPWJ0mLkMfwbu...|2010-01-07 18:32:04|\n","+-------------+---------------+---------------+----------------+--------------+---------------+---------------+---------------+-----------------+----------------+------------------+-----------------+-----+--------------------+----+--------------------+-----+--------+------------+------+--------------------+-------------------+\n","only showing top 10 rows\n","\n"]}]},{"cell_type":"code","source":["# file_paths = ['/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json', '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json']\n","\n","df2 = spark.read.json('/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json')\n","df2.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yQ1lV_GaRV82","executionInfo":{"status":"ok","timestamp":1726195633013,"user_tz":240,"elapsed":69880,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"}},"outputId":"d6fbbd6f-77a9-4b2f-e758-aaafee6cafbd"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["+--------------------+----+-------------------+-----+--------------------+-----+--------------------+------+--------------------+\n","| business_id|cool| date|funny| review_id|stars| text|useful| user_id|\n","+--------------------+----+-------------------+-----+--------------------+-----+--------------------+------+--------------------+\n","|XQfwVwDr-v0ZS3_Cb...| 0|2018-07-07 22:09:11| 0|KU_O5udG6zpxOg-Vc...| 3.0|If you decide to ...| 0|mh_-eMZ6K5RLWhZyI...|\n","|7ATYjTIgM3jUlt4UM...| 1|2012-01-03 15:28:18| 0|BiTunyQ73aT9WBnpR...| 5.0|I've taken a lot ...| 1|OyoGAe7OKpv6SyGZT...|\n","|YjUWPpI6HXG530lwP...| 0|2014-02-05 20:30:30| 0|saUsX_uimxRlCVr67...| 3.0|Family diner. Had...| 0|8g_iMtfSiwikVnbP2...|\n","|kxX2SOes4o-D3ZQBk...| 1|2015-01-04 00:01:03| 0|AqPFMleE6RsU23_au...| 5.0|Wow! Yummy, diff...| 1|_7bHUi9Uuf5__HHc_...|\n","|e4Vwtrqf-wpJfwesg...| 1|2017-01-14 20:54:15| 0|Sx8TMOWLNuJBWer-0...| 4.0|Cute interior and...| 1|bcjbaE6dDog4jkNY9...|\n","|04UD14gamNjLY0IDY...| 1|2015-09-23 23:10:31| 2|JrIxlS1TzJ-iCu79u...| 1.0|I am a long term ...| 1|eUta8W_HdHMXPzLBB...|\n","|gmjsEdUsKpj9Xxu6p...| 0|2015-01-03 23:21:18| 2|6AxgBCNX_PNTOxmbR...| 5.0|Loved this tour! ...| 0|r3zeYsv1XFBRA4dJp...|\n","|LHSTtnW3YHCeUkRDG...| 0|2015-08-07 02:29:16| 0|_ZeMknuYdlQcUqng_...| 5.0|Amazingly amazing...| 2|yfFzsLmaWF2d4Sr0U...|\n","|B5XSoSG3SfvQGtKEG...| 0|2016-03-30 22:46:33| 1|ZKvDG2sBvHVdF5oBN...| 3.0|This easter inste...| 1|wSTuiTk-sKNdcFypr...|\n","|gebiRewfieSdtt17P...| 0|2016-07-25 07:31:06| 0|pUycOfUwM8vqX7KjR...| 3.0|Had a party of 6 ...| 0|59MxRhNVhU9MYndMk...|\n","|uMvVYRgGNXf5boolA...| 0|2015-06-21 14:48:06| 0|rGQRf8UafX7OTlMNN...| 5.0|My experience wit...| 2|1WHRWwQmZOZDAhp2Q...|\n","|EQ-TZ2eeD_E0BHuvo...| 0|2015-08-19 14:31:45| 0|l3Wk_mvAog6XANIuG...| 4.0|Locals recommende...| 0|ZbqSHbgCjzVAqaa7N...|\n","|lj-E32x9_FA7GmUrB...| 0|2014-06-27 22:44:01| 0|XW_LfMv0fV21l9c6x...| 4.0|Love going here f...| 0|9OAtfnWag-ajVxRbU...|\n","|RZtGWDLCAtuipwaZ-...| 0|2009-10-14 19:57:14| 0|8JFGBuHMoiNDyfcxu...| 4.0|Good food--loved ...| 0|smOvOajNG0lS4Pq7d...|\n","|otQS34_MymijPTdNB...| 0|2011-10-27 17:12:05| 2|UBp0zWyH60Hmw6Fsa...| 4.0|The bun makes the...| 0|4Uh27DgGzsp6PqrH9...|\n","|BVndHaLihEYbr76Z0...| 0|2014-10-11 16:22:06| 0|OAhBYw8IQ6wlfw1ow...| 5.0|Great place for b...| 0|1C2lxzUo1Hyye4RFI...|\n","|YtSqYv1Q_pOltsVPS...| 0|2013-06-24 11:21:25| 0|oyaMhzBSwfGgemSGu...| 5.0|Tremendous servic...| 0|Dd1jQj7S-BFGqRbAp...|\n","|rBdG_23USc7DletfZ...| 0|2014-08-10 19:41:43| 0|LnGZB0fjfgeVDVz5I...| 4.0|The hubby and I h...| 1|j2wlzrntrbKwyOcOi...|\n","|CLEWowfkj-wKYJlQD...| 1|2016-03-07 00:02:18| 0|u2vzZaOqJ2feRshaa...| 5.0|I go to blow bar ...| 2|NDZvyYHTUWWu-kqgQ...|\n","|eFvzHawVJofxSnD7T...| 0|2014-11-12 15:30:27| 0|Xs8Z8lmKkosqW5mw_...| 5.0|My absolute favor...| 0|IQsF3Rc6IgCzjVV9D...|\n","+--------------------+----+-------------------+-----+--------------------+-----+--------------------+------+--------------------+\n","only showing top 20 rows\n","\n"]}]},{"cell_type":"code","source":["# Import necessary libraries\n","import pyarrow\n","from pyspark.sql import SparkSession\n","\n","# Create a SparkSession\n","# spark = SparkSession.builder.appName(\"YourAppName\").getOrCreate()\n","\n","# Enable Arrow optimization\n","# spark.conf.set(\"spark.sql.execution.arrow.pyspark.enabled\", \"true\")\n","\n","# file_paths = ['/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json', '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json']\n","\n","df = spark.read.json('/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json')\n","\n","\n","pandas_df = df.toPandas()\n","\n","pandas_df.to_excel('/content/drive/MyDrive/CSV/yelp_academic_dataset_user.xlsx', index=False)\n","\n","# main code\n","\n","# file_paths = ['/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json',\n","# '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json']\n","\n","# reading an coverting each json file to csv\n","\n","# for filepath in file_paths:\n","\n","# csv_root_path = '/content/drive/MyDrive/CSV/'\n","\n","# rt_df = read_json(filepath)\n","\n","# csvfilename = filepath.split('/')[-1].split('.')[0]\n","\n","# print(filepath, ' -----> ', csvfilename, '.xlsx')\n","\n","# write_As_xl1(rt_df, csv_root_path, csvfilename)\n","\n","# Stop the SparkSession when done to release resources\n","spark.stop()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":373},"id":"shIBbE00S4K_","executionInfo":{"status":"error","timestamp":1726195989619,"user_tz":240,"elapsed":181,"user":{"displayName":"BZRecProject","userId":"17728562531777262703"}},"outputId":"9956378d-43b5-44af-d496-2dc099d00040"},"execution_count":null,"outputs":[{"output_type":"error","ename":"ConnectionRefusedError","evalue":"[Errno 111] Connection refused","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mConnectionRefusedError\u001b[0m Traceback (most recent call last)","\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;31m# file_paths = ['/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json', '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json']\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyspark/sql/session.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1704\u001b[0m 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\u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 89\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 90\u001b[0;31m \u001b[0mjconf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparkSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jconf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 91\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 92\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mjconf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrowPySparkEnabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pyspark/sql/session.py\u001b[0m in \u001b[0;36m_jconf\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 622\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_jconf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m\"JavaObject\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 623\u001b[0m \u001b[0;34m\"\"\"Accessor for the JVM SQL-specific configurations\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 624\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jsparkSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msessionState\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 625\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 626\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mnewSession\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m 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self.name)\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/java_gateway.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, command, retry, binary)\u001b[0m\n\u001b[1;32m 1034\u001b[0m \u001b[0;32mif\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mbinary\u001b[0m\u001b[0;31m`\u001b[0m \u001b[0;32mis\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1035\u001b[0m \"\"\"\n\u001b[0;32m-> 1036\u001b[0;31m \u001b[0mconnection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1037\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1038\u001b[0m \u001b[0mresponse\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_create_new_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 285\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/clientserver.py\u001b[0m in \u001b[0;36m_create_new_connection\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_parameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpython_parameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 290\u001b[0m self.gateway_property, self)\n\u001b[0;32m--> 291\u001b[0;31m \u001b[0mconnection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect_to_java_server\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 292\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_thread_connection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mconnection\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mconnection\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/py4j/clientserver.py\u001b[0m in \u001b[0;36mconnect_to_java_server\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 436\u001b[0m self.socket = self.ssl_context.wrap_socket(\n\u001b[1;32m 437\u001b[0m self.socket, server_hostname=self.java_address)\n\u001b[0;32m--> 438\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_address\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjava_port\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 439\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstream\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msocket\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmakefile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rb\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 440\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_connected\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mConnectionRefusedError\u001b[0m: [Errno 111] Connection refused"]}],"source":["pandas_df = df.toPandas()\n","\n","pandas_df.to_excel('/content/drive/MyDrive/CSV/yelp_academic_dataset_user.xlsx', index=False)\n","\n","# main code\n","\n","# file_paths = ['/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_user.json',\n","# '/content/drive/MyDrive/yelp_dataset___/yelp_academic_dataset_review.json']\n","\n","# reading an coverting each json file to csv\n","\n","# for filepath in file_paths:\n","\n","# csv_root_path = '/content/drive/MyDrive/CSV/'\n","\n","# rt_df = read_json(filepath)\n","\n","# csvfilename = filepath.split('/')[-1].split('.')[0]\n","\n","# print(filepath, ' -----> ', csvfilename, '.xlsx')\n","\n","# write_As_xl1(rt_df, csv_root_path, csvfilename)\n","\n","# # Stop the SparkSession when done to release resources\n","# spark.stop()"]}],"metadata":{"colab":{"provenance":[],"gpuType":"T4","mount_file_id":"1LtxiSfF9eW_LSRlSL0F7jdFIGlIOupvt","authorship_tag":"ABX9TyNmr6c5rIOo2N6dgTWc1nqU"},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"},"accelerator":"GPU"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/NoteBook/lime_intro.ipynb b/NoteBook/lime_intro.ipynb new file mode 100644 index 0000000..ff804b0 --- /dev/null +++ b/NoteBook/lime_intro.ipynb @@ -0,0 +1,126 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "authorship_tag": "ABX9TyMMXaToAGLsMRMF3TPy274u", + "include_colab_link": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "view-in-github", + "colab_type": "text" + }, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "aBo23Oai58-f", + "outputId": "0b47140e-6181-49ea-9635-0c80a6ca623b" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting lime\n", + " Downloading lime-0.2.0.1.tar.gz (275 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/275.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m275.7/275.7 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from lime) (3.8.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lime) (1.26.4)\n", + "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lime) (1.13.1)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from lime) (4.66.6)\n", + "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.10/dist-packages (from lime) (1.5.2)\n", + "Requirement already satisfied: scikit-image>=0.12 in /usr/local/lib/python3.10/dist-packages (from lime) (0.24.0)\n", + "Requirement already satisfied: networkx>=2.8 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (3.4.2)\n", + "Requirement already satisfied: pillow>=9.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (10.4.0)\n", + "Requirement already satisfied: imageio>=2.33 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (2.36.0)\n", + "Requirement already satisfied: tifffile>=2022.8.12 in /usr/local/lib/python3.10/dist-packages (from scikit-image>=0.12->lime) (2024.9.20)\n", + "Requirement already satisfied: 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(from matplotlib->lime) (1.4.7)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->lime) (1.16.0)\n", + "Building wheels for collected packages: lime\n", + " Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283834 sha256=b515d66069bac0a1158b6deb11ab86d9339f6b016d519094703a56f0f62b4246\n", + " Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89\n", + "Successfully built lime\n", + "Installing collected packages: lime\n", + "Successfully installed lime-0.2.0.1\n" + ] + } + ], + "source": [ + "pip install lime\n" + ] + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from lime.lime_tabular import LimeTabularExplainer\n", + "\n", + "\n", + "# Split the data\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Train a Random Forest Regressor\n", + "rf = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "rf.fit(X_train, y_train)\n", + "\n", + "# Select an instance to explain\n", + "instance = X_test.iloc[0].values.reshape(1, -1)\n", + "\n", + "# Set up LIME for tabular data\n", + "explainer = LimeTabularExplainer(\n", + " training_data=X_train.values,\n", + " feature_names=X.columns,\n", + " mode='regression'\n", + ")\n", + "\n", + "# Generate explanation for the chosen instance\n", + "exp = explainer.explain_instance(\n", + " data_row=instance[0],\n", + " predict_fn=rf.predict\n", + ")\n", + "\n", + "# Display the explanation\n", + "exp.show_in_notebook(show_table=True)\n" + ], + "metadata": { + "id": "LVPfcEZI8oYd" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/NoteBook/model2notebook.ipynb b/NoteBook/model2notebook.ipynb new file mode 100644 index 0000000..2ef351e --- /dev/null +++ b/NoteBook/model2notebook.ipynb @@ -0,0 +1,225487 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [], + "gpuType": "T4", + "mount_file_id": "1ssZMRtvaaiAMIY1AzRKa-gPDyn29rr6I", + "authorship_tag": "ABX9TyOBseNjxLCiqYAdq3czbrjy" + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "Mounting Google Drive" + ], + "metadata": { + "id": "7LhktZzCy7UK" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "k67xsYuHcCX1" + }, + "outputs": [], + "source": [ + "# code to drive mount\n", + "\n", + "# from google.colab import drive\n", + "# drive.mount('/content/drive')" + ] + }, + { + "cell_type": "markdown", + "source": [ + "Install all necessary dependancies" + ], + "metadata": { + "id": "R5mmoVVDzBBr" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install pyspark\n", + "!pip install lightfm\n", + "!pip install gensim\n", + "!pip install textblob\n", + "!pip install scikit-learn" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "-ND3dikzcYZI", + "outputId": "5255ca3a-0808-4c3c-db6f-f45f2a1adb9b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (3.5.3)\n", + "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n", + "Collecting lightfm\n", + " Downloading lightfm-1.17.tar.gz (316 kB)\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m316.4/316.4 kB\u001b[0m \u001b[31m21.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lightfm) (1.26.4)\n", + "Requirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from lightfm) (1.13.1)\n", + "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from lightfm) (2.32.3)\n", + "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from lightfm) (1.5.2)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->lightfm) (3.4.0)\n", + 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/root/.cache/pip/wheels/4f/9b/7e/0b256f2168511d8fa4dae4fae0200fdbd729eb424a912ad636\n", + "Successfully built lightfm\n", + "Installing collected packages: lightfm\n", + "Successfully installed lightfm-1.17\n", + "Requirement already satisfied: gensim in /usr/local/lib/python3.10/dist-packages (4.3.3)\n", + "Requirement already satisfied: numpy<2.0,>=1.18.5 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.26.4)\n", + "Requirement already satisfied: scipy<1.14.0,>=1.7.0 in /usr/local/lib/python3.10/dist-packages (from gensim) (1.13.1)\n", + "Requirement already satisfied: smart-open>=1.8.1 in /usr/local/lib/python3.10/dist-packages (from gensim) (7.0.5)\n", + "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open>=1.8.1->gensim) (1.16.0)\n", + "Requirement already satisfied: textblob in /usr/local/lib/python3.10/dist-packages (0.17.1)\n", + "Requirement already satisfied: nltk>=3.1 in /usr/local/lib/python3.10/dist-packages (from 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"Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "Importing all needed libraries" + ], + "metadata": { + "id": "2nCLfYTMzFo7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# import all necessay libraries\n", + "\n", + "from pyspark.sql import SparkSession\n", + "from pyspark.sql import functions as F\n", + "from pyspark.sql.types import StructType, StructField, StringType, IntegerType, DoubleType, BooleanType, DateType\n", + "from pyspark.sql.functions import to_date\n", + "import pandas as pd\n", + "import os\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import MinMaxScaler\n", + "from pyspark.sql.functions import udf, col\n", + "from pyspark.sql.types import DoubleType\n" + ], + "metadata": { + "id": "0UOBLHVPJHDA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Creating a spark session" + ], + "metadata": { + "id": "HaSQyMdpzI-F" + } + }, + { + "cell_type": "code", + "source": [ + "# Creating a spark session\n", + "\n", + "spark = SparkSession.builder.appName(\"model2\").getOrCreate()" + ], + "metadata": { + "id": "DY65RA3icl4v" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Reading Business CSV" + ], + "metadata": { + "id": "vMwXywA4zLPC" + } + }, + { + "cell_type": "code", + "source": [ + "#loading the business dataframe\n", + "file_path = \"/content/drive/MyDrive/yelp_business1.csv\"\n", + "\n", + "# Define the schema for the business dataframe\n", + "business_schema = StructType([\n", + " StructField(\"business_id\", StringType(), True),\n", + " StructField(\"name\", StringType(), True),\n", + " StructField(\"address\", StringType(), True),\n", + " StructField(\"city\", StringType(), True),\n", + " StructField(\"state\", StringType(), True),\n", + " StructField(\"postal_code\", StringType(), True),\n", + " StructField(\"latitude\", DoubleType(), True),\n", + " StructField(\"longitude\", DoubleType(), True),\n", + " StructField(\"stars\", DoubleType(), True),\n", + " StructField(\"review_count\", IntegerType(), True),\n", + " StructField(\"is_open\", IntegerType(), True),\n", + " StructField(\"attributes\", StringType(), True),\n", + " StructField(\"categories\", StringType(), True),\n", + " StructField(\"hours\", StringType(), True)\n", + "])\n", + "\n", + "# Read the CSV file with the defined schema\n", + "business_df = spark.read.csv(file_path, header=True, schema=business_schema)\n", + "\n", + "# Display the dataframe\n", + "business_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "iyn_FDyNBBT3", + "outputId": "8ef8b535-38b2-4637-a0d8-38e6889e3c61" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+--------------+-----+-----------+-------------+--------------+-----+------------+-------+----------+--------------------+-----+\n", + "| business_id| name| address| city|state|postal_code| latitude| longitude|stars|review_count|is_open|attributes| categories|hours|\n", + "+--------------------+--------------------+--------------------+--------------+-----+-----------+-------------+--------------+-----+------------+-------+----------+--------------------+-----+\n", + "|Pns2l4eNsfO8kk83d...|Abby Rappoport, L...|1616 Chapala St, ...| Santa Barbara| CA| 93101| 34.4266787| -119.7111968| 5.0| 7| 0| True|Doctors, Traditio...| NULL|\n", + "|mpf3x-BjTdTEA3yCZ...| The UPS Store|87 Grasso Plaza S...| Affton| MO| 63123| 38.551126| -90.335695| 3.0| 15| 1| NULL|Shipping Centers,...| NULL|\n", + "|tUFrWirKiKi_TAnsV...| Target|5255 E Broadway Blvd| Tucson| AZ| 85711| 32.223236| -110.880452| 3.5| 22| 0| False|Department Stores...| NULL|\n", + "|MTSW4McQd7CbVtyjq...| St Honore Pastries| 935 Race St| Philadelphia| PA| 19107| 39.9555052| -75.1555641| 4.0| 80| 1| False|Restaurants, Food...| NULL|\n", + "|mWMc6_wTdE0EUBKIG...|Perkiomen Valley ...| 101 Walnut St| Green Lane| PA| 18054| 40.3381827| -75.4716585| 4.5| 13| 1| NULL|Brewpubs, Breweri...| NULL|\n", + "|CF33F8-E6oudUQ46H...| Sonic Drive-In| 615 S Main St| Ashland City| TN| 37015| 36.269593| -87.058943| 2.0| 6| 1| False|Burgers, Fast Foo...| NULL|\n", + "|n_0UpQx1hsNbnPUSl...| Famous Footwear|8522 Eager Road, ...| Brentwood| MO| 63144| 38.627695| -90.340465| 2.5| 13| 1| NULL|Sporting Goods, F...| NULL|\n", + "|qkRM_2X51Yqxk3btl...| Temple Beth-El| 400 Pasadena Ave S|St. Petersburg| FL| 33707| 27.76659| -82.732983| 3.5| 5| 1| NULL|Synagogues, Relig...| NULL|\n", + "|k0hlBqXX-Bt0vf1op...|Tsevi's Pub And G...| 8025 Mackenzie Rd| Affton| MO| 63123| 38.5651648| -90.3210868| 3.0| 19| 0| NULL|Pubs, Restaurants...| NULL|\n", + "|bBDDEgkFA1Otx9Lfe...| Sonic Drive-In| 2312 Dickerson Pike| Nashville| TN| 37207| 36.2081024| -86.7681696| 1.5| 10| 1| False|Ice Cream & Froze...| NULL|\n", + "|UJsufbvfyfONHeWdv...| Marshalls|21705 Village Lak...| Land O' Lakes| FL| 34639|28.1904587953|-82.4573802199| 3.5| 6| 1| NULL|Department Stores...| NULL|\n", + "|eEOYSgkmpB90uNA7l...|Vietnamese Food T...| NULL| Tampa Bay| FL| 33602| 27.9552692| -82.4563199| 4.0| 10| 1| NULL|Vietnamese, Food,...| NULL|\n", + "|il_Ro8jwPlHresjw9...| Denny's| 8901 US 31 S| Indianapolis| IN| 46227|39.6371332838| -86.127217412| 2.5| 28| 1| NULL|American (Traditi...| NULL|\n", + "|jaxMSoInw8Poo3XeM...| Adams Dental| 15 N Missouri Ave| Clearwater| FL| 33755| 27.966235| -82.787412| 5.0| 10| 1| True|General Dentistry...| NULL|\n", + "|0bPLkL0QhhPO5kt1_...|Zio's Italian Market| 2575 E Bay Dr| Largo| FL| 33771| 27.9161159| -82.7604608| 4.5| 100| 0| NULL|Food, Delis, Ital...| NULL|\n", + "|MUTTqe8uqyMdBl186...| Tuna Bar| 205 Race St| Philadelphia| PA| 19106| 39.953949| -75.1432262| 4.0| 245| 1| NULL|Sushi Bars, Resta...| NULL|\n", + "|rBmpy_Y1UbBx8ggHl...|Arizona Truck Out...| 625 N Stone Ave| Tucson| AZ| 85705| 32.2298719| -110.9723419| 4.5| 10| 1| False|Automotive, Auto ...| NULL|\n", + "|M0XSSHqrASOnhgbWD...| Herb Import Co| 712 Adams St| New Orleans| LA| 70118|29.9414679565| -90.129952757| 4.0| 5| 1| NULL|Vape Shops, Tobac...| NULL|\n", + "|8wGISYjYkE2tSqn3c...| Nifty Car Rental| 1241 Airline Dr| Kenner| LA| 70062| 29.981183| -90.2540123| 3.5| 14| 1| NULL|Automotive, Car R...| NULL|\n", + "|ROeacJQwBeh05Rqg7...| BAP| 1224 South St| Philadelphia| PA| 19147| 39.943223| -75.162568| 4.5| 205| 1| NULL| Korean, Restaurants| NULL|\n", + "+--------------------+--------------------+--------------------+--------------+-----+-----------+-------------+--------------+-----+------------+-------+----------+--------------------+-----+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "# drop attributes, hours from businessdf\n", + "\n", + "business_df = business_df.drop(\"attributes\", \"hours\")\n", + "business_df = business_df.filter(business_df.stars >= 2 ).filter(business_df.review_count > 20)\n", + "\n", + "business_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ugqoFsUqPQJL", + "outputId": "fbdc6ec1-95d3-4376-cb9d-fef335efe214" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+-------------+-----+-----------+-------------+---------------+-----+------------+-------+--------------------+\n", + "| business_id| name| address| city|state|postal_code| latitude| longitude|stars|review_count|is_open| categories|\n", + "+--------------------+--------------------+--------------------+-------------+-----+-----------+-------------+---------------+-----+------------+-------+--------------------+\n", + "|tUFrWirKiKi_TAnsV...| Target|5255 E Broadway Blvd| Tucson| AZ| 85711| 32.223236| -110.880452| 3.5| 22| 0|Department Stores...|\n", + "|MTSW4McQd7CbVtyjq...| St Honore Pastries| 935 Race St| Philadelphia| PA| 19107| 39.9555052| -75.1555641| 4.0| 80| 1|Restaurants, Food...|\n", + "|il_Ro8jwPlHresjw9...| Denny's| 8901 US 31 S| Indianapolis| IN| 46227|39.6371332838| -86.127217412| 2.5| 28| 1|American (Traditi...|\n", + "|0bPLkL0QhhPO5kt1_...|Zio's Italian Market| 2575 E Bay Dr| Largo| FL| 33771| 27.9161159| -82.7604608| 4.5| 100| 0|Food, Delis, Ital...|\n", + "|MUTTqe8uqyMdBl186...| Tuna Bar| 205 Race St| Philadelphia| PA| 19106| 39.953949| -75.1432262| 4.0| 245| 1|Sushi Bars, Resta...|\n", + "|ROeacJQwBeh05Rqg7...| BAP| 1224 South St| Philadelphia| PA| 19147| 39.943223| -75.162568| 4.5| 205| 1| Korean, Restaurants|\n", + "|WKMJwqnfZKsAae75R...|Roast Coffeehouse...| 10359 104 Street NW| Edmonton| AB| T5J 1B9| 53.5460453| -113.4991693| 4.0| 40| 0|Coffee & Tea, Foo...|\n", + "|qhDdDeI3K4jy2Kyzw...|Barnes & Noble Bo...|8675 River Crossi...| Indianapolis| IN| 46240| 39.91382| -86.105739| 4.0| 38| 1|Shopping, Books, ...|\n", + "|9OG5YkX1g2GReZM0A...|Romano's Macaroni...| 5505 S Virginia St| Reno| NV| 89502| 39.4761165| -119.7893392| 2.5| 339| 1|Restaurants, Italian|\n", + "|noByYNtDLQAra9ccq...| H&M| 827-833 State St|Santa Barbara| CA| 93101|34.4202089255|-119.7004597389| 3.0| 24| 1|Women's Clothing,...|\n", + "|tMkwHmWFUEXrC9Zdu...| The Green Pheasant| 215 1st Ave S| Nashville| TN| 37201| 36.1598858| -86.7731974| 4.0| 161| 0|Restaurants, Japa...|\n", + "|QdN72BWoyFypdGJhh...| Bar One| 767 S 9th St| Philadelphia| PA| 19147|39.9398245705| -75.1574465632| 4.0| 65| 0|Cocktail Bars, Ba...|\n", + "|fvWn8oXXwbj2l79co...|Altitude Trampoli...| 1301 N Milwaukee St| Boise| ID| 83704| 43.6167635| -116.2853821| 5.0| 30| 1|Trampoline Parks,...|\n", + "|Mjboz24M9NlBeiOJK...| DeSandro on Main| 4105 Main St| Philadelphia| PA| 19127| 40.0224662| -75.218314| 3.0| 41| 0|Pizza, Restaurant...|\n", + "|kV_Q1oqis8Qli8dUo...| Ardmore Pizza| 10 Rittenhouse Pl| Ardmore| PA| 19003| 40.0067071| -75.289671| 3.5| 109| 1| Pizza, Restaurants|\n", + "|aPNXGTDkf-4bjhyMB...| Craft Hall| 901 N Delaware Ave| Philadelphia| PA| 19123| 39.9625821| -75.1356571| 3.5| 65| 1|Eatertainment, Ar...|\n", + "|ljxNT9p0y7YMPx0fc...|Tony's Restaurant...| 312 Piasa St| Alton| IL| 62002| 38.896563| -90.1862032987| 3.0| 94| 1|Restaurants, Spec...|\n", + "|wghnIlMb_i5U46HMB...|China Dragon Rest...|1625 W Valencia R...| Tucson| AZ| 85746| 32.1323047| -110.9999851| 3.0| 23| 0|Restaurants, Chinese|\n", + "|lk9IwjZXqUMqqOhM7...| Caviar & Bananas| 2031 Broadway| Nashville| TN| 37203| 36.1483712| -86.7988947| 3.5| 159| 0|Coffee & Tea, Res...|\n", + "|5BmQX4UVJY19mMtaf...|Breadland Organic...|11642 104 Avenue,...| Edmonton| AB| T5K 2T7| 53.547205| -113.522448| 4.0| 23| 1|Specialty Food, B...|\n", + "+--------------------+--------------------+--------------------+-------------+-----+-----------+-------------+---------------+-----+------------+-------+--------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# reaname stars column in businessdf to business_stars\n", + "\n", + "business_df = business_df.withColumnRenamed(\"stars\", \"business_stars\")\n", + "business_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "GpYp7kecTZiy", + "outputId": "1d4cca8d-67b9-4f58-b347-8f6415be38d0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+-------------+-----+-----------+-------------+---------------+--------------+------------+-------+--------------------+\n", + "| business_id| name| address| city|state|postal_code| latitude| longitude|business_stars|review_count|is_open| categories|\n", + "+--------------------+--------------------+--------------------+-------------+-----+-----------+-------------+---------------+--------------+------------+-------+--------------------+\n", + "|tUFrWirKiKi_TAnsV...| Target|5255 E Broadway Blvd| Tucson| AZ| 85711| 32.223236| -110.880452| 3.5| 22| 0|Department Stores...|\n", + "|MTSW4McQd7CbVtyjq...| St Honore Pastries| 935 Race St| Philadelphia| PA| 19107| 39.9555052| -75.1555641| 4.0| 80| 1|Restaurants, Food...|\n", + "|il_Ro8jwPlHresjw9...| Denny's| 8901 US 31 S| Indianapolis| IN| 46227|39.6371332838| -86.127217412| 2.5| 28| 1|American (Traditi...|\n", + "|0bPLkL0QhhPO5kt1_...|Zio's Italian Market| 2575 E Bay Dr| Largo| FL| 33771| 27.9161159| -82.7604608| 4.5| 100| 0|Food, Delis, Ital...|\n", + "|MUTTqe8uqyMdBl186...| Tuna Bar| 205 Race St| Philadelphia| PA| 19106| 39.953949| -75.1432262| 4.0| 245| 1|Sushi Bars, Resta...|\n", + "|ROeacJQwBeh05Rqg7...| BAP| 1224 South St| Philadelphia| PA| 19147| 39.943223| -75.162568| 4.5| 205| 1| Korean, Restaurants|\n", + "|WKMJwqnfZKsAae75R...|Roast Coffeehouse...| 10359 104 Street NW| Edmonton| AB| T5J 1B9| 53.5460453| -113.4991693| 4.0| 40| 0|Coffee & Tea, Foo...|\n", + "|qhDdDeI3K4jy2Kyzw...|Barnes & Noble Bo...|8675 River Crossi...| Indianapolis| IN| 46240| 39.91382| -86.105739| 4.0| 38| 1|Shopping, Books, ...|\n", + "|9OG5YkX1g2GReZM0A...|Romano's Macaroni...| 5505 S Virginia St| Reno| NV| 89502| 39.4761165| -119.7893392| 2.5| 339| 1|Restaurants, Italian|\n", + "|noByYNtDLQAra9ccq...| H&M| 827-833 State St|Santa Barbara| CA| 93101|34.4202089255|-119.7004597389| 3.0| 24| 1|Women's Clothing,...|\n", + "|tMkwHmWFUEXrC9Zdu...| The Green Pheasant| 215 1st Ave S| Nashville| TN| 37201| 36.1598858| -86.7731974| 4.0| 161| 0|Restaurants, Japa...|\n", + "|QdN72BWoyFypdGJhh...| Bar One| 767 S 9th St| Philadelphia| PA| 19147|39.9398245705| -75.1574465632| 4.0| 65| 0|Cocktail Bars, Ba...|\n", + "|fvWn8oXXwbj2l79co...|Altitude Trampoli...| 1301 N Milwaukee St| Boise| ID| 83704| 43.6167635| -116.2853821| 5.0| 30| 1|Trampoline Parks,...|\n", + "|Mjboz24M9NlBeiOJK...| DeSandro on Main| 4105 Main St| Philadelphia| PA| 19127| 40.0224662| -75.218314| 3.0| 41| 0|Pizza, Restaurant...|\n", + "|kV_Q1oqis8Qli8dUo...| Ardmore Pizza| 10 Rittenhouse Pl| Ardmore| PA| 19003| 40.0067071| -75.289671| 3.5| 109| 1| Pizza, Restaurants|\n", + "|aPNXGTDkf-4bjhyMB...| Craft Hall| 901 N Delaware Ave| Philadelphia| PA| 19123| 39.9625821| -75.1356571| 3.5| 65| 1|Eatertainment, Ar...|\n", + "|ljxNT9p0y7YMPx0fc...|Tony's Restaurant...| 312 Piasa St| Alton| IL| 62002| 38.896563| -90.1862032987| 3.0| 94| 1|Restaurants, Spec...|\n", + "|wghnIlMb_i5U46HMB...|China Dragon Rest...|1625 W Valencia R...| Tucson| AZ| 85746| 32.1323047| -110.9999851| 3.0| 23| 0|Restaurants, Chinese|\n", + "|lk9IwjZXqUMqqOhM7...| Caviar & Bananas| 2031 Broadway| Nashville| TN| 37203| 36.1483712| -86.7988947| 3.5| 159| 0|Coffee & Tea, Res...|\n", + "|5BmQX4UVJY19mMtaf...|Breadland Organic...|11642 104 Avenue,...| Edmonton| AB| T5K 2T7| 53.547205| -113.522448| 4.0| 23| 1|Specialty Food, B...|\n", + "+--------------------+--------------------+--------------------+-------------+-----+-----------+-------------+---------------+--------------+------------+-------+--------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Reading Review CSV" + ], + "metadata": { + "id": "BlibwgsmzO5y" + } + }, + { + "cell_type": "code", + "source": [ + "review_df = spark.read.csv(\"/content/drive/MyDrive/ProcessedCSV/final_review.csv\", header=True, inferSchema=True)\n", + "columns_to_drop = review_df.columns\n", + "review_df = review_df.filter(F.col(columns_to_drop[0]) != review_df.first()[columns_to_drop[0]])\n", + "for col in columns_to_drop[1:]:\n", + " review_df = review_df.filter(F.col(col) != review_df.first()[col])\n", + "review_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "B0QnpmVJSbVm", + "outputId": "017444a6-5c73-4128-fa41-4333f5f5ab65" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+\n", + "| review_id| user_id| business_id|stars|useful|funny|cool| text| date| time|\n", + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+\n", + "|5Vy-AFz0Ek1FF6s1a...|tbI8YO0O51Mrm_yIi...|4QjOsH83KEcjen94P...| 4| 8| 5| 9|This review is fo...|2016-03-07|2024-11-04 22:17:05|\n", + "|HsthKXBHUDCDOUH1z...|rytAuNSEgYf_xqkKZ...|j-qtdD55OLfSqfsWu...| 4| 1| 1| 0|Somehow we got ca...|2015-08-31|2024-11-04 20:11:08|\n", + "|zg5iA1-3eoozePgAS...|Q8V8JWWQNrpvD2bXC...|9cybDNxJ7cyNVkmwm...| 5| 0| 1| 0|Huge fan of this ...|2013-05-12|2024-11-04 01:44:00|\n", + "|FYj6Tx_Ft4VvkGFgD...|2lFni8ituUYceQJPx...|Iw8uqNPxviwcgxtru...| 1| 7| 5| 3|Things you should...|2010-12-05|2024-11-04 09:18:11|\n", + "|vmaErJz1v5tr0Vybf...|HkbiKPFKaJPdLwPQG...|ixAWNAUrA7JjwvMJz...| 4| 2| 1| 1|Was informed by d...|2015-05-29|2024-11-04 23:20:05|\n", + "|JYJ_EsFX8DQAvlTYx...|9Ba1QLi-_n5ijOUYp...|-bUlpYP74Ei8VkSGp...| 1| 2| 1| 0|Someone from this...|2015-03-23|2024-11-04 14:12:06|\n", + "|eibqXCayeqBuRIlBL...|w-hg9fO4EYJTffKbW...|KnsY8rh5tigp5t6Wp...| 4| 2| 4| 1|Whole Foods is li...|2011-08-29|2024-11-04 18:14:47|\n", + "|L9YHX9CVevipN49BN...|xMGWttm6V-cvP3V2f...|HP03UxUpfwic04tBu...| 2| 1| 1| 1|Ordered delivery....|2018-06-21|2024-11-04 01:57:19|\n", + "|Y7Tk2c14VsaO_nHvR...|I99z9bNf82ESzBJoY...|dKAu4j6GskdIvyiBy...| 1| 6| 2| 0|Update: I changed...|2012-06-30|2024-11-04 21:07:00|\n", + "|T_iM9qQ_JF97ZNeod...|HPvG3bwrp1U1VRafj...|JLumGKzGu9kqGTdwY...| 2| 0| 1| 0|This place has po...|2011-09-29|2024-11-04 01:12:11|\n", + "|ZnA9BxZQA8UAqzbi5...|ooLcX8Ey8fMTB81tT...|CTvHlHQ4jzRO8gkkl...| 5| 3| 1| 0|By far the most a...|2014-01-03|2024-11-04 00:44:51|\n", + "|-88EuW9LAa3UUJ1pi...|3tAW8YObI7gpFggaz...|1YflE3DkiCZGgLnf3...| 5| 14| 2| 8|I live just South...|2014-09-29|2024-11-04 13:51:41|\n", + "|lVHdykXMARM_Nqfup...|kzDzdbPupHiglDzs9...|IvAa7PvfOYl0xRML5...| 4| 12| 4| 7|People will fight...|2017-05-15|2024-11-04 02:22:14|\n", + "|4zHPPk6HtTtbfExrM...|ApwWZBmsF9_gCwam6...|TunmRrfZb7bt53T6H...| 1| 2| 5| 1|DO NOT ORDER DELI...|2015-02-21|2024-11-04 01:52:19|\n", + "|BjxWsDxpO8YPV-El9...|OI8taAco53qGwEzbE...|dL8yaO7NtWpBar8CH...| 4| 6| 2| 3|Wandered in here ...|2011-07-04|2024-11-04 15:45:27|\n", + "|RiydWL4uWz2W3F0xI...|8O5tGAz_LFuX3JTim...|NgCDBhNiVpUDwvpi0...| 2| 1| 1| 0|There are way too...|2011-07-07|2024-11-04 18:47:28|\n", + "|2wCabGwTJKPhiPZpb...|6Q8dQGr_DBKcmQ1aX...|qjSU3SL8CaGfmOL_B...| 2| 1| 2| 0|The theater smell...|2014-09-29|2024-11-04 13:47:40|\n", + "|kdD6T52KAsdv-m_Mu...|zK9xI4tX-Qk43pfc4...|z8YngIHNZI0A05DeM...| 1| 0| 1| 0|Well let's start ...|2016-11-24|2024-11-04 12:58:55|\n", + "|8t-tX1V56l9QKPOA3...|VBxscaJ2Q3YH4Vs8W...|elnmOdMKGQvAF9FoV...| 5| 0| 1| 1|Great bikes. Very...|2014-12-01|2024-11-04 18:20:41|\n", + "|cfFKGABwOJYSsxkiR...|f302ihzVqr3CC4nBo...|3cfo_0_GjpJ2mWRYk...| 4| 0| 1| 0|Nice hotel if you...|2017-09-22|2024-11-04 05:19:32|\n", + "+--------------------+--------------------+--------------------+-----+------+-----+----+--------------------+----------+-------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# rename stars to review_stars in review_df\n", + "\n", + "review_df = review_df.withColumnRenamed(\"stars\", \"review_stars\")\n", + "review_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5KLt9tjzTkzW", + "outputId": "3258412f-4576-4d39-abac-73910d66f419" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "| review_id| user_id| business_id|review_stars|useful|funny|cool| text| date| time|\n", + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "|5Vy-AFz0Ek1FF6s1a...|tbI8YO0O51Mrm_yIi...|4QjOsH83KEcjen94P...| 4| 8| 5| 9|This review is fo...|2016-03-07|2024-11-04 22:17:05|\n", + "|HsthKXBHUDCDOUH1z...|rytAuNSEgYf_xqkKZ...|j-qtdD55OLfSqfsWu...| 4| 1| 1| 0|Somehow we got ca...|2015-08-31|2024-11-04 20:11:08|\n", + "|zg5iA1-3eoozePgAS...|Q8V8JWWQNrpvD2bXC...|9cybDNxJ7cyNVkmwm...| 5| 0| 1| 0|Huge fan of this ...|2013-05-12|2024-11-04 01:44:00|\n", + "|FYj6Tx_Ft4VvkGFgD...|2lFni8ituUYceQJPx...|Iw8uqNPxviwcgxtru...| 1| 7| 5| 3|Things you should...|2010-12-05|2024-11-04 09:18:11|\n", + "|vmaErJz1v5tr0Vybf...|HkbiKPFKaJPdLwPQG...|ixAWNAUrA7JjwvMJz...| 4| 2| 1| 1|Was informed by d...|2015-05-29|2024-11-04 23:20:05|\n", + "|JYJ_EsFX8DQAvlTYx...|9Ba1QLi-_n5ijOUYp...|-bUlpYP74Ei8VkSGp...| 1| 2| 1| 0|Someone from this...|2015-03-23|2024-11-04 14:12:06|\n", + "|eibqXCayeqBuRIlBL...|w-hg9fO4EYJTffKbW...|KnsY8rh5tigp5t6Wp...| 4| 2| 4| 1|Whole Foods is li...|2011-08-29|2024-11-04 18:14:47|\n", + "|L9YHX9CVevipN49BN...|xMGWttm6V-cvP3V2f...|HP03UxUpfwic04tBu...| 2| 1| 1| 1|Ordered delivery....|2018-06-21|2024-11-04 01:57:19|\n", + "|Y7Tk2c14VsaO_nHvR...|I99z9bNf82ESzBJoY...|dKAu4j6GskdIvyiBy...| 1| 6| 2| 0|Update: I changed...|2012-06-30|2024-11-04 21:07:00|\n", + "|T_iM9qQ_JF97ZNeod...|HPvG3bwrp1U1VRafj...|JLumGKzGu9kqGTdwY...| 2| 0| 1| 0|This place has po...|2011-09-29|2024-11-04 01:12:11|\n", + "|ZnA9BxZQA8UAqzbi5...|ooLcX8Ey8fMTB81tT...|CTvHlHQ4jzRO8gkkl...| 5| 3| 1| 0|By far the most a...|2014-01-03|2024-11-04 00:44:51|\n", + "|-88EuW9LAa3UUJ1pi...|3tAW8YObI7gpFggaz...|1YflE3DkiCZGgLnf3...| 5| 14| 2| 8|I live just South...|2014-09-29|2024-11-04 13:51:41|\n", + "|lVHdykXMARM_Nqfup...|kzDzdbPupHiglDzs9...|IvAa7PvfOYl0xRML5...| 4| 12| 4| 7|People will fight...|2017-05-15|2024-11-04 02:22:14|\n", + "|4zHPPk6HtTtbfExrM...|ApwWZBmsF9_gCwam6...|TunmRrfZb7bt53T6H...| 1| 2| 5| 1|DO NOT ORDER DELI...|2015-02-21|2024-11-04 01:52:19|\n", + "|BjxWsDxpO8YPV-El9...|OI8taAco53qGwEzbE...|dL8yaO7NtWpBar8CH...| 4| 6| 2| 3|Wandered in here ...|2011-07-04|2024-11-04 15:45:27|\n", + "|RiydWL4uWz2W3F0xI...|8O5tGAz_LFuX3JTim...|NgCDBhNiVpUDwvpi0...| 2| 1| 1| 0|There are way too...|2011-07-07|2024-11-04 18:47:28|\n", + "|2wCabGwTJKPhiPZpb...|6Q8dQGr_DBKcmQ1aX...|qjSU3SL8CaGfmOL_B...| 2| 1| 2| 0|The theater smell...|2014-09-29|2024-11-04 13:47:40|\n", + "|kdD6T52KAsdv-m_Mu...|zK9xI4tX-Qk43pfc4...|z8YngIHNZI0A05DeM...| 1| 0| 1| 0|Well let's start ...|2016-11-24|2024-11-04 12:58:55|\n", + "|8t-tX1V56l9QKPOA3...|VBxscaJ2Q3YH4Vs8W...|elnmOdMKGQvAF9FoV...| 5| 0| 1| 1|Great bikes. Very...|2014-12-01|2024-11-04 18:20:41|\n", + "|cfFKGABwOJYSsxkiR...|f302ihzVqr3CC4nBo...|3cfo_0_GjpJ2mWRYk...| 4| 0| 1| 0|Nice hotel if you...|2017-09-22|2024-11-04 05:19:32|\n", + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# filter reviews_df to get records on or after 2017\n", + "\n", + "# Assuming 'date' is the column containing the review date in your DataFrame\n", + "review_df = review_df.withColumn(\"date\", to_date(review_df[\"date\"], \"yyyy-MM-dd\"))\n", + "\n", + "# Filter reviews on or after 2017-01-01\n", + "filtered_review_df = review_df.filter(review_df.date >= \"2017-01-01\")\n", + "\n", + "filtered_review_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VNDTjl1WUHeR", + "outputId": "424f62ef-e2e4-448e-9b45-3ca6b9c6f8bc" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "| review_id| user_id| business_id|review_stars|useful|funny|cool| text| date| time|\n", + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "|L9YHX9CVevipN49BN...|xMGWttm6V-cvP3V2f...|HP03UxUpfwic04tBu...| 2| 1| 1| 1|Ordered delivery....|2018-06-21|2024-11-04 01:57:19|\n", + "|lVHdykXMARM_Nqfup...|kzDzdbPupHiglDzs9...|IvAa7PvfOYl0xRML5...| 4| 12| 4| 7|People will fight...|2017-05-15|2024-11-04 02:22:14|\n", + "|cfFKGABwOJYSsxkiR...|f302ihzVqr3CC4nBo...|3cfo_0_GjpJ2mWRYk...| 4| 0| 1| 0|Nice hotel if you...|2017-09-22|2024-11-04 05:19:32|\n", + "|KoXZUf2e8CzuUDjYG...|cY_KlFsqTSkLwfblI...|QsxDgLLRXaneBe5De...| 2| 1| 1| 1|Ok; not great. Se...|2017-09-22|2024-11-04 13:13:18|\n", + "|wyfhBjpZYkR_rsCfK...|Pv3PBDhoCXmnjxmif...|bp5Mk2d0qofUeF5uL...| 1| 0| 1| 0|\"I had to give on...|2017-08-19|2024-11-04 03:29:21|\n", + "|xubs4xQ16WoembWk7...|9fv1YJZZK2Kc6egNF...|DK98TgLNmIguxMMPG...| 5| 0| 1| 0|I love this place...|2017-11-20|2024-11-04 15:35:17|\n", + "|KVlU9P9cStKO-b98a...|gVvkw-bW7hcrs9P5x...|rBhEQ5KjkF1FLQpss...| 1| 2| 1| 0|Sold warm beer in...|2017-03-05|2024-11-04 20:34:02|\n", + "|QpbmU93jmJmKaWbba...|OGA-hrRk2U9rtyToV...|tbxYCbPNLzdCTibop...| 5| 1| 1| 1|I wanted to wait ...|2017-03-28|2024-11-04 17:45:08|\n", + "|CREbSsdFAGNIFIo21...|VuhLmCIwYJC3mASEq...|Z7-p5k2Z89KAsWmy5...| 2| 1| 2| 1|I've been to Gree...|2017-07-29|2024-11-04 11:29:01|\n", + "|v-uA9cLogRW1VQi3v...|1OxjwCO1s799zzYnF...|Nyo4NgBif6I8639ya...| 1| 0| 1| 0|I got the complet...|2018-04-16|2024-11-04 03:30:13|\n", + "|ucG4QbySH4C7fqcRH...|k31iJgDTpquKJX37G...|f_QxYk7vE2rjn8u7q...| 4| 1| 1| 1|Visited St.Louis ...|2017-12-07|2024-11-04 01:53:02|\n", + "|otr5EsMlwUGHuSrk5...|E60ho2jBSjPL6wE0m...|QC5-DP-O0qpawYnQ_...| 5| 2| 1| 3|Craig Walker is p...|2018-05-31|2024-11-04 14:25:38|\n", + "|aJhRB3Zq_xv3VFoRb...|NaWhsjb5GJMRYfZGf...|jbJTF1zVmZsk4hea8...| 5| 1| 1| 1|A friend and I we...|2018-09-28|2024-11-04 19:54:21|\n", + "|Ny1kgj44t-yczYzz0...|5riCVoAxB0Nu5EWtC...|7hzMziJEmZ0O6s9Rt...| 1| 1| 1| 0|We are regulars a...|2017-04-08|2024-11-04 22:21:01|\n", + "|CGviZC0IBcl4IASip...|kWmpeR3Y-uNdH6Jcn...|ESPdkRZVHOI5r_GxN...| 1| 0| 1| 0|Terrible service ...|2018-03-25|2024-11-04 17:12:32|\n", + "|Vk7PTyfo9L3p_7lOY...|7l9dBKDL-ms_mempH...|1xOyqnO5FtxSLwRfT...| 1| 0| 1| 0|I asked for 100 n...|2018-03-25|2024-11-04 05:17:36|\n", + "|mCZNwaTparL3BLyyL...|OwpwF4L7I-MSQQiG6...|HzUJO2v5lUEIeYoOY...| 5| 1| 1| 1|\"In the captured ...|2017-03-27|2024-11-04 21:55:44|\n", + "|TKqhEMOf6Vsb45KDh...|xXs2pVWg_88WVq8ls...|cSknwu7DA2QbbezL-...| 1| 1| 1| 0|Horrible had to w...|2018-03-16|2024-11-04 16:04:39|\n", + "|bAIlY4nr-2aXlFrt6...|PfKfbvqiGTUbOEM1w...|_5_x1-g7vMxag8fPN...| 1| 1| 1| 0|Lovely owner. Mot...|2017-05-15|2024-11-04 02:22:03|\n", + "|e0YUlchvIpagG0jxb...|Iq9dOhX77DKUzK_W6...|Jw3mUNF9_ZiawPDZr...| 5| 0| 1| 0|Stacy @ the Iguan...|2017-03-06|2024-11-04 19:07:07|\n", + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Sentiment Analysis On Review" + ], + "metadata": { + "id": "dSNQiNODzTJ9" + } + }, + { + "cell_type": "code", + "source": [ + "#SENTIMENT ANALYSIS ON REVIEW" + ], + "metadata": { + "id": "Qg6BqKrnSlSy" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "from pyspark.sql.functions import udf, col\n", + "from textblob import TextBlob\n", + "from pyspark.sql.types import FloatType\n", + "\n", + "# Create a UDF to calculate sentiment using TextBlob\n", + "@udf(FloatType())\n", + "def calculate_sentiment(text):\n", + " try:\n", + " sentiment = TextBlob(text).sentiment.polarity\n", + " return float(sentiment)\n", + " except Exception as e:\n", + " print(f\"Error with text: {text}, Error: {e}\")\n", + " return 0.0 # Return a default value in case of error\n", + "\n", + "# Apply the UDF to the 'text' column to create a new 'sentiment' column\n", + "filtered_review_df = filtered_review_df.withColumn(\"sentiment\", calculate_sentiment(col(\"text\")))\n", + "\n", + "# Calculate the average sentiment score for each business\n", + "avg_sentiment = filtered_review_df.groupBy('business_id').agg({'sentiment': 'mean'}) \\\n", + " .withColumnRenamed(\"avg(sentiment)\", \"avg_sentiment\")\n", + "\n", + "avg_sentiment.show(10)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5oHh8NJkyQa6", + "outputId": "5e545326-7ed8-43e8-f52c-e406aad88069" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+-------------------+\n", + "| business_id| avg_sentiment|\n", + "+--------------------+-------------------+\n", + "|VwZ5NDbIu3elGQI6M...| 0.2655506036602534|\n", + "|HC0g0xOuGDqn1jCCM...|-0.2891581624746323|\n", + "|P2XJbQZmf1zvWp9L_...|0.36262180441274094|\n", + "|NLV0ppsHTiJk6JVdF...| 0.1318848542869091|\n", + "|3VrqxApK-iwfRohlA...|0.17360846195369958|\n", + "|BnibjYoTYefJXQ_ZV...|0.27276167273521423|\n", + "|eXMfOA7nAh3dlLgyw...| 0.2873173728585243|\n", + "|XTHUxyHgR7WEfdXgZ...|0.44007351994514465|\n", + "|bncTqUdA8ZPcUkDDm...|0.08524322616202491|\n", + "|-ZzCVD9Ge7KCy4ffh...|0.01465756595134735|\n", + "+--------------------+-------------------+\n", + "only showing top 10 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Calculate average review stars and review count per business\n", + "review_aggregates = filtered_review_df.groupBy('business_id').agg(\n", + " F.avg('review_stars').alias('avg_review_stars'),\n", + " F.count('review_stars').alias('total_reviews')\n", + ")\n", + "\n", + "\n", + "review_aggregates.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UVvkSDEHY3nK", + "outputId": "53ac40ee-d764-4eb6-a646-673073b8838d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+------------------+-------------+\n", + "| business_id| avg_review_stars|total_reviews|\n", + "+--------------------+------------------+-------------+\n", + "|VwZ5NDbIu3elGQI6M...|3.9615384615384617| 26|\n", + "|HC0g0xOuGDqn1jCCM...| 1.0| 5|\n", + "|P2XJbQZmf1zvWp9L_...|4.3076923076923075| 13|\n", + "|NLV0ppsHTiJk6JVdF...| 3.6875| 16|\n", + "|3VrqxApK-iwfRohlA...| 4.0| 10|\n", + "|BnibjYoTYefJXQ_ZV...| 4.0| 8|\n", + "|eXMfOA7nAh3dlLgyw...| 4.5| 2|\n", + "|XTHUxyHgR7WEfdXgZ...| 5.0| 2|\n", + "|bncTqUdA8ZPcUkDDm...|3.2857142857142856| 7|\n", + "|-ZzCVD9Ge7KCy4ffh...| 2.2| 5|\n", + "|0jpFptAehZhLl3nkh...|3.3529411764705883| 17|\n", + "|OLS8elt9V1aoqE0Sj...|2.3333333333333335| 3|\n", + "|ErvTvPF3vtLYsEqXq...| 3.0| 4|\n", + "|-9rRsa_9GTkSYkPdK...|1.4444444444444444| 9|\n", + "|w3giBYDmPWWnsNq5S...| 2.5| 2|\n", + "|LOzj0BsudeOeqoN5d...| 4.8| 5|\n", + "|gzb347pBsdfPDHck-...| 5.0| 1|\n", + "|mqpXIXRuaWdKhWmeN...| 2.5| 2|\n", + "|SzSLKafTPOQzp3pfz...| 1.9| 10|\n", + "|iBtVe23EYetWRjwT3...| 4.0| 2|\n", + "+--------------------+------------------+-------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Read Checkin CSV" + ], + "metadata": { + "id": "oz94FCNYzZEI" + } + }, + { + "cell_type": "code", + "source": [ + "#CHECKIN" + ], + "metadata": { + "id": "HmOiDLbuW9N9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "checkin_df = spark.read.csv(\"/content/drive/MyDrive/ProcessedCSV/final_checkin.csv\", header=True, inferSchema=True)\n", + "columns_to_drop = checkin_df.columns\n", + "checkin_df = checkin_df.filter(F.col(columns_to_drop[0]) != checkin_df.first()[columns_to_drop[0]])\n", + "for col in columns_to_drop[1:]:\n", + " checkin_df = checkin_df.filter(F.col(col) != checkin_df.first()[col])\n", + "checkin_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "V4A9xvHlW9Kf", + "outputId": "01af6723-12b2-4e36-8c4c-64a027943fe2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+----------+-------------------+---------+\n", + "| business_id| date|total_checkin_count| time|\n", + "+--------------------+----------+-------------------+---------+\n", + "|--7jw19RH9JKXgFoh...|2014-04-21| 26|20:42:11,|\n", + "|--8IbOsAAxjKRoYsB...|2015-06-06| 32|01:03:19,|\n", + "|--9osgUCSDUWUkoTL...|2015-06-13| 24|02:00:57,|\n", + "|--ARBQr1WMsTWiwOK...|2014-12-12| 34|00:44:23,|\n", + "|--FWWsIwxRwuw9vIM...|2010-09-11| 7|16:28:39,|\n", + "|--FcbSxK1AoEtEAxO...|2017-08-18| 82|19:43:50,|\n", + "|--LC8cIrALInl2vyo...|2017-01-12| 7|19:10:02,|\n", + "|--MbOh2O1pATkXa7x...|2013-04-21| 103|01:52:06,|\n", + "|--N9yp3ZWqQIm7DqK...|2012-10-06| 7|20:46:47,|\n", + "|--O3ip9NpXTKD4oBS...|2010-04-17| 456|21:07:32,|\n", + "|--OS_I7dnABrXvRCC...|2018-05-11| 1| 18:23:36|\n", + "|--S43ruInmIsGrnnk...|2010-08-29| 309|01:17:44,|\n", + "|--SJXpAa0E-GCp2sm...|2014-04-06| 45|22:23:56,|\n", + "|--Sd93OFWITqDHifM...|2013-01-09| 48|17:42:28,|\n", + "|--ZVrH2X2QXBFdCil...|2010-08-12| 67|18:21:29,|\n", + "|--ZWv8kGlM2YL58uK...|2010-10-13| 13|18:41:45,|\n", + "|--_9CAxgfXZmoFdNI...|2011-01-28| 136|02:59:05,|\n", + "|--_lZuj_WCGnDG6n0...|2013-01-02| 30|23:01:14,|\n", + "|--a_r_w1HTsOY-fag...|2012-04-07| 43|16:13:35,|\n", + "|--eBbs3HpZYIym5pE...|2013-05-07| 28|02:43:13,|\n", + "+--------------------+----------+-------------------+---------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Calculate total check-ins per business\n", + "total_checkins = checkin_df.groupBy('business_id').agg(F.sum('total_checkin_count').alias('total_checkin_count'))\n", + "total_checkins.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ml1A8zpaXDRe", + "outputId": "03ef8523-ddd6-4e1f-f38d-4f20e5c88d40" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+-------------------+\n", + "| business_id|total_checkin_count|\n", + "+--------------------+-------------------+\n", + "|-0iIxySkp97WNlwK6...| 449|\n", + "|-1dARtemb2Gy7Xlft...| 2|\n", + "|-BB9Xn-ak_23FxVEW...| 2|\n", + "|-JAwNIEJoDXmdE_8s...| 360|\n", + "|-UADx0eR5Huq6s1CP...| 20|\n", + "|-WUHfRZ7a5NQA4TyG...| 2|\n", + "|-WvUB8bDBNXuvTU9i...| 22|\n", + "|-ZzCVD9Ge7KCy4ffh...| 43|\n", + "|-ajaASaDA_77I6pK3...| 238|\n", + "|-dB0vYhiTeBDlayTz...| 3|\n", + "|-f5A6KKUu4jZN1qpt...| 1|\n", + "|-fjIX9bvBKwXjQWfd...| 21|\n", + "|-mxgEmVaB0Izs2-qa...| 4|\n", + "|-nEqIKUP2ykB7rSIh...| 6|\n", + "|-rKaikuKBl1BwDPNJ...| 33|\n", + "|04Itzpsew47i9UJ3u...| 9|\n", + "|05qVGl0KbrzU9gbTp...| 2|\n", + "|07uojIeVeauFxa7yO...| 5|\n", + "|0Y8xQvpbmO02SsFZt...| 44|\n", + "|0jgz7W-ytCK-LmN84...| 8|\n", + "+--------------------+-------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# check for missing values in total_checkins\n", + "\n", + "# Check for missing values in total_checkins\n", + "missing_checkins = total_checkins.filter(total_checkins.total_checkin_count.isNull())\n", + "missing_checkins.show()\n", + "\n", + "# Count the number of missing values\n", + "num_missing_checkins = missing_checkins.count()\n", + "\n", + "if num_missing_checkins > 0:\n", + " print(f\"There are {num_missing_checkins} businesses with missing total check-in counts.\")\n", + "else:\n", + " print(\"There are no missing total check-in counts.\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ih8AMoXDZNgx", + "outputId": "d6a3e161-be70-4da5-b202-eff5c73b9696" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+-----------+-------------------+\n", + "|business_id|total_checkin_count|\n", + "+-----------+-------------------+\n", + "+-----------+-------------------+\n", + "\n", + "There are no missing total check-in counts.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Joining all DF's to create one single DF" + ], + "metadata": { + "id": "o5WZ7-exzc7P" + } + }, + { + "cell_type": "code", + "source": [ + "# Join all" + ], + "metadata": { + "id": "2BL9UKZlX6Cm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Merge avg_sentiment with business_df\n", + "features_df = business_df.join(avg_sentiment, on='business_id', how='inner')\n", + "\n", + "# Merge review_aggregates with features_df\n", + "features_df = features_df.join(review_aggregates, on='business_id', how='inner')\n", + "\n", + "# Merge total_checkins with features_df\n", + "features_df = features_df.join(total_checkins, on='business_id', how='inner')\n", + "\n", + "# Fill missing values for sentiment and check-ins (if any) with 0\n", + "features_df = features_df.fillna(0, subset=['avg_sentiment', 'total_checkin_count'])\n", + "\n", + "features_df.show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "qbD0khuPaOMU", + "outputId": "07c6fbfa-d6f3-4a89-fa45-6506a8ae5bae" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------------+------------------+-----+-----------+-------------+---------------+--------------+------------+-------+--------------------+--------------------+------------------+-------------+-------------------+\n", + "| business_id| name| address| city|state|postal_code| latitude| longitude|business_stars|review_count|is_open| categories| avg_sentiment| avg_review_stars|total_reviews|total_checkin_count|\n", + "+--------------------+--------------------+--------------------+------------------+-----+-----------+-------------+---------------+--------------+------------+-------+--------------------+--------------------+------------------+-------------+-------------------+\n", + "|NQhyMw8SOU1HB-V9X...|Champion Chevrole...| 800 Kietzke Ln| Reno| NV| 89502| 39.518222529|-119.7877136963| 3.0| 45| 1|Automotive, Auto ...| 0.04007936641573906| 1.0| 1| 8|\n", + "|2y_CdkxEOJEJGyJAp...|Rode's Fireside R...| 533 Kings Hwy| Woolwich Twp| NJ| 08085| 39.7532524| -75.3024859| 3.5| 104| 1|Restaurants, Amer...| 0.3157407343387604| 4.0| 1| 110|\n", + "|8PNKnlnJg6snf-HUg...| U Dirty Dawg| 2435 S State Rd 135| Greenwood| IN| 46143| 39.579628| -86.158564| 4.0| 32| 1|Pet Sitting, Pet ...| 0.675000011920929| 4.0| 1| 23|\n", + "|sk2lZI4zmuGAccd3D...|Boyd Hill Nature ...|1101 Country Club...| Saint Petersburg| FL| 33705|27.7251226771| -82.6500804812| 5.0| 33| 1|Active Life, Park...| 0.18730159103870392| 4.0| 1| 115|\n", + "|83IeQHroXEctmMpK1...|The Richel D'Ambr...|10 Avenue of the ...| Philadelphia| PA| 19102|39.9512980845| -75.1642701024| 3.5| 65| 1|Hair Salons, Day ...| 0.4623374839623769| 4.666666666666667| 3| 41|\n", + "|KBvdN8Apn4DIxuNW3...| NJ Weedman's Joint| 322 E State St| Trenton| NJ| 08608|40.2208764427| -74.7588232612| 4.0| 42| 1|Chicken Wings, Mu...| 0.1913304105401039| 3.75| 4| 38|\n", + "|zJErbOQMKX-MwHs_u...|Philadelphia Marr...|One Dock St, 2nd ...| Philadelphia| PA| 19106|39.9452460633| -75.1433300972| 3.0| 217| 1|Event Planning & ...| 0.2346357504526774|3.6666666666666665| 3| 503|\n", + "|TuRy46Cyb7MWjV7VM...| Kosmos Pizza| 5 Egypt Rd| Norristown| PA| 19403| 40.132814| -75.379524| 3.0| 71| 1|Pizza, Restaurant...| 0.1791877912150489| 2.888888888888889| 9| 37|\n", + "|I0053JmJ5DEFUWSJ8...|Golden Corral Buf...| 4380 E 22nd St| Tucson| AZ| 85711| 32.2065256| -110.8991584| 2.5| 84| 1|Buffets, Restaura...| 0.0760422629925112| 1.25| 12| 143|\n", + "|AwmeLVLEfdFoCa0La...| The Beer Store|399 Franklin Mill...| Philadelphia| PA| 19154|40.0844192769| -74.9649981286| 3.5| 25| 1|Food, Beer, Wine ...| 0.14942185580730438| 1.5| 2| 52|\n", + "|lpbt16sSm4BTcfeq4...| Super Wok| 778 E Johnson Hwy| Norristown| PA| 19409|40.1248349994| -75.3167966008| 3.5| 52| 1|Restaurants, Chinese| 0.42499999701976776| 3.5| 2| 151|\n", + "|2JX7Y4QQQZW_LYneU...|Sloane Toyota of ...| 593 Lancaster Ave| Malvern| PA| 19355| 40.035778| -75.5742999| 3.0| 105| 1|Car Dealers, Auto...| 0.16773595213890075| 2.6| 5| 23|\n", + "|Y1ZdPCfQ4ndVAmj9l...|China Olive Super...|1350 W Irvington ...| Tucson| AZ| 85746| 32.1639837| -110.993741| 2.5| 38| 1|Buffets, Restaura...|-0.11887420155107975|1.8333333333333333| 6| 25|\n", + "|LnJSsNVZkStgtj86f...|J D's Restaurant ...| 125 Gulf Blvd|Indian Rocks Beach| FL| 33785|27.8791056191| -82.8501277135| 3.0| 200| 1|Bars, Seafood, Ni...| 0.08851684619245741|2.3333333333333335| 6| 512|\n", + "|IiatvDg0R1qwPtKPm...|Panda Pavilion of...|2814 Audubon Vill...| Norristown| PA| 19403| 40.127396| -75.433888| 4.0| 29| 1|Restaurants, Chinese| 0.1425719577819109| 3.5| 4| 28|\n", + "|vQ5Qa6kvlXiUHT60G...| Condado Tacos|12545 Old Meridia...| Carmel| IN| 46032| 39.972309072| -86.1507987043| 4.0| 84| 1|Tacos, Mexican, N...| 0.29971843063831327| 3.6| 5| 62|\n", + "|WOPAPwhaPqQg0cn4t...| Paw Pals| 310 Eddy Ct| Franklin| TN| 37064| 35.9290674| -86.8576755| 3.5| 26| 1|Pet Sitting, Pet ...| 0.10000000149011612| 1.0| 1| 2|\n", + "|X58bbekzeDgD-UHcj...| Buffalo Wild Wings| 2938 Tyrone Blvd N| St Petersburg| FL| 33710| 27.7971469| -82.7350935| 2.5| 86| 1|Chicken Wings, Ni...| 0.2424138536055883|3.3333333333333335| 3| 79|\n", + "|P2XJbQZmf1zvWp9L_...| Sauce on the Side| 411 N 8th St| Saint Louis| MO| 63101|38.6297807094| -90.1920634268| 4.5| 502| 1|Nightlife, Food, ...| 0.36262180441274094|4.3076923076923075| 13| 716|\n", + "|LmRyuir7orBNN43fy...|Soulshine Pizza F...| 1907 Division St| Nashville| TN| 37203| 36.149797| -86.796315| 3.5| 211| 0|Pizza, Food, Arts...| 0.13848957978188992| 3.5| 2| 704|\n", + "+--------------------+--------------------+--------------------+------------------+-----+-----------+-------------+---------------+--------------+------------+-------+--------------------+--------------------+------------------+-------------+-------------------+\n", + "only showing top 20 rows\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Write the Merged/Joined DF to storage to use later" + ], + "metadata": { + "id": "o_Bk5yw3zjB7" + } + }, + { + "cell_type": "code", + "source": [ + "# write features_df as a pandas csv\n", + "\n", + "# Convert the Spark DataFrame to a Pandas DataFrame\n", + "features_pd = features_df.toPandas()\n", + "\n", + "# Save the Pandas DataFrame to a CSV file\n", + "features_pd.to_csv('/content/drive/MyDrive/ProcessedCSV/features_df_pandas.csv', index=False)" + ], + "metadata": { + "id": "QxmFJmqWDqt8" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Write it using pyspark if needed" + ], + "metadata": { + "id": "6OBUmq1czrIk" + } + }, + { + "cell_type": "code", + "source": [ + "# # write features_df as a csv using pyspark\n", + "\n", + "# # Write features_df to a CSV file in Google Drive\n", + "# features_df.write.csv(\"/content/drive/MyDrive/ProcessedCSV/features_df_.csv\", header=True, mode=\"overwrite\")" + ], + "metadata": { + "id": "l4ZTaQQEaK6K" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "w7hggtkAaK2q" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Read the saved CSV (merged)" + ], + "metadata": { + "id": "yAykFElszu6K" + } + }, + { + "cell_type": "code", + "source": [ + "# Read the saved features dataframe from the csv\n", + "\n", + "# Replace 'your_file_path.csv' with the actual path to your CSV file\n", + "file_path = '/content/drive/MyDrive/ProcessedCSV/features_df_.csv'\n", + "\n", + "# Get a list of all part files in the directory\n", + "part_files = [os.path.join(file_path, f) for f in os.listdir(file_path) if f.endswith('.csv')]\n", + "\n", + "# Concatenate all part files into a single DataFrame\n", + "df = pd.concat([pd.read_csv(f) for f in part_files], ignore_index=True)\n", + "\n", + "# Now you can work with the DataFrame 'df'\n", + "print(df.head()) # Display the first few rows" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "JBNI32TPqsmp", + "outputId": "024d4a0c-5b45-4a8f-c849-b6e60f0f0ef0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " business_id name address \\\n", + "0 L-VNs3YquPGKVsXl2Ze-Yg Peacemaker Lobster and Crab 1831 Sidney St \n", + "1 NY_sXepAjc83v58_1w_T_A Insomnia Cookies 226 N Euclid Ave \n", + "2 r6Unwg-mxv_3JrEjQ24_TQ Costco Wholesale 3901 W Costco Dr \n", + "3 vcVOU0oQrYRQfwKK3Vmfxg Aversa's Bakery 801 Rt 168 \n", + "4 fR6WUIlq9RvC_gTC72gg8g The District on 5th 550 N 5th Ave \n", + "\n", + " city state postal_code latitude longitude business_stars \\\n", + "0 Saint Louis MO 63104 38.603194 -90.215495 4.0 \n", + "1 Saint Louis MO 63108 38.644157 -90.261390 3.0 \n", + "2 Tucson AZ 85741 32.326181 -111.049966 3.5 \n", + "3 Blackwood NJ 08012 39.775754 -75.053249 3.5 \n", + "4 Tucson AZ 85705 32.228757 -110.967057 2.0 \n", + "\n", + " review_count is_open categories \\\n", + "0 822 1 Seafood, Restaurants \n", + "1 91 1 Food, Food Delivery Services, Ice Cream & Froz... \n", + "2 98 1 Department Stores, Tires, Food, Automotive, Wh... \n", + "3 63 1 Event Planning & Services, Bakeries, Caterers,... \n", + "4 52 1 Apartments, Real Estate, Home Services \n", + "\n", + " avg_sentiment avg_review_stars total_reviews total_checkin_count \n", + "0 0.277522 3.933333 30 1303 \n", + "1 0.016105 1.625000 8 132 \n", + "2 0.056109 2.750000 4 1434 \n", + "3 0.140095 2.666667 6 47 \n", + "4 0.030885 1.000000 2 2 \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Model" + ], + "metadata": { + "id": "IFOy97yZz3tj" + } + }, + { + "cell_type": "code", + "source": [ + "# # Update weights to include avg_review_stars and total_reviews\n", + "# weights = {\n", + "# \"business_stars\": 0.3,\n", + "# \"review_count\": 0.2,\n", + "# \"avg_review_stars\": 0.2,\n", + "# \"total_reviews\": 0.05,\n", + "# \"avg_sentiment\": 0.2,\n", + "# \"total_checkin_count\": 0.05\n", + "# }\n", + "\n", + "# # Calculate the updated success score using Pandas DataFrame 'df'\n", + "# df['success_score'] = (\n", + "# weights[\"business_stars\"] * df[\"business_stars\"] +\n", + "# weights[\"review_count\"] * df[\"review_count\"] +\n", + "# weights[\"avg_review_stars\"] * df[\"avg_review_stars\"] +\n", + "# weights[\"total_reviews\"] * df[\"total_reviews\"] +\n", + "# weights[\"avg_sentiment\"] * df[\"avg_sentiment\"] +\n", + "# weights[\"total_checkin_count\"] * df[\"total_checkin_count\"]\n", + "# )\n", + "\n", + "# # Display the first few rows with the new 'success_score' column\n", + "# print(df.head())\n", + "\n", + "\n", + "\n", + "# Update weights to include avg_review_stars and total_reviews\n", + "weights = {\n", + " \"business_stars\": 0.4,\n", + " # \"review_count\": 0.2,\n", + " \"avg_review_stars\": 0.3,\n", + " # \"total_reviews\": 0.05,\n", + " \"avg_sentiment\": 0.3\n", + " # \"total_checkin_count\": 0.05\n", + "}\n", + "\n", + "# Calculate the updated success score using Pandas DataFrame 'df'\n", + "df['success_score'] = (\n", + " weights[\"business_stars\"] * df[\"business_stars\"] +\n", + " # weights[\"review_count\"] * df[\"review_count\"] +\n", + " weights[\"avg_review_stars\"] * df[\"avg_review_stars\"] +\n", + " # weights[\"total_reviews\"] * df[\"total_reviews\"] +\n", + " weights[\"avg_sentiment\"] * df[\"avg_sentiment\"] #+\n", + " # weights[\"total_checkin_count\"] * df[\"total_checkin_count\"]\n", + ")\n", + "\n", + "# Display the first few rows with the new 'success_score' column\n", + "print(df.head())" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "nanKBHtdp5lp", + "outputId": "e84f2903-6c5d-4081-fd73-cc6a8d06205a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " business_id name address \\\n", + "0 L-VNs3YquPGKVsXl2Ze-Yg Peacemaker Lobster and Crab 1831 Sidney St \n", + "1 NY_sXepAjc83v58_1w_T_A Insomnia Cookies 226 N Euclid Ave \n", + "2 r6Unwg-mxv_3JrEjQ24_TQ Costco Wholesale 3901 W Costco Dr \n", + "3 vcVOU0oQrYRQfwKK3Vmfxg Aversa's Bakery 801 Rt 168 \n", + "4 fR6WUIlq9RvC_gTC72gg8g The District on 5th 550 N 5th Ave \n", + "\n", + " city state postal_code latitude longitude business_stars \\\n", + "0 Saint Louis MO 63104 38.603194 -90.215495 4.0 \n", + "1 Saint Louis MO 63108 38.644157 -90.261390 3.0 \n", + "2 Tucson AZ 85741 32.326181 -111.049966 3.5 \n", + "3 Blackwood NJ 08012 39.775754 -75.053249 3.5 \n", + "4 Tucson AZ 85705 32.228757 -110.967057 2.0 \n", + "\n", + " review_count is_open categories \\\n", + "0 822 1 Seafood, Restaurants \n", + "1 91 1 Food, Food Delivery Services, Ice Cream & Froz... \n", + "2 98 1 Department Stores, Tires, Food, Automotive, Wh... \n", + "3 63 1 Event Planning & Services, Bakeries, Caterers,... \n", + "4 52 1 Apartments, Real Estate, Home Services \n", + "\n", + " avg_sentiment avg_review_stars total_reviews total_checkin_count \\\n", + "0 0.277522 3.933333 30 1303 \n", + "1 0.016105 1.625000 8 132 \n", + "2 0.056109 2.750000 4 1434 \n", + "3 0.140095 2.666667 6 47 \n", + "4 0.030885 1.000000 2 2 \n", + "\n", + " success_score \n", + "0 2.863257 \n", + "1 1.692331 \n", + "2 2.241833 \n", + "3 2.242028 \n", + "4 1.109266 \n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# pyspark code for # Normalize the success score\n", + "scaler = MinMaxScaler()\n", + "df[\"success_score_normalized\"] = scaler.fit_transform(df[[\"success_score\"]])\n", + "df.head()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 521 + }, + "id": "386jSNEjrBr3", + "outputId": "23dab153-0d82-48fd-cb32-a7819ff3325b" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + " business_id name address \\\n", + "0 L-VNs3YquPGKVsXl2Ze-Yg Peacemaker Lobster and Crab 1831 Sidney St \n", + "1 NY_sXepAjc83v58_1w_T_A Insomnia Cookies 226 N Euclid Ave \n", + "2 r6Unwg-mxv_3JrEjQ24_TQ Costco Wholesale 3901 W Costco Dr \n", + "3 vcVOU0oQrYRQfwKK3Vmfxg Aversa's Bakery 801 Rt 168 \n", + "4 fR6WUIlq9RvC_gTC72gg8g The District on 5th 550 N 5th Ave \n", + "\n", + " city state postal_code latitude longitude business_stars \\\n", + "0 Saint Louis MO 63104 38.603194 -90.215495 4.0 \n", + "1 Saint Louis MO 63108 38.644157 -90.261390 3.0 \n", + "2 Tucson AZ 85741 32.326181 -111.049966 3.5 \n", + "3 Blackwood NJ 08012 39.775754 -75.053249 3.5 \n", + "4 Tucson AZ 85705 32.228757 -110.967057 2.0 \n", + "\n", + " review_count is_open categories \\\n", + "0 822 1 Seafood, Restaurants \n", + "1 91 1 Food, Food Delivery Services, Ice Cream & Froz... \n", + "2 98 1 Department Stores, Tires, Food, Automotive, Wh... \n", + "3 63 1 Event Planning & Services, Bakeries, Caterers,... \n", + "4 52 1 Apartments, Real Estate, Home Services \n", + "\n", + " avg_sentiment avg_review_stars total_reviews total_checkin_count \\\n", + "0 0.277522 3.933333 30 1303 \n", + "1 0.016105 1.625000 8 132 \n", + "2 0.056109 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0L-VNs3YquPGKVsXl2Ze-YgPeacemaker Lobster and Crab1831 Sidney StSaint LouisMO6310438.603194-90.2154954.08221Seafood, Restaurants0.2775223.9333333013032.8632570.677934
1NY_sXepAjc83v58_1w_T_AInsomnia Cookies226 N Euclid AveSaint LouisMO6310838.644157-90.2613903.0911Food, Food Delivery Services, Ice Cream & Froz...0.0161051.62500081321.6923310.275352
2r6Unwg-mxv_3JrEjQ24_TQCostco Wholesale3901 W Costco DrTucsonAZ8574132.326181-111.0499663.5981Department Stores, Tires, Food, Automotive, Wh...0.0561092.750000414342.2418330.464279
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4fR6WUIlq9RvC_gTC72gg8gThe District on 5th550 N 5th AveTucsonAZ8570532.228757-110.9670572.0521Apartments, Real Estate, Home Services0.0308851.000000221.1092660.074885
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{\n \"column\": \"city\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 789,\n \"samples\": [\n \"St. Leo\",\n \"Lambertville\",\n \"Prospect Park\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"state\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 14,\n \"samples\": [\n \"ID\",\n \"CA\",\n \"MO\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"postal_code\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 1425,\n \"samples\": [\n \"18928\",\n \"T5T 0W5\",\n \"T5K 1H6\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"latitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 5.473253312580115,\n \"min\": 27.5644572387,\n \"max\": 53.6497432,\n \"num_unique_values\": 41180,\n \"samples\": [\n 27.9576752063,\n 27.9676252,\n 27.7728341\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"longitude\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 15.028606689513992,\n \"min\": -120.084046,\n \"max\": -74.661348,\n \"num_unique_values\": 40581,\n \"samples\": [\n -86.119413,\n -110.6919319065,\n -75.05398\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"business_stars\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7443006880254324,\n \"min\": 2.0,\n \"max\": 5.0,\n \"num_unique_values\": 7,\n \"samples\": [\n 4.0,\n 3.0,\n 5.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"review_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 206,\n \"min\": 21,\n \"max\": 7568,\n \"num_unique_values\": 1140,\n \"samples\": [\n 509,\n 1289,\n 651\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"is_open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"categories\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 29969,\n \"samples\": [\n \"Breakfast & Brunch, Restaurants, Food Trucks, Fish & Chips, Sushi Bars, Food, Seafood\",\n \"Restaurants, Sandwiches, Salad, Delis, Fast Food\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"avg_sentiment\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.17209486766870022,\n \"min\": -1.0,\n \"max\": 1.0,\n \"num_unique_values\": 39886,\n \"samples\": [\n 0.1598771484568715,\n 0.4266666769981384\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"avg_review_stars\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.311193415141757,\n \"min\": 1.0,\n \"max\": 5.0,\n \"num_unique_values\": 1218,\n \"samples\": [\n 4.029411764705882,\n 4.466666666666667\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_reviews\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 9,\n \"min\": 1,\n \"max\": 453,\n \"num_unique_values\": 132,\n \"samples\": [\n 65,\n 66\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"total_checkin_count\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 702,\n \"min\": 1,\n \"max\": 52144,\n \"num_unique_values\": 2201,\n \"samples\": [\n 1322,\n 240\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"success_score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.6414509799952147,\n \"min\": 0.8914583384990693,\n \"max\": 3.8,\n \"num_unique_values\": 41979,\n \"samples\": [\n 2.903565210035152,\n 3.2074164181947706\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"success_score_normalized\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.2205404132544566,\n \"min\": 0.0,\n \"max\": 1.0,\n \"num_unique_values\": 41975,\n \"samples\": [\n 0.7941827611913486,\n 0.8091770941588066\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" + } + }, + "metadata": {}, + "execution_count": 8 + } + ] + }, + { + "cell_type": "code", + "source": [ + "# # Select features for modeling\n", + "# feature_columns = [\"business_stars\", \"review_count\", \"avg_sentiment\", \"avg_review_stars\", \"total_reviews\", \"total_checkin_count\"]\n", + "# X = df[feature_columns]\n", + "# y = df[\"success_score_normalized\"]\n", + "\n", + "\n", + "# Select features for modeling\n", + "feature_columns = [\"business_stars\", \"avg_sentiment\", \"avg_review_stars\"]\n", + "X = df[feature_columns]\n", + "y = df[\"success_score_normalized\"]" + ], + "metadata": { + "id": "Q9_AKZT5aKvm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "lUS7pkLko74E" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Split data into training and testing sets\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n" + ], + "metadata": { + "id": "DXZsA-gmnl7t" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Train a Random Forest Regressor\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "model = RandomForestRegressor(n_estimators=100, random_state=42)\n", + "model.fit(X_train, y_train)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 80 + }, + "id": "HgLxZg75kQf7", + "outputId": "3d8da084-c619-4bd9-91bd-5a51f3b28a1f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "RandomForestRegressor(random_state=42)" + ], + "text/html": [ + "
RandomForestRegressor(random_state=42)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" + ] + }, + "metadata": {}, + "execution_count": 11 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Evaluation" + ], + "metadata": { + "id": "4zDSToFzz7ht" + } + }, + { + "cell_type": "code", + "source": [ + "# Evaluate the model\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "\n", + "y_pred = model.predict(X_test)\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "\n", + "print(f\"Mean Squared Error: {mse}\")\n", + "print(f\"R^2 Score: {r2}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IwQFGO4hkQdz", + "outputId": "9106a7b2-8cf4-41ee-b576-887ccce1ebdb" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean Squared Error: 2.091459863929542e-06\n", + "R^2 Score: 0.9999572432944943\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "Save model" + ], + "metadata": { + "id": "q1x5KDGQz_rN" + } + }, + { + "cell_type": "code", + "source": [ + "# # saving model\n", + "\n", + "# import pickle\n", + "\n", + "# # Save theu trained model to a file\n", + "# filename = '/content/drive/MyDrive/Models/random_forest_model_3p.pkl'\n", + "# pickle.dump(model, open(filename, 'wb'))" + ], + "metadata": { + "id": "V3B0Hd0m3cco" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# all evluation matrives\n", + "\n", + "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n", + "\n", + "# Regression Metrics\n", + "mse = mean_squared_error(y_test, y_pred)\n", + "rmse = mean_squared_error(y_test, y_pred, squared=False) # Root Mean Squared Error\n", + "mae = mean_absolute_error(y_test, y_pred)\n", + "r2 = r2_score(y_test, y_pred)\n", + "\n", + "print(f\"Mean Squared Error (MSE): {mse}\")\n", + "print(f\"Root Mean Squared Error (RMSE): {rmse}\")\n", + "print(f\"Mean Absolute Error (MAE): {mae}\")\n", + "print(f\"R-squared (R2): {r2}\")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vWtD_HCny1gE", + "outputId": "0b6b5b7d-774a-4755-ee23-71160d9f6134" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Mean Squared Error (MSE): 2.091459863929542e-06\n", + "Root Mean Squared Error (RMSE): 0.0014461880458396625\n", + "Mean Absolute Error (MAE): 0.00039489890147529456\n", + "R-squared (R2): 0.9999572432944943\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n", + " warnings.warn(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "xsculoVuNvbk" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Fetch business ids corresponding to the numerical indices" + ], + "metadata": { + "id": "Vfu7S0db0CfL" + } + }, + { + "cell_type": "code", + "source": [ + "# reading business id and index mapping" + ], + "metadata": { + "id": "XUwOe2QpRUDz" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# read json from file_path = \"/content/drive/MyDrive/Models/businessmapping.json\"\n", + "\n", + "import json\n", + "\n", + "file_path = \"/content/drive/MyDrive/Models/businessmapping.json\"\n", + "\n", + "with open(file_path, 'r') as f:\n", + " data = json.load(f)\n", + "\n", + "print(data)" + ], + "metadata": { + "colab": { + "base_uri": 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" business_list =[]\n", + "\n", + " for i in indexes:\n", + " for key, val in data.items():\n", + " if val == float(i):\n", + " business_list.append(key)\n", + " return business_list\n", + "\n", + "indexes = [6022, 4094, 1765, 8782, 2590, 7514] # please update the output from model1 here, as a list. it should automatically fetch the corresponding business ids\n", + "bisiness_id_list = get_business_id(indexes)\n", + "print(bisiness_id_list)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "p1KYd2JkSOuY", + "outputId": "e45bd9ff-81a6-4255-f943-fe918ce59184" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "['MnjCw3oaM0Hq39bjONDU0Q', 'R46XVcmUzy8qeerHyZQtEg', 'GAXuzDNbzfZbtZYxqrdHnw', 'IZAMcBLQ365GsrgonoM79w', 'W57360g1e7xMGerm7wPW0Q', 'n5TNfoXg1i8wX5R3iY4WUA']\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "loaded_model = model" + ], + "metadata": { + "id": "kZ68gxMvVtK4" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Read saved model" + ], + "metadata": { + "id": "kBamd7Pp0KL5" + } + }, + { + "cell_type": "code", + "source": [ + "# read a saved model file from /content/drive/MyDrive/Models/random_forest_model_.pkl,\n", + "\n", + "import pickle\n", + "\n", + "# Load the saved model from the specified path\n", + "with open('/content/drive/MyDrive/Models/random_forest_model2.pkl', 'rb') as f:\n", + " loaded_model = pickle.load(f)" + ], + "metadata": { + "id": "4gMeAviDNlNb" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Predicting Probabilities" + ], + "metadata": { + "id": "iLzUT2eK0qyb" + } + }, + { + "cell_type": "code", + "source": [ + "# feature_columns = [\"business_stars\", \"review_count\", \"avg_sentiment\", \"avg_review_stars\", \"total_reviews\", \"total_checkin_count\"]\n", + "\n", + "feature_columns = [\"business_stars\", \"avg_sentiment\", \"avg_review_stars\"]\n", + "\n", + "\n", + "def predict_success_for_business_ids(business_ids, model, features_df, feature_columns):\n", + " # Filter the dataset for the provided business IDs\n", + " input_data = features_df[features_df[\"business_id\"].isin(business_ids)]\n", + " X_input = input_data[feature_columns]\n", + "\n", + " # Predict success probability\n", + " success_probabilities = model.predict(X_input)\n", + "\n", + " # Create a DataFrame to display results\n", + " result_df = pd.DataFrame({\n", + " \"business_id\": input_data[\"business_id\"],\n", + " \"success_probability\": success_probabilities\n", + " })\n", + "\n", + " return result_df\n", + "\n", + "# Example usage\n", + "business_ids_to_predict = bisiness_id_list#['IZAMcBLQ365GsrgonoM79w']\n", + "success_probabilities = predict_success_for_business_ids(business_ids_to_predict, loaded_model, df, feature_columns)\n", + "\n", + "print(\"Predicted Success Probabilities:\")\n", + "print(success_probabilities)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "tSn100l3VaYP", + "outputId": "617faa0c-d811-4495-e31c-4f1e29829028" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Predicted Success Probabilities:\n", + " business_id success_probability\n", + "9341 GAXuzDNbzfZbtZYxqrdHnw 0.306973\n", + "21841 n5TNfoXg1i8wX5R3iY4WUA 0.656852\n", + "21947 W57360g1e7xMGerm7wPW0Q 0.712218\n", + "29017 R46XVcmUzy8qeerHyZQtEg 0.784166\n", + "31496 IZAMcBLQ365GsrgonoM79w 0.518287\n", + "41796 MnjCw3oaM0Hq39bjONDU0Q 0.928099\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "kQl9-XSeOF2t" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "LIME INTERPRETATION" + ], + "metadata": { + "id": "GaDVpo5yKMHX" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install lime" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "gbtz3HxvKXBE", + "outputId": "d1e6b0cd-53c9-4861-af18-50309d455ebd" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Collecting lime\n", + " Downloading lime-0.2.0.1.tar.gz (275 kB)\n", + "\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/275.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m275.7/275.7 kB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from lime) (3.8.0)\n", + "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lime) (1.26.4)\n", + "Requirement already satisfied: scipy in 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(from matplotlib->lime) (1.4.7)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (3.2.0)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->lime) (2.8.2)\n", + "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->lime) (1.16.0)\n", + "Building wheels for collected packages: lime\n", + " Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Created wheel for lime: filename=lime-0.2.0.1-py3-none-any.whl size=283834 sha256=2c487a82aba446ce05d0f9bd22d0dd8c744a15c8fad3d7728742c53332f2a77e\n", + " Stored in directory: /root/.cache/pip/wheels/fd/a2/af/9ac0a1a85a27f314a06b39e1f492bee1547d52549a4606ed89\n", + "Successfully built lime\n", + "Installing collected packages: lime\n", + "Successfully installed lime-0.2.0.1\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "import lime\n", + "from lime.lime_tabular import LimeTabularExplainer\n", + "import numpy as np\n", + "\n", + "# Convert feature DataFrame to a numpy array if needed by LIME\n", + "X = df[feature_columns].values\n", + "\n", + "# Initialize the LIME explainer for regression\n", + "explainer = LimeTabularExplainer(\n", + " training_data=X,\n", + " feature_names=feature_columns,\n", + " mode='regression', # Mode is set to 'regression' for a continuous outcome\n", + " discretize_continuous=True # Optional: bins continuous features for interpretability\n", + ")\n" + ], + "metadata": { + "id": "dJNrdI0EKLPZ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import lime\n", + "from lime.lime_tabular import LimeTabularExplainer\n", + "\n", + "\n", + "# Define feature columns and target column\n", + "# feature_columns = [\"business_stars\", \"review_count\", \"avg_sentiment\", \"avg_review_stars\", \"total_reviews\", \"total_checkin_count\"]\n", + "\n", + "feature_columns = [\"business_stars\", \"avg_sentiment\", \"avg_review_stars\"]\n", + "\n", + "# Step 1: Filter the dataset for the provided business IDs\n", + "business_ids_to_predict = bisiness_id_list # ['MnjCw3oaM0Hq39bjONDU0Q','R46XVcmUzy8qeerHyZQtEg', 'GAXuzDNbzfZbtZYxqrdHnw', 'IZAMcBLQ365GsrgonoM79w', 'W57360g1e7xMGerm7wPW0Q']\n", + "input_data = df[df[\"business_id\"].isin(business_ids_to_predict)]\n", + "X_input = input_data[feature_columns].values\n", + "\n", + "# Step 2: Initialize the LIME Explainer\n", + "explainer = LimeTabularExplainer(\n", + " training_data=df[feature_columns].values, # Full training data as required by LIME\n", + " feature_names=feature_columns,\n", + " mode=\"regression\"\n", + ")\n", + "\n", + "# Step 3: Predict and interpret each business ID\n", + "for i, business_id in enumerate(business_ids_to_predict):\n", + " # Get the single prediction row for explanation\n", + " prediction_row = X_input[i].reshape(1, -1)\n", + "\n", + " # Make the prediction\n", + " predicted_success = model.predict(prediction_row)\n", + "\n", + " # Generate LIME explanation for this specific prediction\n", + " explanation = explainer.explain_instance(\n", + " data_row=prediction_row[0], # Single business features\n", + " predict_fn=model.predict\n", + " )\n", + "\n", + " # Display the explanation\n", + " print(f\"\\nExplanation for Business ID: {business_id}\")\n", + " print(f\"Predicted Success Probability: {predicted_success}\")\n", + " explanation.show_in_notebook(show_table=True)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + }, + "id": "EuTiOQxDKKSZ", + "outputId": "89938a25-a8f1-4a7f-b41c-942b525242e0" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Explanation for Business ID: MnjCw3oaM0Hq39bjONDU0Q\n", + "Predicted Success Probability: [0.30697299]\n" + ] + }, + { + "output_type": 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\n", + " \n", + " \n", + " " + ] + }, + "metadata": {} + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n", + " warnings.warn(\n", + "/usr/local/lib/python3.10/dist-packages/sklearn/base.py:493: UserWarning: X does not have valid feature names, but RandomForestRegressor was fitted with feature names\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "Explanation for Business ID: n5TNfoXg1i8wX5R3iY4WUA\n", + "Predicted Success Probability: [0.92809878]\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + " \n", + " \n", + "
\n", + " \n", + " \n", + " " + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "nLk1JykZMSjS" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "CHECKING DETAILS OF A PARTICULAR BUSINESS ID" + ], + "metadata": { + "id": "DWg0Vc5kvDQZ" + } + }, + { + "cell_type": "code", + "source": [ + "#\n", + "business_df.createOrReplaceTempView(\"business_temp\")\n", + "review_df.createOrReplaceTempView(\"review_temp\")\n", + "checkin_df.createOrReplaceTempView(\"checkin_temp\")" + ], + "metadata": { + "id": "70Q6vSxdeNpm" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "business_id_to_search = 'n5TNfoXg1i8wX5R3iY4WUA' # Replace with variable\n", + "\n", + "spark.sql(f\"SELECT * FROM business_temp WHERE business_id = '{business_id_to_search}'\").show()\n", + "spark.sql(f\"SELECT * FROM review_temp WHERE business_id = '{business_id_to_search}'\").show()\n", + "spark.sql(f\"SELECT * FROM checkin_temp WHERE business_id = '{business_id_to_search}'\").show()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "MAJpWClHvtCY", + "outputId": "4db99fdd-a894-4b24-eb47-d88ff76672c4" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "+--------------------+--------------------+--------------+------------+-----+-----------+-------------+--------------+--------------+------------+-------+--------------------+\n", + "| business_id| name| address| city|state|postal_code| latitude| longitude|business_stars|review_count|is_open| categories|\n", + "+--------------------+--------------------+--------------+------------+-----+-----------+-------------+--------------+--------------+------------+-------+--------------------+\n", + "|n5TNfoXg1i8wX5R3i...|Cinder Copper & Lace|1500 Locust St|Philadelphia| PA| 19102|39.9482528775|-75.1666254178| 3.5| 157| 0|Beer, Wine & Spir...|\n", + "+--------------------+--------------------+--------------+------------+-----+-----------+-------------+--------------+--------------+------------+-------+--------------------+\n", + "\n", + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "| review_id| user_id| business_id|review_stars|useful|funny|cool| text| date| time|\n", + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "|8iTFekm-SjwniH8CA...|GcdYgbaF75vj7RO6E...|n5TNfoXg1i8wX5R3i...| 4| 3| 3| 5|Since returning t...|2017-02-12|2024-11-04 08:14:38|\n", + "|eu_OUWSzmQwerc90W...|EuopqHg-cCgb0MjbI...|n5TNfoXg1i8wX5R3i...| 4| 2| 1| 0|Ever since Cinder...|2017-02-12|2024-11-04 21:57:13|\n", + "|wcS-umg95hhmjwX2U...|GcdYgbaF75vj7RO6E...|n5TNfoXg1i8wX5R3i...| 5| 6| 2| 4|Okay yeah, it's t...|2017-06-13|2024-11-04 00:38:18|\n", + "|VaK3v7nDuMRUHDt42...|_hlhhNACb1VylCPDs...|n5TNfoXg1i8wX5R3i...| 4| 1| 1| 1|I was here on a T...|2016-12-13|2024-11-04 03:06:38|\n", + "|CmbYZ7w7dOiCMLXuM...|j2vna6nKnOLk6WQdr...|n5TNfoXg1i8wX5R3i...| 5| 5| 1| 4|I had the pleasur...|2018-06-24|2024-11-04 22:20:37|\n", + "|cbGyJOkpxjyb1KVWI...|ET8n-r7glWYqZhuR6...|n5TNfoXg1i8wX5R3i...| 4| 15| 2| 7|Really enjoyed my...|2018-02-20|2024-11-04 16:39:18|\n", + "|SpxpBORYyNGX06dZE...|DqkSHfAfJCwXaOQYi...|n5TNfoXg1i8wX5R3i...| 4| 0| 1| 0|Cinder was a fant...|2017-01-10|2024-11-04 21:25:33|\n", + "|KkKjuswP6LWJC62Mh...|Te3BwftEFN8C9WxIm...|n5TNfoXg1i8wX5R3i...| 4| 2| 1| 1|I loved this casu...|2018-12-09|2024-11-04 22:12:12|\n", + "|rzPEA1SkuQKOFoRy-...|BgRhdl9PNVTkM7AKE...|n5TNfoXg1i8wX5R3i...| 4| 2| 1| 5|Stopped by Cinder...|2017-05-18|2024-11-04 16:12:02|\n", + "|DezwQJS4GcU_31Cn0...|N9hqTPQu2bmI8c2Pj...|n5TNfoXg1i8wX5R3i...| 5| 27| 9| 17|Philadelphia, we'...|2017-03-01|2024-11-04 14:20:11|\n", + "|vfySxrxlK_sWDUGWw...|9qTmQeOKZMppgnySd...|n5TNfoXg1i8wX5R3i...| 5| 5| 2| 6|We stopped in for...|2017-01-20|2024-11-04 12:33:33|\n", + "|_9ilsQgKYXms2RN1k...|OLGQ7alK4VKl3YdQk...|n5TNfoXg1i8wX5R3i...| 4| 3| 1| 3|Pizzas and sours ...|2016-11-26|2024-11-04 20:44:29|\n", + "|3LeeWefPex4V8kmP-...|OLGQ7alK4VKl3YdQk...|n5TNfoXg1i8wX5R3i...| 5| 31| 8| 16|I love this bar. ...|2017-11-17|2024-11-04 10:19:45|\n", + "|6UsFZbf6n6e-nY0s0...|q_dZIvQMsaidgHJN_...|n5TNfoXg1i8wX5R3i...| 4| 0| 1| 0|Came here for dri...|2018-10-03|2024-11-04 20:26:42|\n", + "+--------------------+--------------------+--------------------+------------+------+-----+----+--------------------+----------+-------------------+\n", + "\n", + "+--------------------+----------+-------------------+---------+\n", + "| business_id| date|total_checkin_count| time|\n", + "+--------------------+----------+-------------------+---------+\n", + "|n5TNfoXg1i8wX5R3i...|2016-11-11| 349|03:14:02,|\n", + "+--------------------+----------+-------------------+---------+\n", + "\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "\n" + ], + "metadata": { + "id": "1KfxdblxHfmb" + }, + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/NoteBook/review_sentiment_code.ipynb b/NoteBook/review_sentiment_code.ipynb new file mode 100644 index 0000000..204893c --- /dev/null +++ b/NoteBook/review_sentiment_code.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "XU9lMuZdVJya", + "outputId": "61bb0d36-5bb4-46bd-accb-a45bfe942fe8" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pyspark in /usr/local/lib/python3.10/dist-packages (3.5.3)\n", + "Requirement already satisfied: py4j==0.10.9.7 in /usr/local/lib/python3.10/dist-packages (from pyspark) (0.10.9.7)\n" + ] + } + ], + "source": [ + "!pip install pyspark" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 219 + }, + "id": "Z6QU3P9Gnn_e", + "outputId": "2289cc66-8b31-460b-d711-893542b014de" + }, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + "

SparkSession - in-memory

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SparkContext

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Spark UI

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Version
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v3.5.3
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Master
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local[*]
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AppName
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MySparkSession
\n", + "
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\n", + " " + ] + }, + "metadata": {}, + "execution_count": 2 + } + ], + "source": [ + "\n", + "from pyspark.sql import SparkSession\n", + "\n", + "spark = SparkSession.builder.appName(\"MySparkSession\").getOrCreate()\n", + "\n", + "# Check if the SparkSession is created successfully\n", + "spark" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "0Q_wxl8osDnA" + }, + "outputs": [], + "source": [ + "# import pandas as pd\n", + "from textblob import TextBlob\n", + "import nltk\n", + "from nltk.tokenize import sent_tokenize" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pBwb1Nt1tbfA" + }, + "outputs": [], + "source": [ + "df_reviews = spark.read.csv('/content/drive/MyDrive/ProcessedCSV/final_review.csv', header=True, inferSchema=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QItJizQrdxm5" + }, + "outputs": [], + "source": [ + "# reviews.info()\n", + "# tips.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "njh0uOuHTyY_" + }, + "outputs": [], + "source": [ + "# reviews.head()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "pwUQV23TLkcn" + }, + "outputs": [], + "source": [ + "# tips.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "2RDxxtJhn7fs" + }, + "outputs": [], + "source": [ + "from pyspark.sql.functions import udf\n", + "from pyspark.sql.types import StringType\n", + "from textblob import TextBlob\n", + "\n", + "# Function to determine sentiment\n", + "def get_sentiment(text):\n", + " analysis = TextBlob(text)\n", + " # Classify sentiment\n", + " if analysis.sentiment.polarity > 0:\n", + " return 'positive'\n", + " elif analysis.sentiment.polarity < 0:\n", + " return 'negative'\n", + " else:\n", + " return 'neutral'\n", + "\n", + "# Create a UDF (User-Defined Function) from the get_sentiment function\n", + "sentiment_udf = udf(get_sentiment, StringType())\n", + "\n", + "# Apply the UDF to the 'text' column\n", + "df_reviews = df_reviews.withColumn('sentiment', sentiment_udf(df_reviews['text']))\n", + "\n", + "# Save the updated DataFrame to a new CSV file\n", + "df_reviews.write.csv('reviews_with_sentiment.csv', header=True, mode='overwrite')\n", + "\n", + "print(\"Sentiment analysis completed and saved to 'reviews_with_sentiment.csv'.\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "xNUHi2OwoYTk" + }, + "outputs": [], + "source": [ + "# read and display csv file\n", + "df_reviews_with_sentiment = spark.read.csv('/content/reviews_with_sentiment.csv', header=True, inferSchema=True)\n", + "df_reviews_with_sentiment.show()" + ] + }, + { + "cell_type": "code", + "source": [ + "\n", + "# Drop the first row from the DataFrame\n", + "df_reviews_with_sentiment = df_reviews_with_sentiment.filter(df_reviews_with_sentiment.index != 0)\n", + "\n", + "# Show the updated DataFrame\n", + "df_reviews_with_sentiment.show()" + ], + "metadata": { + "id": "wpyC_3Uok9BG" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "FhbqCJiroYQG" + }, + "outputs": [], + "source": [ + "# save CSV file in the 'ProcessedCSV' folder\n", + "df_reviews_with_sentiment.write.csv('/content/drive/MyDrive/ProcessedCSV/reviews_with_sentiment.csv', header=True, mode='overwrite')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "A9IZAWrFoYN_" + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "UIJBifIIoYLP" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "colab": { + "machine_shape": "hm", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/README.md b/README.md index 0a9fa09..8ea3df8 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,58 @@ -# Business Recommendation System +# Success Rate Prediction and Business Recommendation Platform -This repository contains the analysis and modeling of the Yelp business dataset. The project includes data cleaning, feature engineering, model building, and evaluation. +Welcome to the Success Rate Prediction and Business Recommendation Model repository! This project leverages machine learning to help entrepreneurs and SMEs predict business success rates and get personalized recommendations. Using real-world data from Yelp and other sources, the model provides actionable insights into business viability. -# BizRecProject +--- +## Table of Contents + +- [Introduction](#introduction) +- [Features](#features) +- [Dataset](#dataset) +- [Contributing](#contributing) +- [License](#license) + +--- + +## Introduction + +High failure rates among small and medium-sized enterprises (SMEs) highlight the need for data-driven tools that provide actionable insights into business viability. This project offers a **Machine Learning-based prediction model** that: + +- Predicts the likelihood of business success based on key factors. +- Provides business recommendations tailored to location, industry, and customer sentiment. +- Helps entrepreneurs make better market entry decisions and allocate resources effectively. + +--- + +## Features + +- **Business Success Prediction**: Provides a success rate (%) prediction for each business based on real-time data. +- **Personalized Recommendations**: Ranks and recommends the top N businesses in a given industry or location. +- **Sentiment Analysis**: Incorporates customer reviews to assess customer sentiment and predict business success. +- **Real-Time Data Integration**: Uses real-time market and competitor data for accurate, up-to-date predictions. +- **User-Friendly Dashboard**: Simple and intuitive interface for non-technical users. + +--- + +## Dataset + +The project uses the [Yelp Open Dataset](https://www.yelp.com/dataset) along with additional sources for financial and geographic data. Key features include: + +- **Business Data**: Name, location, category, rating, review count, operating hours, etc. +- **Customer Review Data**: Text reviews, ratings, sentiment analysis. +- **User Data**: Information about reviewers, including review history and credibility. +- **Check-In Data**: Customer check-ins, which help measure business popularity. + +--- + +## Contributing + +We welcome contributions to improve the platform! Please fork the repository, create a new branch, and submit a pull request with your changes. For major changes, please open an issue first to discuss what you would like to change. + +--- + +## License + +This project is licensed under the MIT License. See `LICENSE` for more information. + +--- diff --git a/api/app.py b/api/app.py new file mode 100644 index 0000000..4a2b998 --- /dev/null +++ b/api/app.py @@ -0,0 +1,446 @@ +from flask import Flask, request, jsonify +from flask_cors import CORS +from pyspark.ml.regression import LinearRegressionModel +from pyspark.sql import SparkSession +from pyspark.sql.window import Window +from pyspark.ml.feature import StringIndexer, VectorAssembler, StandardScaler +from pymongo.mongo_client import MongoClient +from pymongo.server_api import ServerApi +import os +import pyspark +from google.oauth2.service_account import Credentials +from googleapiclient.discovery import build +from pyspark.sql.functions import col, expr, radians, lit, row_number, sqrt, sin, cos, asin, power, count, when, collect_list +from pyspark.ml import PipelineModel +import pandas as pd + + + +app = Flask(__name__) +CORS(app) + +# Load the saved Spark model +model_path ="restaurant_recommender_Final_model" +data_path = "Recommender_System_Newdata.csv" +review_model_path = "topic_senti_model" +try: + spark_session = SparkSession.builder.appName("BizRecApi").getOrCreate() + # spark_session.sparkContext.setLogLevel("ERROR") + # model = LinearRegressionModel.load(spark_session, model_path) +except Exception as e: + print(f"Error creating SparkSession: {e}") + +class RestaurantRecommenderPredictor: + def __init__(self, spark_session): + """ + Initialize the Restaurant Recommender Predictor + """ + self.spark = spark_session + self.df = None + self.kmeans_model = None + self.cuisine_indexer = None + self.vector_assembler = None + self.scaler = None + + def load_data(self, data_path): + """ + Load and preprocess restaurant data + """ + # Read the CSV file + self.df = self.spark.read.csv(data_path, header=True, inferSchema=True) + + # Data cleaning and preprocessing + self.df = self.df.na.drop() # Remove rows with null values + + # Convert boolean columns + self.df = (self.df.withColumn("has_parking", + expr("CASE WHEN parking = 'Yes' THEN true ELSE false END")) + .withColumn("has_wifi", + expr("CASE WHEN WiFi = 'Yes' THEN true ELSE false END"))) + + # Encode categorical variables + self.cuisine_indexer = StringIndexer( + inputCol="cuisine_type", + outputCol="cuisine_type_encoded" + ) + self.df = self.cuisine_indexer.fit(self.df).transform(self.df) + + return self.df + + def load_saved_model(self, model_path): + """ + Load the saved PySpark Pipeline Model + """ + try: + self.kmeans_model = PipelineModel.load(model_path) + + # Extract specific stages from the pipeline + stages = self.kmeans_model.stages + + # Find and store vector assembler and scaler + for stage in stages: + if isinstance(stage, VectorAssembler): + self.vector_assembler = stage + elif isinstance(stage, StandardScaler): + self.scaler = stage + + return self.kmeans_model + except Exception as e: + print(f"Error loading model: {e}") + return None + + def haversine_distance(self, lat1, lon1, lat2, lon2): + """ + Calculate distance between two geographical points + """ + lat1_rad = radians(lit(lat1)) + lon1_rad = radians(lit(lon1)) + lat2_rad = radians(lat2) + lon2_rad = radians(lon2) + + return ( + 6371 * 2 * asin( + sqrt( + sin((lat2_rad - lat1_rad) / 2) ** 2 + + cos(lat1_rad) * cos(lat2_rad) * + sin((lon2_rad - lon1_rad) / 2) ** 2 + ) + ) + ) + + def recommend_restaurants( + self, + user_location, + min_rating, + need_parking, + need_wifi, + cuisine_type, + max_distance=60 + ): + """ + Recommend restaurants based on user preferences + """ + user_lat, user_lon = user_location + + # Apply initial filters + filtered_df = self.df.filter( + (col("rating") >= min_rating) & + (col("cuisine_type") == cuisine_type) + ) + + # Apply parking filter + if need_parking: + filtered_df = filtered_df.filter(col("has_parking") == True) + + # Apply WiFi filter + if need_wifi: + filtered_df = filtered_df.filter(col("has_wifi") == True) + + # Add distance column + with_distance_df = filtered_df.withColumn( + "distance", + self.haversine_distance(user_lat, user_lon, col("latitude"), col("longitude")) + ) + + # Filter by distance + nearby_restaurants = with_distance_df.filter( + col("distance") <= max_distance + ) + + # Rank restaurants + window_spec = Window.partitionBy("bus_name").orderBy(col("rating").desc()) + + recommended_restaurants = ( + nearby_restaurants + .withColumn("rank", row_number().over(window_spec)) + .filter(col("rank") == 1) + .orderBy(col("rating").desc()) + .select("business_id", "bus_name", "address", "rating", "distance", "cuisine_type") + .limit(5) + ) + + return recommended_restaurants + + def analyze_sentiments_and_topics(self, recommendation_df, data_path, model_path): + """ + Analyze sentiments and topics for recommended restaurants + """ + # Load the saved model + loaded_model = PipelineModel.load(model_path) + + # Load review data + review_df = self.spark.read.csv(data_path, header=True, inferSchema=True).select("business_id", "rev_text") + + # Filter reviews for recommended restaurants + business_ids = recommendation_df.select("business_id").distinct().rdd.flatMap(lambda x: x).collect() + filtered_reviews = review_df.filter(col("business_id").isin(business_ids)) + + # Transform reviews using the sentiment model + predictions = loaded_model.transform(filtered_reviews) + predictions = predictions.withColumnRenamed("prediction", "sentimentPrediction") + + # Calculate sentiment percentages + sentiment_count = predictions.groupBy("business_id", "sentimentPrediction").agg(count("sentimentPrediction").alias("sentiment_count")) + total_reviews = predictions.groupBy("business_id").agg(count("rev_text").alias("total_reviews")) + + sentiment_percentage = sentiment_count.join(total_reviews, on="business_id") + sentiment_percentage = sentiment_percentage.withColumn( + "sentiment_percentage", + (col("sentiment_count") / col("total_reviews") * 100) + ) + + # Add topic analysis + predictions = predictions.withColumn( + "topic", + when(col("rev_text").contains("food"), "food_quality") + .when(col("rev_text").contains("service"), "customer_service") + .when(col("rev_text").contains("clean"), "cleanliness") + .otherwise("other") + ) + + topic_count = predictions.groupBy("business_id", "topic").agg(count("topic").alias("topic_count")) + topic_percentage = topic_count.join(total_reviews, on="business_id") + topic_percentage = topic_percentage.withColumn( + "topic_percentage", + (col("topic_count") / col("total_reviews") * 100) + ) + + return sentiment_percentage, topic_percentage + +@app.route("/") +def index(): + return "

BizRec System Api

" + +#cache api response + +@app.route('/settings', methods=['GET']) +def settings(): + # spark_session = SparkSession.builder.appName("BizRecApi").getOrCreate() + # df_loc = spark_session.read.csv("data\city.csv", header=True, inferSchema=True) + # loc_data = df_loc.rdd.flatMap(lambda x: x).collect() + # loc_data = ["Ontario", "Alberta", "Chicago"] + + df_loc = pd.read_csv("data/city.csv", header=0) + loc_data = df_loc.values.tolist() + + cuz_data = ['American', + 'Burgers', + 'Cafe', + 'Chinese', + 'Creole', + 'Indian', + 'Italian', + 'Japanese', + 'Juice Bars', + 'Korean', + 'Mexican', + 'Seafood', + 'Southern', + 'Thai', + 'Vegan', + 'Vegetarian'] + + return jsonify({"location" : loc_data, "cuizine" : cuz_data}) + +@app.route('/location', methods=['GET']) +def location(): + data = ["Ontario", "Alberta", "Chicago"] + return jsonify(data) + +@app.route('/cuizine', methods=['GET']) +def cuizine(): + data = ["American", "Chinese", "Indian"] + return jsonify(data) + +def haversine_distance( lat1, lon1, lat2, lon2): + """ + Calculate distance between two geographical points + """ + lat1_rad = radians(lit(lat1)) + lon1_rad = radians(lit(lon1)) + lat2_rad = radians(lat2) + lon2_rad = radians(lon2) + + return ( + 6371 * 2 * asin( + sqrt( + sin((lat2_rad - lat1_rad) / 2) ** 2 + + cos(lat1_rad) * cos(lat2_rad) * + sin((lon2_rad - lon1_rad) / 2) ** 2 + ) + ) + ) + +@app.route('/recommend', methods=['POST']) +def recommend(): + data = request.get_json() + print(data) + location = data['user_location'] + rating = data['min_rating'] + cuisine = data['cuisine_type'] + parking = (data['need_parking']).lower() + wifi = (data['need_wifi']).lower() + + # # Preprocess the input data (e.g., convert to Spark DataFrame) + df_loc = pd.read_csv("data/city_map.csv", header=0) + filtered_df = df_loc[df_loc['city'] == location] + lat = filtered_df.iloc[0]['latitude'] + long = filtered_df.iloc[0]['longitude'] + + spark = SparkSession.builder \ + .appName("Restaurant Recommender and Sentiment Analyzer") \ + .getOrCreate() + + try: + # Initialize predictor + predictor = RestaurantRecommenderPredictor(spark) + + # Load dataset + predictor.load_data(data_path) + + # Load saved model + loaded_model = predictor.load_saved_model(model_path) + + if loaded_model: + # Interactive recommendation + recommendations = predictor.recommend_restaurants( + user_location=(lat, long), + min_rating=rating, + need_parking=parking, + need_wifi=wifi, + cuisine_type=cuisine + ) + + # Analyze sentiments and topics + sentiment_percentage, topic_percentage = predictor.analyze_sentiments_and_topics( + recommendations, data_path, review_model_path + ) + + # print("Recommended:") + # recommendations.show() + + # print("Sentiment Percentages:") + # sentiment_percentage.show() + + # print("Topic Percentages:") + # topic_percentage.show() + + Sentiment_df = sentiment_percentage.groupBy("business_id", "total_reviews").agg( + collect_list("sentimentPrediction").alias("sentimentPrediction_list"), + collect_list("sentiment_count").alias("sentiment_count_list"), + collect_list("sentiment_percentage").alias("sentiment_percentage_list") +) + # print("Sentiment:") + # Sentiment_df.show() + + topic_df = topic_percentage.groupBy("business_id").agg( + collect_list("topic").alias("topic_list"), + collect_list("topic_count").alias("topic_count_list"), + collect_list("topic_percentage").alias("topic_percentage_list") +) + # print("Topic:") + # topic_df.show() + + # Joining DataFrames + final_df = ( + recommendations + .join(Sentiment_df, on="business_id", how="inner") + .join(topic_df, on="business_id", how="inner") + ) + + # Showing the result + # final_df.show(truncate=False) + final = final_df.toPandas() + + # recommendation = recommendations.toPandas() + # sentiment = sentiment_percentage.toPandas() + # topic = topic_percentage.toPandas() + # pandas_df = pd.merge(recommendation, sentiment, on='business_id', how='inner') + # pandas_df = pd.merge(pandas_df, topic, on='business_id', how='inner') + # pandas_df = pandas_df.drop_duplicates(subset=['business_id']) + # pandas_df = pandas_df.drop(columns=['business_id', 'sentimentPrediction', 'topic_count', 'total_reviews']) + # pandas_df = pandas_df.rename(columns={'topic_percentage': 'topic'}) + # pandas_df = pandas_df.rename(columns={'sentiment_percentage': 'sentiment'}) + + dict_data = final.to_dict(orient='records') + + return jsonify({'message': 'Data received successfully','data': dict_data}) + + except Exception as e: + return jsonify({'message': f"An error occurred: {e}",'data': None}) + finally: + spark.stop() + + #load the model + # spark_session = SparkSession.builder.appName("BizRecApi").getOrCreate() + + # kmeans_model = PipelineModel.load(model_path) + # # Extract specific stages from the pipeline + # stages = kmeans_model.stages + # # Find and store vector assembler and scaler + # for stage in stages: + # if isinstance(stage, VectorAssembler): + # vector_assembler = stage + # elif isinstance(stage, StandardScaler): + # scaler = stage + + # df = spark_session.read.csv(data_path, header=True, inferSchema=True) + # df = df.na.drop() + # df = (df.withColumn("has_parking", + # expr("CASE WHEN parking = 'Yes' THEN true ELSE false END")) + # .withColumn("has_wifi", + # expr("CASE WHEN WiFi = 'Yes' THEN true ELSE false END"))) + + # # Encode categorical variables + # cuisine_indexer = StringIndexer( + # inputCol="cuisine_type", + # outputCol="cuisine_type_encoded" + # ) + # df = cuisine_indexer.fit(df).transform(df) + + # filtered_df = df.filter( + # (col("rating") >= rating) & + # (col("cuisine_type") == cuisine) + # ) + + # # Apply parking filter + # if parking: + # filtered_df = filtered_df.filter(col("has_parking") == True) + + # # Apply WiFi filter + # if wifi: + # filtered_df = filtered_df.filter(col("has_wifi") == True) + + # # Add distance column + # with_distance_df = filtered_df.withColumn( + # "distance", + # haversine_distance(lat, long, col("latitude"), col("longitude")) + # ) + + # # Filter by distance + # max_distance=60 + # nearby_restaurants = with_distance_df.filter( + # col("distance") <= max_distance + # ) + + # # Rank restaurants + # window_spec = Window.partitionBy("bus_name").orderBy(col("rating").desc()) + + # recommended_restaurants = ( + # nearby_restaurants + # .withColumn("rank", row_number().over(window_spec)) + # .filter(col("rank") == 1) + # .orderBy(col("rating").desc()) + # .select("business_id", "bus_name", "address", "rating", "distance", "cuisine_type") + # .limit(5) + # ) + + # pandas_df = recommended_restaurants.toPandas() + # dict_data = pandas_df.to_dict(orient='records') + + # return jsonify({'message': 'Data received successfully','data': dict_data}) + + # return jsonify({'data': prediction}) + +if __name__ == '__main__': + app.run(host='0.0.0.0', port=5000) \ No newline at end of file diff --git a/api/data/city.csv b/api/data/city.csv new file mode 100644 index 0000000..84f205b --- /dev/null +++ b/api/data/city.csv @@ -0,0 +1,732 @@ +city +Abington +Abington Township +Affton +Aldan +Alloway +Alton +Ambler +Antioch +Apollo Beach +Arabi +Ardmore +Arnold +Ashland +Ashland City +Aston +Atco +Audubon +Avon +Avondale +BRANDON +Bala Cynwyd +Ballwin +Bargersville +Barrington +Bayonet Point +Beaumont +Beech Grove +Belle Chasse +Belle Meade +Belleair Blf +Belleair Bluffs +Bellefontaine Neighbors +Belleville +Bellevue +Bellmawr +Belmont Hills +Bensalem +Bensalem Township +Bensalem. Pa +Berkeley +Berlin +Berlin Township +Berry Hill +Berwyn +Bethalto +Beverly +Birchrunville +Blackwood +Blue Bell +Blvd +Boise +Boise City +Boothwyn +Bordentown +Boyertown +Breckenridge Hills +Brentwood +Bridgeport +Bridgeton +Bristol +Bristol Twp +Brookhaven +Brooklawn +Broomall +Brownsburg +Bryn Athyn +Bryn Mawr +Buckingham +Bucks +Bucktown +Burlington +Burlington Township +Bywater +CLEARWATER +CLIFTON HEIGHTS +Cahokia +Camby +Camden +Cane Ridge +Carmel +Carney's Point +Carneys Point +Carpinteria +Carrollwood +Carversville +Caseyville +Castleton +Catalina +Cedar Brook +Cedars +Chadds Ford +Chalfont +Chalmette +Cheltenham +Cheltenham Township +Cherry Hill +Cherry Hill Mall +Chesilhurst +Chester +Chester Springs +Chesterbrook +Chesterfield +Chichester +Christiana +Churchville +Cinnaminson +Citrus Park +Clarksboro +Clarksville +Claymont +Clayton +Clearwater Beach +Clementon +Clermont +Coatesville +Cold Springs +Collegeville +Collingdale +Collingswood +Collinsville +Colmar +Columbia +Columbus +Concordville +Conshohocken +Conshohoeken +Cornwells Heights +Corona De Tucson +Cottage Hills +Crestwood +Creve Coeur +Croydon +Crum Lynne +Cumberland +DONELSON +Dade City +Danville +Darby +Delran +Deptford +Deptford Township +Des Peres +Devon +Dover +Downingtown +Doylestown +Dresher +Drexel Heights +Drexel Hill +Dublin +Dunedin +Dupo +Eagle +Eagleville +Earth City +East Alton +East Edmonton +East Falls +East Nashville +East Norriton +East Saint Louis +East St. Louis +Eastampton +Eastampton Township +Eaux Claires +EdMonton +Eddystone +Edgewater Park +Edwardsville +Elkins Park +Elmer +Elmwood +Elsmere +Elverson +Enoch +Erdenheim +Essington +Evesham +Evesham Township +Ewing +Ewing Township +Exton +Fairless Hills +Fairmont City +Fairview Heights +Fairview Hts +Fairview Hts. +Fairview Village +Feasterville +Feasterville Trevose +Feasterville-Trevose +Fenton +Ferguson +Fieldsboro +Fishers +Flanders +Florence +Florence Township +Florissant +Flourtown +Folcroft +Folsom +Fort Washington +Fortville +Franconia +Franklin +Franklinville +Frazer +Freeburg +Freehold +Frontenac +Furlong +Gallatin +Garden City +Gardenville +Garnet Valley +Gentilly +Gibbsboro +Gibbstown +Gibsonton +Gilbertsville +Gladwyne +Glassboro +Glen Carbon +Glen Mills +Glendora +Glenmoore +Glenoldan +Glenolden +Glenside +Gloucester City +Gloucester Township +Godfrey +Goleta +Goodletsville +Goodlettsville +Granite City +Green Lane +Green Park +Green Valley +Greenbrier +Greenfield +Greenville +Greenwood +Gretna +Gulfport +Gulph Mills +Gwynedd +Gwynedd Valley +Had Twp +Haddon Heights +Haddon Township +Haddon Twp +Haddonfield +Hainesport +Hamiltion +Hamilton +Hamilton Township +Hammonton +Hancock +Harahan +Harleysville +Harrison Township +Harvey +Hatboro +Hatfield +Haverford +Havertown +Hazelwood +Hendersonville +Hermitage +Hernando Beach +Hi-Nella +High Ridge +Hockessin +Holicong +Holiday +Holland +Holland Southampton +Holmes +Horsham +Hudson +Hulmeville +Huntingdon Valley +INpolis +Imperial +Indian Rocks Beach +Indian Shores +Indianapolis +Inglewood +Isla Vista +Ivyland +Jamison +Jefferson +Jeffersonville +Jenkintown +Jennings +Joelton +Kalispell +Kenner +Kenneth City +Kennett Square +Kimberton +Kimmswick +King Of Prussia +Kingston Springs +Kirkwood +Kulpsville +LANGHORNE +LANSDALE +LARGO +LITHIA +La Vergne +Ladue +Lafayette Hill +Lahaska +Lambertville +Land O Lakes +Land O' Lakes +Land O'Lakes +Lansdowne +Laurel Springs +Lawnside +Lawrence +Lawrence Township +Lawrenceville +Lebanon +Lederach +Lester +Levittown +Lima +Limerick +Lindenwold +Linfield +Linwood +Logan Township +London Grove +Luling +Lumberton +Lutz +Lutz fl +MADISON +MEDIA +Madeira Beach +Magnolia +Malaga +Malvern +Manayunk +Manchester +Mansfield +Mantua +Mantua Township +Maple Glen +Maple Shade +Maple Shade Township +Maplewood +Marana +Marcus Hook +Marlborough +Marlton +Marrero +Marshallton +Martinsville +Maryland Heights +Maryville +Mascoutah +McCordsville +Medford +Medford Lakes +Mehlville +Melrose Park +Mendenhall +Meraux +Mercerville +Merchantville +Meridian +Merion Station +Metairie +Metarie +Millstadt +Milmont Park +Monroe Township +Monroeville +Montchanin +Montecito +Montgomeryville +Moorestown +Moorestown-Lenola +Mooresville +Morrisville +Morton +Mount Ephraim +Mount Holly +Mount Juliet +Mount Laurel +Mount Laurel Township +Mount Lemmon +Mt Holly +Mt Juliet +Mt. Holly +Mt. Juliet +Mt. Laurel +Mt.Laurel +Mullica Hill +N Redngtn Bch +N.Wales +NASHVILLE +NEW ORLEANS +NEW PORT RICHEY +NORRISTOWN +NW Edmonton +Narberth +National Park +New Britain +New Castle +New Hope +New Palestine +New Whiteland +Newark +Newfield +Newport +Newtown +Newtown Sqaure +Newtown Square +Noblesville +Nolensville +Normandy +North Redington Bch +North Redington Beach +North Wales +Norwood +O Fallon +O' Fallon +O'Fallon +Oaklyn +Oaks +Oakville +Odessa +Old Hickory +Oldsmar +Olivette +Oreland +Oro Valley +Overland +Ozona +PHILA +PINELLAS PARK +Palm Harbor +Palmetto +Palmyra +Paoli +Paulsboro +Pemberton +Penndel +Pennington +Penns Grove +Pennsauken +Pennsauken Township +Pennsburg +Pennsville +Pennsville Township +Pennsylvania +Perkasie +Perkiomenville +Philadelphia +Philadephia +Phoenixville +Pike Creek +Pilesgrove +Pine Forge +Pine HIll +Pinellas +Pineville +Pipersville +Pitman +Pittsgrove +Pittsgrove Township +Plainfield +Plant City +Pleasant View +Plumsteadville +Plymouth Meeting +Pontoon Beach +Port Richey +Pottstown +Primos +Prospect Park +Quakertown +RADNOR +RIVERVIEW +Red Hill +Redingtn Shor +Redington Shore +Redington Shores +Reno +Richboro +Richmond Heights +Ridley +Ridley Park +River Ridge +Riverside +Riverton +Rock Hill +Rockledge +Roebling +Rosemont +Roslyn +Royersford +Runnemede +Ruskin +S.Pasadena +SPRINGHILL +ST LOUIS +ST. Louis +ST. PETE BEACH +ST. PETERSBURG +Safety Harbor +Sahuarita +Saint Albert +Saint Ann +Saint Bernard +Saint Charles +Saint John +Saint Leo +Saint Louis +Saint Pete Beach +Saint Peters +Saint Petersburg +Saint Petersburg Beach +Saint Rose +Salem +San Antonio +Sanatoga +Santa Barbara +Santa Barbara +Sappington +Sassamansville +Schwenksville +Scott AFB +Scott Air Force Base +Secane +Seffner +Sellersville +Seminole +Sewell +Shamong +Sharon Hill +Sherwood +Sherwood Park +Shiloh +Shrewsbury +Sicklerville +Skippack +Skippack Village +Smithton +Smyrna +Somerdale +Souderton +South Cinnaminson +South Pasadena +South Tampa +South Tucson +Southampton +Southampton Township +Southeast Edmonton +Southport +Southwest Philadelphia +Southwest Tampa +Spanish Springs +Sparks +Speedway +Spring City +Spring Hill +Spring House +Springfield +St Albert +St Ann +St Charles +St Louis County +St Pete +St Pete Beach +St Petersburg +St. Charles +St. Albert +St. Ann +St. Charles +St. Davids +St. Louis County +St. Peters +St. Rose +St.Ann +St.Charles +St.Louis +St.Petersburg +St.Rose +Stowe +Stratford +Sullivan +Summerland +Sumneytown +Sun City +Sun City Center +Sun Valley +Sunset Hills +Swansea +Swarthmore +Swedeland +Swedesboro +TAMPA +TRENTON +TRINITY +TUCSON +Tabernacle +Talleyville +Tampa Bay +Tampa Florida +Tampa Palms +Tarpon Springs +Telford +Temple Terrace +Terra Ceia +Terrytown +Thonotosassa +Thorndale +Thornton +Thorofare +Tierra Verde +Tierre Verde +Tinicum +Titusville +Toughkenamon +Town & Country +Town 'N' Country +Town And Country +Trappe +Treasure Island +Trevose +Trolley Square +Trooper +Troy +Turnersville +Tuscon +Twin Oaks +Twn N Cntry +Tylersport +UPPER MORELAND +Unionville +University City +Upland +Upper Chichester +Upper Darby +Upper Pottsgrove +Upper Southampton +VC Highlands +Vail +Valley Park +Valrico +Verdi +View +Villanova +Vincentown +Vineland +Violet +Virginia City +Voorhees +Voorhees Township +W Cherry Hill +W. Berlin +W.Chester +WILLOW GROVE +Wallingford +Wanamaker +Warminster +Warrington +Warrington Township +Warson Woods +Washington Crossing +Washington Park +Washington Twp +Waterloo +Wayne +Webster Groves +Wenonah +Wesley Chapel +West Berlin +West Bradford Township +West Chester +West Collingswood Heights +West Conshohocken +West Deptford +West Edmonton +West Mount Holly +West Point +West Trenton +Westampton +Westchase +Westmont +Westmont - Haddon Towsship +Westville +Westwego +White House +Whiteland +Whites Creek +Whitestown +Williamstown +Willingboro +Willingboro Township +Wilmington +Wilmington Manor +Wimauma +Winchester +Winslow Township +Wood River +Woodbury +Woodbury Heights +Woodbury Hts. +Woodlyn +Woodson Terrace +Woodstown +Woolwich Township +Woolwich Twp +Worcester +Wycombe +Wyncote +Wyndlake Condominium +Wyndmoor +Wynnewood +Yardley +Yeadon +Zephyrhills +Zionsville \ No newline at end of file diff --git a/api/data/city_map.csv b/api/data/city_map.csv new file mode 100644 index 0000000..4c1f75f --- /dev/null +++ b/api/data/city_map.csv @@ -0,0 +1,30876 @@ +city,longitude,latitude +Abington,-75.13280577,40.12461305 +Abington,-75.118793,40.119112 +Abington,-75.1189178,40.1190304 +Abington,-75.11427004,40.13672793 +Abington,-75.1217458,40.1150951 +Abington,-75.1167098,40.1270789 +Abington,-75.1132549,40.1384684 +Abington,-75.12822,40.129452 +Abington,-75.1158053,40.1231556 +Abington,-75.1314976,40.1269888 +Abington,-75.117823,40.1248371 +Abington,-75.093802,40.084963 +Abington,-75.11578205,40.1311964 +Abington,-75.116683,40.127007 +Abington,-75.1169336,40.129672 +Abington,-75.11585253,40.13258155 +Abington,-75.10440952,40.07389451 +Abington,-75.1296594,40.1289112 +Abington,-75.1329107,40.1240716 +Abington,-75.118188,40.1215968 +Abington,-75.1346695,40.12004 +Abington,-75.117823,40.12478 +Abington,-75.11621,40.12301 +Abington,-75.1209932,40.115446 +Abington,-75.11592737,40.123346 +Abington,-75.11554241,40.13205349 +Abington,-75.1173304,40.122468 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+wimauma,-82.2988251,27.7122123 \ No newline at end of file diff --git a/api/demo.py b/api/demo.py new file mode 100644 index 0000000..e3d2874 --- /dev/null +++ b/api/demo.py @@ -0,0 +1,16 @@ +from pyspark.sql import SparkSession +import pyspark.sql.functions as F +import pyspark +from pyspark.sql import SparkSession +from google.oauth2.service_account import Credentials +from googleapiclient.discovery import build + +# Initialize SparkSession +spark = SparkSession.builder \ + .appName("Filter CSV Data") \ + .getOrCreate() + +# Load the CSV file into a DataFrame +review_df = spark.read.csv("..\Recommender_System_Newdata.csv", header=True, inferSchema=True) +review_df.show(5) + diff --git a/api/gunicorn_config.py b/api/gunicorn_config.py new file mode 100644 index 0000000..7c1e4fc --- /dev/null +++ b/api/gunicorn_config.py @@ -0,0 +1,8 @@ +import os + +workers = int(os.environ.get('GUNICORN_PROCESSES', '2')) +threads = int(os.environ.get('GUNICORN_THREADS', '2')) +# timeout = int(os.environ.get('GUNICORN_TIMEOUT', '120')) +bind = os.environ.get('GUNICORN_BIND', '0.0.0.0:5050') +forwarded_allow_ips = '*' +secure_scheme_headers = { 'X-Forwarded-Proto': 'https' } \ No newline at end of file diff --git a/api/requirements.txt b/api/requirements.txt new file mode 100644 index 0000000..841ada2 --- /dev/null +++ b/api/requirements.txt @@ -0,0 +1,11 @@ +pandas +numpy +scikit-learn +flask +flask_cors +uvicorn +gunicorn +pyspark +pymongo[srv] +google-auth-oauthlib +google-api-python-client \ No newline at end of file diff --git a/business_analytics.pbix b/business_analytics.pbix deleted file mode 100644 index 82c8c84..0000000 Binary files a/business_analytics.pbix and /dev/null differ diff --git a/requirements.txt b/requirements.txt index 8db17a8..841ada2 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,11 @@ pandas numpy -scikit-learn \ No newline at end of file +scikit-learn +flask +flask_cors +uvicorn +gunicorn +pyspark +pymongo[srv] +google-auth-oauthlib +google-api-python-client \ No newline at end of file diff --git a/scripts/km_recsys.py b/scripts/km_recsys.py new file mode 100644 index 0000000..3364410 --- /dev/null +++ b/scripts/km_recsys.py @@ -0,0 +1,243 @@ +# -*- coding: utf-8 -*- +"""KM_RecSys + +Automatically generated by Colab. + +Original file is located at + https://colab.research.google.com/drive/1PLYCnhMfoOrBC8c9azVzMA_yj6Cpdgnt +""" + +import os +import joblib +import numpy as np +import pandas as pd +from pyspark.sql import SparkSession +from pyspark.ml.feature import VectorAssembler, StandardScaler +from pyspark.ml.clustering import KMeans +from pyspark.sql.functions import col, expr, radians, sin, cos, sqrt, asin, lit, row_number, power +from pyspark.sql.window import Window +from pyspark.ml import Pipeline +from pyspark.ml.feature import StringIndexer + +class RestaurantRecommenderSystem: + def __init__(self, spark_session): + """ + Initialize the Restaurant Recommender System + """ + self.spark = spark_session + self.df = None + self.kmeans_model = None + self.scaler = None + self.vector_assembler = None + + def load_and_preprocess_data(self, data_path): + """ + Load and preprocess restaurant data + """ + # Read the CSV file + self.df = self.spark.read.csv('/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv', header=True, inferSchema=True) + + # Data cleaning and preprocessing + self.df = self.df.na.drop() # Remove rows with null values + + # Convert boolean columns + self.df = (self.df.withColumn("has_parking", + expr("CASE WHEN parking = 'Yes' THEN true ELSE false END")) + .withColumn("has_wifi", + expr("CASE WHEN WiFi = 'Yes' THEN true ELSE false END"))) + + # Encode categorical variables + cuisine_indexer = StringIndexer(inputCol="cuisine_type", outputCol="cuisine_type_encoded") + # Fit and transform the DataFrame using the StringIndexer + self.df = cuisine_indexer.fit(self.df).transform(self.df) + + return self.df + + def create_feature_vector(self): + """ + Create feature vector for clustering + """ + # Select features for clustering + feature_columns = [ + "latitude", + "longitude", + "rating", + "review_count", + "cuisine_type_encoded" + ] + + # Vector Assembler to combine features + self.vector_assembler = VectorAssembler( + inputCols=feature_columns, + outputCol="features" + ) + + # Scale features + self.scaler = StandardScaler( + inputCol="features", + outputCol="scaled_features", + withStd=True, + withMean=True + ) + + return self + + def train_kmeans_clustering(self, k=5): + """ + Train K-means clustering model + """ + # Create K-means model + kmeans = KMeans( + featuresCol="scaled_features", + predictionCol="cluster", + k=k + ) + + # Create pipeline + pipeline = Pipeline(stages=[ + self.vector_assembler, + self.scaler, + kmeans + ]) + + # Fit the pipeline + self.kmeans_model = pipeline.fit(self.df) + + return self + + def save_model(self, model_path): + """ + Save the trained model + """ + # Instead of joblib, use PySpark's save method for PipelineModels + self.kmeans_model.save(os.path.join('/content/drive/MyDrive/Models', 'restaurant_recommender_Final_model')) + print(f"Model saved to {model_path}") + + def haversine_distance(self, lat1, lon1, lat2, lon2): + """ + Calculate distance between two geographical points + """ + lat1_rad = radians(lit(lat1)) + lon1_rad = radians(lit(lon1)) + lat2_rad = radians(lat2) + lon2_rad = radians(lon2) + + return ( + 6371 * 2 * asin( + sqrt( + power(sin((lat2_rad - lat1_rad) / 2), 2) + + cos(lat1_rad) * cos(lat2_rad) * + power(sin((lon2_rad - lon1_rad) / 2), 2) + ) + ) + ) + + def recommend_restaurants( + self, + user_location, + min_rating, + need_parking, + need_wifi, + cuisine_type, + max_distance=60 + ): + """ + Recommend restaurants based on user preferences + """ + user_lat, user_lon = user_location + + # Apply initial filters + filtered_df = self.df.filter( + (col("rating") >= min_rating) & + (col("cuisine_type") == cuisine_type) + ) + + # Apply parking filter + if need_parking: + filtered_df = filtered_df.filter(col("has_parking") == True) + + # Apply WiFi filter + if need_wifi: + filtered_df = filtered_df.filter(col("has_wifi") == True) + + # Add distance column + with_distance_df = filtered_df.withColumn( + "distance", + self.haversine_distance(user_lat, user_lon, col("latitude"), col("longitude")) + ) + + # Filter by distance + nearby_restaurants = with_distance_df.filter( + col("distance") <= max_distance + ) + + # Rank restaurants + window_spec = Window.partitionBy("bus_name").orderBy(col("rating").desc()) + + recommended_restaurants = ( + nearby_restaurants + .withColumn("rank", row_number().over(window_spec)) + .filter(col("rank") == 1) + .orderBy(col("rating").desc()) + .select("business_id", "bus_name", "address", "rating", "distance", "cuisine_type") + .limit(5) + ) + + return recommended_restaurants + +def interactive_recommendation(recommender): + """ + Interactive restaurant recommendation interface + """ + print("Restaurant Recommender System") + + # Get user inputs + user_lat = float(input("Enter your latitude: ")) + user_lon = float(input("Enter your longitude: ")) + min_rating = float(input("Enter minimum rating (0-5): ")) + need_parking = input("Need parking? (yes/no): ").lower() == 'yes' + need_wifi = input("Need WiFi? (yes/no): ").lower() == 'yes' + cuisine_type = input("Enter cuisine type: ") + + # Make recommendations + recommendations = recommender.recommend_restaurants( + user_location=(user_lat, user_lon), + min_rating=min_rating, + need_parking=need_parking, + need_wifi=need_wifi, + cuisine_type=cuisine_type + ) + + # Show recommendations + recommendations.show() + +def main(): + # Create Spark Session + spark = SparkSession.builder \ + .appName("RestaurantRecommenderSystem") \ + .getOrCreate() + + # Initialize recommender + recommender = RestaurantRecommenderSystem(spark) + + # Define paths + data_path = "/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv" + model_save_path = "/content/drive/MyDrive/Models/restaurant_recommender_Final_model.pkl" + + # Load and preprocess data + recommender.load_and_preprocess_data(data_path) + + # Create feature vector + recommender.create_feature_vector() + + # Train K-means clustering + recommender.train_kmeans_clustering(k=5) + + # Save model + recommender.save_model(model_save_path) + + # Start interactive recommendation + interactive_recommendation(recommender) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/scripts/model1_hpft.py b/scripts/model1_hpft.py new file mode 100644 index 0000000..52b511b --- /dev/null +++ b/scripts/model1_hpft.py @@ -0,0 +1,253 @@ +# -*- coding: utf-8 -*- +"""Model1_HPFT + +Automatically generated by Colab. + +Original file is located at + https://colab.research.google.com/drive/1zeq3sYtlEwA8vBqjWspTqg83Ca_6J4D3 +""" + +from pyspark.sql import SparkSession +from pyspark.sql.functions import * +from pyspark.sql.types import * +from pyspark.ml.feature import HashingTF, IDF, Tokenizer, StopWordsRemover +from pyspark.ml.linalg import Vectors +from pyspark.ml.recommendation import ALS +from pyspark.ml.evaluation import RegressionEvaluator +from pyspark.ml.tuning import ParamGridBuilder, CrossValidator +from textblob import TextBlob +import re +from collections import defaultdict + +class HybridRecommender: + def __init__(self): + self.spark = SparkSession.builder \ + .appName("YelpHybridRecommender") \ + .config("spark.driver.memory", "4g") \ + .config("spark.executor.memory", "4g") \ + .getOrCreate() + + self.business_df = None + self.user_df = None + self.reviews_df = None + self.tips_df = None + self.checkin_df = None + self.tfidf_model = None + self.best_model = None + self.sentiment_scores = None + self.checkin_scores = None + self.popular_items = None + self.user_profiles = {} + + def load_and_preprocess_data(self, business_path, user_path, reviews_path, tips_path, checkin_path): + """ + Load and preprocess all datasets + """ + # Load data + self.business_df = self.spark.read.csv(business_path, header=True, inferSchema=True) + self.user_df = self.spark.read.csv(user_path, header=True, inferSchema=True) + self.reviews_df = self.spark.read.csv(reviews_path, header=True, inferSchema=True) + self.tips_df = self.spark.read.csv(tips_path, header=True, inferSchema=True) + self.checkin_df = self.spark.read.csv(checkin_path, header=True, inferSchema=True) + + # Preprocess data + self.preprocess_business_data() + self.preprocess_user_data() + self.calculate_sentiment_scores() + self.calculate_checkin_scores() + self.calculate_popular_items() + self.create_user_profiles() + + def preprocess_business_data(self): + """ + Preprocess business data + """ + # Clean categories + self.business_df = self.business_df.withColumn("processed_categories", + self.preprocess_text(col("categories"))) + + # Calculate average ratings + business_avg_ratings = self.reviews_df.groupBy("business_id") \ + .agg(avg("stars").alias("avg_rating"), count("*").alias("review_count")) + self.business_df = self.business_df.join(business_avg_ratings, "business_id", "left") + + # Create business features + self.business_df = self.business_df.withColumn("price_level", + size(split(col("attributes"), "\$")) - 1) + + def preprocess_user_data(self): + """ + Preprocess user data + """ + # Calculate user activity metrics + user_activity = self.reviews_df.groupBy("user_id") \ + .agg(count("business_id").alias("review_count"), + avg("stars").alias("average_rating")) + + self.user_df = self.user_df.join(user_activity, "user_id", "left") + + def create_user_profiles(self): + """ + Create user profiles based on their review history + """ + user_profiles = self.reviews_df.join(self.business_df, "business_id") \ + .groupBy("user_id") \ + .agg(collect_list(struct("categories", "stars")).alias("reviews")) + + def create_profile(reviews): + category_preferences = defaultdict(float) + for review in reviews: + categories = review.categories.split(", ") + for category in categories: + category_preferences[category] += review.stars + total_ratings = sum(category_preferences.values()) + if total_ratings > 0: + category_preferences = {k: v/total_ratings for k, v in category_preferences.items()} + return category_preferences + + create_profile_udf = udf(create_profile, MapType(StringType(), FloatType())) + + user_profiles = user_profiles.withColumn("category_preferences", create_profile_udf("reviews")) + + self.user_profiles = user_profiles.collect() + + def calculate_popular_items(self): + """ + Calculate popular items for cold start recommendations + """ + self.popular_items = self.business_df.join(self.sentiment_scores, "business_id", "left") \ + .join(self.checkin_scores, "business_id", "left") \ + .withColumn("popularity_score", + col("avg_rating") * 0.4 + + col("review_count") * 0.3 + + col("sentiment_score") * 0.2 + + col("checkin_score") * 0.1) \ + .orderBy(desc("popularity_score")) + + def handle_cold_start(self, user_id=None, business_id=None): + """ + Handle cold start problems for new users or items + """ + if user_id and user_id not in [profile.user_id for profile in self.user_profiles]: + # New user: Return popular items + return self.popular_items.limit(10) + + if business_id and business_id not in self.business_df.select("business_id").rdd.flatMap(lambda x: x).collect(): + # New item: Return similar items based on categories + business_categories = self.business_df.filter(col("business_id") == business_id) \ + .select("categories").first().categories + + return self.get_content_based_recommendations(business_categories, n_recommendations=10) + + return None + + def find_best_collaborative_model(self): + """ + Find the best collaborative filtering model using ALS + """ + als = ALS(userCol="user_id", itemCol="business_id", ratingCol="stars", + coldStartStrategy="drop", nonnegative=True) + + paramGrid = ParamGridBuilder() \ + .addGrid(als.rank, [10, 50, 100]) \ + .addGrid(als.regParam, [0.01, 0.1, 1.0]) \ + .addGrid(als.maxIter, [5, 10, 20]) \ + .build() + + evaluator = RegressionEvaluator(metricName="rmse", labelCol="stars", predictionCol="prediction") + + cv = CrossValidator(estimator=als, estimatorParamMaps=paramGrid, evaluator=evaluator, numFolds=3) + + model = cv.fit(self.reviews_df) + + self.best_model = model.bestModel + + return self.best_model, evaluator.evaluate(self.best_model.transform(self.reviews_df)) + + def get_content_based_recommendations(self, categories, n_recommendations=10): + """ + Get content-based recommendations based on business categories + """ + tokenizer = Tokenizer(inputCol="processed_categories", outputCol="words") + wordsData = tokenizer.transform(self.business_df.select("business_id", "processed_categories")) + + hashingTF = HashingTF(inputCol="words", outputCol="rawFeatures", numFeatures=20) + featurizedData = hashingTF.transform(wordsData) + + idf = IDF(inputCol=" rawFeatures", outputCol="features") + idfModel = idf.fit(featurizedData) + tfidfData = idfModel.transform(featurizedData) + + # Create TF-IDF vector for the input categories + input_vector = tfidfModel.transform(self.spark.createDataFrame([(categories,)], ["processed_categories"])) + + # Calculate cosine similarity with existing businesses + cosine_sim = cosine_similarity(input_vector.select("features").rdd.flatMap(lambda x: x).collect(), + tfidfData.select("features").rdd.flatMap(lambda x: x).collect()) + + # Get top N recommendations + sim_scores = list(enumerate(cosine_sim[0])) + sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True) + top_indices = [i[0] for i in sim_scores[:n_recommendations]] + + return self.business_df.select("business_id").collect()[top_indices] + + def preprocess_text(self, text): + """ + Preprocess text data + """ + if isinstance(text, str): + # Convert to lowercase + text = text.lower() + # Remove special characters and digits + text = re.sub(r'[^a-zA-Z\s]', '', text) + return text + return '' + + def calculate_sentiment_scores(self): + """ + Calculate sentiment scores for reviews and tips + """ + # Process reviews + self.reviews_df = self.reviews_df.withColumn("sentiment", + udf(lambda x: TextBlob(str(x)).sentiment.polarity, FloatType())(col("text"))) + + # Process tips + self.tips_df = self.tips_df.withColumn("sentiment", + udf(lambda x: TextBlob(str(x)).sentiment.polarity, FloatType())(col("text"))) + + # Aggregate sentiment scores by business + review_sentiments = self.reviews_df.groupBy("business_id").agg(avg("sentiment").alias("review_sentiment")) + tip_sentiments = self.tips_df.groupBy("business_id").agg(avg("sentiment").alias("tip_sentiment")) + + # Combine sentiment scores + self.sentiment_scores = review_sentiments.join(tip_sentiments, "business_id", "outer").fillna(0) + + def calculate_checkin_scores(self): + """ + Calculate check-in scores for businesses + """ + # A checkin_df has columns: business_id, date + self.checkin_df = self.checkin_df.withColumn("date", to_date(col("date"))) + + # Calculate recency and frequency + now = datetime.now() + checkin_stats = self.checkin_df.groupBy("business_id").agg( + max("date").alias("last_checkin"), + count("date").alias("frequency") + ) + + checkin_stats = checkin_stats.withColumn("recency", datediff(lit(now), col("last_checkin"))) + + # Normalize scores + scaler = MinMaxScaler(inputCol="frequency", outputCol="frequency_scaled") + self.checkin_scores = scaler.fit(checkin_stats).transform(checkin_stats) + + # Invert recency so that lower values (more recent) get higher scores + self.checkin_scores = self.checkin_scores.withColumn("recency_scaled", 1 - (col("recency") / col("recency").max())) + +# Example usage +recommender = HybridRecommender() +recommender.load_and_preprocess_data('business.csv', 'user.csv', 'reviews.csv', 'tips.csv', 'checkin.csv') +best_model, model_results = recommender.find_best_collaborative_model() +print(f"Best Collaborative Model: {best_model}, Results: {model_results}") \ No newline at end of file diff --git a/scripts/recommendation_model_ht.py b/scripts/recommendation_model_ht.py new file mode 100644 index 0000000..3e6f2b9 --- /dev/null +++ b/scripts/recommendation_model_ht.py @@ -0,0 +1,243 @@ +# -*- coding: utf-8 -*- +"""Recommendation_Model_HT + +Automatically generated by Colab. + +Original file is located at + https://colab.research.google.com/drive/1dPJklD5Qa3Q0sQt1zKe2rInpz5p1YxqE +""" + +import pandas as pd +import numpy as np +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.metrics.pairwise import cosine_similarity +from sklearn.preprocessing import MinMaxScaler +from surprise import Dataset, Reader, SVD, KNNBasic, KNNWithMeans, NMF, BaselineOnly +from surprise.model_selection import train_test_split, cross_validate +from textblob import TextBlob +import nltk +from nltk.tokenize import word_tokenize +from nltk.corpus import stopwords +from nltk.stem import WordNetLemmatizer +import re +from sklearn.metrics import mean_squared_error +import optuna +from datetime import datetime +from collections import defaultdict + +# Download required NLTK data +nltk.download('punkt') +nltk.download('stopwords') +nltk.download('wordnet') +nltk.download('averaged_perceptron_tagger') + +class HybridRecommender: + def __init__(self): + self.business_df = None + self.user_df = None + self.reviews_df = None + self.checkin_df = None + self.tfidf_matrix = None + self.cosine_sim = None + self.best_model = None + self.sentiment_scores = None + self.checkin_scores = None + self.popular_items = None + self.user_profiles = {} + + def load_and_preprocess_data(self, business_path, user_path, reviews_path, checkin_path): + """ + Load and preprocess all datasets + """ + # Load data + self.business_df = pd.read_csv(business_path) + self.user_df = pd.read_csv(user_path) + self.reviews_df = pd.read_csv(reviews_path) + self.checkin_df = pd.read_csv(checkin_path) + + # Preprocess data + self.preprocess_business_data() + self.preprocess_user_data() + self.calculate_sentiment_scores() + self.calculate_checkin_scores() + self.calculate_popular_items() + self.create_user_profiles() + + def preprocess_business_data(self): + """ + Preprocess business data + """ + # Clean categories + self.business_df['processed_categories'] = self.business_df['categories'].apply(self.preprocess_text) + + # Calculate average ratings + business_avg_ratings = self.reviews_df.groupby('business_id')['stars'].agg(['mean', 'count']).reset_index() + self.business_df = self.business_df.merge(business_avg_ratings, on='business_id', how='left') + + # Create business features + self.business_df['price_level'] = pd.to_numeric(self.business_df['attributes'].apply( + lambda x: str(x).count('$') if pd.notnull(x) else 0 + )) + + def preprocess_user_data(self): + """ + Preprocess user data + """ + # Calculate user activity metrics + user_activity = self.reviews_df.groupby('user_id').agg({ + 'business_id': 'count', + 'stars': 'mean' + }).reset_index() + user_activity.columns = ['user_id', 'review_count', 'average_rating'] + + self.user_df = self.user_df.merge(user_activity, on='user_id', how='left') + + def create_user_profiles(self): + """ + Create user profiles based on their review history + """ + for user_id in self.user_df['user_id'].unique(): + user_reviews = self.reviews_df[self.reviews_df['user_id'] == user_id] + + if len(user_reviews) > 0: + # Get businesses reviewed by user + reviewed_businesses = self.business_df[ + self.business_df['business_id'].isin(user_reviews['business_id']) + ] + + # Calculate category preferences + category_preferences = defaultdict(float) + for _, business in reviewed_businesses.iterrows(): + categories = str(business['categories']).split(', ') + rating = user_reviews[ + user_reviews['business_id'] == business['business_id'] + ]['stars'].iloc[0] + + for category in categories: + category_preferences[category] += rating + + # Normalize category preferences + total_ratings = sum(category_preferences.values()) + if total_ratings > 0: + category_preferences = { + k: v/total_ratings for k, v in category_preferences.items() + } + + self.user_profiles[user_id] = { + 'category_preferences': category_preferences, + 'avg_rating': user_reviews['stars'].mean(), + 'review_count': len(user_reviews) + } + + def calculate_popular_items(self): + """ + Calculate popular items for cold start recommendations """ + # Combine review ratings, sentiment scores, and check-in data + business_metrics = pd.DataFrame() + business_metrics['business_id'] = self.business_df['business_id'] + business_metrics['avg_rating'] = self.business_df['mean'] + business_metrics['review_count'] = self.business_df['count'] + + # Merge sentiment scores + business_metrics = business_metrics.merge( + self.sentiment_scores, on='business_id', how='left' + ) + + # Merge check-in scores + business_metrics = business_metrics.merge( + self.checkin_scores, on='business_id', how='left' + ) + + # Calculate popularity score + business_metrics['popularity_score'] = ( + business_metrics['avg_rating'] * 0.4 + + business_metrics['review_count'] * 0.3 + + business_metrics['sentiment_score'] * 0.2 + + business_metrics['checkin_score'] * 0.1 + ) + + self.popular_items = business_metrics.sort_values( + 'popularity_score', ascending=False + ) + + def evaluate_recommendations(self, user_id, recommended_items, k=10): + """ + Evaluate recommendations using Recall@K, Precision@K, NDCG, and Diversity Score + """ + # Get actual items rated by the user + actual_items = set(self.reviews_df[self.reviews_df['user_id'] == user_id]['business_id']) + + # Calculate Precision@K + recommended_set = set(recommended_items[:k]) + precision = len(recommended_set.intersection(actual_items)) / k + + # Calculate Recall@K + recall = len(recommended_set.intersection(actual_items)) / len(actual_items) if actual_items else 0 + + # Calculate NDCG + ndcg = self.calculate_ndcg(recommended_items, actual_items, k) + + # Calculate Diversity Score + diversity = self.calculate_diversity(recommended_items) + + return { + 'Precision@K': precision, + 'Recall@K': recall, + 'NDCG': ndcg, + 'Diversity Score': diversity + } + + def calculate_ndcg(self, recommended_items, actual_items, k): + """ + Calculate NDCG for the top K recommendations + """ + dcg = 0 + for i, item in enumerate(recommended_items[:k]): + if item in actual_items: + dcg += 1 / np.log2(i + 2) # +2 because index starts at 0 + + idcg = sum(1 / np.log2(i + 2) for i in range(min(k, len(actual_items)))) + + return dcg / idcg if idcg > 0 else 0 + + def calculate_diversity(self, recommended_items): + """ + Calculate diversity score based on the number of unique categories in the recommended items + """ + unique_categories = set() + for item in recommended_items: + categories = self.business_df[self.business_df['business_id'] == item]['categories'].values + if categories: + unique_categories.update(categories[0].split(', ')) + + return len(unique_categories) / len(recommended_items) if recommended_items else 0 + + def preprocess_text(self, text): + """ + Preprocess text data + """ + if isinstance(text, str): + # Convert to lowercase + text = text.lower() + # Remove special characters and digits + text = re.sub(r'[^a-zA-Z\s]', '', text) + # Tokenization + tokens = word_tokenize(text) + # Remove stopwords and lemmatize + stop_words = set(stopwords.words('english')) + lemmatizer = WordNetLemmatizer() + tokens = [lemmatizer.lemmatize(token) for token in tokens if token not in stop_words] + return ' '.join(tokens) + return '' + +# Example usage +recommender = HybridRecommender() +recommender.load_and_preprocess_data('business.csv', 'user.csv', 'reviews.csv', 'checkin.csv') +best_model, model_results = recommender.find_best_collaborative_model() +print(f"Best Collaborative Model: {best_model}, Results: {model_results}") + +# Example evaluation +user_id = 1 # Replace with a valid user ID +recommended_items = recommender.popular_items['business_id'].head(10).tolist() # Get top 10 recommended items +evaluation_results = recommender.evaluate_recommendations(user_id, recommended_items) +print(f"Evaluation Results: {evaluation_results}") \ No newline at end of file diff --git a/recommendation_system_hybrid.py b/scripts/recommendation_system_hybrid.py similarity index 100% rename from recommendation_system_hybrid.py rename to scripts/recommendation_system_hybrid.py diff --git a/scripts/using_km_recsys_predictions_.py b/scripts/using_km_recsys_predictions_.py new file mode 100644 index 0000000..4e92bb4 --- /dev/null +++ b/scripts/using_km_recsys_predictions_.py @@ -0,0 +1,206 @@ +# -*- coding: utf-8 -*- +"""Using_KM_RecSys_Predictions. + +Automatically generated by Colab. + +Original file is located at + https://colab.research.google.com/drive/1CuqREXyz1x7n9AfusdGgSqlJ-Pw6sf4J +""" + +from pyspark.sql import SparkSession +from pyspark.ml import PipelineModel +from pyspark.sql.functions import col, expr, radians, lit, row_number, sqrt, sin, cos, asin,power +from pyspark.sql.window import Window +from pyspark.ml.feature import StringIndexer, VectorAssembler, StandardScaler + +class RestaurantRecommenderPredictor: + def __init__(self, spark_session): + """ + Initialize the Restaurant Recommender Predictor + """ + self.spark = spark_session + self.df = None + self.kmeans_model = None + self.cuisine_indexer = None + self.vector_assembler = None + self.scaler = None + + def load_data(self, data_path): + """ + Load and preprocess restaurant data + """ + # Read the CSV file + self.df = self.spark.read.csv(data_path, header=True, inferSchema=True) + + # Data cleaning and preprocessing + self.df = self.df.na.drop() # Remove rows with null values + + # Convert boolean columns + self.df = (self.df.withColumn("has_parking", + expr("CASE WHEN parking = 'Yes' THEN true ELSE false END")) + .withColumn("has_wifi", + expr("CASE WHEN WiFi = 'Yes' THEN true ELSE false END"))) + + # Encode categorical variables + self.cuisine_indexer = StringIndexer( + inputCol="cuisine_type", + outputCol="cuisine_type_encoded" + ) + self.df = self.cuisine_indexer.fit(self.df).transform(self.df) + + return self.df + + def load_saved_model(self, model_path): + """ + Load the saved PySpark Pipeline Model + """ + try: + # Load the entire pipeline model + self.kmeans_model = PipelineModel.load(model_path) + + # Extract specific stages from the pipeline + stages = self.kmeans_model.stages + + # Find and store vector assembler and scaler + for stage in stages: + if isinstance(stage, VectorAssembler): + self.vector_assembler = stage + elif isinstance(stage, StandardScaler): + self.scaler = stage + + return self.kmeans_model + except Exception as e: + print(f"Error loading model: {e}") + return None + + def haversine_distance(self, lat1, lon1, lat2, lon2): + """ + Calculate distance between two geographical points + """ + lat1_rad = radians(lit(lat1)) + lon1_rad = radians(lit(lon1)) + lat2_rad = radians(lat2) + lon2_rad = radians(lon2) + + return ( + 6371 * 2 * asin( + sqrt( + sin((lat2_rad - lat1_rad) / 2) ** 2 + + cos(lat1_rad) * cos(lat2_rad) * + sin((lon2_rad - lon1_rad) / 2) ** 2 + ) + ) + ) + + def recommend_restaurants( + self, + user_location, + min_rating, + need_parking, + need_wifi, + cuisine_type, + max_distance=60 + ): + """ + Recommend restaurants based on user preferences + """ + user_lat, user_lon = user_location + + # Apply initial filters + filtered_df = self.df.filter( + (col("rating") >= min_rating) & + (col("cuisine_type") == cuisine_type) + ) + + # Apply parking filter + if need_parking: + filtered_df = filtered_df.filter(col("has_parking") == True) + + # Apply WiFi filter + if need_wifi: + filtered_df = filtered_df.filter(col("has_wifi") == True) + + # Add distance column + with_distance_df = filtered_df.withColumn( + "distance", + self.haversine_distance(user_lat, user_lon, col("latitude"), col("longitude")) + ) + + # Filter by distance + nearby_restaurants = with_distance_df.filter( + col("distance") <= max_distance + ) + + # Rank restaurants + window_spec = Window.partitionBy("bus_name").orderBy(col("rating").desc()) + + recommended_restaurants = ( + nearby_restaurants + .withColumn("rank", row_number().over(window_spec)) + .filter(col("rank") == 1) + .orderBy(col("rating").desc()) + .select("business_id", "bus_name", "address", "rating", "distance", "cuisine_type") + .limit(5) + ) + + return recommended_restaurants + +def interactive_recommendation(predictor): + """ + Interactive restaurant recommendation interface + """ + print("Restaurant Recommender System") + + # Get user inputs + user_lat = float(input("Enter your latitude: ")) + user_lon = float(input("Enter your longitude: ")) + min_rating = float(input("Enter minimum rating (0-5): ")) + need_parking = input("Need parking? (yes/no): ").lower() == 'yes' + need_wifi = input("Need WiFi? (yes/no): ").lower() == 'yes' + cuisine_type = input("Enter cuisine type: ") + + # Make recommendations + recommendations = predictor.recommend_restaurants( + user_location=(user_lat, user_lon), + min_rating=min_rating, + need_parking=need_parking, + need_wifi=need_wifi, + cuisine_type=cuisine_type + ) + + # Show recommendations + recommendations.show() + +def main(): + # Create Spark Session + spark = SparkSession.builder \ + .appName("RestaurantRecommenderPredictor") \ + .getOrCreate() + + # Define paths + model_path = '/content/drive/MyDrive/Models/restaurant_recommender_Final_model' + data_path = '/content/drive/MyDrive/ProcessedCSV/Recommender_System_Newdata.csv' + + try: + # Initialize predictor + predictor = RestaurantRecommenderPredictor(spark) + + # Load dataset + predictor.load_data(data_path) + + # Load saved model + loaded_model = predictor.load_saved_model(model_path) + + if loaded_model: + # Start interactive recommendation + interactive_recommendation(predictor) + else: + print("Failed to load the model.") + + except Exception as e: + print(f"An error occurred: {e}") + finally: + spark.stop() + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/web/css/animate.css b/web/css/animate.css new file mode 100644 index 0000000..57725be --- /dev/null +++ b/web/css/animate.css @@ -0,0 +1,3288 @@ +@charset "UTF-8"; + +/*! +Animate.css - http://daneden.me/animate +Licensed under the MIT license - http://opensource.org/licenses/MIT + +Copyright (c) 2015 Daniel Eden +*/ + +.animated { + -webkit-animation-duration: 1s; + animation-duration: 1s; + -webkit-animation-fill-mode: both; + animation-fill-mode: both; +} + +.animated.infinite { + -webkit-animation-iteration-count: infinite; + animation-iteration-count: infinite; +} + +.animated.hinge { + -webkit-animation-duration: 2s; + animation-duration: 2s; +} + +.animated.bounceIn, +.animated.bounceOut { + -webkit-animation-duration: .75s; + animation-duration: .75s; +} + +.animated.flipOutX, +.animated.flipOutY { + -webkit-animation-duration: .75s; + animation-duration: .75s; +} + +@-webkit-keyframes bounce { + from, 20%, 53%, 80%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + -webkit-transform: translate3d(0,0,0); + transform: translate3d(0,0,0); + } + + 40%, 43% { + -webkit-animation-timing-function: cubic-bezier(0.755, 0.050, 0.855, 0.060); + animation-timing-function: cubic-bezier(0.755, 0.050, 0.855, 0.060); + -webkit-transform: translate3d(0, -30px, 0); + transform: translate3d(0, -30px, 0); + } + + 70% { + -webkit-animation-timing-function: cubic-bezier(0.755, 0.050, 0.855, 0.060); + animation-timing-function: cubic-bezier(0.755, 0.050, 0.855, 0.060); + -webkit-transform: translate3d(0, -15px, 0); + transform: translate3d(0, -15px, 0); + } + + 90% { + -webkit-transform: translate3d(0,-4px,0); + transform: translate3d(0,-4px,0); + } +} + +@keyframes bounce { + from, 20%, 53%, 80%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + -webkit-transform: translate3d(0,0,0); + transform: translate3d(0,0,0); + } + + 40%, 43% { + -webkit-animation-timing-function: cubic-bezier(0.755, 0.050, 0.855, 0.060); + animation-timing-function: cubic-bezier(0.755, 0.050, 0.855, 0.060); + -webkit-transform: translate3d(0, -30px, 0); + transform: translate3d(0, -30px, 0); + } + + 70% { + -webkit-animation-timing-function: cubic-bezier(0.755, 0.050, 0.855, 0.060); + animation-timing-function: cubic-bezier(0.755, 0.050, 0.855, 0.060); + -webkit-transform: translate3d(0, -15px, 0); + transform: translate3d(0, -15px, 0); + } + + 90% { + -webkit-transform: translate3d(0,-4px,0); + transform: translate3d(0,-4px,0); + } +} + +.bounce { + -webkit-animation-name: bounce; + animation-name: bounce; + -webkit-transform-origin: center bottom; + transform-origin: center bottom; +} + +@-webkit-keyframes flash { + from, 50%, to { + opacity: 1; + } + + 25%, 75% { + opacity: 0; + } +} + +@keyframes flash { + from, 50%, to { + opacity: 1; + } + + 25%, 75% { + opacity: 0; + } +} + +.flash { + -webkit-animation-name: flash; + animation-name: flash; +} + +/* originally authored by Nick Pettit - https://github.com/nickpettit/glide */ + +@-webkit-keyframes pulse { + from { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } + + 50% { + -webkit-transform: scale3d(1.05, 1.05, 1.05); + transform: scale3d(1.05, 1.05, 1.05); + } + + to { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } +} + +@keyframes pulse { + from { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } + + 50% { + -webkit-transform: scale3d(1.05, 1.05, 1.05); + transform: scale3d(1.05, 1.05, 1.05); + } + + to { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } +} + +.pulse { + -webkit-animation-name: pulse; + animation-name: pulse; +} + +@-webkit-keyframes rubberBand { + from { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } + + 30% { + -webkit-transform: scale3d(1.25, 0.75, 1); + transform: scale3d(1.25, 0.75, 1); + } + + 40% { + -webkit-transform: scale3d(0.75, 1.25, 1); + transform: scale3d(0.75, 1.25, 1); + } + + 50% { + -webkit-transform: scale3d(1.15, 0.85, 1); + transform: scale3d(1.15, 0.85, 1); + } + + 65% { + -webkit-transform: scale3d(.95, 1.05, 1); + transform: scale3d(.95, 1.05, 1); + } + + 75% { + -webkit-transform: scale3d(1.05, .95, 1); + transform: scale3d(1.05, .95, 1); + } + + to { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } +} + +@keyframes rubberBand { + from { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } + + 30% { + -webkit-transform: scale3d(1.25, 0.75, 1); + transform: scale3d(1.25, 0.75, 1); + } + + 40% { + -webkit-transform: scale3d(0.75, 1.25, 1); + transform: scale3d(0.75, 1.25, 1); + } + + 50% { + -webkit-transform: scale3d(1.15, 0.85, 1); + transform: scale3d(1.15, 0.85, 1); + } + + 65% { + -webkit-transform: scale3d(.95, 1.05, 1); + transform: scale3d(.95, 1.05, 1); + } + + 75% { + -webkit-transform: scale3d(1.05, .95, 1); + transform: scale3d(1.05, .95, 1); + } + + to { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } +} + +.rubberBand { + -webkit-animation-name: rubberBand; + animation-name: rubberBand; +} + +@-webkit-keyframes shake { + from, to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + 10%, 30%, 50%, 70%, 90% { + -webkit-transform: translate3d(-10px, 0, 0); + transform: translate3d(-10px, 0, 0); + } + + 20%, 40%, 60%, 80% { + -webkit-transform: translate3d(10px, 0, 0); + transform: translate3d(10px, 0, 0); + } +} + +@keyframes shake { + from, to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + 10%, 30%, 50%, 70%, 90% { + -webkit-transform: translate3d(-10px, 0, 0); + transform: translate3d(-10px, 0, 0); + } + + 20%, 40%, 60%, 80% { + -webkit-transform: translate3d(10px, 0, 0); + transform: translate3d(10px, 0, 0); + } +} + +.shake { + -webkit-animation-name: shake; + animation-name: shake; +} + +@-webkit-keyframes swing { + 20% { + -webkit-transform: rotate3d(0, 0, 1, 15deg); + transform: rotate3d(0, 0, 1, 15deg); + } + + 40% { + -webkit-transform: rotate3d(0, 0, 1, -10deg); + transform: rotate3d(0, 0, 1, -10deg); + } + + 60% { + -webkit-transform: rotate3d(0, 0, 1, 5deg); + transform: rotate3d(0, 0, 1, 5deg); + } + + 80% { + -webkit-transform: rotate3d(0, 0, 1, -5deg); + transform: rotate3d(0, 0, 1, -5deg); + } + + to { + -webkit-transform: rotate3d(0, 0, 1, 0deg); + transform: rotate3d(0, 0, 1, 0deg); + } +} + +@keyframes swing { + 20% { + -webkit-transform: rotate3d(0, 0, 1, 15deg); + transform: rotate3d(0, 0, 1, 15deg); + } + + 40% { + -webkit-transform: rotate3d(0, 0, 1, -10deg); + transform: rotate3d(0, 0, 1, -10deg); + } + + 60% { + -webkit-transform: rotate3d(0, 0, 1, 5deg); + transform: rotate3d(0, 0, 1, 5deg); + } + + 80% { + -webkit-transform: rotate3d(0, 0, 1, -5deg); + transform: rotate3d(0, 0, 1, -5deg); + } + + to { + -webkit-transform: rotate3d(0, 0, 1, 0deg); + transform: rotate3d(0, 0, 1, 0deg); + } +} + +.swing { + -webkit-transform-origin: top center; + transform-origin: top center; + -webkit-animation-name: swing; + animation-name: swing; +} + +@-webkit-keyframes tada { + from { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } + + 10%, 20% { + -webkit-transform: scale3d(.9, .9, .9) rotate3d(0, 0, 1, -3deg); + transform: scale3d(.9, .9, .9) rotate3d(0, 0, 1, -3deg); + } + + 30%, 50%, 70%, 90% { + -webkit-transform: scale3d(1.1, 1.1, 1.1) rotate3d(0, 0, 1, 3deg); + transform: scale3d(1.1, 1.1, 1.1) rotate3d(0, 0, 1, 3deg); + } + + 40%, 60%, 80% { + -webkit-transform: scale3d(1.1, 1.1, 1.1) rotate3d(0, 0, 1, -3deg); + transform: scale3d(1.1, 1.1, 1.1) rotate3d(0, 0, 1, -3deg); + } + + to { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } +} + +@keyframes tada { + from { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } + + 10%, 20% { + -webkit-transform: scale3d(.9, .9, .9) rotate3d(0, 0, 1, -3deg); + transform: scale3d(.9, .9, .9) rotate3d(0, 0, 1, -3deg); + } + + 30%, 50%, 70%, 90% { + -webkit-transform: scale3d(1.1, 1.1, 1.1) rotate3d(0, 0, 1, 3deg); + transform: scale3d(1.1, 1.1, 1.1) rotate3d(0, 0, 1, 3deg); + } + + 40%, 60%, 80% { + -webkit-transform: scale3d(1.1, 1.1, 1.1) rotate3d(0, 0, 1, -3deg); + transform: scale3d(1.1, 1.1, 1.1) rotate3d(0, 0, 1, -3deg); + } + + to { + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } +} + +.tada { + -webkit-animation-name: tada; + animation-name: tada; +} + +/* originally authored by Nick Pettit - https://github.com/nickpettit/glide */ + +@-webkit-keyframes wobble { + from { + -webkit-transform: none; + transform: none; + } + + 15% { + -webkit-transform: translate3d(-25%, 0, 0) rotate3d(0, 0, 1, -5deg); + transform: translate3d(-25%, 0, 0) rotate3d(0, 0, 1, -5deg); + } + + 30% { + -webkit-transform: translate3d(20%, 0, 0) rotate3d(0, 0, 1, 3deg); + transform: translate3d(20%, 0, 0) rotate3d(0, 0, 1, 3deg); + } + + 45% { + -webkit-transform: translate3d(-15%, 0, 0) rotate3d(0, 0, 1, -3deg); + transform: translate3d(-15%, 0, 0) rotate3d(0, 0, 1, -3deg); + } + + 60% { + -webkit-transform: translate3d(10%, 0, 0) rotate3d(0, 0, 1, 2deg); + transform: translate3d(10%, 0, 0) rotate3d(0, 0, 1, 2deg); + } + + 75% { + -webkit-transform: translate3d(-5%, 0, 0) rotate3d(0, 0, 1, -1deg); + transform: translate3d(-5%, 0, 0) rotate3d(0, 0, 1, -1deg); + } + + to { + -webkit-transform: none; + transform: none; + } +} + +@keyframes wobble { + from { + -webkit-transform: none; + transform: none; + } + + 15% { + -webkit-transform: translate3d(-25%, 0, 0) rotate3d(0, 0, 1, -5deg); + transform: translate3d(-25%, 0, 0) rotate3d(0, 0, 1, -5deg); + } + + 30% { + -webkit-transform: translate3d(20%, 0, 0) rotate3d(0, 0, 1, 3deg); + transform: translate3d(20%, 0, 0) rotate3d(0, 0, 1, 3deg); + } + + 45% { + -webkit-transform: translate3d(-15%, 0, 0) rotate3d(0, 0, 1, -3deg); + transform: translate3d(-15%, 0, 0) rotate3d(0, 0, 1, -3deg); + } + + 60% { + -webkit-transform: translate3d(10%, 0, 0) rotate3d(0, 0, 1, 2deg); + transform: translate3d(10%, 0, 0) rotate3d(0, 0, 1, 2deg); + } + + 75% { + -webkit-transform: translate3d(-5%, 0, 0) rotate3d(0, 0, 1, -1deg); + transform: translate3d(-5%, 0, 0) rotate3d(0, 0, 1, -1deg); + } + + to { + -webkit-transform: none; + transform: none; + } +} + +.wobble { + -webkit-animation-name: wobble; + animation-name: wobble; +} + +@-webkit-keyframes jello { + from, 11.1%, to { + -webkit-transform: none; + transform: none; + } + + 22.2% { + -webkit-transform: skewX(-12.5deg) skewY(-12.5deg); + transform: skewX(-12.5deg) skewY(-12.5deg); + } + + 33.3% { + -webkit-transform: skewX(6.25deg) skewY(6.25deg); + transform: skewX(6.25deg) skewY(6.25deg); + } + + 44.4% { + -webkit-transform: skewX(-3.125deg) skewY(-3.125deg); + transform: skewX(-3.125deg) skewY(-3.125deg); + } + + 55.5% { + -webkit-transform: skewX(1.5625deg) skewY(1.5625deg); + transform: skewX(1.5625deg) skewY(1.5625deg); + } + + 66.6% { + -webkit-transform: skewX(-0.78125deg) skewY(-0.78125deg); + transform: skewX(-0.78125deg) skewY(-0.78125deg); + } + + 77.7% { + -webkit-transform: skewX(0.390625deg) skewY(0.390625deg); + transform: skewX(0.390625deg) skewY(0.390625deg); + } + + 88.8% { + -webkit-transform: skewX(-0.1953125deg) skewY(-0.1953125deg); + transform: skewX(-0.1953125deg) skewY(-0.1953125deg); + } +} + +@keyframes jello { + from, 11.1%, to { + -webkit-transform: none; + transform: none; + } + + 22.2% { + -webkit-transform: skewX(-12.5deg) skewY(-12.5deg); + transform: skewX(-12.5deg) skewY(-12.5deg); + } + + 33.3% { + -webkit-transform: skewX(6.25deg) skewY(6.25deg); + transform: skewX(6.25deg) skewY(6.25deg); + } + + 44.4% { + -webkit-transform: skewX(-3.125deg) skewY(-3.125deg); + transform: skewX(-3.125deg) skewY(-3.125deg); + } + + 55.5% { + -webkit-transform: skewX(1.5625deg) skewY(1.5625deg); + transform: skewX(1.5625deg) skewY(1.5625deg); + } + + 66.6% { + -webkit-transform: skewX(-0.78125deg) skewY(-0.78125deg); + transform: skewX(-0.78125deg) skewY(-0.78125deg); + } + + 77.7% { + -webkit-transform: skewX(0.390625deg) skewY(0.390625deg); + transform: skewX(0.390625deg) skewY(0.390625deg); + } + + 88.8% { + -webkit-transform: skewX(-0.1953125deg) skewY(-0.1953125deg); + transform: skewX(-0.1953125deg) skewY(-0.1953125deg); + } +} + +.jello { + -webkit-animation-name: jello; + animation-name: jello; + -webkit-transform-origin: center; + transform-origin: center; +} + +@-webkit-keyframes bounceIn { + from, 20%, 40%, 60%, 80%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + 0% { + opacity: 0; + -webkit-transform: scale3d(.3, .3, .3); + transform: scale3d(.3, .3, .3); + } + + 20% { + -webkit-transform: scale3d(1.1, 1.1, 1.1); + transform: scale3d(1.1, 1.1, 1.1); + } + + 40% { + -webkit-transform: scale3d(.9, .9, .9); + transform: scale3d(.9, .9, .9); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(1.03, 1.03, 1.03); + transform: scale3d(1.03, 1.03, 1.03); + } + + 80% { + -webkit-transform: scale3d(.97, .97, .97); + transform: scale3d(.97, .97, .97); + } + + to { + opacity: 1; + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } +} + +@keyframes bounceIn { + from, 20%, 40%, 60%, 80%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + 0% { + opacity: 0; + -webkit-transform: scale3d(.3, .3, .3); + transform: scale3d(.3, .3, .3); + } + + 20% { + -webkit-transform: scale3d(1.1, 1.1, 1.1); + transform: scale3d(1.1, 1.1, 1.1); + } + + 40% { + -webkit-transform: scale3d(.9, .9, .9); + transform: scale3d(.9, .9, .9); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(1.03, 1.03, 1.03); + transform: scale3d(1.03, 1.03, 1.03); + } + + 80% { + -webkit-transform: scale3d(.97, .97, .97); + transform: scale3d(.97, .97, .97); + } + + to { + opacity: 1; + -webkit-transform: scale3d(1, 1, 1); + transform: scale3d(1, 1, 1); + } +} + +.bounceIn { + -webkit-animation-name: bounceIn; + animation-name: bounceIn; +} + +@-webkit-keyframes bounceInDown { + from, 60%, 75%, 90%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + 0% { + opacity: 0; + -webkit-transform: translate3d(0, -3000px, 0); + transform: translate3d(0, -3000px, 0); + } + + 60% { + opacity: 1; + -webkit-transform: translate3d(0, 25px, 0); + transform: translate3d(0, 25px, 0); + } + + 75% { + -webkit-transform: translate3d(0, -10px, 0); + transform: translate3d(0, -10px, 0); + } + + 90% { + -webkit-transform: translate3d(0, 5px, 0); + transform: translate3d(0, 5px, 0); + } + + to { + -webkit-transform: none; + transform: none; + } +} + +@keyframes bounceInDown { + from, 60%, 75%, 90%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + 0% { + opacity: 0; + -webkit-transform: translate3d(0, -3000px, 0); + transform: translate3d(0, -3000px, 0); + } + + 60% { + opacity: 1; + -webkit-transform: translate3d(0, 25px, 0); + transform: translate3d(0, 25px, 0); + } + + 75% { + -webkit-transform: translate3d(0, -10px, 0); + transform: translate3d(0, -10px, 0); + } + + 90% { + -webkit-transform: translate3d(0, 5px, 0); + transform: translate3d(0, 5px, 0); + } + + to { + -webkit-transform: none; + transform: none; + } +} + +.bounceInDown { + -webkit-animation-name: bounceInDown; + animation-name: bounceInDown; +} + +@-webkit-keyframes bounceInLeft { + from, 60%, 75%, 90%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + 0% { + opacity: 0; + -webkit-transform: translate3d(-3000px, 0, 0); + transform: translate3d(-3000px, 0, 0); + } + + 60% { + opacity: 1; + -webkit-transform: translate3d(25px, 0, 0); + transform: translate3d(25px, 0, 0); + } + + 75% { + -webkit-transform: translate3d(-10px, 0, 0); + transform: translate3d(-10px, 0, 0); + } + + 90% { + -webkit-transform: translate3d(5px, 0, 0); + transform: translate3d(5px, 0, 0); + } + + to { + -webkit-transform: none; + transform: none; + } +} + +@keyframes bounceInLeft { + from, 60%, 75%, 90%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + 0% { + opacity: 0; + -webkit-transform: translate3d(-3000px, 0, 0); + transform: translate3d(-3000px, 0, 0); + } + + 60% { + opacity: 1; + -webkit-transform: translate3d(25px, 0, 0); + transform: translate3d(25px, 0, 0); + } + + 75% { + -webkit-transform: translate3d(-10px, 0, 0); + transform: translate3d(-10px, 0, 0); + } + + 90% { + -webkit-transform: translate3d(5px, 0, 0); + transform: translate3d(5px, 0, 0); + } + + to { + -webkit-transform: none; + transform: none; + } +} + +.bounceInLeft { + -webkit-animation-name: bounceInLeft; + animation-name: bounceInLeft; +} + +@-webkit-keyframes bounceInRight { + from, 60%, 75%, 90%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + from { + opacity: 0; + -webkit-transform: translate3d(3000px, 0, 0); + transform: translate3d(3000px, 0, 0); + } + + 60% { + opacity: 1; + -webkit-transform: translate3d(-25px, 0, 0); + transform: translate3d(-25px, 0, 0); + } + + 75% { + -webkit-transform: translate3d(10px, 0, 0); + transform: translate3d(10px, 0, 0); + } + + 90% { + -webkit-transform: translate3d(-5px, 0, 0); + transform: translate3d(-5px, 0, 0); + } + + to { + -webkit-transform: none; + transform: none; + } +} + +@keyframes bounceInRight { + from, 60%, 75%, 90%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + from { + opacity: 0; + -webkit-transform: translate3d(3000px, 0, 0); + transform: translate3d(3000px, 0, 0); + } + + 60% { + opacity: 1; + -webkit-transform: translate3d(-25px, 0, 0); + transform: translate3d(-25px, 0, 0); + } + + 75% { + -webkit-transform: translate3d(10px, 0, 0); + transform: translate3d(10px, 0, 0); + } + + 90% { + -webkit-transform: translate3d(-5px, 0, 0); + transform: translate3d(-5px, 0, 0); + } + + to { + -webkit-transform: none; + transform: none; + } +} + +.bounceInRight { + -webkit-animation-name: bounceInRight; + animation-name: bounceInRight; +} + +@-webkit-keyframes bounceInUp { + from, 60%, 75%, 90%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + from { + opacity: 0; + -webkit-transform: translate3d(0, 3000px, 0); + transform: translate3d(0, 3000px, 0); + } + + 60% { + opacity: 1; + -webkit-transform: translate3d(0, -20px, 0); + transform: translate3d(0, -20px, 0); + } + + 75% { + -webkit-transform: translate3d(0, 10px, 0); + transform: translate3d(0, 10px, 0); + } + + 90% { + -webkit-transform: translate3d(0, -5px, 0); + transform: translate3d(0, -5px, 0); + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +@keyframes bounceInUp { + from, 60%, 75%, 90%, to { + -webkit-animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + animation-timing-function: cubic-bezier(0.215, 0.610, 0.355, 1.000); + } + + from { + opacity: 0; + -webkit-transform: translate3d(0, 3000px, 0); + transform: translate3d(0, 3000px, 0); + } + + 60% { + opacity: 1; + -webkit-transform: translate3d(0, -20px, 0); + transform: translate3d(0, -20px, 0); + } + + 75% { + -webkit-transform: translate3d(0, 10px, 0); + transform: translate3d(0, 10px, 0); + } + + 90% { + -webkit-transform: translate3d(0, -5px, 0); + transform: translate3d(0, -5px, 0); + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +.bounceInUp { + -webkit-animation-name: bounceInUp; + animation-name: bounceInUp; +} + +@-webkit-keyframes bounceOut { + 20% { + -webkit-transform: scale3d(.9, .9, .9); + transform: scale3d(.9, .9, .9); + } + + 50%, 55% { + opacity: 1; + -webkit-transform: scale3d(1.1, 1.1, 1.1); + transform: scale3d(1.1, 1.1, 1.1); + } + + to { + opacity: 0; + -webkit-transform: scale3d(.3, .3, .3); + transform: scale3d(.3, .3, .3); + } +} + +@keyframes bounceOut { + 20% { + -webkit-transform: scale3d(.9, .9, .9); + transform: scale3d(.9, .9, .9); + } + + 50%, 55% { + opacity: 1; + -webkit-transform: scale3d(1.1, 1.1, 1.1); + transform: scale3d(1.1, 1.1, 1.1); + } + + to { + opacity: 0; + -webkit-transform: scale3d(.3, .3, .3); + transform: scale3d(.3, .3, .3); + } +} + +.bounceOut { + -webkit-animation-name: bounceOut; + animation-name: bounceOut; +} + +@-webkit-keyframes bounceOutDown { + 20% { + -webkit-transform: translate3d(0, 10px, 0); + transform: translate3d(0, 10px, 0); + } + + 40%, 45% { + opacity: 1; + -webkit-transform: translate3d(0, -20px, 0); + transform: translate3d(0, -20px, 0); + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, 2000px, 0); + transform: translate3d(0, 2000px, 0); + } +} + +@keyframes bounceOutDown { + 20% { + -webkit-transform: translate3d(0, 10px, 0); + transform: translate3d(0, 10px, 0); + } + + 40%, 45% { + opacity: 1; + -webkit-transform: translate3d(0, -20px, 0); + transform: translate3d(0, -20px, 0); + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, 2000px, 0); + transform: translate3d(0, 2000px, 0); + } +} + +.bounceOutDown { + -webkit-animation-name: bounceOutDown; + animation-name: bounceOutDown; +} + +@-webkit-keyframes bounceOutLeft { + 20% { + opacity: 1; + -webkit-transform: translate3d(20px, 0, 0); + transform: translate3d(20px, 0, 0); + } + + to { + opacity: 0; + -webkit-transform: translate3d(-2000px, 0, 0); + transform: translate3d(-2000px, 0, 0); + } +} + +@keyframes bounceOutLeft { + 20% { + opacity: 1; + -webkit-transform: translate3d(20px, 0, 0); + transform: translate3d(20px, 0, 0); + } + + to { + opacity: 0; + -webkit-transform: translate3d(-2000px, 0, 0); + transform: translate3d(-2000px, 0, 0); + } +} + +.bounceOutLeft { + -webkit-animation-name: bounceOutLeft; + animation-name: bounceOutLeft; +} + +@-webkit-keyframes bounceOutRight { + 20% { + opacity: 1; + -webkit-transform: translate3d(-20px, 0, 0); + transform: translate3d(-20px, 0, 0); + } + + to { + opacity: 0; + -webkit-transform: translate3d(2000px, 0, 0); + transform: translate3d(2000px, 0, 0); + } +} + +@keyframes bounceOutRight { + 20% { + opacity: 1; + -webkit-transform: translate3d(-20px, 0, 0); + transform: translate3d(-20px, 0, 0); + } + + to { + opacity: 0; + -webkit-transform: translate3d(2000px, 0, 0); + transform: translate3d(2000px, 0, 0); + } +} + +.bounceOutRight { + -webkit-animation-name: bounceOutRight; + animation-name: bounceOutRight; +} + +@-webkit-keyframes bounceOutUp { + 20% { + -webkit-transform: translate3d(0, -10px, 0); + transform: translate3d(0, -10px, 0); + } + + 40%, 45% { + opacity: 1; + -webkit-transform: translate3d(0, 20px, 0); + transform: translate3d(0, 20px, 0); + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, -2000px, 0); + transform: translate3d(0, -2000px, 0); + } +} + +@keyframes bounceOutUp { + 20% { + -webkit-transform: translate3d(0, -10px, 0); + transform: translate3d(0, -10px, 0); + } + + 40%, 45% { + opacity: 1; + -webkit-transform: translate3d(0, 20px, 0); + transform: translate3d(0, 20px, 0); + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, -2000px, 0); + transform: translate3d(0, -2000px, 0); + } +} + +.bounceOutUp { + -webkit-animation-name: bounceOutUp; + animation-name: bounceOutUp; +} + +@-webkit-keyframes fadeIn { + from { + opacity: 0; + } + + to { + opacity: 1; + } +} + +@keyframes fadeIn { + from { + opacity: 0; + } + + to { + opacity: 1; + } +} + +.fadeIn { + -webkit-animation-name: fadeIn; + animation-name: fadeIn; +} + +@-webkit-keyframes fadeInDown { + from { + opacity: 0; + /*-webkit-transform: translate3d(0, -100%, 0); + transform: translate3d(0, -100%, 0);*/ + -webkit-transform: translate3d(0, -50px, 0); + transform: translate3d(0, -50px, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes fadeInDown { + from { + opacity: 0; + /*-webkit-transform: translate3d(0, -100%, 0); + transform: translate3d(0, -100%, 0);*/ + -webkit-transform: translate3d(0, -50px, 0); + transform: translate3d(0, -50px, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.fadeInDown { + -webkit-animation-name: fadeInDown; + animation-name: fadeInDown; +} + +@-webkit-keyframes fadeInDownBig { + from { + opacity: 0; + -webkit-transform: translate3d(0, -2000px, 0); + transform: translate3d(0, -2000px, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes fadeInDownBig { + from { + opacity: 0; + -webkit-transform: translate3d(0, -2000px, 0); + transform: translate3d(0, -2000px, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.fadeInDownBig { + -webkit-animation-name: fadeInDownBig; + animation-name: fadeInDownBig; +} + +@-webkit-keyframes fadeInLeft { + from { + opacity: 0; + /*-webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0);*/ + -webkit-transform: translate3d(-50px, 0, 0); + transform: translate3d(-50px, 0, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes fadeInLeft { + from { + opacity: 0; + /*-webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0);*/ + -webkit-transform: translate3d(-50px, 0, 0); + transform: translate3d(-50px, 0, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.fadeInLeft { + -webkit-animation-name: fadeInLeft; + animation-name: fadeInLeft; +} + +@-webkit-keyframes fadeInLeftBig { + from { + opacity: 0; + -webkit-transform: translate3d(-2000px, 0, 0); + transform: translate3d(-2000px, 0, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes fadeInLeftBig { + from { + opacity: 0; + -webkit-transform: translate3d(-2000px, 0, 0); + transform: translate3d(-2000px, 0, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.fadeInLeftBig { + -webkit-animation-name: fadeInLeftBig; + animation-name: fadeInLeftBig; +} + +@-webkit-keyframes fadeInRight { + from { + opacity: 0; + /*-webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0);*/ + -webkit-transform: translate3d(50px, 0, 0); + transform: translate3d(50px, 0, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes fadeInRight { + from { + opacity: 0; + /*-webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0);*/ + -webkit-transform: translate3d(50px, 0, 0); + transform: translate3d(50px, 0, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.fadeInRight { + -webkit-animation-name: fadeInRight; + animation-name: fadeInRight; +} + +@-webkit-keyframes fadeInRightBig { + from { + opacity: 0; + -webkit-transform: translate3d(2000px, 0, 0); + transform: translate3d(2000px, 0, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes fadeInRightBig { + from { + opacity: 0; + -webkit-transform: translate3d(2000px, 0, 0); + transform: translate3d(2000px, 0, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.fadeInRightBig { + -webkit-animation-name: fadeInRightBig; + animation-name: fadeInRightBig; +} + +@-webkit-keyframes fadeInUp { + from { + opacity: 0; + /*-webkit-transform: translate3d(0, 100%, 0); + transform: translate3d(0, 100%, 0);*/ + -webkit-transform: translate3d(0, 50px, 0); + transform: translate3d(0, 50px, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes fadeInUp { + from { + opacity: 0; + /*-webkit-transform: translate3d(0, 100%, 0); + transform: translate3d(0, 100%, 0);*/ + -webkit-transform: translate3d(0, 50px, 0); + transform: translate3d(0, 50px, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.fadeInUp { + -webkit-animation-name: fadeInUp; + animation-name: fadeInUp; +} + +@-webkit-keyframes fadeInUpBig { + from { + opacity: 0; + -webkit-transform: translate3d(0, 2000px, 0); + transform: translate3d(0, 2000px, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes fadeInUpBig { + from { + opacity: 0; + -webkit-transform: translate3d(0, 2000px, 0); + transform: translate3d(0, 2000px, 0); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.fadeInUpBig { + -webkit-animation-name: fadeInUpBig; + animation-name: fadeInUpBig; +} + +@-webkit-keyframes fadeOut { + from { + opacity: 1; + } + + to { + opacity: 0; + } +} + +@keyframes fadeOut { + from { + opacity: 1; + } + + to { + opacity: 0; + } +} + +.fadeOut { + -webkit-animation-name: fadeOut; + animation-name: fadeOut; +} + +@-webkit-keyframes fadeOutDown { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, 100%, 0); + transform: translate3d(0, 100%, 0); + } +} + +@keyframes fadeOutDown { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, 100%, 0); + transform: translate3d(0, 100%, 0); + } +} + +.fadeOutDown { + -webkit-animation-name: fadeOutDown; + animation-name: fadeOutDown; +} + +@-webkit-keyframes fadeOutDownBig { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, 2000px, 0); + transform: translate3d(0, 2000px, 0); + } +} + +@keyframes fadeOutDownBig { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, 2000px, 0); + transform: translate3d(0, 2000px, 0); + } +} + +.fadeOutDownBig { + -webkit-animation-name: fadeOutDownBig; + animation-name: fadeOutDownBig; +} + +@-webkit-keyframes fadeOutLeft { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + } +} + +@keyframes fadeOutLeft { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + } +} + +.fadeOutLeft { + -webkit-animation-name: fadeOutLeft; + animation-name: fadeOutLeft; +} + +@-webkit-keyframes fadeOutLeftBig { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(-2000px, 0, 0); + transform: translate3d(-2000px, 0, 0); + } +} + +@keyframes fadeOutLeftBig { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(-2000px, 0, 0); + transform: translate3d(-2000px, 0, 0); + } +} + +.fadeOutLeftBig { + -webkit-animation-name: fadeOutLeftBig; + animation-name: fadeOutLeftBig; +} + +@-webkit-keyframes fadeOutRight { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + } +} + +@keyframes fadeOutRight { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + } +} + +.fadeOutRight { + -webkit-animation-name: fadeOutRight; + animation-name: fadeOutRight; +} + +@-webkit-keyframes fadeOutRightBig { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(2000px, 0, 0); + transform: translate3d(2000px, 0, 0); + } +} + +@keyframes fadeOutRightBig { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(2000px, 0, 0); + transform: translate3d(2000px, 0, 0); + } +} + +.fadeOutRightBig { + -webkit-animation-name: fadeOutRightBig; + animation-name: fadeOutRightBig; +} + +@-webkit-keyframes fadeOutUp { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, -100%, 0); + transform: translate3d(0, -100%, 0); + } +} + +@keyframes fadeOutUp { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, -100%, 0); + transform: translate3d(0, -100%, 0); + } +} + +.fadeOutUp { + -webkit-animation-name: fadeOutUp; + animation-name: fadeOutUp; +} + +@-webkit-keyframes fadeOutUpBig { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, -2000px, 0); + transform: translate3d(0, -2000px, 0); + } +} + +@keyframes fadeOutUpBig { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(0, -2000px, 0); + transform: translate3d(0, -2000px, 0); + } +} + +.fadeOutUpBig { + -webkit-animation-name: fadeOutUpBig; + animation-name: fadeOutUpBig; +} + +@-webkit-keyframes flip { + from { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, -360deg); + transform: perspective(400px) rotate3d(0, 1, 0, -360deg); + -webkit-animation-timing-function: ease-out; + animation-timing-function: ease-out; + } + + 40% { + -webkit-transform: perspective(400px) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -190deg); + transform: perspective(400px) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -190deg); + -webkit-animation-timing-function: ease-out; + animation-timing-function: ease-out; + } + + 50% { + -webkit-transform: perspective(400px) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -170deg); + transform: perspective(400px) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -170deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } + + 80% { + -webkit-transform: perspective(400px) scale3d(.95, .95, .95); + transform: perspective(400px) scale3d(.95, .95, .95); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } + + to { + -webkit-transform: perspective(400px); + transform: perspective(400px); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } +} + +@keyframes flip { + from { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, -360deg); + transform: perspective(400px) rotate3d(0, 1, 0, -360deg); + -webkit-animation-timing-function: ease-out; + animation-timing-function: ease-out; + } + + 40% { + -webkit-transform: perspective(400px) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -190deg); + transform: perspective(400px) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -190deg); + -webkit-animation-timing-function: ease-out; + animation-timing-function: ease-out; + } + + 50% { + -webkit-transform: perspective(400px) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -170deg); + transform: perspective(400px) translate3d(0, 0, 150px) rotate3d(0, 1, 0, -170deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } + + 80% { + -webkit-transform: perspective(400px) scale3d(.95, .95, .95); + transform: perspective(400px) scale3d(.95, .95, .95); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } + + to { + -webkit-transform: perspective(400px); + transform: perspective(400px); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } +} + +.animated.flip { + -webkit-backface-visibility: visible; + backface-visibility: visible; + -webkit-animation-name: flip; + animation-name: flip; +} + +@-webkit-keyframes flipInX { + from { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, 90deg); + transform: perspective(400px) rotate3d(1, 0, 0, 90deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + opacity: 0; + } + + 40% { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, -20deg); + transform: perspective(400px) rotate3d(1, 0, 0, -20deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } + + 60% { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, 10deg); + transform: perspective(400px) rotate3d(1, 0, 0, 10deg); + opacity: 1; + } + + 80% { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, -5deg); + transform: perspective(400px) rotate3d(1, 0, 0, -5deg); + } + + to { + -webkit-transform: perspective(400px); + transform: perspective(400px); + } +} + +@keyframes flipInX { + from { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, 90deg); + transform: perspective(400px) rotate3d(1, 0, 0, 90deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + opacity: 0; + } + + 40% { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, -20deg); + transform: perspective(400px) rotate3d(1, 0, 0, -20deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } + + 60% { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, 10deg); + transform: perspective(400px) rotate3d(1, 0, 0, 10deg); + opacity: 1; + } + + 80% { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, -5deg); + transform: perspective(400px) rotate3d(1, 0, 0, -5deg); + } + + to { + -webkit-transform: perspective(400px); + transform: perspective(400px); + } +} + +.flipInX { + -webkit-backface-visibility: visible !important; + backface-visibility: visible !important; + -webkit-animation-name: flipInX; + animation-name: flipInX; +} + +@-webkit-keyframes flipInY { + from { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, 90deg); + transform: perspective(400px) rotate3d(0, 1, 0, 90deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + opacity: 0; + } + + 40% { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, -20deg); + transform: perspective(400px) rotate3d(0, 1, 0, -20deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } + + 60% { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, 10deg); + transform: perspective(400px) rotate3d(0, 1, 0, 10deg); + opacity: 1; + } + + 80% { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, -5deg); + transform: perspective(400px) rotate3d(0, 1, 0, -5deg); + } + + to { + -webkit-transform: perspective(400px); + transform: perspective(400px); + } +} + +@keyframes flipInY { + from { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, 90deg); + transform: perspective(400px) rotate3d(0, 1, 0, 90deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + opacity: 0; + } + + 40% { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, -20deg); + transform: perspective(400px) rotate3d(0, 1, 0, -20deg); + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; + } + + 60% { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, 10deg); + transform: perspective(400px) rotate3d(0, 1, 0, 10deg); + opacity: 1; + } + + 80% { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, -5deg); + transform: perspective(400px) rotate3d(0, 1, 0, -5deg); + } + + to { + -webkit-transform: perspective(400px); + transform: perspective(400px); + } +} + +.flipInY { + -webkit-backface-visibility: visible !important; + backface-visibility: visible !important; + -webkit-animation-name: flipInY; + animation-name: flipInY; +} + +@-webkit-keyframes flipOutX { + from { + -webkit-transform: perspective(400px); + transform: perspective(400px); + } + + 30% { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, -20deg); + transform: perspective(400px) rotate3d(1, 0, 0, -20deg); + opacity: 1; + } + + to { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, 90deg); + transform: perspective(400px) rotate3d(1, 0, 0, 90deg); + opacity: 0; + } +} + +@keyframes flipOutX { + from { + -webkit-transform: perspective(400px); + transform: perspective(400px); + } + + 30% { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, -20deg); + transform: perspective(400px) rotate3d(1, 0, 0, -20deg); + opacity: 1; + } + + to { + -webkit-transform: perspective(400px) rotate3d(1, 0, 0, 90deg); + transform: perspective(400px) rotate3d(1, 0, 0, 90deg); + opacity: 0; + } +} + +.flipOutX { + -webkit-animation-name: flipOutX; + animation-name: flipOutX; + -webkit-backface-visibility: visible !important; + backface-visibility: visible !important; +} + +@-webkit-keyframes flipOutY { + from { + -webkit-transform: perspective(400px); + transform: perspective(400px); + } + + 30% { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, -15deg); + transform: perspective(400px) rotate3d(0, 1, 0, -15deg); + opacity: 1; + } + + to { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, 90deg); + transform: perspective(400px) rotate3d(0, 1, 0, 90deg); + opacity: 0; + } +} + +@keyframes flipOutY { + from { + -webkit-transform: perspective(400px); + transform: perspective(400px); + } + + 30% { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, -15deg); + transform: perspective(400px) rotate3d(0, 1, 0, -15deg); + opacity: 1; + } + + to { + -webkit-transform: perspective(400px) rotate3d(0, 1, 0, 90deg); + transform: perspective(400px) rotate3d(0, 1, 0, 90deg); + opacity: 0; + } +} + +.flipOutY { + -webkit-backface-visibility: visible !important; + backface-visibility: visible !important; + -webkit-animation-name: flipOutY; + animation-name: flipOutY; +} + +@-webkit-keyframes lightSpeedIn { + from { + -webkit-transform: translate3d(100%, 0, 0) skewX(-30deg); + transform: translate3d(100%, 0, 0) skewX(-30deg); + opacity: 0; + } + + 60% { + -webkit-transform: skewX(20deg); + transform: skewX(20deg); + opacity: 1; + } + + 80% { + -webkit-transform: skewX(-5deg); + transform: skewX(-5deg); + opacity: 1; + } + + to { + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +@keyframes lightSpeedIn { + from { + -webkit-transform: translate3d(100%, 0, 0) skewX(-30deg); + transform: translate3d(100%, 0, 0) skewX(-30deg); + opacity: 0; + } + + 60% { + -webkit-transform: skewX(20deg); + transform: skewX(20deg); + opacity: 1; + } + + 80% { + -webkit-transform: skewX(-5deg); + transform: skewX(-5deg); + opacity: 1; + } + + to { + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +.lightSpeedIn { + -webkit-animation-name: lightSpeedIn; + animation-name: lightSpeedIn; + -webkit-animation-timing-function: ease-out; + animation-timing-function: ease-out; +} + +@-webkit-keyframes lightSpeedOut { + from { + opacity: 1; + } + + to { + -webkit-transform: translate3d(100%, 0, 0) skewX(30deg); + transform: translate3d(100%, 0, 0) skewX(30deg); + opacity: 0; + } +} + +@keyframes lightSpeedOut { + from { + opacity: 1; + } + + to { + -webkit-transform: translate3d(100%, 0, 0) skewX(30deg); + transform: translate3d(100%, 0, 0) skewX(30deg); + opacity: 0; + } +} + +.lightSpeedOut { + -webkit-animation-name: lightSpeedOut; + animation-name: lightSpeedOut; + -webkit-animation-timing-function: ease-in; + animation-timing-function: ease-in; +} + +@-webkit-keyframes rotateIn { + from { + -webkit-transform-origin: center; + transform-origin: center; + -webkit-transform: rotate3d(0, 0, 1, -200deg); + transform: rotate3d(0, 0, 1, -200deg); + opacity: 0; + } + + to { + -webkit-transform-origin: center; + transform-origin: center; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +@keyframes rotateIn { + from { + -webkit-transform-origin: center; + transform-origin: center; + -webkit-transform: rotate3d(0, 0, 1, -200deg); + transform: rotate3d(0, 0, 1, -200deg); + opacity: 0; + } + + to { + -webkit-transform-origin: center; + transform-origin: center; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +.rotateIn { + -webkit-animation-name: rotateIn; + animation-name: rotateIn; +} + +@-webkit-keyframes rotateInDownLeft { + from { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: rotate3d(0, 0, 1, -45deg); + transform: rotate3d(0, 0, 1, -45deg); + opacity: 0; + } + + to { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +@keyframes rotateInDownLeft { + from { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: rotate3d(0, 0, 1, -45deg); + transform: rotate3d(0, 0, 1, -45deg); + opacity: 0; + } + + to { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +.rotateInDownLeft { + -webkit-animation-name: rotateInDownLeft; + animation-name: rotateInDownLeft; +} + +@-webkit-keyframes rotateInDownRight { + from { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: rotate3d(0, 0, 1, 45deg); + transform: rotate3d(0, 0, 1, 45deg); + opacity: 0; + } + + to { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +@keyframes rotateInDownRight { + from { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: rotate3d(0, 0, 1, 45deg); + transform: rotate3d(0, 0, 1, 45deg); + opacity: 0; + } + + to { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +.rotateInDownRight { + -webkit-animation-name: rotateInDownRight; + animation-name: rotateInDownRight; +} + +@-webkit-keyframes rotateInUpLeft { + from { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: rotate3d(0, 0, 1, 45deg); + transform: rotate3d(0, 0, 1, 45deg); + opacity: 0; + } + + to { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +@keyframes rotateInUpLeft { + from { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: rotate3d(0, 0, 1, 45deg); + transform: rotate3d(0, 0, 1, 45deg); + opacity: 0; + } + + to { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +.rotateInUpLeft { + -webkit-animation-name: rotateInUpLeft; + animation-name: rotateInUpLeft; +} + +@-webkit-keyframes rotateInUpRight { + from { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: rotate3d(0, 0, 1, -90deg); + transform: rotate3d(0, 0, 1, -90deg); + opacity: 0; + } + + to { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +@keyframes rotateInUpRight { + from { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: rotate3d(0, 0, 1, -90deg); + transform: rotate3d(0, 0, 1, -90deg); + opacity: 0; + } + + to { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: none; + transform: none; + opacity: 1; + } +} + +.rotateInUpRight { + -webkit-animation-name: rotateInUpRight; + animation-name: rotateInUpRight; +} + +@-webkit-keyframes rotateOut { + from { + -webkit-transform-origin: center; + transform-origin: center; + opacity: 1; + } + + to { + -webkit-transform-origin: center; + transform-origin: center; + -webkit-transform: rotate3d(0, 0, 1, 200deg); + transform: rotate3d(0, 0, 1, 200deg); + opacity: 0; + } +} + +@keyframes rotateOut { + from { + -webkit-transform-origin: center; + transform-origin: center; + opacity: 1; + } + + to { + -webkit-transform-origin: center; + transform-origin: center; + -webkit-transform: rotate3d(0, 0, 1, 200deg); + transform: rotate3d(0, 0, 1, 200deg); + opacity: 0; + } +} + +.rotateOut { + -webkit-animation-name: rotateOut; + animation-name: rotateOut; +} + +@-webkit-keyframes rotateOutDownLeft { + from { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + opacity: 1; + } + + to { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: rotate3d(0, 0, 1, 45deg); + transform: rotate3d(0, 0, 1, 45deg); + opacity: 0; + } +} + +@keyframes rotateOutDownLeft { + from { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + opacity: 1; + } + + to { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: rotate3d(0, 0, 1, 45deg); + transform: rotate3d(0, 0, 1, 45deg); + opacity: 0; + } +} + +.rotateOutDownLeft { + -webkit-animation-name: rotateOutDownLeft; + animation-name: rotateOutDownLeft; +} + +@-webkit-keyframes rotateOutDownRight { + from { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + opacity: 1; + } + + to { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: rotate3d(0, 0, 1, -45deg); + transform: rotate3d(0, 0, 1, -45deg); + opacity: 0; + } +} + +@keyframes rotateOutDownRight { + from { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + opacity: 1; + } + + to { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: rotate3d(0, 0, 1, -45deg); + transform: rotate3d(0, 0, 1, -45deg); + opacity: 0; + } +} + +.rotateOutDownRight { + -webkit-animation-name: rotateOutDownRight; + animation-name: rotateOutDownRight; +} + +@-webkit-keyframes rotateOutUpLeft { + from { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + opacity: 1; + } + + to { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: rotate3d(0, 0, 1, -45deg); + transform: rotate3d(0, 0, 1, -45deg); + opacity: 0; + } +} + +@keyframes rotateOutUpLeft { + from { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + opacity: 1; + } + + to { + -webkit-transform-origin: left bottom; + transform-origin: left bottom; + -webkit-transform: rotate3d(0, 0, 1, -45deg); + transform: rotate3d(0, 0, 1, -45deg); + opacity: 0; + } +} + +.rotateOutUpLeft { + -webkit-animation-name: rotateOutUpLeft; + animation-name: rotateOutUpLeft; +} + +@-webkit-keyframes rotateOutUpRight { + from { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + opacity: 1; + } + + to { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: rotate3d(0, 0, 1, 90deg); + transform: rotate3d(0, 0, 1, 90deg); + opacity: 0; + } +} + +@keyframes rotateOutUpRight { + from { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + opacity: 1; + } + + to { + -webkit-transform-origin: right bottom; + transform-origin: right bottom; + -webkit-transform: rotate3d(0, 0, 1, 90deg); + transform: rotate3d(0, 0, 1, 90deg); + opacity: 0; + } +} + +.rotateOutUpRight { + -webkit-animation-name: rotateOutUpRight; + animation-name: rotateOutUpRight; +} + +@-webkit-keyframes hinge { + 0% { + -webkit-transform-origin: top left; + transform-origin: top left; + -webkit-animation-timing-function: ease-in-out; + animation-timing-function: ease-in-out; + } + + 20%, 60% { + -webkit-transform: rotate3d(0, 0, 1, 80deg); + transform: rotate3d(0, 0, 1, 80deg); + -webkit-transform-origin: top left; + transform-origin: top left; + -webkit-animation-timing-function: ease-in-out; + animation-timing-function: ease-in-out; + } + + 40%, 80% { + -webkit-transform: rotate3d(0, 0, 1, 60deg); + transform: rotate3d(0, 0, 1, 60deg); + -webkit-transform-origin: top left; + transform-origin: top left; + -webkit-animation-timing-function: ease-in-out; + animation-timing-function: ease-in-out; + opacity: 1; + } + + to { + -webkit-transform: translate3d(0, 700px, 0); + transform: translate3d(0, 700px, 0); + opacity: 0; + } +} + +@keyframes hinge { + 0% { + -webkit-transform-origin: top left; + transform-origin: top left; + -webkit-animation-timing-function: ease-in-out; + animation-timing-function: ease-in-out; + } + + 20%, 60% { + -webkit-transform: rotate3d(0, 0, 1, 80deg); + transform: rotate3d(0, 0, 1, 80deg); + -webkit-transform-origin: top left; + transform-origin: top left; + -webkit-animation-timing-function: ease-in-out; + animation-timing-function: ease-in-out; + } + + 40%, 80% { + -webkit-transform: rotate3d(0, 0, 1, 60deg); + transform: rotate3d(0, 0, 1, 60deg); + -webkit-transform-origin: top left; + transform-origin: top left; + -webkit-animation-timing-function: ease-in-out; + animation-timing-function: ease-in-out; + opacity: 1; + } + + to { + -webkit-transform: translate3d(0, 700px, 0); + transform: translate3d(0, 700px, 0); + opacity: 0; + } +} + +.hinge { + -webkit-animation-name: hinge; + animation-name: hinge; +} + +/* originally authored by Nick Pettit - https://github.com/nickpettit/glide */ + +@-webkit-keyframes rollIn { + from { + opacity: 0; + -webkit-transform: translate3d(-100%, 0, 0) rotate3d(0, 0, 1, -120deg); + transform: translate3d(-100%, 0, 0) rotate3d(0, 0, 1, -120deg); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +@keyframes rollIn { + from { + opacity: 0; + -webkit-transform: translate3d(-100%, 0, 0) rotate3d(0, 0, 1, -120deg); + transform: translate3d(-100%, 0, 0) rotate3d(0, 0, 1, -120deg); + } + + to { + opacity: 1; + -webkit-transform: none; + transform: none; + } +} + +.rollIn { + -webkit-animation-name: rollIn; + animation-name: rollIn; +} + +/* originally authored by Nick Pettit - https://github.com/nickpettit/glide */ + +@-webkit-keyframes rollOut { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(100%, 0, 0) rotate3d(0, 0, 1, 120deg); + transform: translate3d(100%, 0, 0) rotate3d(0, 0, 1, 120deg); + } +} + +@keyframes rollOut { + from { + opacity: 1; + } + + to { + opacity: 0; + -webkit-transform: translate3d(100%, 0, 0) rotate3d(0, 0, 1, 120deg); + transform: translate3d(100%, 0, 0) rotate3d(0, 0, 1, 120deg); + } +} + +.rollOut { + -webkit-animation-name: rollOut; + animation-name: rollOut; +} + +@-webkit-keyframes zoomIn { + from { + opacity: 0; + -webkit-transform: scale3d(.3, .3, .3); + transform: scale3d(.3, .3, .3); + } + + 50% { + opacity: 1; + } +} + +@keyframes zoomIn { + from { + opacity: 0; + -webkit-transform: scale3d(.3, .3, .3); + transform: scale3d(.3, .3, .3); + } + + 50% { + opacity: 1; + } +} + +.zoomIn { + -webkit-animation-name: zoomIn; + animation-name: zoomIn; +} + +@-webkit-keyframes zoomInDown { + from { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(0, -1000px, 0); + transform: scale3d(.1, .1, .1) translate3d(0, -1000px, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(0, 60px, 0); + transform: scale3d(.475, .475, .475) translate3d(0, 60px, 0); + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +@keyframes zoomInDown { + from { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(0, -1000px, 0); + transform: scale3d(.1, .1, .1) translate3d(0, -1000px, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(0, 60px, 0); + transform: scale3d(.475, .475, .475) translate3d(0, 60px, 0); + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +.zoomInDown { + -webkit-animation-name: zoomInDown; + animation-name: zoomInDown; +} + +@-webkit-keyframes zoomInLeft { + from { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(-1000px, 0, 0); + transform: scale3d(.1, .1, .1) translate3d(-1000px, 0, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(10px, 0, 0); + transform: scale3d(.475, .475, .475) translate3d(10px, 0, 0); + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +@keyframes zoomInLeft { + from { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(-1000px, 0, 0); + transform: scale3d(.1, .1, .1) translate3d(-1000px, 0, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(10px, 0, 0); + transform: scale3d(.475, .475, .475) translate3d(10px, 0, 0); + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +.zoomInLeft { + -webkit-animation-name: zoomInLeft; + animation-name: zoomInLeft; +} + +@-webkit-keyframes zoomInRight { + from { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(1000px, 0, 0); + transform: scale3d(.1, .1, .1) translate3d(1000px, 0, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(-10px, 0, 0); + transform: scale3d(.475, .475, .475) translate3d(-10px, 0, 0); + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +@keyframes zoomInRight { + from { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(1000px, 0, 0); + transform: scale3d(.1, .1, .1) translate3d(1000px, 0, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(-10px, 0, 0); + transform: scale3d(.475, .475, .475) translate3d(-10px, 0, 0); + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +.zoomInRight { + -webkit-animation-name: zoomInRight; + animation-name: zoomInRight; +} + +@-webkit-keyframes zoomInUp { + from { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(0, 1000px, 0); + transform: scale3d(.1, .1, .1) translate3d(0, 1000px, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(0, -60px, 0); + transform: scale3d(.475, .475, .475) translate3d(0, -60px, 0); + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +@keyframes zoomInUp { + from { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(0, 1000px, 0); + transform: scale3d(.1, .1, .1) translate3d(0, 1000px, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + 60% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(0, -60px, 0); + transform: scale3d(.475, .475, .475) translate3d(0, -60px, 0); + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +.zoomInUp { + -webkit-animation-name: zoomInUp; + animation-name: zoomInUp; +} + +@-webkit-keyframes zoomOut { + from { + opacity: 1; + } + + 50% { + opacity: 0; + -webkit-transform: scale3d(.3, .3, .3); + transform: scale3d(.3, .3, .3); + } + + to { + opacity: 0; + } +} + +@keyframes zoomOut { + from { + opacity: 1; + } + + 50% { + opacity: 0; + -webkit-transform: scale3d(.3, .3, .3); + transform: scale3d(.3, .3, .3); + } + + to { + opacity: 0; + } +} + +.zoomOut { + -webkit-animation-name: zoomOut; + animation-name: zoomOut; +} + +@-webkit-keyframes zoomOutDown { + 40% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(0, -60px, 0); + transform: scale3d(.475, .475, .475) translate3d(0, -60px, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + to { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(0, 2000px, 0); + transform: scale3d(.1, .1, .1) translate3d(0, 2000px, 0); + -webkit-transform-origin: center bottom; + transform-origin: center bottom; + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +@keyframes zoomOutDown { + 40% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(0, -60px, 0); + transform: scale3d(.475, .475, .475) translate3d(0, -60px, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + to { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(0, 2000px, 0); + transform: scale3d(.1, .1, .1) translate3d(0, 2000px, 0); + -webkit-transform-origin: center bottom; + transform-origin: center bottom; + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +.zoomOutDown { + -webkit-animation-name: zoomOutDown; + animation-name: zoomOutDown; +} + +@-webkit-keyframes zoomOutLeft { + 40% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(42px, 0, 0); + transform: scale3d(.475, .475, .475) translate3d(42px, 0, 0); + } + + to { + opacity: 0; + -webkit-transform: scale(.1) translate3d(-2000px, 0, 0); + transform: scale(.1) translate3d(-2000px, 0, 0); + -webkit-transform-origin: left center; + transform-origin: left center; + } +} + +@keyframes zoomOutLeft { + 40% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(42px, 0, 0); + transform: scale3d(.475, .475, .475) translate3d(42px, 0, 0); + } + + to { + opacity: 0; + -webkit-transform: scale(.1) translate3d(-2000px, 0, 0); + transform: scale(.1) translate3d(-2000px, 0, 0); + -webkit-transform-origin: left center; + transform-origin: left center; + } +} + +.zoomOutLeft { + -webkit-animation-name: zoomOutLeft; + animation-name: zoomOutLeft; +} + +@-webkit-keyframes zoomOutRight { + 40% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(-42px, 0, 0); + transform: scale3d(.475, .475, .475) translate3d(-42px, 0, 0); + } + + to { + opacity: 0; + -webkit-transform: scale(.1) translate3d(2000px, 0, 0); + transform: scale(.1) translate3d(2000px, 0, 0); + -webkit-transform-origin: right center; + transform-origin: right center; + } +} + +@keyframes zoomOutRight { + 40% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(-42px, 0, 0); + transform: scale3d(.475, .475, .475) translate3d(-42px, 0, 0); + } + + to { + opacity: 0; + -webkit-transform: scale(.1) translate3d(2000px, 0, 0); + transform: scale(.1) translate3d(2000px, 0, 0); + -webkit-transform-origin: right center; + transform-origin: right center; + } +} + +.zoomOutRight { + -webkit-animation-name: zoomOutRight; + animation-name: zoomOutRight; +} + +@-webkit-keyframes zoomOutUp { + 40% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(0, 60px, 0); + transform: scale3d(.475, .475, .475) translate3d(0, 60px, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + to { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(0, -2000px, 0); + transform: scale3d(.1, .1, .1) translate3d(0, -2000px, 0); + -webkit-transform-origin: center bottom; + transform-origin: center bottom; + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +@keyframes zoomOutUp { + 40% { + opacity: 1; + -webkit-transform: scale3d(.475, .475, .475) translate3d(0, 60px, 0); + transform: scale3d(.475, .475, .475) translate3d(0, 60px, 0); + -webkit-animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + animation-timing-function: cubic-bezier(0.550, 0.055, 0.675, 0.190); + } + + to { + opacity: 0; + -webkit-transform: scale3d(.1, .1, .1) translate3d(0, -2000px, 0); + transform: scale3d(.1, .1, .1) translate3d(0, -2000px, 0); + -webkit-transform-origin: center bottom; + transform-origin: center bottom; + -webkit-animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + animation-timing-function: cubic-bezier(0.175, 0.885, 0.320, 1); + } +} + +.zoomOutUp { + -webkit-animation-name: zoomOutUp; + animation-name: zoomOutUp; +} + +@-webkit-keyframes slideInDown { + from { + -webkit-transform: translate3d(0, -100%, 0); + transform: translate3d(0, -100%, 0); + visibility: visible; + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +@keyframes slideInDown { + from { + -webkit-transform: translate3d(0, -100%, 0); + transform: translate3d(0, -100%, 0); + visibility: visible; + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +.slideInDown { + -webkit-animation-name: slideInDown; + animation-name: slideInDown; +} + +@-webkit-keyframes slideInLeft { + from { + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + visibility: visible; + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +@keyframes slideInLeft { + from { + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + visibility: visible; + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +.slideInLeft { + -webkit-animation-name: slideInLeft; + animation-name: slideInLeft; +} + +@-webkit-keyframes slideInRight { + from { + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + visibility: visible; + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +@keyframes slideInRight { + from { + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + visibility: visible; + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +.slideInRight { + -webkit-animation-name: slideInRight; + animation-name: slideInRight; +} + +@-webkit-keyframes slideInUp { + from { + -webkit-transform: translate3d(0, 100%, 0); + transform: translate3d(0, 100%, 0); + visibility: visible; + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +@keyframes slideInUp { + from { + -webkit-transform: translate3d(0, 100%, 0); + transform: translate3d(0, 100%, 0); + visibility: visible; + } + + to { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } +} + +.slideInUp { + -webkit-animation-name: slideInUp; + animation-name: slideInUp; +} + +@-webkit-keyframes slideOutDown { + from { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + -webkit-transform: translate3d(0, 100%, 0); + transform: translate3d(0, 100%, 0); + } +} + +@keyframes slideOutDown { + from { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + -webkit-transform: translate3d(0, 100%, 0); + transform: translate3d(0, 100%, 0); + } +} + +.slideOutDown { + -webkit-animation-name: slideOutDown; + animation-name: slideOutDown; +} + +@-webkit-keyframes slideOutLeft { + from { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + } +} + +@keyframes slideOutLeft { + from { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + } +} + +.slideOutLeft { + -webkit-animation-name: slideOutLeft; + animation-name: slideOutLeft; +} + +@-webkit-keyframes slideOutRight { + from { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + } +} + +@keyframes slideOutRight { + from { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + } +} + +.slideOutRight { + -webkit-animation-name: slideOutRight; + animation-name: slideOutRight; +} + +@-webkit-keyframes slideOutUp { + from { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + -webkit-transform: translate3d(0, -100%, 0); + transform: translate3d(0, -100%, 0); + } +} + +@keyframes slideOutUp { + from { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + } + + to { + visibility: hidden; + -webkit-transform: translate3d(0, -100%, 0); + transform: translate3d(0, -100%, 0); + } +} + +.slideOutUp { + -webkit-animation-name: slideOutUp; + animation-name: slideOutUp; +} \ No newline at end of file diff --git a/web/css/bootstrap.css b/web/css/bootstrap.css new file mode 100644 index 0000000..641c1bc --- /dev/null +++ b/web/css/bootstrap.css @@ -0,0 +1,8135 @@ +@charset "UTF-8"; +/*! + * Bootstrap v3.3.5 (http://getbootstrap.com) + * Copyright 2011-2015 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + */ +/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ +html { + font-family: sans-serif; + -ms-text-size-adjust: 100%; + -webkit-text-size-adjust: 100%; +} + +body { + margin: 0; +} + +article, +aside, +details, +figcaption, +figure, +footer, +header, +hgroup, +main, +menu, +nav, +section, +summary { + display: block; +} + +audio, +canvas, +progress, +video { + display: inline-block; + vertical-align: baseline; +} + +audio:not([controls]) { + display: none; + height: 0; +} + +[hidden], +template { + display: none; +} + +a { + background-color: transparent; +} + +a:active, +a:hover { + outline: 0; +} + +abbr[title] { + border-bottom: 1px dotted; +} + +b, +strong { + font-weight: bold; +} + +dfn { + font-style: italic; +} + +h1 { + font-size: 2em; + margin: 0.67em 0; +} + +mark { + background: #ff0; + color: #000; +} + +small { + font-size: 80%; +} + +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline; +} + +sup { + top: -0.5em; +} + +sub { + bottom: -0.25em; +} + +img { + border: 0; +} + +svg:not(:root) { + overflow: hidden; +} + +figure { + margin: 1em 40px; +} + +hr { + box-sizing: content-box; + height: 0; +} + +pre { + overflow: auto; +} + +code, +kbd, +pre, +samp { + font-family: monospace, monospace; + font-size: 1em; +} + +button, +input, +optgroup, +select, +textarea { + color: inherit; + font: inherit; + margin: 0; +} + +button { + overflow: visible; +} + +button, +select { + text-transform: none; +} + +button, +html input[type="button"], +input[type="reset"], +input[type="submit"] { + -webkit-appearance: button; + cursor: pointer; +} + +button[disabled], +html input[disabled] { + cursor: default; +} + +button::-moz-focus-inner, +input::-moz-focus-inner { + border: 0; + padding: 0; +} + +input { + line-height: normal; +} + +input[type="checkbox"], +input[type="radio"] { + box-sizing: border-box; + padding: 0; +} + +input[type="number"]::-webkit-inner-spin-button, +input[type="number"]::-webkit-outer-spin-button { + height: auto; +} + +input[type="search"] { + -webkit-appearance: textfield; + box-sizing: content-box; +} + +input[type="search"]::-webkit-search-cancel-button, +input[type="search"]::-webkit-search-decoration { + -webkit-appearance: none; +} + +fieldset { + border: 1px solid #c0c0c0; + margin: 0 2px; + padding: 0.35em 0.625em 0.75em; +} + +legend { + border: 0; + padding: 0; +} + +textarea { + overflow: auto; +} + +optgroup { + font-weight: bold; +} + +table { + border-collapse: collapse; + border-spacing: 0; +} + +td, +th { + padding: 0; +} + +/*! Source: https://github.com/h5bp/html5-boilerplate/blob/master/src/css/main.css */ +@media print { + *, + *:before, + *:after { + background: transparent !important; + color: #000 !important; + box-shadow: none !important; + text-shadow: none !important; + } + + a, + a:visited { + text-decoration: underline; + } + + a[href]:after { + content: " (" attr(href) ")"; + } + + abbr[title]:after { + content: " (" attr(title) ")"; + } + + a[href^="#"]:after, + a[href^="javascript:"]:after { + content: ""; + } + + pre, + blockquote { + border: 1px solid #999; + page-break-inside: avoid; + } + + thead { + display: table-header-group; + } + + tr, + img { + page-break-inside: avoid; + } + + img { + max-width: 100% !important; + } + + p, + h2, + h3 { + orphans: 3; + widows: 3; + } + + h2, + h3 { + page-break-after: avoid; + } + + .navbar { + display: none; + } + + .btn > .caret, + .dropup > .btn > .caret { + border-top-color: #000 !important; + } + + .label { + border: 1px solid #000; + } + + .table { + border-collapse: collapse !important; + } + .table td, + .table th { + background-color: #fff !important; + } + + .table-bordered th, + .table-bordered td { + border: 1px solid #ddd !important; + } +} +@font-face { + font-family: "Glyphicons Halflings"; + src: url("../fonts/bootstrap/glyphicons-halflings-regular.eot"); + src: url("../fonts/bootstrap/glyphicons-halflings-regular.eot?#iefix") + format("embedded-opentype"), + url("../fonts/bootstrap/glyphicons-halflings-regular.woff2") format("woff2"), + url("../fonts/bootstrap/glyphicons-halflings-regular.woff") format("woff"), + url("../fonts/bootstrap/glyphicons-halflings-regular.ttf") + format("truetype"), + url("../fonts/bootstrap/glyphicons-halflings-regular.svg#glyphicons_halflingsregular") + format("svg"); +} +.glyphicon { + position: relative; + top: 1px; + display: inline-block; + font-family: "Glyphicons Halflings"; + font-style: normal; + font-weight: normal; + line-height: 1; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.glyphicon-asterisk:before { + content: "\2a"; +} + +.glyphicon-plus:before { + content: "\2b"; +} + +.glyphicon-euro:before, +.glyphicon-eur:before { + content: "\20ac"; +} + +.glyphicon-minus:before { + content: "\2212"; +} + +.glyphicon-cloud:before { + content: "\2601"; +} + +.glyphicon-envelope:before { + content: "\2709"; +} + +.glyphicon-pencil:before { + content: "\270f"; +} + +.glyphicon-glass:before { + content: "\e001"; +} + +.glyphicon-music:before { + content: "\e002"; +} + +.glyphicon-search:before { + content: "\e003"; +} + +.glyphicon-heart:before { + content: "\e005"; +} + +.glyphicon-star:before { + content: "\e006"; +} + +.glyphicon-star-empty:before { + content: "\e007"; +} + +.glyphicon-user:before { + content: "\e008"; +} + +.glyphicon-film:before { + content: "\e009"; +} + +.glyphicon-th-large:before { + content: "\e010"; +} + +.glyphicon-th:before { + content: "\e011"; +} + +.glyphicon-th-list:before { + content: "\e012"; +} + +.glyphicon-ok:before { + content: "\e013"; +} + +.glyphicon-remove:before { + content: "\e014"; +} + +.glyphicon-zoom-in:before { + content: "\e015"; +} + +.glyphicon-zoom-out:before { + content: "\e016"; +} + +.glyphicon-off:before { + content: "\e017"; +} + +.glyphicon-signal:before { + content: "\e018"; +} + +.glyphicon-cog:before { + content: "\e019"; +} + +.glyphicon-trash:before { + content: "\e020"; +} + +.glyphicon-home:before { + content: "\e021"; +} + +.glyphicon-file:before { + content: "\e022"; +} + +.glyphicon-time:before { + content: "\e023"; +} + +.glyphicon-road:before { + content: "\e024"; +} + +.glyphicon-download-alt:before { + content: "\e025"; +} + +.glyphicon-download:before { + content: "\e026"; +} + +.glyphicon-upload:before { + content: "\e027"; +} + +.glyphicon-inbox:before { + content: "\e028"; +} + +.glyphicon-play-circle:before { + content: "\e029"; +} + +.glyphicon-repeat:before { + content: "\e030"; +} + +.glyphicon-refresh:before { + content: "\e031"; +} + +.glyphicon-list-alt:before { + content: "\e032"; +} + +.glyphicon-lock:before { + content: "\e033"; +} + +.glyphicon-flag:before { + content: "\e034"; +} + +.glyphicon-headphones:before { + content: "\e035"; +} + +.glyphicon-volume-off:before { + content: "\e036"; +} + +.glyphicon-volume-down:before { + content: "\e037"; +} + +.glyphicon-volume-up:before { + content: "\e038"; +} + +.glyphicon-qrcode:before { + content: "\e039"; +} + +.glyphicon-barcode:before { + content: "\e040"; +} + +.glyphicon-tag:before { + content: "\e041"; +} + +.glyphicon-tags:before { + content: "\e042"; +} + +.glyphicon-book:before { + content: "\e043"; +} + +.glyphicon-bookmark:before { + content: "\e044"; +} + +.glyphicon-print:before { + content: "\e045"; +} + +.glyphicon-camera:before { + content: "\e046"; +} + +.glyphicon-font:before { + content: "\e047"; +} + +.glyphicon-bold:before { + content: "\e048"; +} + +.glyphicon-italic:before { + content: "\e049"; +} + +.glyphicon-text-height:before { + content: "\e050"; +} + +.glyphicon-text-width:before { + content: "\e051"; +} + +.glyphicon-align-left:before { + content: "\e052"; +} + +.glyphicon-align-center:before { + content: "\e053"; +} + +.glyphicon-align-right:before { + content: "\e054"; +} + +.glyphicon-align-justify:before { + content: "\e055"; +} + +.glyphicon-list:before { + content: "\e056"; +} + +.glyphicon-indent-left:before { + content: "\e057"; +} + +.glyphicon-indent-right:before { + content: "\e058"; +} + +.glyphicon-facetime-video:before { + content: "\e059"; +} + +.glyphicon-picture:before { + content: "\e060"; +} + +.glyphicon-map-marker:before { + content: "\e062"; +} + +.glyphicon-adjust:before { + content: "\e063"; +} + +.glyphicon-tint:before { + content: "\e064"; +} + +.glyphicon-edit:before { + content: "\e065"; +} + +.glyphicon-share:before { + content: "\e066"; +} + +.glyphicon-check:before { + content: "\e067"; +} + +.glyphicon-move:before { + content: "\e068"; +} + +.glyphicon-step-backward:before { + content: "\e069"; +} + +.glyphicon-fast-backward:before { + content: "\e070"; +} + +.glyphicon-backward:before { + content: "\e071"; +} + +.glyphicon-play:before { + content: "\e072"; +} + +.glyphicon-pause:before { + content: "\e073"; +} + +.glyphicon-stop:before { + content: "\e074"; +} + +.glyphicon-forward:before { + content: "\e075"; +} + +.glyphicon-fast-forward:before { + content: "\e076"; +} + +.glyphicon-step-forward:before { + content: "\e077"; +} + +.glyphicon-eject:before { + content: "\e078"; +} + +.glyphicon-chevron-left:before { + content: "\e079"; +} + +.glyphicon-chevron-right:before { + content: "\e080"; +} + +.glyphicon-plus-sign:before { + content: "\e081"; +} + +.glyphicon-minus-sign:before { + content: "\e082"; +} + +.glyphicon-remove-sign:before { + content: "\e083"; +} + +.glyphicon-ok-sign:before { + content: "\e084"; +} + +.glyphicon-question-sign:before { + content: "\e085"; +} + +.glyphicon-info-sign:before { + content: "\e086"; +} + +.glyphicon-screenshot:before { + content: "\e087"; +} + +.glyphicon-remove-circle:before { + content: "\e088"; +} + +.glyphicon-ok-circle:before { + content: "\e089"; +} + +.glyphicon-ban-circle:before { + content: "\e090"; +} + +.glyphicon-arrow-left:before { + content: "\e091"; +} + +.glyphicon-arrow-right:before { + content: "\e092"; +} + +.glyphicon-arrow-up:before { + content: "\e093"; +} + +.glyphicon-arrow-down:before { + content: "\e094"; +} + +.glyphicon-share-alt:before { + content: "\e095"; +} + +.glyphicon-resize-full:before { + content: "\e096"; +} + +.glyphicon-resize-small:before { + content: "\e097"; +} + +.glyphicon-exclamation-sign:before { + content: "\e101"; +} + +.glyphicon-gift:before { + content: "\e102"; +} + +.glyphicon-leaf:before { + content: "\e103"; +} + +.glyphicon-fire:before { + content: "\e104"; +} + +.glyphicon-eye-open:before { + content: "\e105"; +} + +.glyphicon-eye-close:before { + content: "\e106"; +} + +.glyphicon-warning-sign:before { + content: "\e107"; +} + +.glyphicon-plane:before { + content: "\e108"; +} + +.glyphicon-calendar:before { + content: "\e109"; +} + +.glyphicon-random:before { + content: "\e110"; +} + +.glyphicon-comment:before { + content: "\e111"; +} + +.glyphicon-magnet:before { + content: "\e112"; +} + +.glyphicon-chevron-up:before { + content: "\e113"; +} + +.glyphicon-chevron-down:before { + content: "\e114"; +} + +.glyphicon-retweet:before { + content: "\e115"; +} + +.glyphicon-shopping-cart:before { + content: "\e116"; +} + +.glyphicon-folder-close:before { + content: "\e117"; +} + +.glyphicon-folder-open:before { + content: "\e118"; +} + +.glyphicon-resize-vertical:before { + content: "\e119"; +} + +.glyphicon-resize-horizontal:before { + content: "\e120"; +} + +.glyphicon-hdd:before { + content: "\e121"; +} + +.glyphicon-bullhorn:before { + content: "\e122"; +} + +.glyphicon-bell:before { + content: "\e123"; +} + +.glyphicon-certificate:before { + content: "\e124"; +} + +.glyphicon-thumbs-up:before { + content: "\e125"; +} + +.glyphicon-thumbs-down:before { + content: "\e126"; +} + +.glyphicon-hand-right:before { + content: "\e127"; +} + +.glyphicon-hand-left:before { + content: "\e128"; +} + +.glyphicon-hand-up:before { + content: "\e129"; +} + +.glyphicon-hand-down:before { + content: "\e130"; +} + +.glyphicon-circle-arrow-right:before { + content: "\e131"; +} + +.glyphicon-circle-arrow-left:before { + content: "\e132"; +} + +.glyphicon-circle-arrow-up:before { + content: "\e133"; +} + +.glyphicon-circle-arrow-down:before { + content: "\e134"; +} + +.glyphicon-globe:before { + content: "\e135"; +} + +.glyphicon-wrench:before { + content: "\e136"; +} + +.glyphicon-tasks:before { + content: "\e137"; +} + +.glyphicon-filter:before { + content: "\e138"; +} + +.glyphicon-briefcase:before { + content: "\e139"; +} + +.glyphicon-fullscreen:before { + content: "\e140"; +} + +.glyphicon-dashboard:before { + content: "\e141"; +} + +.glyphicon-paperclip:before { + content: "\e142"; +} + +.glyphicon-heart-empty:before { + content: "\e143"; +} + +.glyphicon-link:before { + content: "\e144"; +} + +.glyphicon-phone:before { + content: "\e145"; +} + +.glyphicon-pushpin:before { + content: "\e146"; +} + +.glyphicon-usd:before { + content: "\e148"; +} + +.glyphicon-gbp:before { + content: "\e149"; +} + +.glyphicon-sort:before { + content: "\e150"; +} + +.glyphicon-sort-by-alphabet:before { + content: "\e151"; +} + +.glyphicon-sort-by-alphabet-alt:before { + content: "\e152"; +} + +.glyphicon-sort-by-order:before { + content: "\e153"; +} + +.glyphicon-sort-by-order-alt:before { + content: "\e154"; +} + +.glyphicon-sort-by-attributes:before { + content: "\e155"; +} + +.glyphicon-sort-by-attributes-alt:before { + content: "\e156"; +} + +.glyphicon-unchecked:before { + content: "\e157"; +} + +.glyphicon-expand:before { + content: "\e158"; +} + +.glyphicon-collapse-down:before { + content: "\e159"; +} + +.glyphicon-collapse-up:before { + content: "\e160"; +} + +.glyphicon-log-in:before { + content: "\e161"; +} + +.glyphicon-flash:before { + content: "\e162"; +} + +.glyphicon-log-out:before { + content: "\e163"; +} + +.glyphicon-new-window:before { + content: "\e164"; +} + +.glyphicon-record:before { + content: "\e165"; +} + +.glyphicon-save:before { + content: "\e166"; +} + +.glyphicon-open:before { + content: "\e167"; +} + +.glyphicon-saved:before { + content: "\e168"; +} + +.glyphicon-import:before { + content: "\e169"; +} + +.glyphicon-export:before { + content: "\e170"; +} + +.glyphicon-send:before { + content: "\e171"; +} + +.glyphicon-floppy-disk:before { + content: "\e172"; +} + +.glyphicon-floppy-saved:before { + content: "\e173"; +} + +.glyphicon-floppy-remove:before { + content: "\e174"; +} + +.glyphicon-floppy-save:before { + content: "\e175"; +} + +.glyphicon-floppy-open:before { + content: "\e176"; +} + +.glyphicon-credit-card:before { + content: "\e177"; +} + +.glyphicon-transfer:before { + content: "\e178"; +} + +.glyphicon-cutlery:before { + content: "\e179"; +} + +.glyphicon-header:before { + content: "\e180"; +} + +.glyphicon-compressed:before { + content: "\e181"; +} + +.glyphicon-earphone:before { + content: "\e182"; +} + +.glyphicon-phone-alt:before { + content: "\e183"; +} + +.glyphicon-tower:before { + content: "\e184"; +} + +.glyphicon-stats:before { + content: "\e185"; +} + +.glyphicon-sd-video:before { + content: "\e186"; +} + +.glyphicon-hd-video:before { + content: "\e187"; +} + +.glyphicon-subtitles:before { + content: "\e188"; +} + +.glyphicon-sound-stereo:before { + content: "\e189"; +} + +.glyphicon-sound-dolby:before { + content: "\e190"; +} + +.glyphicon-sound-5-1:before { + content: "\e191"; +} + +.glyphicon-sound-6-1:before { + content: "\e192"; +} + +.glyphicon-sound-7-1:before { + content: "\e193"; +} + +.glyphicon-copyright-mark:before { + content: "\e194"; +} + +.glyphicon-registration-mark:before { + content: "\e195"; +} + +.glyphicon-cloud-download:before { + content: "\e197"; +} + +.glyphicon-cloud-upload:before { + content: "\e198"; +} + +.glyphicon-tree-conifer:before { + content: "\e199"; +} + +.glyphicon-tree-deciduous:before { + content: "\e200"; +} + +.glyphicon-cd:before { + content: "\e201"; +} + +.glyphicon-save-file:before { + content: "\e202"; +} + +.glyphicon-open-file:before { + content: "\e203"; +} + +.glyphicon-level-up:before { + content: "\e204"; +} + +.glyphicon-copy:before { + content: "\e205"; +} + +.glyphicon-paste:before { + content: "\e206"; +} + +.glyphicon-alert:before { + content: "\e209"; +} + +.glyphicon-equalizer:before { + content: "\e210"; +} + +.glyphicon-king:before { + content: "\e211"; +} + +.glyphicon-queen:before { + content: "\e212"; +} + +.glyphicon-pawn:before { + content: "\e213"; +} + +.glyphicon-bishop:before { + content: "\e214"; +} + +.glyphicon-knight:before { + content: "\e215"; +} + +.glyphicon-baby-formula:before { + content: "\e216"; +} + +.glyphicon-tent:before { + content: "\26fa"; +} + +.glyphicon-blackboard:before { + content: "\e218"; +} + +.glyphicon-bed:before { + content: "\e219"; +} + +.glyphicon-apple:before { + content: "\f8ff"; +} + +.glyphicon-erase:before { + content: "\e221"; +} + +.glyphicon-hourglass:before { + content: "\231b"; +} + +.glyphicon-lamp:before { + content: "\e223"; +} + +.glyphicon-duplicate:before { + content: "\e224"; +} + +.glyphicon-piggy-bank:before { + content: "\e225"; +} + +.glyphicon-scissors:before { + content: "\e226"; +} + +.glyphicon-bitcoin:before { + content: "\e227"; +} + +.glyphicon-btc:before { + content: "\e227"; +} + +.glyphicon-xbt:before { + content: "\e227"; +} + +.glyphicon-yen:before { + content: "\00a5"; +} + +.glyphicon-jpy:before { + content: "\00a5"; +} + +.glyphicon-ruble:before { + content: "\20bd"; +} + +.glyphicon-rub:before { + content: "\20bd"; +} + +.glyphicon-scale:before { + content: "\e230"; +} + +.glyphicon-ice-lolly:before { + content: "\e231"; +} + +.glyphicon-ice-lolly-tasted:before { + content: "\e232"; +} + +.glyphicon-education:before { + content: "\e233"; +} + +.glyphicon-option-horizontal:before { + content: "\e234"; +} + +.glyphicon-option-vertical:before { + content: "\e235"; +} + +.glyphicon-menu-hamburger:before { + content: "\e236"; +} + +.glyphicon-modal-window:before { + content: "\e237"; +} + +.glyphicon-oil:before { + content: "\e238"; +} + +.glyphicon-grain:before { + content: "\e239"; +} + +.glyphicon-sunglasses:before { + content: "\e240"; +} + +.glyphicon-text-size:before { + content: "\e241"; +} + +.glyphicon-text-color:before { + content: "\e242"; +} + +.glyphicon-text-background:before { + content: "\e243"; +} + +.glyphicon-object-align-top:before { + content: "\e244"; +} + +.glyphicon-object-align-bottom:before { + content: "\e245"; +} + +.glyphicon-object-align-horizontal:before { + content: "\e246"; +} + +.glyphicon-object-align-left:before { + content: "\e247"; +} + +.glyphicon-object-align-vertical:before { + content: "\e248"; +} + +.glyphicon-object-align-right:before { + content: "\e249"; +} + +.glyphicon-triangle-right:before { + content: "\e250"; +} + +.glyphicon-triangle-left:before { + content: "\e251"; +} + +.glyphicon-triangle-bottom:before { + content: "\e252"; +} + +.glyphicon-triangle-top:before { + content: "\e253"; +} + +.glyphicon-console:before { + content: "\e254"; +} + +.glyphicon-superscript:before { + content: "\e255"; +} + +.glyphicon-subscript:before { + content: "\e256"; +} + +.glyphicon-menu-left:before { + content: "\e257"; +} + +.glyphicon-menu-right:before { + content: "\e258"; +} + +.glyphicon-menu-down:before { + content: "\e259"; +} + +.glyphicon-menu-up:before { + content: "\e260"; +} + +* { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} + +*:before, +*:after { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} + +html { + font-size: 10px; + -webkit-tap-highlight-color: transparent; +} + +body { + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 14px; + line-height: 1.42857; + color: #333333; + background-color: #fff; +} + +input, +button, +select, +textarea { + font-family: inherit; + font-size: inherit; + line-height: inherit; +} + +a { + color: #337ab7; + text-decoration: none; +} +a:hover, +a:focus { + color: #23527c; + text-decoration: underline; +} +a:focus { + outline: thin dotted; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +figure { + margin: 0; +} + +img { + vertical-align: middle; +} + +.img-responsive { + display: block; + max-width: 100%; + height: auto; +} + +.img-rounded { + border-radius: 6px; +} + +.img-thumbnail { + padding: 4px; + line-height: 1.42857; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 4px; + -webkit-transition: all 0.2s ease-in-out; + -o-transition: all 0.2s ease-in-out; + transition: all 0.2s ease-in-out; + display: inline-block; + max-width: 100%; + height: auto; +} + +.img-circle { + border-radius: 50%; +} + +hr { + margin-top: 20px; + margin-bottom: 20px; + border: 0; + border-top: 1px solid #eeeeee; +} + +.sr-only { + position: absolute; + width: 1px; + height: 1px; + margin: -1px; + padding: 0; + overflow: hidden; + clip: rect(0, 0, 0, 0); + border: 0; +} + +.sr-only-focusable:active, +.sr-only-focusable:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; +} + +[role="button"] { + cursor: pointer; +} + +h1, +h2, +h3, +h4, +h5, +h6, +.h1, +.h2, +.h3, +.h4, +.h5, +.h6 { + font-family: inherit; + font-weight: 500; + line-height: 1.1; + color: inherit; +} +h1 small, +h1 .small, +h2 small, +h2 .small, +h3 small, +h3 .small, +h4 small, +h4 .small, +h5 small, +h5 .small, +h6 small, +h6 .small, +.h1 small, +.h1 .small, +.h2 small, +.h2 .small, +.h3 small, +.h3 .small, +.h4 small, +.h4 .small, +.h5 small, +.h5 .small, +.h6 small, +.h6 .small { + font-weight: normal; + line-height: 1; + color: #777777; +} + +h1, +.h1, +h2, +.h2, +h3, +.h3 { + margin-top: 20px; + margin-bottom: 10px; +} +h1 small, +h1 .small, +.h1 small, +.h1 .small, +h2 small, +h2 .small, +.h2 small, +.h2 .small, +h3 small, +h3 .small, +.h3 small, +.h3 .small { + font-size: 65%; +} + +h4, +.h4, +h5, +.h5, +h6, +.h6 { + margin-top: 10px; + margin-bottom: 10px; +} +h4 small, +h4 .small, +.h4 small, +.h4 .small, +h5 small, +h5 .small, +.h5 small, +.h5 .small, +h6 small, +h6 .small, +.h6 small, +.h6 .small { + font-size: 75%; +} + +h1, +.h1 { + font-size: 36px; +} + +h2, +.h2 { + font-size: 30px; +} + +h3, +.h3 { + font-size: 24px; +} + +h4, +.h4 { + font-size: 18px; +} + +h5, +.h5 { + font-size: 14px; +} + +h6, +.h6 { + font-size: 12px; +} + +p { + margin: 0 0 10px; +} + +.lead { + margin-bottom: 20px; + font-size: 16px; + font-weight: 300; + line-height: 1.4; +} +@media (min-width: 768px) { + .lead { + font-size: 21px; + } +} + +small, +.small { + font-size: 85%; +} + +mark, +.mark { + background-color: #fcf8e3; + padding: 0.2em; +} + +.text-left { + text-align: left; +} + +.text-right { + text-align: right; +} + +.text-center { + text-align: center; +} + +.text-justify { + text-align: justify; +} + +.text-nowrap { + white-space: nowrap; +} + +.text-lowercase { + text-transform: lowercase; +} + +.text-uppercase, +.initialism { + text-transform: uppercase; +} + +.text-capitalize { + text-transform: capitalize; +} + +.text-muted { + color: #777777; +} + +.text-primary { + color: #337ab7; +} + +a.text-primary:hover, +a.text-primary:focus { + color: #286090; +} + +.text-success { + color: #3c763d; +} + +a.text-success:hover, +a.text-success:focus { + color: #2b542c; +} + +.text-info { + color: #31708f; +} + +a.text-info:hover, +a.text-info:focus { + color: #245269; +} + +.text-warning { + color: #8a6d3b; +} + +a.text-warning:hover, +a.text-warning:focus { + color: #66512c; +} + +.text-danger { + color: #a94442; +} + +a.text-danger:hover, +a.text-danger:focus { + color: #843534; +} + +.bg-primary { + color: #fff; +} + +.bg-primary { + background-color: #337ab7; +} + +a.bg-primary:hover, +a.bg-primary:focus { + background-color: #286090; +} + +.bg-success { + background-color: #dff0d8; +} + +a.bg-success:hover, +a.bg-success:focus { + background-color: #c1e2b3; +} + +.bg-info { + background-color: #d9edf7; +} + +a.bg-info:hover, +a.bg-info:focus { + background-color: #afd9ee; +} + +.bg-warning { + background-color: #fcf8e3; +} + +a.bg-warning:hover, +a.bg-warning:focus { + background-color: #f7ecb5; +} + +.bg-danger { + background-color: #f2dede; +} + +a.bg-danger:hover, +a.bg-danger:focus { + background-color: #e4b9b9; +} + +.page-header { + padding-bottom: 9px; + margin: 40px 0 20px; + border-bottom: 1px solid #eeeeee; +} + +ul, +ol { + margin-top: 0; + margin-bottom: 10px; +} +ul ul, +ul ol, +ol ul, +ol ol { + margin-bottom: 0; +} + +.list-unstyled { + padding-left: 0; + list-style: none; +} + +.list-inline { + padding-left: 0; + list-style: none; + margin-left: -5px; +} +.list-inline > li { + display: inline-block; + padding-left: 5px; + padding-right: 5px; +} + +dl { + margin-top: 0; + margin-bottom: 20px; +} + +dt, +dd { + line-height: 1.42857; +} + +dt { + font-weight: bold; +} + +dd { + margin-left: 0; +} + +.dl-horizontal dd:before, +.dl-horizontal dd:after { + content: " "; + display: table; +} +.dl-horizontal dd:after { + clear: both; +} +@media (min-width: 768px) { + .dl-horizontal dt { + float: left; + width: 160px; + clear: left; + text-align: right; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + } + .dl-horizontal dd { + margin-left: 180px; + } +} + +abbr[title], +abbr[data-original-title] { + cursor: help; + border-bottom: 1px dotted #777777; +} + +.initialism { + font-size: 90%; +} + +blockquote { + padding: 10px 20px; + margin: 0 0 20px; + font-size: 17.5px; + border-left: 5px solid #eeeeee; +} +blockquote p:last-child, +blockquote ul:last-child, +blockquote ol:last-child { + margin-bottom: 0; +} +blockquote footer, +blockquote small, +blockquote .small { + display: block; + font-size: 80%; + line-height: 1.42857; + color: #777777; +} +blockquote footer:before, +blockquote small:before, +blockquote .small:before { + content: "\2014 \00A0"; +} + +.blockquote-reverse, +blockquote.pull-right { + padding-right: 15px; + padding-left: 0; + border-right: 5px solid #eeeeee; + border-left: 0; + text-align: right; +} +.blockquote-reverse footer:before, +.blockquote-reverse small:before, +.blockquote-reverse .small:before, +blockquote.pull-right footer:before, +blockquote.pull-right small:before, +blockquote.pull-right .small:before { + content: ""; +} +.blockquote-reverse footer:after, +.blockquote-reverse small:after, +.blockquote-reverse .small:after, +blockquote.pull-right footer:after, +blockquote.pull-right small:after, +blockquote.pull-right .small:after { + content: "\00A0 \2014"; +} + +address { + margin-bottom: 20px; + font-style: normal; + line-height: 1.42857; +} + +code, +kbd, +pre, +samp { + font-family: Menlo, Monaco, Consolas, "Courier New", monospace; +} + +code { + padding: 2px 4px; + font-size: 90%; + color: #c7254e; + background-color: #f9f2f4; + border-radius: 4px; +} + +kbd { + padding: 2px 4px; + font-size: 90%; + color: #fff; + background-color: #333; + border-radius: 3px; + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); +} +kbd kbd { + padding: 0; + font-size: 100%; + font-weight: bold; + box-shadow: none; +} + +pre { + display: block; + padding: 9.5px; + margin: 0 0 10px; + font-size: 13px; + line-height: 1.42857; + word-break: break-all; + word-wrap: break-word; + color: #333333; + background-color: #f5f5f5; + border: 1px solid #ccc; + border-radius: 4px; +} +pre code { + padding: 0; + font-size: inherit; + color: inherit; + white-space: pre-wrap; + background-color: transparent; + border-radius: 0; +} + +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} + +.container { + margin-right: auto; + margin-left: auto; + padding-left: 5px; + padding-right: 5px; +} +.container:before, +.container:after { + content: " "; + display: table; +} +.container:after { + clear: both; +} +@media (min-width: 768px) { + .container { + width: 750px; + } +} +@media (min-width: 992px) { + .container { + width: 970px; + } +} +@media (min-width: 1200px) { + .container { + width: 1250px; + } +} + +.container-fluid { + margin-right: auto; + margin-left: auto; + padding-left: 15px; + padding-right: 15px; +} +.container-fluid:before, +.container-fluid:after { + content: " "; + display: table; +} +.container-fluid:after { + clear: both; +} + +.row { + margin-left: -15px; + margin-right: -15px; +} +.row:before, +.row:after { + content: " "; + display: table; +} +.row:after { + clear: both; +} + +.col-xs-1, +.col-sm-1, +.col-md-1, +.col-lg-1, +.col-xs-2, +.col-sm-2, +.col-md-2, +.col-lg-2, +.col-xs-3, +.col-sm-3, +.col-md-3, +.col-lg-3, +.col-xs-4, +.col-sm-4, +.col-md-4, +.col-lg-4, +.col-xs-5, +.col-sm-5, +.col-md-5, +.col-lg-5, +.col-xs-6, +.col-sm-6, +.col-md-6, +.col-lg-6, +.col-xs-7, +.col-sm-7, +.col-md-7, +.col-lg-7, +.col-xs-8, +.col-sm-8, +.col-md-8, +.col-lg-8, +.col-xs-9, +.col-sm-9, +.col-md-9, +.col-lg-9, +.col-xs-10, +.col-sm-10, +.col-md-10, +.col-lg-10, +.col-xs-11, +.col-sm-11, +.col-md-11, +.col-lg-11, +.col-xs-12, +.col-sm-12, +.col-md-12, +.col-lg-12 { + position: relative; + min-height: 1px; + padding-left: 15px; + padding-right: 15px; +} + +.col-xs-1, +.col-xs-2, +.col-xs-3, +.col-xs-4, +.col-xs-5, +.col-xs-6, +.col-xs-7, +.col-xs-8, +.col-xs-9, +.col-xs-10, +.col-xs-11, +.col-xs-12 { + float: left; +} + +.col-xs-1 { + width: 8.33333%; +} + +.col-xs-2 { + width: 16.66667%; +} + +.col-xs-3 { + width: 25%; +} + +.col-xs-4 { + width: 33.33333%; +} + +.col-xs-5 { + width: 41.66667%; +} + +.col-xs-6 { + width: 50%; +} + +.col-xs-7 { + width: 58.33333%; +} + +.col-xs-8 { + width: 66.66667%; +} + +.col-xs-9 { + width: 75%; +} + +.col-xs-10 { + width: 83.33333%; +} + +.col-xs-11 { + width: 91.66667%; +} + +.col-xs-12 { + width: 100%; +} + +.col-xs-pull-0 { + right: auto; +} + +.col-xs-pull-1 { + right: 8.33333%; +} + +.col-xs-pull-2 { + right: 16.66667%; +} + +.col-xs-pull-3 { + right: 25%; +} + +.col-xs-pull-4 { + right: 33.33333%; +} + +.col-xs-pull-5 { + right: 41.66667%; +} + +.col-xs-pull-6 { + right: 50%; +} + +.col-xs-pull-7 { + right: 58.33333%; +} + +.col-xs-pull-8 { + right: 66.66667%; +} + +.col-xs-pull-9 { + right: 75%; +} + +.col-xs-pull-10 { + right: 83.33333%; +} + +.col-xs-pull-11 { + right: 91.66667%; +} + +.col-xs-pull-12 { + right: 100%; +} + +.col-xs-push-0 { + left: auto; +} + +.col-xs-push-1 { + left: 8.33333%; +} + +.col-xs-push-2 { + left: 16.66667%; +} + +.col-xs-push-3 { + left: 25%; +} + +.col-xs-push-4 { + left: 33.33333%; +} + +.col-xs-push-5 { + left: 41.66667%; +} + +.col-xs-push-6 { + left: 50%; +} + +.col-xs-push-7 { + left: 58.33333%; +} + +.col-xs-push-8 { + left: 66.66667%; +} + +.col-xs-push-9 { + left: 75%; +} + +.col-xs-push-10 { + left: 83.33333%; +} + +.col-xs-push-11 { + left: 91.66667%; +} + +.col-xs-push-12 { + left: 100%; +} + +.col-xs-offset-0 { + margin-left: 0%; +} + +.col-xs-offset-1 { + margin-left: 8.33333%; +} + +.col-xs-offset-2 { + margin-left: 16.66667%; +} + +.col-xs-offset-3 { + margin-left: 25%; +} + +.col-xs-offset-4 { + margin-left: 33.33333%; +} + +.col-xs-offset-5 { + margin-left: 41.66667%; +} + +.col-xs-offset-6 { + margin-left: 50%; +} + +.col-xs-offset-7 { + margin-left: 58.33333%; +} + +.col-xs-offset-8 { + margin-left: 66.66667%; +} + +.col-xs-offset-9 { + margin-left: 75%; +} + +.col-xs-offset-10 { + margin-left: 83.33333%; +} + +.col-xs-offset-11 { + margin-left: 91.66667%; +} + +.col-xs-offset-12 { + margin-left: 100%; +} + +@media (min-width: 768px) { + .col-sm-1, + .col-sm-2, + .col-sm-3, + .col-sm-4, + .col-sm-5, + .col-sm-6, + .col-sm-7, + .col-sm-8, + .col-sm-9, + .col-sm-10, + .col-sm-11, + .col-sm-12 { + float: left; + } + + .col-sm-1 { + width: 8.33333%; + } + + .col-sm-2 { + width: 16.66667%; + } + + .col-sm-3 { + width: 25%; + } + + .col-sm-4 { + width: 33.33333%; + } + + .col-sm-5 { + width: 41.66667%; + } + + .col-sm-6 { + width: 50%; + } + + .col-sm-7 { + width: 58.33333%; + } + + .col-sm-8 { + width: 66.66667%; + } + + .col-sm-9 { + width: 75%; + } + + .col-sm-10 { + width: 83.33333%; + } + + .col-sm-11 { + width: 91.66667%; + } + + .col-sm-12 { + width: 100%; + } + + .col-sm-pull-0 { + right: auto; + } + + .col-sm-pull-1 { + right: 8.33333%; + } + + .col-sm-pull-2 { + right: 16.66667%; + } + + .col-sm-pull-3 { + right: 25%; + } + + .col-sm-pull-4 { + right: 33.33333%; + } + + .col-sm-pull-5 { + right: 41.66667%; + } + + .col-sm-pull-6 { + right: 50%; + } + + .col-sm-pull-7 { + right: 58.33333%; + } + + .col-sm-pull-8 { + right: 66.66667%; + } + + .col-sm-pull-9 { + right: 75%; + } + + .col-sm-pull-10 { + right: 83.33333%; + } + + .col-sm-pull-11 { + right: 91.66667%; + } + + .col-sm-pull-12 { + right: 100%; + } + + .col-sm-push-0 { + left: auto; + } + + .col-sm-push-1 { + left: 8.33333%; + } + + .col-sm-push-2 { + left: 16.66667%; + } + + .col-sm-push-3 { + left: 25%; + } + + .col-sm-push-4 { + left: 33.33333%; + } + + .col-sm-push-5 { + left: 41.66667%; + } + + .col-sm-push-6 { + left: 50%; + } + + .col-sm-push-7 { + left: 58.33333%; + } + + .col-sm-push-8 { + left: 66.66667%; + } + + .col-sm-push-9 { + left: 75%; + } + + .col-sm-push-10 { + left: 83.33333%; + } + + .col-sm-push-11 { + left: 91.66667%; + } + + .col-sm-push-12 { + left: 100%; + } + + .col-sm-offset-0 { + margin-left: 0%; + } + + .col-sm-offset-1 { + margin-left: 8.33333%; + } + + .col-sm-offset-2 { + margin-left: 16.66667%; + } + + .col-sm-offset-3 { + margin-left: 25%; + } + + .col-sm-offset-4 { + margin-left: 33.33333%; + } + + .col-sm-offset-5 { + margin-left: 41.66667%; + } + + .col-sm-offset-6 { + margin-left: 50%; + } + + .col-sm-offset-7 { + margin-left: 58.33333%; + } + + .col-sm-offset-8 { + margin-left: 66.66667%; + } + + .col-sm-offset-9 { + margin-left: 75%; + } + + .col-sm-offset-10 { + margin-left: 83.33333%; + } + + .col-sm-offset-11 { + margin-left: 91.66667%; + } + + .col-sm-offset-12 { + margin-left: 100%; + } +} +@media (min-width: 992px) { + .col-md-1, + .col-md-2, + .col-md-3, + .col-md-4, + .col-md-5, + .col-md-6, + .col-md-7, + .col-md-8, + .col-md-9, + .col-md-10, + .col-md-11, + .col-md-12 { + float: left; + } + + .col-md-1 { + width: 8.33333%; + } + + .col-md-2 { + width: 16.66667%; + } + + .col-md-3 { + width: 25%; + } + + .col-md-4 { + width: 33.33333%; + } + + .col-md-5 { + width: 41.66667%; + } + + .col-md-6 { + width: 50%; + } + + .col-md-7 { + width: 58.33333%; + } + + .col-md-8 { + width: 66.66667%; + } + + .col-md-9 { + width: 75%; + } + + .col-md-10 { + width: 83.33333%; + } + + .col-md-11 { + width: 91.66667%; + } + + .col-md-12 { + width: 100%; + } + + .col-md-pull-0 { + right: auto; + } + + .col-md-pull-1 { + right: 8.33333%; + } + + .col-md-pull-2 { + right: 16.66667%; + } + + .col-md-pull-3 { + right: 25%; + } + + .col-md-pull-4 { + right: 33.33333%; + } + + .col-md-pull-5 { + right: 41.66667%; + } + + .col-md-pull-6 { + right: 50%; + } + + .col-md-pull-7 { + right: 58.33333%; + } + + .col-md-pull-8 { + right: 66.66667%; + } + + .col-md-pull-9 { + right: 75%; + } + + .col-md-pull-10 { + right: 83.33333%; + } + + .col-md-pull-11 { + right: 91.66667%; + } + + .col-md-pull-12 { + right: 100%; + } + + .col-md-push-0 { + left: auto; + } + + .col-md-push-1 { + left: 8.33333%; + } + + .col-md-push-2 { + left: 16.66667%; + } + + .col-md-push-3 { + left: 25%; + } + + .col-md-push-4 { + left: 33.33333%; + } + + .col-md-push-5 { + left: 41.66667%; + } + + .col-md-push-6 { + left: 50%; + } + + .col-md-push-7 { + left: 58.33333%; + } + + .col-md-push-8 { + left: 66.66667%; + } + + .col-md-push-9 { + left: 75%; + } + + .col-md-push-10 { + left: 83.33333%; + } + + .col-md-push-11 { + left: 91.66667%; + } + + .col-md-push-12 { + left: 100%; + } + + .col-md-offset-0 { + margin-left: 0%; + } + + .col-md-offset-1 { + margin-left: 8.33333%; + } + + .col-md-offset-2 { + margin-left: 16.66667%; + } + + .col-md-offset-3 { + margin-left: 25%; + } + + .col-md-offset-4 { + margin-left: 33.33333%; + } + + .col-md-offset-5 { + margin-left: 41.66667%; + } + + .col-md-offset-6 { + margin-left: 50%; + } + + .col-md-offset-7 { + margin-left: 58.33333%; + } + + .col-md-offset-8 { + margin-left: 66.66667%; + } + + .col-md-offset-9 { + margin-left: 75%; + } + + .col-md-offset-10 { + margin-left: 83.33333%; + } + + .col-md-offset-11 { + margin-left: 91.66667%; + } + + .col-md-offset-12 { + margin-left: 100%; + } +} +@media (min-width: 1200px) { + .col-lg-1, + .col-lg-2, + .col-lg-3, + .col-lg-4, + .col-lg-5, + .col-lg-6, + .col-lg-7, + .col-lg-8, + .col-lg-9, + .col-lg-10, + .col-lg-11, + .col-lg-12 { + float: left; + } + + .col-lg-1 { + width: 8.33333%; + } + + .col-lg-2 { + width: 16.66667%; + } + + .col-lg-3 { + width: 25%; + } + + .col-lg-4 { + width: 33.33333%; + } + + .col-lg-5 { + width: 41.66667%; + } + + .col-lg-6 { + width: 50%; + } + + .col-lg-7 { + width: 58.33333%; + } + + .col-lg-8 { + width: 66.66667%; + } + + .col-lg-9 { + width: 75%; + } + + .col-lg-10 { + width: 83.33333%; + } + + .col-lg-11 { + width: 91.66667%; + } + + .col-lg-12 { + width: 100%; + } + + .col-lg-pull-0 { + right: auto; + } + + .col-lg-pull-1 { + right: 8.33333%; + } + + .col-lg-pull-2 { + right: 16.66667%; + } + + .col-lg-pull-3 { + right: 25%; + } + + .col-lg-pull-4 { + right: 33.33333%; + } + + .col-lg-pull-5 { + right: 41.66667%; + } + + .col-lg-pull-6 { + right: 50%; + } + + .col-lg-pull-7 { + right: 58.33333%; + } + + .col-lg-pull-8 { + right: 66.66667%; + } + + .col-lg-pull-9 { + right: 75%; + } + + .col-lg-pull-10 { + right: 83.33333%; + } + + .col-lg-pull-11 { + right: 91.66667%; + } + + .col-lg-pull-12 { + right: 100%; + } + + .col-lg-push-0 { + left: auto; + } + + .col-lg-push-1 { + left: 8.33333%; + } + + .col-lg-push-2 { + left: 16.66667%; + } + + .col-lg-push-3 { + left: 25%; + } + + .col-lg-push-4 { + left: 33.33333%; + } + + .col-lg-push-5 { + left: 41.66667%; + } + + .col-lg-push-6 { + left: 50%; + } + + .col-lg-push-7 { + left: 58.33333%; + } + + .col-lg-push-8 { + left: 66.66667%; + } + + .col-lg-push-9 { + left: 75%; + } + + .col-lg-push-10 { + left: 83.33333%; + } + + .col-lg-push-11 { + left: 91.66667%; + } + + .col-lg-push-12 { + left: 100%; + } + + .col-lg-offset-0 { + margin-left: 0%; + } + + .col-lg-offset-1 { + margin-left: 8.33333%; + } + + .col-lg-offset-2 { + margin-left: 16.66667%; + } + + .col-lg-offset-3 { + margin-left: 25%; + } + + .col-lg-offset-4 { + margin-left: 33.33333%; + } + + .col-lg-offset-5 { + margin-left: 41.66667%; + } + + .col-lg-offset-6 { + margin-left: 50%; + } + + .col-lg-offset-7 { + margin-left: 58.33333%; + } + + .col-lg-offset-8 { + margin-left: 66.66667%; + } + + .col-lg-offset-9 { + margin-left: 75%; + } + + .col-lg-offset-10 { + margin-left: 83.33333%; + } + + .col-lg-offset-11 { + margin-left: 91.66667%; + } + + .col-lg-offset-12 { + margin-left: 100%; + } +} +table { + background-color: transparent; +} + +caption { + padding-top: 8px; + padding-bottom: 8px; + color: #777777; + text-align: left; +} + +th { + text-align: left; +} + +.table { + width: 100%; + max-width: 100%; + margin-bottom: 20px; +} +.table > thead > tr > th, +.table > thead > tr > td, +.table > tbody > tr > th, +.table > tbody > tr > td, +.table > tfoot > tr > th, +.table > tfoot > tr > td { + padding: 8px; + line-height: 1.42857; + vertical-align: top; + border-top: 1px solid #ddd; +} +.table > thead > tr > th { + vertical-align: bottom; + border-bottom: 2px solid #ddd; +} +.table > caption + thead > tr:first-child > th, +.table > caption + thead > tr:first-child > td, +.table > colgroup + thead > tr:first-child > th, +.table > colgroup + thead > tr:first-child > td, +.table > thead:first-child > tr:first-child > th, +.table > thead:first-child > tr:first-child > td { + border-top: 0; +} +.table > tbody + tbody { + border-top: 2px solid #ddd; +} +.table .table { + background-color: #fff; +} + +.table-condensed > thead > tr > th, +.table-condensed > thead > tr > td, +.table-condensed > tbody > tr > th, +.table-condensed > tbody > tr > td, +.table-condensed > tfoot > tr > th, +.table-condensed > tfoot > tr > td { + padding: 5px; +} + +.table-bordered { + border: 1px solid #ddd; +} +.table-bordered > thead > tr > th, +.table-bordered > thead > tr > td, +.table-bordered > tbody > tr > th, +.table-bordered > tbody > tr > td, +.table-bordered > tfoot > tr > th, +.table-bordered > tfoot > tr > td { + border: 1px solid #ddd; +} +.table-bordered > thead > tr > th, +.table-bordered > thead > tr > td { + border-bottom-width: 2px; +} + +.table-striped > tbody > tr:nth-of-type(odd) { + background-color: #f9f9f9; +} + +.table-hover > tbody > tr:hover { + background-color: #f5f5f5; +} + +table col[class*="col-"] { + position: static; + float: none; + display: table-column; +} + +table td[class*="col-"], +table th[class*="col-"] { + position: static; + float: none; + display: table-cell; +} + +.table > thead > tr > td.active, +.table > thead > tr > th.active, +.table > thead > tr.active > td, +.table > thead > tr.active > th, +.table > tbody > tr > td.active, +.table > tbody > tr > th.active, +.table > tbody > tr.active > td, +.table > tbody > tr.active > th, +.table > tfoot > tr > td.active, +.table > tfoot > tr > th.active, +.table > tfoot > tr.active > td, +.table > tfoot > tr.active > th { + background-color: #f5f5f5; +} + +.table-hover > tbody > tr > td.active:hover, +.table-hover > tbody > tr > th.active:hover, +.table-hover > tbody > tr.active:hover > td, +.table-hover > tbody > tr:hover > .active, +.table-hover > tbody > tr.active:hover > th { + background-color: #e8e8e8; +} + +.table > thead > tr > td.success, +.table > thead > tr > th.success, +.table > thead > tr.success > td, +.table > thead > tr.success > th, +.table > tbody > tr > td.success, +.table > tbody > tr > th.success, +.table > tbody > tr.success > td, +.table > tbody > tr.success > th, +.table > tfoot > tr > td.success, +.table > tfoot > tr > th.success, +.table > tfoot > tr.success > td, +.table > tfoot > tr.success > th { + background-color: #dff0d8; +} + +.table-hover > tbody > tr > td.success:hover, +.table-hover > tbody > tr > th.success:hover, +.table-hover > tbody > tr.success:hover > td, +.table-hover > tbody > tr:hover > .success, +.table-hover > tbody > tr.success:hover > th { + background-color: #d0e9c6; +} + +.table > thead > tr > td.info, +.table > thead > tr > th.info, +.table > thead > tr.info > td, +.table > thead > tr.info > th, +.table > tbody > tr > td.info, +.table > tbody > tr > th.info, +.table > tbody > tr.info > td, +.table > tbody > tr.info > th, +.table > tfoot > tr > td.info, +.table > tfoot > tr > th.info, +.table > tfoot > tr.info > td, +.table > tfoot > tr.info > th { + background-color: #d9edf7; +} + +.table-hover > tbody > tr > td.info:hover, +.table-hover > tbody > tr > th.info:hover, +.table-hover > tbody > tr.info:hover > td, +.table-hover > tbody > tr:hover > .info, +.table-hover > tbody > tr.info:hover > th { + background-color: #c4e3f3; +} + +.table > thead > tr > td.warning, +.table > thead > tr > th.warning, +.table > thead > tr.warning > td, +.table > thead > tr.warning > th, +.table > tbody > tr > td.warning, +.table > tbody > tr > th.warning, +.table > tbody > tr.warning > td, +.table > tbody > tr.warning > th, +.table > tfoot > tr > td.warning, +.table > tfoot > tr > th.warning, +.table > tfoot > tr.warning > td, +.table > tfoot > tr.warning > th { + background-color: #fcf8e3; +} + +.table-hover > tbody > tr > td.warning:hover, +.table-hover > tbody > tr > th.warning:hover, +.table-hover > tbody > tr.warning:hover > td, +.table-hover > tbody > tr:hover > .warning, +.table-hover > tbody > tr.warning:hover > th { + background-color: #faf2cc; +} + +.table > thead > tr > td.danger, +.table > thead > tr > th.danger, +.table > thead > tr.danger > td, +.table > thead > tr.danger > th, +.table > tbody > tr > td.danger, +.table > tbody > tr > th.danger, +.table > tbody > tr.danger > td, +.table > tbody > tr.danger > th, +.table > tfoot > tr > td.danger, +.table > tfoot > tr > th.danger, +.table > tfoot > tr.danger > td, +.table > tfoot > tr.danger > th { + background-color: #f2dede; +} + +.table-hover > tbody > tr > td.danger:hover, +.table-hover > tbody > tr > th.danger:hover, +.table-hover > tbody > tr.danger:hover > td, +.table-hover > tbody > tr:hover > .danger, +.table-hover > tbody > tr.danger:hover > th { + background-color: #ebcccc; +} + +.table-responsive { + overflow-x: auto; + min-height: 0.01%; +} +@media screen and (max-width: 767px) { + .table-responsive { + width: 100%; + margin-bottom: 15px; + overflow-y: hidden; + -ms-overflow-style: -ms-autohiding-scrollbar; + border: 1px solid #ddd; + } + .table-responsive > .table { + margin-bottom: 0; + } + .table-responsive > .table > thead > tr > th, + .table-responsive > .table > thead > tr > td, + .table-responsive > .table > tbody > tr > th, + .table-responsive > .table > tbody > tr > td, + .table-responsive > .table > tfoot > tr > th, + .table-responsive > .table > tfoot > tr > td { + white-space: nowrap; + } + .table-responsive > .table-bordered { + border: 0; + } + .table-responsive > .table-bordered > thead > tr > th:first-child, + .table-responsive > .table-bordered > thead > tr > td:first-child, + .table-responsive > .table-bordered > tbody > tr > th:first-child, + .table-responsive > .table-bordered > tbody > tr > td:first-child, + .table-responsive > .table-bordered > tfoot > tr > th:first-child, + .table-responsive > .table-bordered > tfoot > tr > td:first-child { + border-left: 0; + } + .table-responsive > .table-bordered > thead > tr > th:last-child, + .table-responsive > .table-bordered > thead > tr > td:last-child, + .table-responsive > .table-bordered > tbody > tr > th:last-child, + .table-responsive > .table-bordered > tbody > tr > td:last-child, + .table-responsive > .table-bordered > tfoot > tr > th:last-child, + .table-responsive > .table-bordered > tfoot > tr > td:last-child { + border-right: 0; + } + .table-responsive > .table-bordered > tbody > tr:last-child > th, + .table-responsive > .table-bordered > tbody > tr:last-child > td, + .table-responsive > .table-bordered > tfoot > tr:last-child > th, + .table-responsive > .table-bordered > tfoot > tr:last-child > td { + border-bottom: 0; + } +} + +fieldset { + padding: 0; + margin: 0; + border: 0; + min-width: 0; +} + +legend { + display: block; + width: 100%; + padding: 0; + margin-bottom: 20px; + font-size: 21px; + line-height: inherit; + color: #333333; + border: 0; + border-bottom: 1px solid #e5e5e5; +} + +label { + display: inline-block; + max-width: 100%; + margin-bottom: 5px; + font-weight: bold; +} + +input[type="search"] { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} + +input[type="radio"], +input[type="checkbox"] { + margin: 4px 0 0; + margin-top: 1px \9; + line-height: normal; +} + +input[type="file"] { + display: block; +} + +input[type="range"] { + display: block; + width: 100%; +} + +select[multiple], +select[size] { + height: auto; +} + +input[type="file"]:focus, +input[type="radio"]:focus, +input[type="checkbox"]:focus { + outline: thin dotted; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} + +output { + display: block; + padding-top: 11px; + font-size: 14px; + line-height: 1.42857; + color: #555555; +} + +.form-control { + display: block; + width: 100%; + height: 42px; + padding: 10px 20px; + font-size: 14px; + line-height: 1.42857; + color: #555555; + background-color: #fff; + background-image: none; + border: 1px solid #ccc; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border-color ease-in-out 0.15s, + box-shadow ease-in-out 0.15s; + -o-transition: border-color ease-in-out 0.15s, box-shadow ease-in-out 0.15s; + transition: border-color ease-in-out 0.15s, box-shadow ease-in-out 0.15s; +} +.form-control:focus { + border-color: #66afe9; + outline: 0; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), + 0 0 8px rgba(102, 175, 233, 0.6); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), + 0 0 8px rgba(102, 175, 233, 0.6); +} +.form-control::-moz-placeholder { + color: #999; + opacity: 1; +} +.form-control:-ms-input-placeholder { + color: #999; +} +.form-control::-webkit-input-placeholder { + color: #999; +} +.form-control[disabled], +.form-control[readonly], +fieldset[disabled] .form-control { + background-color: #eeeeee; + opacity: 1; +} +.form-control[disabled], +fieldset[disabled] .form-control { + cursor: not-allowed; +} + +textarea.form-control { + height: auto; +} + +input[type="search"] { + -webkit-appearance: none; +} + +@media screen and (-webkit-min-device-pixel-ratio: 0) { + input[type="date"].form-control, + input[type="time"].form-control, + input[type="datetime-local"].form-control, + input[type="month"].form-control { + line-height: 42px; + } + input[type="date"].input-sm, + .input-group-sm > input[type="date"].form-control, + .input-group-sm > input[type="date"].input-group-addon, + .input-group-sm > .input-group-btn > input[type="date"].btn, + .input-group-sm input[type="date"], + input[type="time"].input-sm, + .input-group-sm > input[type="time"].form-control, + .input-group-sm > input[type="time"].input-group-addon, + .input-group-sm > .input-group-btn > input[type="time"].btn, + .input-group-sm input[type="time"], + input[type="datetime-local"].input-sm, + .input-group-sm > input[type="datetime-local"].form-control, + .input-group-sm > input[type="datetime-local"].input-group-addon, + .input-group-sm > .input-group-btn > input[type="datetime-local"].btn, + .input-group-sm input[type="datetime-local"], + input[type="month"].input-sm, + .input-group-sm > input[type="month"].form-control, + .input-group-sm > input[type="month"].input-group-addon, + .input-group-sm > .input-group-btn > input[type="month"].btn, + .input-group-sm input[type="month"] { + line-height: 30px; + } + input[type="date"].input-lg, + .input-group-lg > input[type="date"].form-control, + .input-group-lg > input[type="date"].input-group-addon, + .input-group-lg > .input-group-btn > input[type="date"].btn, + .input-group-lg input[type="date"], + input[type="time"].input-lg, + .input-group-lg > input[type="time"].form-control, + .input-group-lg > input[type="time"].input-group-addon, + .input-group-lg > .input-group-btn > input[type="time"].btn, + .input-group-lg input[type="time"], + input[type="datetime-local"].input-lg, + .input-group-lg > input[type="datetime-local"].form-control, + .input-group-lg > input[type="datetime-local"].input-group-addon, + .input-group-lg > .input-group-btn > input[type="datetime-local"].btn, + .input-group-lg input[type="datetime-local"], + input[type="month"].input-lg, + .input-group-lg > input[type="month"].form-control, + .input-group-lg > input[type="month"].input-group-addon, + .input-group-lg > .input-group-btn > input[type="month"].btn, + .input-group-lg input[type="month"] { + line-height: 46px; + } +} +.form-group { + margin-bottom: 15px; +} + +.radio, +.checkbox { + position: relative; + display: block; + margin-top: 10px; + margin-bottom: 10px; +} +.radio label, +.checkbox label { + min-height: 20px; + padding-left: 20px; + margin-bottom: 0; + font-weight: normal; + cursor: pointer; +} + +.radio input[type="radio"], +.radio-inline input[type="radio"], +.checkbox input[type="checkbox"], +.checkbox-inline input[type="checkbox"] { + position: absolute; + margin-left: -20px; + margin-top: 4px \9; +} + +.radio + .radio, +.checkbox + .checkbox { + margin-top: -5px; +} + +.radio-inline, +.checkbox-inline { + position: relative; + display: inline-block; + padding-left: 20px; + margin-bottom: 0; + vertical-align: middle; + font-weight: normal; + cursor: pointer; +} + +.radio-inline + .radio-inline, +.checkbox-inline + .checkbox-inline { + margin-top: 0; + margin-left: 10px; +} + +input[type="radio"][disabled], +input[type="radio"].disabled, +fieldset[disabled] input[type="radio"], +input[type="checkbox"][disabled], +input[type="checkbox"].disabled, +fieldset[disabled] input[type="checkbox"] { + cursor: not-allowed; +} + +.radio-inline.disabled, +fieldset[disabled] .radio-inline, +.checkbox-inline.disabled, +fieldset[disabled] .checkbox-inline { + cursor: not-allowed; +} + +.radio.disabled label, +fieldset[disabled] .radio label, +.checkbox.disabled label, +fieldset[disabled] .checkbox label { + cursor: not-allowed; +} + +.form-control-static { + padding-top: 11px; + padding-bottom: 11px; + margin-bottom: 0; + min-height: 34px; +} +.form-control-static.input-lg, +.input-group-lg > .form-control-static.form-control, +.input-group-lg > .form-control-static.input-group-addon, +.input-group-lg > .input-group-btn > .form-control-static.btn, +.form-control-static.input-sm, +.input-group-sm > .form-control-static.form-control, +.input-group-sm > .form-control-static.input-group-addon, +.input-group-sm > .input-group-btn > .form-control-static.btn { + padding-left: 0; + padding-right: 0; +} + +.input-sm, +.input-group-sm > .form-control, +.input-group-sm > .input-group-addon, +.input-group-sm > .input-group-btn > .btn { + height: 30px; + padding: 5px 20px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} + +select.input-sm, +.input-group-sm > select.form-control, +.input-group-sm > select.input-group-addon, +.input-group-sm > .input-group-btn > select.btn { + height: 30px; + line-height: 30px; +} + +textarea.input-sm, +.input-group-sm > textarea.form-control, +.input-group-sm > textarea.input-group-addon, +.input-group-sm > .input-group-btn > textarea.btn, +select[multiple].input-sm, +.input-group-sm > select[multiple].form-control, +.input-group-sm > select[multiple].input-group-addon, +.input-group-sm > .input-group-btn > select[multiple].btn { + height: auto; +} + +.form-group-sm .form-control { + height: 30px; + padding: 5px 20px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} +.form-group-sm select.form-control { + height: 30px; + line-height: 30px; +} +.form-group-sm textarea.form-control, +.form-group-sm select[multiple].form-control { + height: auto; +} +.form-group-sm .form-control-static { + height: 30px; + min-height: 32px; + padding: 6px 20px; + font-size: 12px; + line-height: 1.5; +} + +.input-lg, +.input-group-lg > .form-control, +.input-group-lg > .input-group-addon, +.input-group-lg > .input-group-btn > .btn { + height: 46px; + padding: 10px 20px; + font-size: 18px; + line-height: 1.33333; + border-radius: 6px; +} + +select.input-lg, +.input-group-lg > select.form-control, +.input-group-lg > select.input-group-addon, +.input-group-lg > .input-group-btn > select.btn { + height: 46px; + line-height: 46px; +} + +textarea.input-lg, +.input-group-lg > textarea.form-control, +.input-group-lg > textarea.input-group-addon, +.input-group-lg > .input-group-btn > textarea.btn, +select[multiple].input-lg, +.input-group-lg > select[multiple].form-control, +.input-group-lg > select[multiple].input-group-addon, +.input-group-lg > .input-group-btn > select[multiple].btn { + height: auto; +} + +.form-group-lg .form-control { + height: 46px; + padding: 10px 20px; + font-size: 18px; + line-height: 1.33333; + border-radius: 6px; +} +.form-group-lg select.form-control { + height: 46px; + line-height: 46px; +} +.form-group-lg textarea.form-control, +.form-group-lg select[multiple].form-control { + height: auto; +} +.form-group-lg .form-control-static { + height: 46px; + min-height: 38px; + padding: 11px 20px; + font-size: 18px; + line-height: 1.33333; +} + +.has-feedback { + position: relative; +} +.has-feedback .form-control { + padding-right: 52.5px; +} + +.form-control-feedback { + position: absolute; + top: 0; + right: 0; + z-index: 2; + display: block; + width: 42px; + height: 42px; + line-height: 42px; + text-align: center; + pointer-events: none; +} + +.input-lg + .form-control-feedback, +.input-group-lg > .form-control + .form-control-feedback, +.input-group-lg > .input-group-addon + .form-control-feedback, +.input-group-lg > .input-group-btn > .btn + .form-control-feedback, +.input-group-lg + .form-control-feedback, +.form-group-lg .form-control + .form-control-feedback { + width: 46px; + height: 46px; + line-height: 46px; +} + +.input-sm + .form-control-feedback, +.input-group-sm > .form-control + .form-control-feedback, +.input-group-sm > .input-group-addon + .form-control-feedback, +.input-group-sm > .input-group-btn > .btn + .form-control-feedback, +.input-group-sm + .form-control-feedback, +.form-group-sm .form-control + .form-control-feedback { + width: 30px; + height: 30px; + line-height: 30px; +} + +.has-success .help-block, +.has-success .control-label, +.has-success .radio, +.has-success .checkbox, +.has-success .radio-inline, +.has-success .checkbox-inline, +.has-success.radio label, +.has-success.checkbox label, +.has-success.radio-inline label, +.has-success.checkbox-inline label { + color: #3c763d; +} +.has-success .form-control { + border-color: #3c763d; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-success .form-control:focus { + border-color: #2b542c; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; +} +.has-success .input-group-addon { + color: #3c763d; + border-color: #3c763d; + background-color: #dff0d8; +} +.has-success .form-control-feedback { + color: #3c763d; +} + +.has-warning .help-block, +.has-warning .control-label, +.has-warning .radio, +.has-warning .checkbox, +.has-warning .radio-inline, +.has-warning .checkbox-inline, +.has-warning.radio label, +.has-warning.checkbox label, +.has-warning.radio-inline label, +.has-warning.checkbox-inline label { + color: #8a6d3b; +} +.has-warning .form-control { + border-color: #8a6d3b; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-warning .form-control:focus { + border-color: #66512c; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; +} +.has-warning .input-group-addon { + color: #8a6d3b; + border-color: #8a6d3b; + background-color: #fcf8e3; +} +.has-warning .form-control-feedback { + color: #8a6d3b; +} + +.has-error .help-block, +.has-error .control-label, +.has-error .radio, +.has-error .checkbox, +.has-error .radio-inline, +.has-error .checkbox-inline, +.has-error.radio label, +.has-error.checkbox label, +.has-error.radio-inline label, +.has-error.checkbox-inline label { + color: #a94442; +} +.has-error .form-control { + border-color: #a94442; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-error .form-control:focus { + border-color: #843534; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; +} +.has-error .input-group-addon { + color: #a94442; + border-color: #a94442; + background-color: #f2dede; +} +.has-error .form-control-feedback { + color: #a94442; +} + +.has-feedback label ~ .form-control-feedback { + top: 25px; +} +.has-feedback label.sr-only ~ .form-control-feedback { + top: 0; +} + +.help-block { + display: block; + margin-top: 5px; + margin-bottom: 10px; + color: #737373; +} + +@media (min-width: 768px) { + .form-inline .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .form-inline .form-control-static { + display: inline-block; + } + .form-inline .input-group { + display: inline-table; + vertical-align: middle; + } + .form-inline .input-group .input-group-addon, + .form-inline .input-group .input-group-btn, + .form-inline .input-group .form-control { + width: auto; + } + .form-inline .input-group > .form-control { + width: 100%; + } + .form-inline .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .radio, + .form-inline .checkbox { + display: inline-block; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .radio label, + .form-inline .checkbox label { + padding-left: 0; + } + .form-inline .radio input[type="radio"], + .form-inline .checkbox input[type="checkbox"] { + position: relative; + margin-left: 0; + } + .form-inline .has-feedback .form-control-feedback { + top: 0; + } +} + +.form-horizontal .radio, +.form-horizontal .checkbox, +.form-horizontal .radio-inline, +.form-horizontal .checkbox-inline { + margin-top: 0; + margin-bottom: 0; + padding-top: 11px; +} +.form-horizontal .radio, +.form-horizontal .checkbox { + min-height: 31px; +} +.form-horizontal .form-group { + margin-left: -15px; + margin-right: -15px; +} +.form-horizontal .form-group:before, +.form-horizontal .form-group:after { + content: " "; + display: table; +} +.form-horizontal .form-group:after { + clear: both; +} +@media (min-width: 768px) { + .form-horizontal .control-label { + text-align: right; + margin-bottom: 0; + padding-top: 11px; + } +} +.form-horizontal .has-feedback .form-control-feedback { + right: 15px; +} +@media (min-width: 768px) { + .form-horizontal .form-group-lg .control-label { + padding-top: 14.33333px; + font-size: 18px; + } +} +@media (min-width: 768px) { + .form-horizontal .form-group-sm .control-label { + padding-top: 6px; + font-size: 12px; + } +} + +.btn { + display: inline-block; + margin-bottom: 0; + font-weight: normal; + text-align: center; + vertical-align: middle; + touch-action: manipulation; + cursor: pointer; + background-image: none; + border: 1px solid transparent; + white-space: nowrap; + padding: 10px 20px; + font-size: 14px; + line-height: 1.42857; + border-radius: 4px; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} +.btn:focus, +.btn.focus, +.btn:active:focus, +.btn:active.focus, +.btn.active:focus, +.btn.active.focus { + outline: thin dotted; + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +.btn:hover, +.btn:focus, +.btn.focus { + color: #333; + text-decoration: none; +} +.btn:active, +.btn.active { + outline: 0; + background-image: none; + -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); + box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); +} +.btn.disabled, +.btn[disabled], +fieldset[disabled] .btn { + cursor: not-allowed; + opacity: 0.65; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + box-shadow: none; +} + +a.btn.disabled, +fieldset[disabled] a.btn { + pointer-events: none; +} + +.btn-default { + color: #333; + background-color: #fff; + border-color: #ccc; +} +.btn-default:focus, +.btn-default.focus { + color: #333; + background-color: #e6e6e6; + border-color: #8c8c8c; +} +.btn-default:hover { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +.btn-default:active, +.btn-default.active, +.open > .btn-default.dropdown-toggle { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +.btn-default:active:hover, +.btn-default:active:focus, +.btn-default:active.focus, +.btn-default.active:hover, +.btn-default.active:focus, +.btn-default.active.focus, +.open > .btn-default.dropdown-toggle:hover, +.open > .btn-default.dropdown-toggle:focus, +.open > .btn-default.dropdown-toggle.focus { + color: #333; + background-color: #d4d4d4; + border-color: #8c8c8c; +} +.btn-default:active, +.btn-default.active, +.open > .btn-default.dropdown-toggle { + background-image: none; +} +.btn-default.disabled, +.btn-default.disabled:hover, +.btn-default.disabled:focus, +.btn-default.disabled.focus, +.btn-default.disabled:active, +.btn-default.disabled.active, +.btn-default[disabled], +.btn-default[disabled]:hover, +.btn-default[disabled]:focus, +.btn-default[disabled].focus, +.btn-default[disabled]:active, +.btn-default[disabled].active, +fieldset[disabled] .btn-default, +fieldset[disabled] .btn-default:hover, +fieldset[disabled] .btn-default:focus, +fieldset[disabled] .btn-default.focus, +fieldset[disabled] .btn-default:active, +fieldset[disabled] .btn-default.active { + background-color: #fff; + border-color: #ccc; +} +.btn-default .badge { + color: #fff; + background-color: #333; +} + +.btn-primary { + color: #fff; + background-color: #337ab7; + border-color: #2e6da4; +} +.btn-primary:focus, +.btn-primary.focus { + color: #fff; + background-color: #286090; + border-color: #122b40; +} +.btn-primary:hover { + color: #fff; + background-color: #286090; + border-color: #204d74; +} +.btn-primary:active, +.btn-primary.active, +.open > .btn-primary.dropdown-toggle { + color: #fff; + background-color: #286090; + border-color: #204d74; +} +.btn-primary:active:hover, +.btn-primary:active:focus, +.btn-primary:active.focus, +.btn-primary.active:hover, +.btn-primary.active:focus, +.btn-primary.active.focus, +.open > .btn-primary.dropdown-toggle:hover, +.open > .btn-primary.dropdown-toggle:focus, +.open > .btn-primary.dropdown-toggle.focus { + color: #fff; + background-color: #204d74; + border-color: #122b40; +} +.btn-primary:active, +.btn-primary.active, +.open > .btn-primary.dropdown-toggle { + background-image: none; +} +.btn-primary.disabled, +.btn-primary.disabled:hover, +.btn-primary.disabled:focus, +.btn-primary.disabled.focus, +.btn-primary.disabled:active, +.btn-primary.disabled.active, +.btn-primary[disabled], +.btn-primary[disabled]:hover, +.btn-primary[disabled]:focus, +.btn-primary[disabled].focus, +.btn-primary[disabled]:active, +.btn-primary[disabled].active, +fieldset[disabled] .btn-primary, +fieldset[disabled] .btn-primary:hover, +fieldset[disabled] .btn-primary:focus, +fieldset[disabled] .btn-primary.focus, +fieldset[disabled] .btn-primary:active, +fieldset[disabled] .btn-primary.active { + background-color: #337ab7; + border-color: #2e6da4; +} +.btn-primary .badge { + color: #337ab7; + background-color: #fff; +} + +.btn-success { + color: #fff; + background-color: #5cb85c; + border-color: #4cae4c; +} +.btn-success:focus, +.btn-success.focus { + color: #fff; + background-color: #449d44; + border-color: #255625; +} +.btn-success:hover { + color: #fff; + background-color: #449d44; + border-color: #398439; +} +.btn-success:active, +.btn-success.active, +.open > .btn-success.dropdown-toggle { + color: #fff; + background-color: #449d44; + border-color: #398439; +} +.btn-success:active:hover, +.btn-success:active:focus, +.btn-success:active.focus, +.btn-success.active:hover, +.btn-success.active:focus, +.btn-success.active.focus, +.open > .btn-success.dropdown-toggle:hover, +.open > .btn-success.dropdown-toggle:focus, +.open > .btn-success.dropdown-toggle.focus { + color: #fff; + background-color: #398439; + border-color: #255625; +} +.btn-success:active, +.btn-success.active, +.open > .btn-success.dropdown-toggle { + background-image: none; +} +.btn-success.disabled, +.btn-success.disabled:hover, +.btn-success.disabled:focus, +.btn-success.disabled.focus, +.btn-success.disabled:active, +.btn-success.disabled.active, +.btn-success[disabled], +.btn-success[disabled]:hover, +.btn-success[disabled]:focus, +.btn-success[disabled].focus, +.btn-success[disabled]:active, +.btn-success[disabled].active, +fieldset[disabled] .btn-success, +fieldset[disabled] .btn-success:hover, +fieldset[disabled] .btn-success:focus, +fieldset[disabled] .btn-success.focus, +fieldset[disabled] .btn-success:active, +fieldset[disabled] .btn-success.active { + background-color: #5cb85c; + border-color: #4cae4c; +} +.btn-success .badge { + color: #5cb85c; + background-color: #fff; +} + +.btn-info { + color: #fff; + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-info:focus, +.btn-info.focus { + color: #fff; + background-color: #31b0d5; + border-color: #1b6d85; +} +.btn-info:hover { + color: #fff; + background-color: #31b0d5; + border-color: #269abc; +} +.btn-info:active, +.btn-info.active, +.open > .btn-info.dropdown-toggle { + color: #fff; + background-color: #31b0d5; + border-color: #269abc; +} +.btn-info:active:hover, +.btn-info:active:focus, +.btn-info:active.focus, +.btn-info.active:hover, +.btn-info.active:focus, +.btn-info.active.focus, +.open > .btn-info.dropdown-toggle:hover, +.open > .btn-info.dropdown-toggle:focus, +.open > .btn-info.dropdown-toggle.focus { + color: #fff; + background-color: #269abc; + border-color: #1b6d85; +} +.btn-info:active, +.btn-info.active, +.open > .btn-info.dropdown-toggle { + background-image: none; +} +.btn-info.disabled, +.btn-info.disabled:hover, +.btn-info.disabled:focus, +.btn-info.disabled.focus, +.btn-info.disabled:active, +.btn-info.disabled.active, +.btn-info[disabled], +.btn-info[disabled]:hover, +.btn-info[disabled]:focus, +.btn-info[disabled].focus, +.btn-info[disabled]:active, +.btn-info[disabled].active, +fieldset[disabled] .btn-info, +fieldset[disabled] .btn-info:hover, +fieldset[disabled] .btn-info:focus, +fieldset[disabled] .btn-info.focus, +fieldset[disabled] .btn-info:active, +fieldset[disabled] .btn-info.active { + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-info .badge { + color: #5bc0de; + background-color: #fff; +} + +.btn-warning { + color: #fff; + background-color: #f0ad4e; + border-color: #eea236; +} +.btn-warning:focus, +.btn-warning.focus { + color: #fff; + background-color: #ec971f; + border-color: #985f0d; +} +.btn-warning:hover { + color: #fff; + background-color: #ec971f; + border-color: #d58512; +} +.btn-warning:active, +.btn-warning.active, +.open > .btn-warning.dropdown-toggle { + color: #fff; + background-color: #ec971f; + border-color: #d58512; +} +.btn-warning:active:hover, +.btn-warning:active:focus, +.btn-warning:active.focus, +.btn-warning.active:hover, +.btn-warning.active:focus, +.btn-warning.active.focus, +.open > .btn-warning.dropdown-toggle:hover, +.open > .btn-warning.dropdown-toggle:focus, +.open > .btn-warning.dropdown-toggle.focus { + color: #fff; + background-color: #d58512; + border-color: #985f0d; +} +.btn-warning:active, +.btn-warning.active, +.open > .btn-warning.dropdown-toggle { + background-image: none; +} +.btn-warning.disabled, +.btn-warning.disabled:hover, +.btn-warning.disabled:focus, +.btn-warning.disabled.focus, +.btn-warning.disabled:active, +.btn-warning.disabled.active, +.btn-warning[disabled], +.btn-warning[disabled]:hover, +.btn-warning[disabled]:focus, +.btn-warning[disabled].focus, +.btn-warning[disabled]:active, +.btn-warning[disabled].active, +fieldset[disabled] .btn-warning, +fieldset[disabled] .btn-warning:hover, +fieldset[disabled] .btn-warning:focus, +fieldset[disabled] .btn-warning.focus, +fieldset[disabled] .btn-warning:active, +fieldset[disabled] .btn-warning.active { + background-color: #f0ad4e; + border-color: #eea236; +} +.btn-warning .badge { + color: #f0ad4e; + background-color: #fff; +} + +.btn-danger { + color: #fff; + background-color: #d9534f; + border-color: #d43f3a; +} +.btn-danger:focus, +.btn-danger.focus { + color: #fff; + background-color: #c9302c; + border-color: #761c19; +} +.btn-danger:hover { + color: #fff; + background-color: #c9302c; + border-color: #ac2925; +} +.btn-danger:active, +.btn-danger.active, +.open > .btn-danger.dropdown-toggle { + color: #fff; + background-color: #c9302c; + border-color: #ac2925; +} +.btn-danger:active:hover, +.btn-danger:active:focus, +.btn-danger:active.focus, +.btn-danger.active:hover, +.btn-danger.active:focus, +.btn-danger.active.focus, +.open > .btn-danger.dropdown-toggle:hover, +.open > .btn-danger.dropdown-toggle:focus, +.open > .btn-danger.dropdown-toggle.focus { + color: #fff; + background-color: #ac2925; + border-color: #761c19; +} +.btn-danger:active, +.btn-danger.active, +.open > .btn-danger.dropdown-toggle { + background-image: none; +} +.btn-danger.disabled, +.btn-danger.disabled:hover, +.btn-danger.disabled:focus, +.btn-danger.disabled.focus, +.btn-danger.disabled:active, +.btn-danger.disabled.active, +.btn-danger[disabled], +.btn-danger[disabled]:hover, +.btn-danger[disabled]:focus, +.btn-danger[disabled].focus, +.btn-danger[disabled]:active, +.btn-danger[disabled].active, +fieldset[disabled] .btn-danger, +fieldset[disabled] .btn-danger:hover, +fieldset[disabled] .btn-danger:focus, +fieldset[disabled] .btn-danger.focus, +fieldset[disabled] .btn-danger:active, +fieldset[disabled] .btn-danger.active { + background-color: #d9534f; + border-color: #d43f3a; +} +.btn-danger .badge { + color: #d9534f; + background-color: #fff; +} + +.btn-link { + color: #337ab7; + font-weight: normal; + border-radius: 0; +} +.btn-link, +.btn-link:active, +.btn-link.active, +.btn-link[disabled], +fieldset[disabled] .btn-link { + background-color: transparent; + -webkit-box-shadow: none; + box-shadow: none; +} +.btn-link, +.btn-link:hover, +.btn-link:focus, +.btn-link:active { + border-color: transparent; +} +.btn-link:hover, +.btn-link:focus { + color: #23527c; + text-decoration: underline; + background-color: transparent; +} +.btn-link[disabled]:hover, +.btn-link[disabled]:focus, +fieldset[disabled] .btn-link:hover, +fieldset[disabled] .btn-link:focus { + color: #777777; + text-decoration: none; +} + +.btn-lg, +.btn-group-lg > .btn { + padding: 10px 20px; + font-size: 18px; + line-height: 1.33333; + border-radius: 6px; +} + +.btn-sm, +.btn-group-sm > .btn { + padding: 5px 20px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} + +.btn-xs, +.btn-group-xs > .btn { + padding: 1px 5px; + font-size: 12px; + line-height: 1.5; + border-radius: 3px; +} + +.btn-block { + display: block; + width: 100%; +} + +.btn-block + .btn-block { + margin-top: 5px; +} + +input[type="submit"].btn-block, +input[type="reset"].btn-block, +input[type="button"].btn-block { + width: 100%; +} + +.fade { + opacity: 0; + -webkit-transition: opacity 0.15s linear; + -o-transition: opacity 0.15s linear; + transition: opacity 0.15s linear; +} +.fade.in { + opacity: 1; +} + +.collapse { + display: none; +} +.collapse.in { + display: block; +} + +tr.collapse.in { + display: table-row; +} + +tbody.collapse.in { + display: table-row-group; +} + +.collapsing { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition-property: height, visibility; + transition-property: height, visibility; + -webkit-transition-duration: 0.35s; + transition-duration: 0.35s; + -webkit-transition-timing-function: ease; + transition-timing-function: ease; +} + +.caret { + display: inline-block; + width: 0; + height: 0; + margin-left: 2px; + vertical-align: middle; + border-top: 4px dashed; + border-top: 4px solid \9; + border-right: 4px solid transparent; + border-left: 4px solid transparent; +} + +.dropup, +.dropdown { + position: relative; +} + +.dropdown-toggle:focus { + outline: 0; +} + +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 5px 0; + margin: 2px 0 0; + list-style: none; + font-size: 14px; + text-align: left; + background-color: #fff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.15); + border-radius: 4px; + -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); + box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); + background-clip: padding-box; +} +.dropdown-menu.pull-right { + right: 0; + left: auto; +} +.dropdown-menu .divider { + height: 1px; + margin: 9px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.dropdown-menu > li > a { + display: block; + padding: 3px 20px; + clear: both; + font-weight: normal; + line-height: 1.42857; + color: #333333; + white-space: nowrap; +} + +.dropdown-menu > li > a:hover, +.dropdown-menu > li > a:focus { + text-decoration: none; + color: #262626; + background-color: #f5f5f5; +} + +.dropdown-menu > .active > a, +.dropdown-menu > .active > a:hover, +.dropdown-menu > .active > a:focus { + color: #fff; + text-decoration: none; + outline: 0; + background-color: #337ab7; +} + +.dropdown-menu > .disabled > a, +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + color: #777777; +} +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + text-decoration: none; + background-color: transparent; + background-image: none; + filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); + cursor: not-allowed; +} + +.open > .dropdown-menu { + display: block; +} +.open > a { + outline: 0; +} + +.dropdown-menu-right { + left: auto; + right: 0; +} + +.dropdown-menu-left { + left: 0; + right: auto; +} + +.dropdown-header { + display: block; + padding: 3px 20px; + font-size: 12px; + line-height: 1.42857; + color: #777777; + white-space: nowrap; +} + +.dropdown-backdrop { + position: fixed; + left: 0; + right: 0; + bottom: 0; + top: 0; + z-index: 990; +} + +.pull-right > .dropdown-menu { + right: 0; + left: auto; +} + +.dropup .caret, +.navbar-fixed-bottom .dropdown .caret { + border-top: 0; + border-bottom: 4px dashed; + border-bottom: 4px solid \9; + content: ""; +} +.dropup .dropdown-menu, +.navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 2px; +} + +@media (min-width: 768px) { + .navbar-right .dropdown-menu { + right: 0; + left: auto; + } + .navbar-right .dropdown-menu-left { + left: 0; + right: auto; + } +} +.btn-group, +.btn-group-vertical { + position: relative; + display: inline-block; + vertical-align: middle; +} +.btn-group > .btn, +.btn-group-vertical > .btn { + position: relative; + float: left; +} +.btn-group > .btn:hover, +.btn-group > .btn:focus, +.btn-group > .btn:active, +.btn-group > .btn.active, +.btn-group-vertical > .btn:hover, +.btn-group-vertical > .btn:focus, +.btn-group-vertical > .btn:active, +.btn-group-vertical > .btn.active { + z-index: 2; +} + +.btn-group .btn + .btn, +.btn-group .btn + .btn-group, +.btn-group .btn-group + .btn, +.btn-group .btn-group + .btn-group { + margin-left: -1px; +} + +.btn-toolbar { + margin-left: -5px; +} +.btn-toolbar:before, +.btn-toolbar:after { + content: " "; + display: table; +} +.btn-toolbar:after { + clear: both; +} +.btn-toolbar .btn, +.btn-toolbar .btn-group, +.btn-toolbar .input-group { + float: left; +} +.btn-toolbar > .btn, +.btn-toolbar > .btn-group, +.btn-toolbar > .input-group { + margin-left: 5px; +} + +.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { + border-radius: 0; +} + +.btn-group > .btn:first-child { + margin-left: 0; +} +.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { + border-bottom-right-radius: 0; + border-top-right-radius: 0; +} + +.btn-group > .btn:last-child:not(:first-child), +.btn-group > .dropdown-toggle:not(:first-child) { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} + +.btn-group > .btn-group { + float: left; +} + +.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { + border-radius: 0; +} + +.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, +.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { + border-bottom-right-radius: 0; + border-top-right-radius: 0; +} + +.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} + +.btn-group .dropdown-toggle:active, +.btn-group.open .dropdown-toggle { + outline: 0; +} + +.btn-group > .btn + .dropdown-toggle { + padding-left: 8px; + padding-right: 8px; +} + +.btn-group > .btn-lg + .dropdown-toggle, +.btn-group-lg.btn-group > .btn + .dropdown-toggle { + padding-left: 12px; + padding-right: 12px; +} + +.btn-group.open .dropdown-toggle { + -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); + box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); +} +.btn-group.open .dropdown-toggle.btn-link { + -webkit-box-shadow: none; + box-shadow: none; +} + +.btn .caret { + margin-left: 0; +} + +.btn-lg .caret, +.btn-group-lg > .btn .caret { + border-width: 5px 5px 0; + border-bottom-width: 0; +} + +.dropup .btn-lg .caret, +.dropup .btn-group-lg > .btn .caret { + border-width: 0 5px 5px; +} + +.btn-group-vertical > .btn, +.btn-group-vertical > .btn-group, +.btn-group-vertical > .btn-group > .btn { + display: block; + float: none; + width: 100%; + max-width: 100%; +} +.btn-group-vertical > .btn-group:before, +.btn-group-vertical > .btn-group:after { + content: " "; + display: table; +} +.btn-group-vertical > .btn-group:after { + clear: both; +} +.btn-group-vertical > .btn-group > .btn { + float: none; +} +.btn-group-vertical > .btn + .btn, +.btn-group-vertical > .btn + .btn-group, +.btn-group-vertical > .btn-group + .btn, +.btn-group-vertical > .btn-group + .btn-group { + margin-top: -1px; + margin-left: 0; +} + +.btn-group-vertical > .btn:not(:first-child):not(:last-child) { + border-radius: 0; +} +.btn-group-vertical > .btn:first-child:not(:last-child) { + border-top-right-radius: 4px; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical > .btn:last-child:not(:first-child) { + border-bottom-left-radius: 4px; + border-top-right-radius: 0; + border-top-left-radius: 0; +} + +.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { + border-radius: 0; +} + +.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, +.btn-group-vertical + > .btn-group:first-child:not(:last-child) + > .dropdown-toggle { + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} + +.btn-group-vertical + > .btn-group:last-child:not(:first-child) + > .btn:first-child { + border-top-right-radius: 0; + border-top-left-radius: 0; +} + +.btn-group-justified { + display: table; + width: 100%; + table-layout: fixed; + border-collapse: separate; +} +.btn-group-justified > .btn, +.btn-group-justified > .btn-group { + float: none; + display: table-cell; + width: 1%; +} +.btn-group-justified > .btn-group .btn { + width: 100%; +} +.btn-group-justified > .btn-group .dropdown-menu { + left: auto; +} + +[data-toggle="buttons"] > .btn input[type="radio"], +[data-toggle="buttons"] > .btn input[type="checkbox"], +[data-toggle="buttons"] > .btn-group > .btn input[type="radio"], +[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { + position: absolute; + clip: rect(0, 0, 0, 0); + pointer-events: none; +} + +.input-group { + position: relative; + display: table; + border-collapse: separate; +} +.input-group[class*="col-"] { + float: none; + padding-left: 0; + padding-right: 0; +} +.input-group .form-control { + position: relative; + z-index: 2; + float: left; + width: 100%; + margin-bottom: 0; +} + +.input-group-addon, +.input-group-btn, +.input-group .form-control { + display: table-cell; +} +.input-group-addon:not(:first-child):not(:last-child), +.input-group-btn:not(:first-child):not(:last-child), +.input-group .form-control:not(:first-child):not(:last-child) { + border-radius: 0; +} + +.input-group-addon, +.input-group-btn { + width: 1%; + white-space: nowrap; + vertical-align: middle; +} + +.input-group-addon { + padding: 10px 20px; + font-size: 14px; + font-weight: normal; + line-height: 1; + color: #555555; + text-align: center; + background-color: #eeeeee; + border: 1px solid #ccc; + border-radius: 4px; +} +.input-group-addon.input-sm, +.input-group-sm > .input-group-addon, +.input-group-sm > .input-group-btn > .input-group-addon.btn { + padding: 5px 20px; + font-size: 12px; + border-radius: 3px; +} +.input-group-addon.input-lg, +.input-group-lg > .input-group-addon, +.input-group-lg > .input-group-btn > .input-group-addon.btn { + padding: 10px 20px; + font-size: 18px; + border-radius: 6px; +} +.input-group-addon input[type="radio"], +.input-group-addon input[type="checkbox"] { + margin-top: 0; +} + +.input-group .form-control:first-child, +.input-group-addon:first-child, +.input-group-btn:first-child > .btn, +.input-group-btn:first-child > .btn-group > .btn, +.input-group-btn:first-child > .dropdown-toggle, +.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), +.input-group-btn:last-child > .btn-group:not(:last-child) > .btn { + border-bottom-right-radius: 0; + border-top-right-radius: 0; +} + +.input-group-addon:first-child { + border-right: 0; +} + +.input-group .form-control:last-child, +.input-group-addon:last-child, +.input-group-btn:last-child > .btn, +.input-group-btn:last-child > .btn-group > .btn, +.input-group-btn:last-child > .dropdown-toggle, +.input-group-btn:first-child > .btn:not(:first-child), +.input-group-btn:first-child > .btn-group:not(:first-child) > .btn { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} + +.input-group-addon:last-child { + border-left: 0; +} + +.input-group-btn { + position: relative; + font-size: 0; + white-space: nowrap; +} +.input-group-btn > .btn { + position: relative; +} +.input-group-btn > .btn + .btn { + margin-left: -1px; +} +.input-group-btn > .btn:hover, +.input-group-btn > .btn:focus, +.input-group-btn > .btn:active { + z-index: 2; +} +.input-group-btn:first-child > .btn, +.input-group-btn:first-child > .btn-group { + margin-right: -1px; +} +.input-group-btn:last-child > .btn, +.input-group-btn:last-child > .btn-group { + z-index: 2; + margin-left: -1px; +} + +.nav { + margin-bottom: 0; + padding-left: 0; + list-style: none; +} +.nav:before, +.nav:after { + content: " "; + display: table; +} +.nav:after { + clear: both; +} +.nav > li { + position: relative; + display: block; +} +.nav > li > a { + position: relative; + display: block; + padding: 10px 15px; +} +.nav > li > a:hover, +.nav > li > a:focus { + text-decoration: none; + background-color: #eeeeee; +} +.nav > li.disabled > a { + color: #777777; +} +.nav > li.disabled > a:hover, +.nav > li.disabled > a:focus { + color: #777777; + text-decoration: none; + background-color: transparent; + cursor: not-allowed; +} +.nav .open > a, +.nav .open > a:hover, +.nav .open > a:focus { + background-color: #eeeeee; + border-color: #337ab7; +} +.nav .nav-divider { + height: 1px; + margin: 9px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.nav > li > a > img { + max-width: none; +} + +.nav-tabs { + border-bottom: 1px solid #ddd; +} +.nav-tabs > li { + float: left; + margin-bottom: -1px; +} +.nav-tabs > li > a { + margin-right: 2px; + line-height: 1.42857; + border: 1px solid transparent; + border-radius: 4px 4px 0 0; +} +.nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee #ddd; +} +.nav-tabs > li.active > a, +.nav-tabs > li.active > a:hover, +.nav-tabs > li.active > a:focus { + color: #555555; + background-color: #fff; + border: 1px solid #ddd; + border-bottom-color: transparent; + cursor: default; +} + +.nav-pills > li { + float: left; +} +.nav-pills > li > a { + border-radius: 4px; +} +.nav-pills > li + li { + margin-left: 2px; +} +.nav-pills > li.active > a, +.nav-pills > li.active > a:hover, +.nav-pills > li.active > a:focus { + color: #fff; + background-color: #337ab7; +} + +.nav-stacked > li { + float: none; +} +.nav-stacked > li + li { + margin-top: 2px; + margin-left: 0; +} + +.nav-justified, +.nav-tabs.nav-justified { + width: 100%; +} +.nav-justified > li, +.nav-tabs.nav-justified > li { + float: none; +} +.nav-justified > li > a, +.nav-tabs.nav-justified > li > a { + text-align: center; + margin-bottom: 5px; +} +.nav-justified > .dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media (min-width: 768px) { + .nav-justified > li, + .nav-tabs.nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-justified > li > a, + .nav-tabs.nav-justified > li > a { + margin-bottom: 0; + } +} + +.nav-tabs-justified, +.nav-tabs.nav-justified { + border-bottom: 0; +} +.nav-tabs-justified > li > a, +.nav-tabs.nav-justified > li > a { + margin-right: 0; + border-radius: 4px; +} +.nav-tabs-justified > .active > a, +.nav-tabs.nav-justified > .active > a, +.nav-tabs-justified > .active > a:hover, +.nav-tabs.nav-justified > .active > a:hover, +.nav-tabs-justified > .active > a:focus, +.nav-tabs.nav-justified > .active > a:focus { + border: 1px solid #ddd; +} +@media (min-width: 768px) { + .nav-tabs-justified > li > a, + .nav-tabs.nav-justified > li > a { + border-bottom: 1px solid #ddd; + border-radius: 4px 4px 0 0; + } + .nav-tabs-justified > .active > a, + .nav-tabs.nav-justified > .active > a, + .nav-tabs-justified > .active > a:hover, + .nav-tabs.nav-justified > .active > a:hover, + .nav-tabs-justified > .active > a:focus, + .nav-tabs.nav-justified > .active > a:focus { + border-bottom-color: #fff; + } +} + +.tab-content > .tab-pane { + display: none; +} +.tab-content > .active { + display: block; +} + +.nav-tabs .dropdown-menu { + margin-top: -1px; + border-top-right-radius: 0; + border-top-left-radius: 0; +} + +.navbar { + position: relative; + min-height: 55px; + margin-bottom: 20px; + border: 1px solid transparent; +} +.navbar:before, +.navbar:after { + content: " "; + display: table; +} +.navbar:after { + clear: both; +} +@media (min-width: 768px) { + .navbar { + border-radius: 4px; + } +} + +.navbar-header:before, +.navbar-header:after { + content: " "; + display: table; +} +.navbar-header:after { + clear: both; +} +@media (min-width: 768px) { + .navbar-header { + float: left; + } +} + +.navbar-collapse { + overflow-x: visible; + padding-right: 15px; + padding-left: 15px; + border-top: 1px solid transparent; + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); + -webkit-overflow-scrolling: touch; +} +.navbar-collapse:before, +.navbar-collapse:after { + content: " "; + display: table; +} +.navbar-collapse:after { + clear: both; +} +.navbar-collapse.in { + overflow-y: auto; +} +@media (min-width: 768px) { + .navbar-collapse { + width: auto; + border-top: 0; + box-shadow: none; + } + .navbar-collapse.collapse { + display: block !important; + height: auto !important; + padding-bottom: 0; + overflow: visible !important; + } + .navbar-collapse.in { + overflow-y: visible; + } + .navbar-fixed-top .navbar-collapse, + .navbar-static-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + padding-left: 0; + padding-right: 0; + } +} + +.navbar-fixed-top .navbar-collapse, +.navbar-fixed-bottom .navbar-collapse { + max-height: 340px; +} +@media (max-device-width: 480px) and (orientation: landscape) { + .navbar-fixed-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + max-height: 200px; + } +} + +.container > .navbar-header, +.container > .navbar-collapse, +.container-fluid > .navbar-header, +.container-fluid > .navbar-collapse { + margin-right: -15px; + margin-left: -15px; +} +@media (min-width: 768px) { + .container > .navbar-header, + .container > .navbar-collapse, + .container-fluid > .navbar-header, + .container-fluid > .navbar-collapse { + margin-right: 0; + margin-left: 0; + } +} + +.navbar-static-top { + z-index: 1000; + border-width: 0 0 1px; +} +@media (min-width: 768px) { + .navbar-static-top { + border-radius: 0; + } +} + +.navbar-fixed-top, +.navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; +} +@media (min-width: 768px) { + .navbar-fixed-top, + .navbar-fixed-bottom { + border-radius: 0; + } +} + +.navbar-fixed-top { + top: 0; + border-width: 0 0 1px; +} + +.navbar-fixed-bottom { + bottom: 0; + margin-bottom: 0; + border-width: 1px 0 0; +} + +.navbar-brand { + float: left; + padding: 17.5px 15px; + font-size: 18px; + line-height: 20px; + height: 55px; +} +.navbar-brand:hover, +.navbar-brand:focus { + text-decoration: none; +} +.navbar-brand > img { + display: block; +} +@media (min-width: 768px) { + .navbar > .container .navbar-brand, + .navbar > .container-fluid .navbar-brand { + margin-left: -15px; + } +} + +.navbar-toggle { + position: relative; + float: right; + margin-right: 15px; + padding: 9px 10px; + margin-top: 10.5px; + margin-bottom: 10.5px; + background-color: transparent; + background-image: none; + border: 1px solid transparent; + border-radius: 4px; +} +.navbar-toggle:focus { + outline: 0; +} +.navbar-toggle .icon-bar { + display: block; + width: 22px; + height: 2px; + border-radius: 1px; +} +.navbar-toggle .icon-bar + .icon-bar { + margin-top: 4px; +} +@media (min-width: 768px) { + .navbar-toggle { + display: none; + } +} + +.navbar-nav { + margin: 8.75px -15px; +} +.navbar-nav > li > a { + padding-top: 10px; + padding-bottom: 10px; + line-height: 20px; +} +@media (max-width: 767px) { + .navbar-nav .open .dropdown-menu { + position: static; + float: none; + width: auto; + margin-top: 0; + background-color: transparent; + border: 0; + box-shadow: none; + } + .navbar-nav .open .dropdown-menu > li > a, + .navbar-nav .open .dropdown-menu .dropdown-header { + padding: 5px 15px 5px 25px; + } + .navbar-nav .open .dropdown-menu > li > a { + line-height: 20px; + } + .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-nav .open .dropdown-menu > li > a:focus { + background-image: none; + } +} +@media (min-width: 768px) { + .navbar-nav { + float: left; + margin: 0; + } + .navbar-nav > li { + float: left; + } + .navbar-nav > li > a { + padding-top: 17.5px; + padding-bottom: 17.5px; + } +} + +.navbar-form { + margin-left: -15px; + margin-right: -15px; + padding: 10px 15px; + border-top: 1px solid transparent; + border-bottom: 1px solid transparent; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), + 0 1px 0 rgba(255, 255, 255, 0.1); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), + 0 1px 0 rgba(255, 255, 255, 0.1); + margin-top: 6.5px; + margin-bottom: 6.5px; +} +@media (min-width: 768px) { + .navbar-form .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .navbar-form .form-control-static { + display: inline-block; + } + .navbar-form .input-group { + display: inline-table; + vertical-align: middle; + } + .navbar-form .input-group .input-group-addon, + .navbar-form .input-group .input-group-btn, + .navbar-form .input-group .form-control { + width: auto; + } + .navbar-form .input-group > .form-control { + width: 100%; + } + .navbar-form .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio, + .navbar-form .checkbox { + display: inline-block; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio label, + .navbar-form .checkbox label { + padding-left: 0; + } + .navbar-form .radio input[type="radio"], + .navbar-form .checkbox input[type="checkbox"] { + position: relative; + margin-left: 0; + } + .navbar-form .has-feedback .form-control-feedback { + top: 0; + } +} +@media (max-width: 767px) { + .navbar-form .form-group { + margin-bottom: 5px; + } + .navbar-form .form-group:last-child { + margin-bottom: 0; + } +} +@media (min-width: 768px) { + .navbar-form { + width: auto; + border: 0; + margin-left: 0; + margin-right: 0; + padding-top: 0; + padding-bottom: 0; + -webkit-box-shadow: none; + box-shadow: none; + } +} + +.navbar-nav > li > .dropdown-menu { + margin-top: 0; + border-top-right-radius: 0; + border-top-left-radius: 0; +} + +.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { + margin-bottom: 0; + border-top-right-radius: 4px; + border-top-left-radius: 4px; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} + +.navbar-btn { + margin-top: 6.5px; + margin-bottom: 6.5px; +} +.navbar-btn.btn-sm, +.btn-group-sm > .navbar-btn.btn { + margin-top: 12.5px; + margin-bottom: 12.5px; +} +.navbar-btn.btn-xs, +.btn-group-xs > .navbar-btn.btn { + margin-top: 16.5px; + margin-bottom: 16.5px; +} + +.navbar-text { + margin-top: 17.5px; + margin-bottom: 17.5px; +} +@media (min-width: 768px) { + .navbar-text { + float: left; + margin-left: 15px; + margin-right: 15px; + } +} + +@media (min-width: 768px) { + .navbar-left { + float: left !important; + } + + .navbar-right { + float: right !important; + margin-right: -15px; + } + .navbar-right ~ .navbar-right { + margin-right: 0; + } +} +.navbar-default { + background-color: #f8f8f8; + border-color: #e7e7e7; +} +.navbar-default .navbar-brand { + color: #777; +} +.navbar-default .navbar-brand:hover, +.navbar-default .navbar-brand:focus { + color: #5e5e5e; + background-color: transparent; +} +.navbar-default .navbar-text { + color: #777; +} +.navbar-default .navbar-nav > li > a { + color: #777; +} +.navbar-default .navbar-nav > li > a:hover, +.navbar-default .navbar-nav > li > a:focus { + color: #333; + background-color: transparent; +} +.navbar-default .navbar-nav > .active > a, +.navbar-default .navbar-nav > .active > a:hover, +.navbar-default .navbar-nav > .active > a:focus { + color: #555; + background-color: #e7e7e7; +} +.navbar-default .navbar-nav > .disabled > a, +.navbar-default .navbar-nav > .disabled > a:hover, +.navbar-default .navbar-nav > .disabled > a:focus { + color: #ccc; + background-color: transparent; +} +.navbar-default .navbar-toggle { + border-color: #ddd; +} +.navbar-default .navbar-toggle:hover, +.navbar-default .navbar-toggle:focus { + background-color: #ddd; +} +.navbar-default .navbar-toggle .icon-bar { + background-color: #888; +} +.navbar-default .navbar-collapse, +.navbar-default .navbar-form { + border-color: #e7e7e7; +} +.navbar-default .navbar-nav > .open > a, +.navbar-default .navbar-nav > .open > a:hover, +.navbar-default .navbar-nav > .open > a:focus { + background-color: #e7e7e7; + color: #555; +} +@media (max-width: 767px) { + .navbar-default .navbar-nav .open .dropdown-menu > li > a { + color: #777; + } + .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { + color: #333; + background-color: transparent; + } + .navbar-default .navbar-nav .open .dropdown-menu > .active > a, + .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { + color: #555; + background-color: #e7e7e7; + } + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { + color: #ccc; + background-color: transparent; + } +} +.navbar-default .navbar-link { + color: #777; +} +.navbar-default .navbar-link:hover { + color: #333; +} +.navbar-default .btn-link { + color: #777; +} +.navbar-default .btn-link:hover, +.navbar-default .btn-link:focus { + color: #333; +} +.navbar-default .btn-link[disabled]:hover, +.navbar-default .btn-link[disabled]:focus, +fieldset[disabled] .navbar-default .btn-link:hover, +fieldset[disabled] .navbar-default .btn-link:focus { + color: #ccc; +} + +.navbar-inverse { + background-color: #222; + border-color: #090909; +} +.navbar-inverse .navbar-brand { + color: #9d9d9d; +} +.navbar-inverse .navbar-brand:hover, +.navbar-inverse .navbar-brand:focus { + color: #fff; + background-color: transparent; +} +.navbar-inverse .navbar-text { + color: #9d9d9d; +} +.navbar-inverse .navbar-nav > li > a { + color: #9d9d9d; +} +.navbar-inverse .navbar-nav > li > a:hover, +.navbar-inverse .navbar-nav > li > a:focus { + color: #fff; + background-color: transparent; +} +.navbar-inverse .navbar-nav > .active > a, +.navbar-inverse .navbar-nav > .active > a:hover, +.navbar-inverse .navbar-nav > .active > a:focus { + color: #fff; + background-color: #090909; +} +.navbar-inverse .navbar-nav > .disabled > a, +.navbar-inverse .navbar-nav > .disabled > a:hover, +.navbar-inverse .navbar-nav > .disabled > a:focus { + color: #444; + background-color: transparent; +} +.navbar-inverse .navbar-toggle { + border-color: #333; +} +.navbar-inverse .navbar-toggle:hover, +.navbar-inverse .navbar-toggle:focus { + background-color: #333; +} +.navbar-inverse .navbar-toggle .icon-bar { + background-color: #fff; +} +.navbar-inverse .navbar-collapse, +.navbar-inverse .navbar-form { + border-color: #101010; +} +.navbar-inverse .navbar-nav > .open > a, +.navbar-inverse .navbar-nav > .open > a:hover, +.navbar-inverse .navbar-nav > .open > a:focus { + background-color: #090909; + color: #fff; +} +@media (max-width: 767px) { + .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { + border-color: #090909; + } + .navbar-inverse .navbar-nav .open .dropdown-menu .divider { + background-color: #090909; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { + color: #9d9d9d; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { + color: #fff; + background-color: transparent; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { + color: #fff; + background-color: #090909; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { + color: #444; + background-color: transparent; + } +} +.navbar-inverse .navbar-link { + color: #9d9d9d; +} +.navbar-inverse .navbar-link:hover { + color: #fff; +} +.navbar-inverse .btn-link { + color: #9d9d9d; +} +.navbar-inverse .btn-link:hover, +.navbar-inverse .btn-link:focus { + color: #fff; +} +.navbar-inverse .btn-link[disabled]:hover, +.navbar-inverse .btn-link[disabled]:focus, +fieldset[disabled] .navbar-inverse .btn-link:hover, +fieldset[disabled] .navbar-inverse .btn-link:focus { + color: #444; +} + +.breadcrumb { + padding: 8px 15px; + margin-bottom: 20px; + list-style: none; + background-color: #f5f5f5; + border-radius: 4px; +} +.breadcrumb > li { + display: inline-block; +} +.breadcrumb > li + li:before { + content: "/ "; + padding: 0 5px; + color: #ccc; +} +.breadcrumb > .active { + color: #777777; +} + +.pagination { + display: inline-block; + padding-left: 0; + margin: 20px 0; + border-radius: 4px; +} +.pagination > li { + display: inline; +} +.pagination > li > a, +.pagination > li > span { + position: relative; + float: left; + padding: 10px 20px; + line-height: 1.42857; + text-decoration: none; + color: #337ab7; + background-color: #fff; + border: 1px solid #ddd; + margin-left: -1px; +} +.pagination > li:first-child > a, +.pagination > li:first-child > span { + margin-left: 0; + border-bottom-left-radius: 4px; + border-top-left-radius: 4px; +} +.pagination > li:last-child > a, +.pagination > li:last-child > span { + border-bottom-right-radius: 4px; + border-top-right-radius: 4px; +} +.pagination > li > a:hover, +.pagination > li > a:focus, +.pagination > li > span:hover, +.pagination > li > span:focus { + z-index: 3; + color: #23527c; + background-color: #eeeeee; + border-color: #ddd; +} +.pagination > .active > a, +.pagination > .active > a:hover, +.pagination > .active > a:focus, +.pagination > .active > span, +.pagination > .active > span:hover, +.pagination > .active > span:focus { + z-index: 2; + color: #fff; + background-color: #337ab7; + border-color: #337ab7; + cursor: default; +} +.pagination > .disabled > span, +.pagination > .disabled > span:hover, +.pagination > .disabled > span:focus, +.pagination > .disabled > a, +.pagination > .disabled > a:hover, +.pagination > .disabled > a:focus { + color: #777777; + background-color: #fff; + border-color: #ddd; + cursor: not-allowed; +} + +.pagination-lg > li > a, +.pagination-lg > li > span { + padding: 10px 20px; + font-size: 18px; + line-height: 1.33333; +} +.pagination-lg > li:first-child > a, +.pagination-lg > li:first-child > span { + border-bottom-left-radius: 6px; + border-top-left-radius: 6px; +} +.pagination-lg > li:last-child > a, +.pagination-lg > li:last-child > span { + border-bottom-right-radius: 6px; + border-top-right-radius: 6px; +} + +.pagination-sm > li > a, +.pagination-sm > li > span { + padding: 5px 20px; + font-size: 12px; + line-height: 1.5; +} +.pagination-sm > li:first-child > a, +.pagination-sm > li:first-child > span { + border-bottom-left-radius: 3px; + border-top-left-radius: 3px; +} +.pagination-sm > li:last-child > a, +.pagination-sm > li:last-child > span { + border-bottom-right-radius: 3px; + border-top-right-radius: 3px; +} + +.pager { + padding-left: 0; + margin: 20px 0; + list-style: none; + text-align: center; +} +.pager:before, +.pager:after { + content: " "; + display: table; +} +.pager:after { + clear: both; +} +.pager li { + display: inline; +} +.pager li > a, +.pager li > span { + display: inline-block; + padding: 5px 14px; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 15px; +} +.pager li > a:hover, +.pager li > a:focus { + text-decoration: none; + background-color: #eeeeee; +} +.pager .next > a, +.pager .next > span { + float: right; +} +.pager .previous > a, +.pager .previous > span { + float: left; +} +.pager .disabled > a, +.pager .disabled > a:hover, +.pager .disabled > a:focus, +.pager .disabled > span { + color: #777777; + background-color: #fff; + cursor: not-allowed; +} + +.label { + display: inline; + padding: 0.2em 0.6em 0.3em; + font-size: 75%; + font-weight: bold; + line-height: 1; + color: #fff; + text-align: center; + white-space: nowrap; + vertical-align: baseline; + border-radius: 0.25em; +} +.label:empty { + display: none; +} +.btn .label { + position: relative; + top: -1px; +} + +a.label:hover, +a.label:focus { + color: #fff; + text-decoration: none; + cursor: pointer; +} + +.label-default { + background-color: #777777; +} +.label-default[href]:hover, +.label-default[href]:focus { + background-color: #5e5e5e; +} + +.label-primary { + background-color: #337ab7; +} +.label-primary[href]:hover, +.label-primary[href]:focus { + background-color: #286090; +} + +.label-success { + background-color: #5cb85c; +} +.label-success[href]:hover, +.label-success[href]:focus { + background-color: #449d44; +} + +.label-info { + background-color: #5bc0de; +} +.label-info[href]:hover, +.label-info[href]:focus { + background-color: #31b0d5; +} + +.label-warning { + background-color: #f0ad4e; +} +.label-warning[href]:hover, +.label-warning[href]:focus { + background-color: #ec971f; +} + +.label-danger { + background-color: #d9534f; +} +.label-danger[href]:hover, +.label-danger[href]:focus { + background-color: #c9302c; +} + +.badge { + display: inline-block; + min-width: 10px; + padding: 3px 7px; + font-size: 12px; + font-weight: bold; + color: #fff; + line-height: 1; + vertical-align: middle; + white-space: nowrap; + text-align: center; + background-color: #777777; + border-radius: 10px; +} +.badge:empty { + display: none; +} +.btn .badge { + position: relative; + top: -1px; +} +.btn-xs .badge, +.btn-group-xs > .btn .badge, +.btn-group-xs > .btn .badge { + top: 0; + padding: 1px 5px; +} +.list-group-item.active > .badge, +.nav-pills > .active > a > .badge { + color: #337ab7; + background-color: #fff; +} +.list-group-item > .badge { + float: right; +} +.list-group-item > .badge + .badge { + margin-right: 5px; +} +.nav-pills > li > a > .badge { + margin-left: 3px; +} + +a.badge:hover, +a.badge:focus { + color: #fff; + text-decoration: none; + cursor: pointer; +} + +.jumbotron { + padding-top: 30px; + padding-bottom: 30px; + margin-bottom: 30px; + color: inherit; + background-color: #eeeeee; +} +.jumbotron h1, +.jumbotron .h1 { + color: inherit; +} +.jumbotron p { + margin-bottom: 15px; + font-size: 21px; + font-weight: 200; +} +.jumbotron > hr { + border-top-color: #d5d5d5; +} +.container .jumbotron, +.container-fluid .jumbotron { + border-radius: 6px; +} +.jumbotron .container { + max-width: 100%; +} +@media screen and (min-width: 768px) { + .jumbotron { + padding-top: 48px; + padding-bottom: 48px; + } + .container .jumbotron, + .container-fluid .jumbotron { + padding-left: 60px; + padding-right: 60px; + } + .jumbotron h1, + .jumbotron .h1 { + font-size: 63px; + } +} + +.thumbnail { + display: block; + padding: 4px; + margin-bottom: 20px; + line-height: 1.42857; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 4px; + -webkit-transition: border 0.2s ease-in-out; + -o-transition: border 0.2s ease-in-out; + transition: border 0.2s ease-in-out; +} +.thumbnail > img, +.thumbnail a > img { + display: block; + max-width: 100%; + height: auto; + margin-left: auto; + margin-right: auto; +} +.thumbnail .caption { + padding: 9px; + color: #333333; +} + +a.thumbnail:hover, +a.thumbnail:focus, +a.thumbnail.active { + border-color: #337ab7; +} + +.alert { + padding: 15px; + margin-bottom: 20px; + border: 1px solid transparent; + border-radius: 4px; +} +.alert h4 { + margin-top: 0; + color: inherit; +} +.alert .alert-link { + font-weight: bold; +} +.alert > p, +.alert > ul { + margin-bottom: 0; +} +.alert > p + p { + margin-top: 5px; +} + +.alert-dismissable, +.alert-dismissible { + padding-right: 35px; +} +.alert-dismissable .close, +.alert-dismissible .close { + position: relative; + top: -2px; + right: -21px; + color: inherit; +} + +.alert-success { + background-color: #dff0d8; + border-color: #d6e9c6; + color: #3c763d; +} +.alert-success hr { + border-top-color: #c9e2b3; +} +.alert-success .alert-link { + color: #2b542c; +} + +.alert-info { + background-color: #d9edf7; + border-color: #bce8f1; + color: #31708f; +} +.alert-info hr { + border-top-color: #a6e1ec; +} +.alert-info .alert-link { + color: #245269; +} + +.alert-warning { + background-color: #fcf8e3; + border-color: #faebcc; + color: #8a6d3b; +} +.alert-warning hr { + border-top-color: #f7e1b5; +} +.alert-warning .alert-link { + color: #66512c; +} + +.alert-danger { + background-color: #f2dede; + border-color: #ebccd1; + color: #a94442; +} +.alert-danger hr { + border-top-color: #e4b9c0; +} +.alert-danger .alert-link { + color: #843534; +} + +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +@keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +.progress { + overflow: hidden; + height: 20px; + margin-bottom: 20px; + background-color: #f5f5f5; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} + +.progress-bar { + float: left; + width: 0%; + height: 100%; + font-size: 12px; + line-height: 20px; + color: #fff; + text-align: center; + background-color: #337ab7; + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -webkit-transition: width 0.6s ease; + -o-transition: width 0.6s ease; + transition: width 0.6s ease; +} + +.progress-striped .progress-bar, +.progress-bar-striped { + background-image: -webkit-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: -o-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-size: 40px 40px; +} + +.progress.active .progress-bar, +.progress-bar.active { + -webkit-animation: progress-bar-stripes 2s linear infinite; + -o-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} + +.progress-bar-success { + background-color: #5cb85c; +} +.progress-striped .progress-bar-success { + background-image: -webkit-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: -o-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); +} + +.progress-bar-info { + background-color: #5bc0de; +} +.progress-striped .progress-bar-info { + background-image: -webkit-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: -o-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); +} + +.progress-bar-warning { + background-color: #f0ad4e; +} +.progress-striped .progress-bar-warning { + background-image: -webkit-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: -o-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); +} + +.progress-bar-danger { + background-color: #d9534f; +} +.progress-striped .progress-bar-danger { + background-image: -webkit-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: -o-linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); + background-image: linear-gradient( + 45deg, + rgba(255, 255, 255, 0.15) 25%, + transparent 25%, + transparent 50%, + rgba(255, 255, 255, 0.15) 50%, + rgba(255, 255, 255, 0.15) 75%, + transparent 75%, + transparent + ); +} + +.media { + margin-top: 15px; +} +.media:first-child { + margin-top: 0; +} + +.media, +.media-body { + zoom: 1; + overflow: hidden; +} + +.media-body { + width: 10000px; +} + +.media-object { + display: block; +} +.media-object.img-thumbnail { + max-width: none; +} + +.media-right, +.media > .pull-right { + padding-left: 10px; +} + +.media-left, +.media > .pull-left { + padding-right: 10px; +} + +.media-left, +.media-right, +.media-body { + display: table-cell; + vertical-align: top; +} + +.media-middle { + vertical-align: middle; +} + +.media-bottom { + vertical-align: bottom; +} + +.media-heading { + margin-top: 0; + margin-bottom: 5px; +} + +.media-list { + padding-left: 0; + list-style: none; +} + +.list-group { + margin-bottom: 20px; + padding-left: 0; +} + +.list-group-item { + position: relative; + display: block; + padding: 10px 15px; + margin-bottom: -1px; + background-color: #fff; + border: 1px solid #ddd; +} +.list-group-item:first-child { + border-top-right-radius: 4px; + border-top-left-radius: 4px; +} +.list-group-item:last-child { + margin-bottom: 0; + border-bottom-right-radius: 4px; + border-bottom-left-radius: 4px; +} + +a.list-group-item, +button.list-group-item { + color: #555; +} +a.list-group-item .list-group-item-heading, +button.list-group-item .list-group-item-heading { + color: #333; +} +a.list-group-item:hover, +a.list-group-item:focus, +button.list-group-item:hover, +button.list-group-item:focus { + text-decoration: none; + color: #555; + background-color: #f5f5f5; +} + +button.list-group-item { + width: 100%; + text-align: left; +} + +.list-group-item.disabled, +.list-group-item.disabled:hover, +.list-group-item.disabled:focus { + background-color: #eeeeee; + color: #777777; + cursor: not-allowed; +} +.list-group-item.disabled .list-group-item-heading, +.list-group-item.disabled:hover .list-group-item-heading, +.list-group-item.disabled:focus .list-group-item-heading { + color: inherit; +} +.list-group-item.disabled .list-group-item-text, +.list-group-item.disabled:hover .list-group-item-text, +.list-group-item.disabled:focus .list-group-item-text { + color: #777777; +} +.list-group-item.active, +.list-group-item.active:hover, +.list-group-item.active:focus { + z-index: 2; + color: #fff; + background-color: #337ab7; + border-color: #337ab7; +} +.list-group-item.active .list-group-item-heading, +.list-group-item.active .list-group-item-heading > small, +.list-group-item.active .list-group-item-heading > .small, +.list-group-item.active:hover .list-group-item-heading, +.list-group-item.active:hover .list-group-item-heading > small, +.list-group-item.active:hover .list-group-item-heading > .small, +.list-group-item.active:focus .list-group-item-heading, +.list-group-item.active:focus .list-group-item-heading > small, +.list-group-item.active:focus .list-group-item-heading > .small { + color: inherit; +} +.list-group-item.active .list-group-item-text, +.list-group-item.active:hover .list-group-item-text, +.list-group-item.active:focus .list-group-item-text { + color: #c7ddef; +} + +.list-group-item-success { + color: #3c763d; + background-color: #dff0d8; +} + +a.list-group-item-success, +button.list-group-item-success { + color: #3c763d; +} +a.list-group-item-success .list-group-item-heading, +button.list-group-item-success .list-group-item-heading { + color: inherit; +} +a.list-group-item-success:hover, +a.list-group-item-success:focus, +button.list-group-item-success:hover, +button.list-group-item-success:focus { + color: #3c763d; + background-color: #d0e9c6; +} +a.list-group-item-success.active, +a.list-group-item-success.active:hover, +a.list-group-item-success.active:focus, +button.list-group-item-success.active, +button.list-group-item-success.active:hover, +button.list-group-item-success.active:focus { + color: #fff; + background-color: #3c763d; + border-color: #3c763d; +} + +.list-group-item-info { + color: #31708f; + background-color: #d9edf7; +} + +a.list-group-item-info, +button.list-group-item-info { + color: #31708f; +} +a.list-group-item-info .list-group-item-heading, +button.list-group-item-info .list-group-item-heading { + color: inherit; +} +a.list-group-item-info:hover, +a.list-group-item-info:focus, +button.list-group-item-info:hover, +button.list-group-item-info:focus { + color: #31708f; + background-color: #c4e3f3; +} +a.list-group-item-info.active, +a.list-group-item-info.active:hover, +a.list-group-item-info.active:focus, +button.list-group-item-info.active, +button.list-group-item-info.active:hover, +button.list-group-item-info.active:focus { + color: #fff; + background-color: #31708f; + border-color: #31708f; +} + +.list-group-item-warning { + color: #8a6d3b; + background-color: #fcf8e3; +} + +a.list-group-item-warning, +button.list-group-item-warning { + color: #8a6d3b; +} +a.list-group-item-warning .list-group-item-heading, +button.list-group-item-warning .list-group-item-heading { + color: inherit; +} +a.list-group-item-warning:hover, +a.list-group-item-warning:focus, +button.list-group-item-warning:hover, +button.list-group-item-warning:focus { + color: #8a6d3b; + background-color: #faf2cc; +} +a.list-group-item-warning.active, +a.list-group-item-warning.active:hover, +a.list-group-item-warning.active:focus, +button.list-group-item-warning.active, +button.list-group-item-warning.active:hover, +button.list-group-item-warning.active:focus { + color: #fff; + background-color: #8a6d3b; + border-color: #8a6d3b; +} + +.list-group-item-danger { + color: #a94442; + background-color: #f2dede; +} + +a.list-group-item-danger, +button.list-group-item-danger { + color: #a94442; +} +a.list-group-item-danger .list-group-item-heading, +button.list-group-item-danger .list-group-item-heading { + color: inherit; +} +a.list-group-item-danger:hover, +a.list-group-item-danger:focus, +button.list-group-item-danger:hover, +button.list-group-item-danger:focus { + color: #a94442; + background-color: #ebcccc; +} +a.list-group-item-danger.active, +a.list-group-item-danger.active:hover, +a.list-group-item-danger.active:focus, +button.list-group-item-danger.active, +button.list-group-item-danger.active:hover, +button.list-group-item-danger.active:focus { + color: #fff; + background-color: #a94442; + border-color: #a94442; +} + +.list-group-item-heading { + margin-top: 0; + margin-bottom: 5px; +} + +.list-group-item-text { + margin-bottom: 0; + line-height: 1.3; +} + +.panel { + margin-bottom: 20px; + background-color: #fff; + border: 1px solid transparent; + border-radius: 4px; + -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); +} + +.panel-body { + padding: 15px; +} +.panel-body:before, +.panel-body:after { + content: " "; + display: table; +} +.panel-body:after { + clear: both; +} + +.panel-heading { + padding: 10px 15px; + border-bottom: 1px solid transparent; + border-top-right-radius: 3px; + border-top-left-radius: 3px; +} +.panel-heading > .dropdown .dropdown-toggle { + color: inherit; +} + +.panel-title { + margin-top: 0; + margin-bottom: 0; + font-size: 16px; + color: inherit; +} +.panel-title > a, +.panel-title > small, +.panel-title > .small, +.panel-title > small > a, +.panel-title > .small > a { + color: inherit; +} + +.panel-footer { + padding: 10px 15px; + background-color: #f5f5f5; + border-top: 1px solid #ddd; + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} + +.panel > .list-group, +.panel > .panel-collapse > .list-group { + margin-bottom: 0; +} +.panel > .list-group .list-group-item, +.panel > .panel-collapse > .list-group .list-group-item { + border-width: 1px 0; + border-radius: 0; +} +.panel > .list-group:first-child .list-group-item:first-child, +.panel + > .panel-collapse + > .list-group:first-child + .list-group-item:first-child { + border-top: 0; + border-top-right-radius: 3px; + border-top-left-radius: 3px; +} +.panel > .list-group:last-child .list-group-item:last-child, +.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { + border-bottom: 0; + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} +.panel + > .panel-heading + + .panel-collapse + > .list-group + .list-group-item:first-child { + border-top-right-radius: 0; + border-top-left-radius: 0; +} + +.panel-heading + .list-group .list-group-item:first-child { + border-top-width: 0; +} + +.list-group + .panel-footer { + border-top-width: 0; +} + +.panel > .table, +.panel > .table-responsive > .table, +.panel > .panel-collapse > .table { + margin-bottom: 0; +} +.panel > .table caption, +.panel > .table-responsive > .table caption, +.panel > .panel-collapse > .table caption { + padding-left: 15px; + padding-right: 15px; +} +.panel > .table:first-child, +.panel > .table-responsive:first-child > .table:first-child { + border-top-right-radius: 3px; + border-top-left-radius: 3px; +} +.panel > .table:first-child > thead:first-child > tr:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child, +.panel + > .table-responsive:first-child + > .table:first-child + > thead:first-child + > tr:first-child, +.panel + > .table-responsive:first-child + > .table:first-child + > tbody:first-child + > tr:first-child { + border-top-left-radius: 3px; + border-top-right-radius: 3px; +} +.panel > .table:first-child > thead:first-child > tr:first-child td:first-child, +.panel > .table:first-child > thead:first-child > tr:first-child th:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, +.panel + > .table-responsive:first-child + > .table:first-child + > thead:first-child + > tr:first-child + td:first-child, +.panel + > .table-responsive:first-child + > .table:first-child + > thead:first-child + > tr:first-child + th:first-child, +.panel + > .table-responsive:first-child + > .table:first-child + > tbody:first-child + > tr:first-child + td:first-child, +.panel + > .table-responsive:first-child + > .table:first-child + > tbody:first-child + > tr:first-child + th:first-child { + border-top-left-radius: 3px; +} +.panel > .table:first-child > thead:first-child > tr:first-child td:last-child, +.panel > .table:first-child > thead:first-child > tr:first-child th:last-child, +.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, +.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, +.panel + > .table-responsive:first-child + > .table:first-child + > thead:first-child + > tr:first-child + td:last-child, +.panel + > .table-responsive:first-child + > .table:first-child + > thead:first-child + > tr:first-child + th:last-child, +.panel + > .table-responsive:first-child + > .table:first-child + > tbody:first-child + > tr:first-child + td:last-child, +.panel + > .table-responsive:first-child + > .table:first-child + > tbody:first-child + > tr:first-child + th:last-child { + border-top-right-radius: 3px; +} +.panel > .table:last-child, +.panel > .table-responsive:last-child > .table:last-child { + border-bottom-right-radius: 3px; + border-bottom-left-radius: 3px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tbody:last-child + > tr:last-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tfoot:last-child + > tr:last-child { + border-bottom-left-radius: 3px; + border-bottom-right-radius: 3px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, +.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tbody:last-child + > tr:last-child + td:first-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tbody:last-child + > tr:last-child + th:first-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tfoot:last-child + > tr:last-child + td:first-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tfoot:last-child + > tr:last-child + th:first-child { + border-bottom-left-radius: 3px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, +.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tbody:last-child + > tr:last-child + td:last-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tbody:last-child + > tr:last-child + th:last-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tfoot:last-child + > tr:last-child + td:last-child, +.panel + > .table-responsive:last-child + > .table:last-child + > tfoot:last-child + > tr:last-child + th:last-child { + border-bottom-right-radius: 3px; +} +.panel > .panel-body + .table, +.panel > .panel-body + .table-responsive, +.panel > .table + .panel-body, +.panel > .table-responsive + .panel-body { + border-top: 1px solid #ddd; +} +.panel > .table > tbody:first-child > tr:first-child th, +.panel > .table > tbody:first-child > tr:first-child td { + border-top: 0; +} +.panel > .table-bordered, +.panel > .table-responsive > .table-bordered { + border: 0; +} +.panel > .table-bordered > thead > tr > th:first-child, +.panel > .table-bordered > thead > tr > td:first-child, +.panel > .table-bordered > tbody > tr > th:first-child, +.panel > .table-bordered > tbody > tr > td:first-child, +.panel > .table-bordered > tfoot > tr > th:first-child, +.panel > .table-bordered > tfoot > tr > td:first-child, +.panel > .table-responsive > .table-bordered > thead > tr > th:first-child, +.panel > .table-responsive > .table-bordered > thead > tr > td:first-child, +.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, +.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { + border-left: 0; +} +.panel > .table-bordered > thead > tr > th:last-child, +.panel > .table-bordered > thead > tr > td:last-child, +.panel > .table-bordered > tbody > tr > th:last-child, +.panel > .table-bordered > tbody > tr > td:last-child, +.panel > .table-bordered > tfoot > tr > th:last-child, +.panel > .table-bordered > tfoot > tr > td:last-child, +.panel > .table-responsive > .table-bordered > thead > tr > th:last-child, +.panel > .table-responsive > .table-bordered > thead > tr > td:last-child, +.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, +.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { + border-right: 0; +} +.panel > .table-bordered > thead > tr:first-child > td, +.panel > .table-bordered > thead > tr:first-child > th, +.panel > .table-bordered > tbody > tr:first-child > td, +.panel > .table-bordered > tbody > tr:first-child > th, +.panel > .table-responsive > .table-bordered > thead > tr:first-child > td, +.panel > .table-responsive > .table-bordered > thead > tr:first-child > th, +.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, +.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { + border-bottom: 0; +} +.panel > .table-bordered > tbody > tr:last-child > td, +.panel > .table-bordered > tbody > tr:last-child > th, +.panel > .table-bordered > tfoot > tr:last-child > td, +.panel > .table-bordered > tfoot > tr:last-child > th, +.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, +.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, +.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, +.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { + border-bottom: 0; +} +.panel > .table-responsive { + border: 0; + margin-bottom: 0; +} + +.panel-group { + margin-bottom: 20px; +} +.panel-group .panel { + margin-bottom: 0; + border-radius: 4px; +} +.panel-group .panel + .panel { + margin-top: 5px; +} +.panel-group .panel-heading { + border-bottom: 0; +} +.panel-group .panel-heading + .panel-collapse > .panel-body, +.panel-group .panel-heading + .panel-collapse > .list-group { + border-top: 1px solid #ddd; +} +.panel-group .panel-footer { + border-top: 0; +} +.panel-group .panel-footer + .panel-collapse .panel-body { + border-bottom: 1px solid #ddd; +} + +.panel-default { + border-color: #ddd; +} +.panel-default > .panel-heading { + color: #333333; + background-color: #f5f5f5; + border-color: #ddd; +} +.panel-default > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #ddd; +} +.panel-default > .panel-heading .badge { + color: #f5f5f5; + background-color: #333333; +} +.panel-default > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #ddd; +} + +.panel-primary { + border-color: #337ab7; +} +.panel-primary > .panel-heading { + color: #fff; + background-color: #337ab7; + border-color: #337ab7; +} +.panel-primary > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #337ab7; +} +.panel-primary > .panel-heading .badge { + color: #337ab7; + background-color: #fff; +} +.panel-primary > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #337ab7; +} + +.panel-success { + border-color: #d6e9c6; +} +.panel-success > .panel-heading { + color: #3c763d; + background-color: #dff0d8; + border-color: #d6e9c6; +} +.panel-success > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #d6e9c6; +} +.panel-success > .panel-heading .badge { + color: #dff0d8; + background-color: #3c763d; +} +.panel-success > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #d6e9c6; +} + +.panel-info { + border-color: #bce8f1; +} +.panel-info > .panel-heading { + color: #31708f; + background-color: #d9edf7; + border-color: #bce8f1; +} +.panel-info > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #bce8f1; +} +.panel-info > .panel-heading .badge { + color: #d9edf7; + background-color: #31708f; +} +.panel-info > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #bce8f1; +} + +.panel-warning { + border-color: #faebcc; +} +.panel-warning > .panel-heading { + color: #8a6d3b; + background-color: #fcf8e3; + border-color: #faebcc; +} +.panel-warning > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #faebcc; +} +.panel-warning > .panel-heading .badge { + color: #fcf8e3; + background-color: #8a6d3b; +} +.panel-warning > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #faebcc; +} + +.panel-danger { + border-color: #ebccd1; +} +.panel-danger > .panel-heading { + color: #a94442; + background-color: #f2dede; + border-color: #ebccd1; +} +.panel-danger > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #ebccd1; +} +.panel-danger > .panel-heading .badge { + color: #f2dede; + background-color: #a94442; +} +.panel-danger > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #ebccd1; +} + +.embed-responsive { + position: relative; + display: block; + height: 0; + padding: 0; + overflow: hidden; +} +.embed-responsive .embed-responsive-item, +.embed-responsive iframe, +.embed-responsive embed, +.embed-responsive object, +.embed-responsive video { + position: absolute; + top: 0; + left: 0; + bottom: 0; + height: 100%; + width: 100%; + border: 0; +} + +.embed-responsive-16by9 { + padding-bottom: 56.25%; +} + +.embed-responsive-4by3 { + padding-bottom: 75%; +} + +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #f5f5f5; + border: 1px solid #e3e3e3; + border-radius: 4px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); +} +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, 0.15); +} + +.well-lg { + padding: 24px; + border-radius: 6px; +} + +.well-sm { + padding: 9px; + border-radius: 3px; +} + +.close { + float: right; + font-size: 21px; + font-weight: bold; + line-height: 1; + color: #000; + text-shadow: 0 1px 0 #fff; + opacity: 0.2; + filter: alpha(opacity=20); +} +.close:hover, +.close:focus { + color: #000; + text-decoration: none; + cursor: pointer; + opacity: 0.5; + filter: alpha(opacity=50); +} + +button.close { + padding: 0; + cursor: pointer; + background: transparent; + border: 0; + -webkit-appearance: none; +} + +.modal-open { + overflow: hidden; +} + +.modal { + display: none; + overflow: hidden; + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1050; + -webkit-overflow-scrolling: touch; + outline: 0; +} +.modal.fade .modal-dialog { + -webkit-transform: translate(0, -25%); + -ms-transform: translate(0, -25%); + -o-transform: translate(0, -25%); + transform: translate(0, -25%); + -webkit-transition: -webkit-transform 0.3s ease-out; + -moz-transition: -moz-transform 0.3s ease-out; + -o-transition: -o-transform 0.3s ease-out; + transition: transform 0.3s ease-out; +} +.modal.in .modal-dialog { + -webkit-transform: translate(0, 0); + -ms-transform: translate(0, 0); + -o-transform: translate(0, 0); + transform: translate(0, 0); +} + +.modal-open .modal { + overflow-x: hidden; + overflow-y: auto; +} + +.modal-dialog { + position: relative; + width: auto; + margin: 10px; +} + +.modal-content { + position: relative; + background-color: #fff; + border: 1px solid #999; + border: 1px solid rgba(0, 0, 0, 0.2); + border-radius: 6px; + -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); + box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); + background-clip: padding-box; + outline: 0; +} + +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000; +} +.modal-backdrop.fade { + opacity: 0; + filter: alpha(opacity=0); +} +.modal-backdrop.in { + opacity: 0.5; + filter: alpha(opacity=50); +} + +.modal-header { + padding: 15px; + border-bottom: 1px solid #e5e5e5; + min-height: 16.42857px; +} + +.modal-header .close { + margin-top: -2px; +} + +.modal-title { + margin: 0; + line-height: 1.42857; +} + +.modal-body { + position: relative; + padding: 15px; +} + +.modal-footer { + padding: 15px; + text-align: right; + border-top: 1px solid #e5e5e5; +} +.modal-footer:before, +.modal-footer:after { + content: " "; + display: table; +} +.modal-footer:after { + clear: both; +} +.modal-footer .btn + .btn { + margin-left: 5px; + margin-bottom: 0; +} +.modal-footer .btn-group .btn + .btn { + margin-left: -1px; +} +.modal-footer .btn-block + .btn-block { + margin-left: 0; +} + +.modal-scrollbar-measure { + position: absolute; + top: -9999px; + width: 50px; + height: 50px; + overflow: scroll; +} + +@media (min-width: 768px) { + .modal-dialog { + width: 600px; + margin: 30px auto; + } + + .modal-content { + -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); + } + + .modal-sm { + width: 300px; + } +} +@media (min-width: 992px) { + .modal-lg { + width: 900px; + } +} +.tooltip { + position: absolute; + z-index: 1070; + display: block; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-style: normal; + font-weight: normal; + letter-spacing: normal; + line-break: auto; + line-height: 1.42857; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + white-space: normal; + word-break: normal; + word-spacing: normal; + word-wrap: normal; + font-size: 12px; + opacity: 0; + filter: alpha(opacity=0); +} +.tooltip.in { + opacity: 0.9; + filter: alpha(opacity=90); +} +.tooltip.top { + margin-top: -3px; + padding: 5px 0; +} +.tooltip.right { + margin-left: 3px; + padding: 0 5px; +} +.tooltip.bottom { + margin-top: 3px; + padding: 5px 0; +} +.tooltip.left { + margin-left: -3px; + padding: 0 5px; +} + +.tooltip-inner { + max-width: 200px; + padding: 3px 8px; + color: #fff; + text-align: center; + background-color: #000; + border-radius: 4px; +} + +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} + +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.top-left .tooltip-arrow { + bottom: 0; + right: 5px; + margin-bottom: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.top-right .tooltip-arrow { + bottom: 0; + left: 5px; + margin-bottom: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-width: 5px 5px 5px 0; + border-right-color: #000; +} +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-width: 5px 0 5px 5px; + border-left-color: #000; +} +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} +.tooltip.bottom-left .tooltip-arrow { + top: 0; + right: 5px; + margin-top: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} +.tooltip.bottom-right .tooltip-arrow { + top: 0; + left: 5px; + margin-top: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} + +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1060; + display: none; + max-width: 276px; + padding: 1px; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-style: normal; + font-weight: normal; + letter-spacing: normal; + line-break: auto; + line-height: 1.42857; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + white-space: normal; + word-break: normal; + word-spacing: normal; + word-wrap: normal; + font-size: 14px; + background-color: #fff; + background-clip: padding-box; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.2); + border-radius: 6px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); +} +.popover.top { + margin-top: -10px; +} +.popover.right { + margin-left: 10px; +} +.popover.bottom { + margin-top: 10px; +} +.popover.left { + margin-left: -10px; +} + +.popover-title { + margin: 0; + padding: 8px 14px; + font-size: 14px; + background-color: #f7f7f7; + border-bottom: 1px solid #ebebeb; + border-radius: 5px 5px 0 0; +} + +.popover-content { + padding: 9px 14px; +} + +.popover > .arrow, +.popover > .arrow:after { + position: absolute; + display: block; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} + +.popover > .arrow { + border-width: 11px; +} + +.popover > .arrow:after { + border-width: 10px; + content: ""; +} + +.popover.top > .arrow { + left: 50%; + margin-left: -11px; + border-bottom-width: 0; + border-top-color: #999999; + border-top-color: rgba(0, 0, 0, 0.25); + bottom: -11px; +} +.popover.top > .arrow:after { + content: " "; + bottom: 1px; + margin-left: -10px; + border-bottom-width: 0; + border-top-color: #fff; +} +.popover.right > .arrow { + top: 50%; + left: -11px; + margin-top: -11px; + border-left-width: 0; + border-right-color: #999999; + border-right-color: rgba(0, 0, 0, 0.25); +} +.popover.right > .arrow:after { + content: " "; + left: 1px; + bottom: -10px; + border-left-width: 0; + border-right-color: #fff; +} +.popover.bottom > .arrow { + left: 50%; + margin-left: -11px; + border-top-width: 0; + border-bottom-color: #999999; + border-bottom-color: rgba(0, 0, 0, 0.25); + top: -11px; +} +.popover.bottom > .arrow:after { + content: " "; + top: 1px; + margin-left: -10px; + border-top-width: 0; + border-bottom-color: #fff; +} +.popover.left > .arrow { + top: 50%; + right: -11px; + margin-top: -11px; + border-right-width: 0; + border-left-color: #999999; + border-left-color: rgba(0, 0, 0, 0.25); +} +.popover.left > .arrow:after { + content: " "; + right: 1px; + border-right-width: 0; + border-left-color: #fff; + bottom: -10px; +} + +.carousel { + position: relative; +} + +.carousel-inner { + position: relative; + overflow: hidden; + width: 100%; +} +.carousel-inner > .item { + display: none; + position: relative; + -webkit-transition: 0.6s ease-in-out left; + -o-transition: 0.6s ease-in-out left; + transition: 0.6s ease-in-out left; +} +.carousel-inner > .item > img, +.carousel-inner > .item > a > img { + display: block; + max-width: 100%; + height: auto; + line-height: 1; +} +@media all and (transform-3d), (-webkit-transform-3d) { + .carousel-inner > .item { + -webkit-transition: -webkit-transform 0.6s ease-in-out; + -moz-transition: -moz-transform 0.6s ease-in-out; + -o-transition: -o-transform 0.6s ease-in-out; + transition: transform 0.6s ease-in-out; + -webkit-backface-visibility: hidden; + -moz-backface-visibility: hidden; + backface-visibility: hidden; + -webkit-perspective: 1000px; + -moz-perspective: 1000px; + perspective: 1000px; + } + .carousel-inner > .item.next, + .carousel-inner > .item.active.right { + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + left: 0; + } + .carousel-inner > .item.prev, + .carousel-inner > .item.active.left { + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + left: 0; + } + .carousel-inner > .item.next.left, + .carousel-inner > .item.prev.right, + .carousel-inner > .item.active { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + left: 0; + } +} +.carousel-inner > .active, +.carousel-inner > .next, +.carousel-inner > .prev { + display: block; +} +.carousel-inner > .active { + left: 0; +} +.carousel-inner > .next, +.carousel-inner > .prev { + position: absolute; + top: 0; + width: 100%; +} +.carousel-inner > .next { + left: 100%; +} +.carousel-inner > .prev { + left: -100%; +} +.carousel-inner > .next.left, +.carousel-inner > .prev.right { + left: 0; +} +.carousel-inner > .active.left { + left: -100%; +} +.carousel-inner > .active.right { + left: 100%; +} + +.carousel-control { + position: absolute; + top: 0; + left: 0; + bottom: 0; + width: 15%; + opacity: 0.5; + filter: alpha(opacity=50); + font-size: 20px; + color: #fff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); +} +.carousel-control.left { + background-image: -webkit-linear-gradient( + left, + rgba(0, 0, 0, 0.5) 0%, + rgba(0, 0, 0, 0.0001) 100% + ); + background-image: -o-linear-gradient( + left, + rgba(0, 0, 0, 0.5) 0%, + rgba(0, 0, 0, 0.0001) 100% + ); + background-image: linear-gradient( + to right, + rgba(0, 0, 0, 0.5) 0%, + rgba(0, 0, 0, 0.0001) 100% + ); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); +} +.carousel-control.right { + left: auto; + right: 0; + background-image: -webkit-linear-gradient( + left, + rgba(0, 0, 0, 0.0001) 0%, + rgba(0, 0, 0, 0.5) 100% + ); + background-image: -o-linear-gradient( + left, + rgba(0, 0, 0, 0.0001) 0%, + rgba(0, 0, 0, 0.5) 100% + ); + background-image: linear-gradient( + to right, + rgba(0, 0, 0, 0.0001) 0%, + rgba(0, 0, 0, 0.5) 100% + ); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); +} +.carousel-control:hover, +.carousel-control:focus { + outline: 0; + color: #fff; + text-decoration: none; + opacity: 0.9; + filter: alpha(opacity=90); +} +.carousel-control .icon-prev, +.carousel-control .icon-next, +.carousel-control .glyphicon-chevron-left, +.carousel-control .glyphicon-chevron-right { + position: absolute; + top: 50%; + margin-top: -10px; + z-index: 5; + display: inline-block; +} +.carousel-control .icon-prev, +.carousel-control .glyphicon-chevron-left { + left: 50%; + margin-left: -10px; +} +.carousel-control .icon-next, +.carousel-control .glyphicon-chevron-right { + right: 50%; + margin-right: -10px; +} +.carousel-control .icon-prev, +.carousel-control .icon-next { + width: 20px; + height: 20px; + line-height: 1; + font-family: serif; +} +.carousel-control .icon-prev:before { + content: "\2039"; +} +.carousel-control .icon-next:before { + content: "\203a"; +} + +.carousel-indicators { + position: absolute; + bottom: 10px; + left: 50%; + z-index: 15; + width: 60%; + margin-left: -30%; + padding-left: 0; + list-style: none; + text-align: center; +} +.carousel-indicators li { + display: inline-block; + width: 10px; + height: 10px; + margin: 1px; + text-indent: -999px; + border: 1px solid #fff; + border-radius: 10px; + cursor: pointer; + background-color: #000 \9; + background-color: transparent; +} +.carousel-indicators .active { + margin: 0; + width: 12px; + height: 12px; + background-color: #fff; +} + +.carousel-caption { + position: absolute; + left: 15%; + right: 15%; + bottom: 20px; + z-index: 10; + padding-top: 20px; + padding-bottom: 20px; + color: #fff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); +} +.carousel-caption .btn { + text-shadow: none; +} + +@media screen and (min-width: 768px) { + .carousel-control .glyphicon-chevron-left, + .carousel-control .glyphicon-chevron-right, + .carousel-control .icon-prev, + .carousel-control .icon-next { + width: 30px; + height: 30px; + margin-top: -15px; + font-size: 30px; + } + .carousel-control .glyphicon-chevron-left, + .carousel-control .icon-prev { + margin-left: -15px; + } + .carousel-control .glyphicon-chevron-right, + .carousel-control .icon-next { + margin-right: -15px; + } + + .carousel-caption { + left: 20%; + right: 20%; + padding-bottom: 30px; + } + + .carousel-indicators { + bottom: 20px; + } +} +.clearfix:before, +.clearfix:after { + content: " "; + display: table; +} +.clearfix:after { + clear: both; +} + +.center-block { + display: block; + margin-left: auto; + margin-right: auto; +} + +.pull-right { + float: right !important; +} + +.pull-left { + float: left !important; +} + +.hide { + display: none !important; +} + +.show { + display: block !important; +} + +.invisible { + visibility: hidden; +} + +.text-hide { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} + +.hidden { + display: none !important; +} + +.affix { + position: fixed; +} + +@-ms-viewport { + width: device-width; +} +.visible-xs { + display: none !important; +} + +.visible-sm { + display: none !important; +} + +.visible-md { + display: none !important; +} + +.visible-lg { + display: none !important; +} + +.visible-xs-block, +.visible-xs-inline, +.visible-xs-inline-block, +.visible-sm-block, +.visible-sm-inline, +.visible-sm-inline-block, +.visible-md-block, +.visible-md-inline, +.visible-md-inline-block, +.visible-lg-block, +.visible-lg-inline, +.visible-lg-inline-block { + display: none !important; +} + +@media (max-width: 767px) { + .visible-xs { + display: block !important; + } + + table.visible-xs { + display: table !important; + } + + tr.visible-xs { + display: table-row !important; + } + + th.visible-xs, + td.visible-xs { + display: table-cell !important; + } +} +@media (max-width: 767px) { + .visible-xs-block { + display: block !important; + } +} + +@media (max-width: 767px) { + .visible-xs-inline { + display: inline !important; + } +} + +@media (max-width: 767px) { + .visible-xs-inline-block { + display: inline-block !important; + } +} + +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm { + display: block !important; + } + + table.visible-sm { + display: table !important; + } + + tr.visible-sm { + display: table-row !important; + } + + th.visible-sm, + td.visible-sm { + display: table-cell !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-block { + display: block !important; + } +} + +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-inline { + display: inline !important; + } +} + +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-inline-block { + display: inline-block !important; + } +} + +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md { + display: block !important; + } + + table.visible-md { + display: table !important; + } + + tr.visible-md { + display: table-row !important; + } + + th.visible-md, + td.visible-md { + display: table-cell !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-block { + display: block !important; + } +} + +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-inline { + display: inline !important; + } +} + +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-inline-block { + display: inline-block !important; + } +} + +@media (min-width: 1200px) { + .visible-lg { + display: block !important; + } + + table.visible-lg { + display: table !important; + } + + tr.visible-lg { + display: table-row !important; + } + + th.visible-lg, + td.visible-lg { + display: table-cell !important; + } +} +@media (min-width: 1200px) { + .visible-lg-block { + display: block !important; + } +} + +@media (min-width: 1200px) { + .visible-lg-inline { + display: inline !important; + } +} + +@media (min-width: 1200px) { + .visible-lg-inline-block { + display: inline-block !important; + } +} + +@media (max-width: 767px) { + .hidden-xs { + display: none !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .hidden-sm { + display: none !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .hidden-md { + display: none !important; + } +} +@media (min-width: 1200px) { + .hidden-lg { + display: none !important; + } +} +.visible-print { + display: none !important; +} + +@media print { + .visible-print { + display: block !important; + } + + table.visible-print { + display: table !important; + } + + tr.visible-print { + display: table-row !important; + } + + th.visible-print, + td.visible-print { + display: table-cell !important; + } +} +.visible-print-block { + display: none !important; +} +@media print { + .visible-print-block { + display: block !important; + } +} + +.visible-print-inline { + display: none !important; +} +@media print { + .visible-print-inline { + display: inline !important; + } +} + +.visible-print-inline-block { + display: none !important; +} +@media print { + .visible-print-inline-block { + display: inline-block !important; + } +} + +@media print { + .hidden-print { + display: none !important; + } +} + +/*# sourceMappingURL=bootstrap.css.map */ diff --git a/web/css/icheck.css b/web/css/icheck.css new file mode 100644 index 0000000..3d6c828 --- /dev/null +++ b/web/css/icheck.css @@ -0,0 +1,551 @@ +/*! + * icheck-bootstrap v3.0.1 (https://github.com/bantikyan/icheck-bootstrap) + * Copyright 2018 Hovhannes Bantikyan. + * Licensed under MIT (https://github.com/bantikyan/icheck-bootstrap/blob/master/LICENSE) + */ +[class*="icheck-"] { + min-height: 22px; + margin-top: 6px !important; + margin-bottom: 6px !important; + padding-left: 0; +} +.icheck-inline { + display: inline-block; +} +.icheck-inline + .icheck-inline { + margin-left: 0.75rem; + margin-top: 6px; +} +[class*="icheck-"] > label { + padding-left: 29px !important; + min-height: 22px; + line-height: 22px; + display: inline-block; + position: relative; + vertical-align: top; + margin-bottom: 0; + font-weight: 400; + cursor: pointer; +} +[class*="icheck-"] > input:first-child { + position: absolute !important; + opacity: 0; + margin: 0; +} +[class*="icheck-"] > input:first-child:disabled { + cursor: default; +} +[class*="icheck-"] > input:first-child + input[type="hidden"] + label::before, +[class*="icheck-"] > input:first-child + label::before { + content: ""; + display: inline-block; + position: absolute; + width: 22px; + height: 22px; + border: 1px solid #d3cfc8; + border-radius: 0; + margin-left: -29px; +} +[class*="icheck-"] + > input:first-child:checked + + input[type="hidden"] + + label::after, +[class*="icheck-"] > input:first-child:checked + label::after { + content: ""; + display: inline-block; + position: absolute; + top: 0; + left: 0; + width: 7px; + height: 10px; + border: 2px solid #fff; + border-left: none; + border-top: none; + transform: translate(7.75px, 4.5px) rotate(45deg); + -ms-transform: translate(7.75px, 4.5px) rotate(45deg); +} +[class*="icheck-"] + > input[type="radio"]:first-child + + input[type="hidden"] + + label::before, +[class*="icheck-"] > input[type="radio"]:first-child + label::before { + border-radius: 50%; +} +[class*="icheck-"] + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +[class*="icheck-"] + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-width: 2px; +} +[class*="icheck-"] > input:first-child:disabled + input[type="hidden"] + label, +[class*="icheck-"] + > input:first-child:disabled + + input[type="hidden"] + + label::before, +[class*="icheck-"] > input:first-child:disabled + label, +[class*="icheck-"] > input:first-child:disabled + label::before { + pointer-events: none; + cursor: default; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + box-shadow: none; + opacity: 0.65; +} +.icheck-default + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-default + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #adadad; +} +.icheck-default + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-default > input:first-child:checked + label::before { + background-color: #e6e6e6; + border-color: #adadad; +} +.icheck-default + > input:first-child:checked + + input[type="hidden"] + + label::after, +.icheck-default > input:first-child:checked + label::after { + border-bottom-color: #333; + border-right-color: #333; +} +.icheck-primary + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-primary + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #2e6da4; +} +.icheck-primary + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-primary > input:first-child:checked + label::before { + background-color: #337ab7; + border-color: #2e6da4; +} +.icheck-success + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-success + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #4cae4c; +} +.icheck-success + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-success > input:first-child:checked + label::before { + background-color: #5cb85c; + border-color: #4cae4c; +} +.icheck-info + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-info + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #46b8da; +} +.icheck-info > input:first-child:checked + input[type="hidden"] + label::before, +.icheck-info > input:first-child:checked + label::before { + background-color: #5bc0de; + border-color: #46b8da; +} +.icheck-warning + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-warning + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #eea236; +} +.icheck-warning + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-warning > input:first-child:checked + label::before { + background-color: #f0ad4e; + border-color: #eea236; +} +.icheck-danger + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-danger + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #d43f3a; +} +.icheck-danger + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-danger > input:first-child:checked + label::before { + background-color: #d9534f; + border-color: #d43f3a; +} +.icheck-turquoise + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-turquoise + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #1abc9c; +} +.icheck-turquoise + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-turquoise > input:first-child:checked + label::before { + background-color: #1abc9c; + border-color: #1abc9c; +} +.icheck-emerland + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-emerland + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #2ecc71; +} +.icheck-emerland + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-emerland > input:first-child:checked + label::before { + background-color: #2ecc71; + border-color: #2ecc71; +} +.icheck-peterriver + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-peterriver + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #3498db; +} +.icheck-peterriver + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-peterriver > input:first-child:checked + label::before { + background-color: #3498db; + border-color: #3498db; +} +.icheck-amethyst + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-amethyst + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #9b59b6; +} +.icheck-amethyst + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-amethyst > input:first-child:checked + label::before { + background-color: #9b59b6; + border-color: #9b59b6; +} +.icheck-wetasphalt + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-wetasphalt + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #34495e; +} +.icheck-wetasphalt + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-wetasphalt > input:first-child:checked + label::before { + background-color: #34495e; + border-color: #34495e; +} +.icheck-greensea + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-greensea + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #16a085; +} +.icheck-greensea + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-greensea > input:first-child:checked + label::before { + background-color: #16a085; + border-color: #16a085; +} +.icheck-nephritis + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-nephritis + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #27ae60; +} +.icheck-nephritis + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-nephritis > input:first-child:checked + label::before { + background-color: #27ae60; + border-color: #27ae60; +} +.icheck-belizehole + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-belizehole + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #2980b9; +} +.icheck-belizehole + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-belizehole > input:first-child:checked + label::before { + background-color: #2980b9; + border-color: #2980b9; +} +.icheck-wisteria + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-wisteria + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #8e44ad; +} +.icheck-wisteria + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-wisteria > input:first-child:checked + label::before { + background-color: #8e44ad; + border-color: #8e44ad; +} +.icheck-midnightblue + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-midnightblue + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #2c3e50; +} +.icheck-midnightblue + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-midnightblue > input:first-child:checked + label::before { + background-color: #2c3e50; + border-color: #2c3e50; +} +.icheck-sunflower + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-sunflower + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #f1c40f; +} +.icheck-sunflower + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-sunflower > input:first-child:checked + label::before { + background-color: #f1c40f; + border-color: #f1c40f; +} +.icheck-carrot + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-carrot + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #e67e22; +} +.icheck-carrot + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-carrot > input:first-child:checked + label::before { + background-color: #e67e22; + border-color: #e67e22; +} +.icheck-alizarin + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-alizarin + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #e74c3c; +} +.icheck-alizarin + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-alizarin > input:first-child:checked + label::before { + background-color: #e74c3c; + border-color: #e74c3c; +} +.icheck-clouds + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-clouds + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #ecf0f1; +} +.icheck-clouds + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-clouds > input:first-child:checked + label::before { + background-color: #ecf0f1; + border-color: #ecf0f1; +} +.icheck-clouds + > input:first-child:checked + + input[type="hidden"] + + label::after, +.icheck-clouds > input:first-child:checked + label::after { + border-bottom-color: #95a5a6; + border-right-color: #95a5a6; +} +.icheck-concrete + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-concrete + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #95a5a6; +} +.icheck-concrete + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-concrete > input:first-child:checked + label::before { + background-color: #95a5a6; + border-color: #95a5a6; +} +.icheck-orange + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-orange + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #f39c12; +} +.icheck-orange + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-orange > input:first-child:checked + label::before { + background-color: #f39c12; + border-color: #f39c12; +} +.icheck-pumpkin + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-pumpkin + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #d35400; +} +.icheck-pumpkin + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-pumpkin > input:first-child:checked + label::before { + background-color: #d35400; + border-color: #d35400; +} +.icheck-pomegranate + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-pomegranate + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #c0392b; +} +.icheck-pomegranate + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-pomegranate > input:first-child:checked + label::before { + background-color: #c0392b; + border-color: #c0392b; +} +.icheck-silver + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-silver + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #bdc3c7; +} +.icheck-silver + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-silver > input:first-child:checked + label::before { + background-color: #bdc3c7; + border-color: #bdc3c7; +} +.icheck-asbestos + > input:first-child:not(:checked):not(:disabled):hover + + input[type="hidden"] + + label::before, +.icheck-asbestos + > input:first-child:not(:checked):not(:disabled):hover + + label::before { + border-color: #7f8c8d; +} +.icheck-asbestos + > input:first-child:checked + + input[type="hidden"] + + label::before, +.icheck-asbestos > input:first-child:checked + label::before { + background-color: #7f8c8d; + border-color: #7f8c8d; +} diff --git a/web/css/icomoon.css b/web/css/icomoon.css new file mode 100644 index 0000000..28c290a --- /dev/null +++ b/web/css/icomoon.css @@ -0,0 +1,3135 @@ +@font-face { + font-family: 'icomoon'; + src: url('fonts/icomoon.eot?1oniuf'); + src: url('fonts/icomoon.eot?1oniuf#iefix') format('embedded-opentype'), + url('fonts/icomoon.ttf?1oniuf') format('truetype'), + url('fonts/icomoon.woff?1oniuf') format('woff'), + url('fonts/icomoon.svg?1oniuf#icomoon') format('svg'); + font-weight: normal; + font-style: normal; +} + +[class^="icon-"], [class*=" icon-"] { + /* use !important to prevent issues with browser extensions that change fonts */ + font-family: 'icomoon' !important; + speak: none; + font-style: normal; + font-weight: normal; + font-variant: normal; + text-transform: none; + line-height: 1; + + /* Better Font Rendering =========== */ + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} + +.icon-mobile:before { + content: "\e000"; +} +.icon-laptop:before { + content: "\e001"; +} +.icon-desktop:before { + content: "\e002"; +} +.icon-tablet:before { + content: "\e003"; +} +.icon-phone:before { + content: "\e004"; +} +.icon-document:before { + content: "\e005"; +} +.icon-documents:before { + content: "\e006"; +} +.icon-search:before { + content: "\e007"; +} +.icon-clipboard:before { + content: "\e008"; +} +.icon-newspaper:before { + content: "\e009"; +} +.icon-notebook:before { + content: "\e00a"; +} +.icon-book-open:before { + content: "\e00b"; +} +.icon-browser:before { + content: "\e00c"; +} +.icon-calendar:before { + content: "\e00d"; +} +.icon-presentation:before { + content: "\e00e"; +} +.icon-picture:before { + content: "\e00f"; +} +.icon-pictures:before { + content: "\e010"; +} +.icon-video:before { + content: "\e011"; +} +.icon-camera:before { + content: "\e012"; +} +.icon-printer:before { + content: "\e013"; +} +.icon-toolbox:before { + content: "\e014"; +} +.icon-briefcase:before { + content: "\e015"; +} +.icon-wallet:before { + content: "\e016"; +} +.icon-gift:before { + content: "\e017"; +} +.icon-bargraph:before { + content: "\e018"; +} +.icon-grid:before { + content: "\e019"; +} +.icon-expand:before { + content: "\e01a"; +} +.icon-focus:before { + content: "\e01b"; +} +.icon-edit:before { + content: "\e01c"; +} +.icon-adjustments:before { + content: "\e01d"; +} +.icon-ribbon:before { + content: "\e01e"; +} +.icon-hourglass:before { + content: "\e01f"; +} +.icon-lock:before { + content: "\e020"; +} +.icon-megaphone:before { + content: "\e021"; +} +.icon-shield:before { + content: "\e022"; +} +.icon-trophy:before { + content: "\e023"; +} +.icon-flag:before { + content: "\e024"; +} +.icon-map:before { + content: "\e025"; +} +.icon-puzzle:before { + content: "\e026"; +} +.icon-basket:before { + content: "\e027"; +} +.icon-envelope:before { + content: "\e028"; +} +.icon-streetsign:before { + content: "\e029"; +} +.icon-telescope:before { + content: "\e02a"; +} +.icon-gears:before { + content: "\e02b"; +} +.icon-key:before { + content: "\e02c"; +} +.icon-paperclip:before { + content: "\e02d"; +} +.icon-attachment:before { + content: "\e02e"; +} +.icon-pricetags:before { + content: "\e02f"; +} +.icon-lightbulb:before { + content: "\e030"; +} +.icon-layers:before { + content: "\e031"; +} +.icon-pencil:before { + content: "\e032"; +} +.icon-tools:before { + content: "\e033"; +} +.icon-tools-2:before { + content: "\e034"; +} +.icon-scissors:before { + content: "\e035"; +} 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+} +.icon-cloud:before { + content: "\e04b"; +} +.icon-upload:before { + content: "\e04c"; +} +.icon-download:before { + content: "\e04d"; +} +.icon-target:before { + content: "\e04e"; +} +.icon-hazardous:before { + content: "\e04f"; +} +.icon-piechart:before { + content: "\e050"; +} +.icon-speedometer:before { + content: "\e051"; +} +.icon-global:before { + content: "\e052"; +} +.icon-compass:before { + content: "\e053"; +} +.icon-lifesaver:before { + content: "\e054"; +} +.icon-clock:before { + content: "\e055"; +} +.icon-aperture:before { + content: "\e056"; +} +.icon-quote:before { + content: "\e057"; +} +.icon-scope:before { + content: "\e058"; +} +.icon-alarmclock:before { + content: "\e059"; +} +.icon-refresh:before { + content: "\e05a"; +} +.icon-happy:before { + content: "\e05b"; +} +.icon-sad:before { + content: "\e05c"; +} +.icon-facebook:before { + content: "\e05d"; +} +.icon-twitter:before { + content: "\e05e"; +} +.icon-googleplus:before { + content: "\e05f"; +} 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content: "\ea93"; +} +.icon-sun:before { + content: "\ea94"; +} +.icon-moon2:before { + content: "\ea95"; +} +.icon-bell2:before { + content: "\ea96"; +} +.icon-folder2:before { + content: "\ea97"; +} +.icon-pin2:before { + content: "\ea98"; +} +.icon-sound2:before { + content: "\ea99"; +} +.icon-microphone:before { + content: "\ea9a"; +} +.icon-camera2:before { + content: "\ea9b"; +} +.icon-image2:before { + content: "\ea9c"; +} +.icon-cog2:before { + content: "\ea9d"; +} +.icon-calendar2:before { + content: "\ea9e"; +} +.icon-book2:before { + content: "\ea9f"; +} +.icon-map-marker:before { + content: "\eaa0"; +} +.icon-store:before { + content: "\eaa1"; +} +.icon-support:before { + content: "\eaa2"; +} +.icon-tag2:before { + content: "\eaa3"; +} +.icon-heart2:before { + content: "\eaa4"; +} +.icon-video-camera:before { + content: "\eaa5"; +} +.icon-trophy2:before { + content: "\eaa6"; +} +.icon-cart:before { + content: "\eaa7"; +} +.icon-eye2:before { + content: "\eaa8"; +} 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+ content: "\e96d"; +} +.icon-bell:before { + content: "\e96e"; +} +.icon-blackboard:before { + content: "\e96f"; +} +.icon-block:before { + content: "\e970"; +} +.icon-book:before { + content: "\e971"; +} +.icon-bowl:before { + content: "\e972"; +} +.icon-box:before { + content: "\e973"; +} +.icon-briefcase2:before { + content: "\e974"; +} +.icon-browser3:before { + content: "\e975"; +} +.icon-brush:before { + content: "\e976"; +} +.icon-bucket:before { + content: "\e977"; +} +.icon-cake:before { + content: "\e978"; +} +.icon-calculator:before { + content: "\e979"; +} +.icon-calendar3:before { + content: "\e97a"; +} +.icon-camera3:before { + content: "\e97b"; +} +.icon-ccw:before { + content: "\e97c"; +} +.icon-chat2:before { + content: "\e97d"; +} +.icon-check:before { + content: "\e97e"; +} +.icon-circle-with-cross:before { + content: "\e97f"; +} +.icon-circle-with-minus:before { + content: "\e980"; +} +.icon-circle-with-plus:before { + content: "\e981"; +} +.icon-circle:before { + content: "\e982"; +} +.icon-circular-graph:before { + content: "\e983"; +} +.icon-clapperboard:before { + content: "\e984"; +} +.icon-clipboard2:before { + content: "\e985"; +} +.icon-clock3:before { + content: "\e986"; +} +.icon-code:before { + content: "\e987"; +} +.icon-cog:before { + content: "\e988"; +} +.icon-colours:before { + content: "\e989"; +} +.icon-compass2:before { + content: "\e98a"; +} +.icon-copy:before { + content: "\e98b"; +} +.icon-credit-card:before { + content: "\e98c"; +} +.icon-credit:before { + content: "\e98d"; +} +.icon-cross:before { + content: "\e98e"; +} +.icon-cup:before { + content: "\e98f"; +} +.icon-cw:before { + content: "\e990"; +} +.icon-cycle:before { + content: "\e991"; +} +.icon-database:before { + content: "\e992"; +} +.icon-dial-pad:before { + content: "\e993"; +} +.icon-direction:before { + content: "\e994"; +} +.icon-document2:before { + content: "\e995"; +} +.icon-documents2:before { + content: "\e996"; +} +.icon-drink:before { + content: "\e997"; +} +.icon-drive:before { + content: "\e998"; +} +.icon-drop:before { + content: "\e999"; +} +.icon-edit2:before { + content: "\e99a"; +} +.icon-email:before { + content: "\e99b"; +} +.icon-emoji-happy:before { + content: "\e99c"; +} +.icon-emoji-neutral:before { + content: "\e99d"; +} +.icon-emoji-sad:before { + content: "\e99e"; +} +.icon-erase:before { + content: "\e99f"; +} +.icon-eraser:before { + content: "\e9a0"; +} +.icon-export:before { + content: "\e9a1"; +} +.icon-eye:before { + content: "\e9a2"; +} +.icon-feather:before { + content: "\e9a3"; +} +.icon-flag3:before { + content: "\e9a4"; +} +.icon-flash:before { + content: "\e9a5"; +} +.icon-flashlight:before { + content: "\e9a6"; +} +.icon-flat-brush:before { + content: "\e9a7"; +} +.icon-folder-images:before { + content: "\e9a8"; +} +.icon-folder-music:before { + content: "\e9a9"; +} +.icon-folder-video:before { + content: "\e9aa"; +} +.icon-folder:before { + content: "\e9ab"; +} +.icon-forward:before { + content: "\e9ac"; +} +.icon-funnel:before { + content: "\e9ad"; +} +.icon-game-controller:before { + content: "\e9ae"; +} +.icon-gauge:before { + content: "\e9af"; +} +.icon-globe2:before { + content: "\e9b0"; +} +.icon-graduation-cap:before { + content: "\e9b1"; +} +.icon-grid2:before { + content: "\e9b2"; +} +.icon-hair-cross:before { + content: "\e9b3"; +} +.icon-hand:before { + content: "\e9b4"; +} +.icon-heart-outlined:before { + content: "\e9b5"; +} +.icon-heart3:before { + content: "\e9b6"; +} +.icon-help-with-circle:before { + content: "\e9b7"; +} +.icon-help:before { + content: "\e9b8"; +} +.icon-home:before { + content: "\e9b9"; +} +.icon-hour-glass:before { + content: "\e9ba"; +} +.icon-image-inverted:before { + content: "\e9bb"; +} +.icon-image:before { + content: "\e9bc"; +} +.icon-images:before { + content: "\e9bd"; +} +.icon-inbox:before { + content: "\e9be"; +} +.icon-infinity:before { + content: "\e9bf"; +} +.icon-info-with-circle:before { + content: "\e9c0"; +} 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content: "\e9eb"; +} +.icon-new:before { + content: "\e9ec"; +} +.icon-news:before { + content: "\e9ed"; +} +.icon-note:before { + content: "\e9ee"; +} +.icon-notification:before { + content: "\e9ef"; +} +.icon-old-mobile:before { + content: "\e9f0"; +} +.icon-old-phone:before { + content: "\e9f1"; +} +.icon-palette:before { + content: "\e9f2"; +} +.icon-paper-plane:before { + content: "\e9f3"; +} +.icon-pencil2:before { + content: "\e9f4"; +} +.icon-phone2:before { + content: "\e9f5"; +} +.icon-pie-chart:before { + content: "\e9f6"; +} +.icon-pin:before { + content: "\e9f7"; +} +.icon-plus:before { + content: "\e9f8"; +} +.icon-popup:before { + content: "\e9f9"; +} +.icon-power-plug:before { + content: "\e9fa"; +} +.icon-price-ribbon:before { + content: "\e9fb"; +} +.icon-price-tag:before { + content: "\e9fc"; +} +.icon-print:before { + content: "\e9fd"; +} +.icon-progress-empty:before { + content: "\e9fe"; +} +.icon-progress-full:before { + content: "\e9ff"; +} 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content: "\ea14"; +} +.icon-shuffle:before { + content: "\ea15"; +} +.icon-signal:before { + content: "\ea16"; +} +.icon-sound-mix:before { + content: "\ea17"; +} +.icon-sports-club:before { + content: "\ea18"; +} +.icon-spreadsheet:before { + content: "\ea19"; +} +.icon-squared-cross:before { + content: "\ea1a"; +} +.icon-squared-minus:before { + content: "\ea1b"; +} +.icon-squared-plus:before { + content: "\ea1c"; +} +.icon-star-outlined:before { + content: "\ea1d"; +} +.icon-star:before { + content: "\ea1e"; +} +.icon-stopwatch:before { + content: "\ea1f"; +} +.icon-suitcase:before { + content: "\ea20"; +} +.icon-swap:before { + content: "\ea21"; +} +.icon-sweden:before { + content: "\ea22"; +} +.icon-switch:before { + content: "\ea23"; +} +.icon-tablet2:before { + content: "\ea24"; +} +.icon-tag:before { + content: "\ea25"; +} +.icon-text-document-inverted:before { + content: "\ea26"; +} +.icon-text-document:before { + content: "\ea27"; +} +.icon-text:before { + content: "\ea28"; +} +.icon-thermometer:before { + content: "\ea29"; +} +.icon-thumbs-down:before { + content: "\ea2a"; +} +.icon-thumbs-up:before { + content: "\ea2b"; +} +.icon-thunder-cloud:before { + content: "\ea2c"; +} +.icon-ticket:before { + content: "\ea2d"; +} +.icon-time-slot:before { + content: "\ea2e"; +} +.icon-tools2:before { + content: "\ea2f"; +} +.icon-traffic-cone:before { + content: "\ea30"; +} +.icon-tree:before { + content: "\ea31"; +} +.icon-trophy3:before { + content: "\ea32"; +} +.icon-tv:before { + content: "\ea33"; +} +.icon-typing:before { + content: "\ea34"; +} +.icon-unread:before { + content: "\ea35"; +} +.icon-untag:before { + content: "\ea36"; +} +.icon-user:before { + content: "\ea37"; +} +.icon-users:before { + content: "\ea38"; +} +.icon-v-card:before { + content: "\ea39"; +} +.icon-video2:before { + content: "\ea3a"; +} +.icon-vinyl:before { + content: "\ea3b"; +} +.icon-voicemail:before { + content: "\ea3c"; +} +.icon-wallet2:before { + content: "\ea3d"; +} 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content: "\ea52"; +} +.icon-scribd:before { + content: "\ea53"; +} +.icon-sina-weibo:before { + content: "\ea54"; +} +.icon-skype-with-circle:before { + content: "\ea55"; +} +.icon-skype:before { + content: "\ea56"; +} +.icon-slideshare:before { + content: "\ea57"; +} +.icon-smashing:before { + content: "\ea58"; +} +.icon-soundcloud:before { + content: "\ea59"; +} +.icon-spotify-with-circle:before { + content: "\ea5a"; +} +.icon-spotify:before { + content: "\ea5b"; +} +.icon-swarm:before { + content: "\ea5c"; +} +.icon-vine-with-circle:before { + content: "\ea5d"; +} +.icon-vine:before { + content: "\ea5e"; +} +.icon-vk-alternitive:before { + content: "\ea5f"; +} +.icon-vk-with-circle:before { + content: "\ea60"; +} +.icon-vk:before { + content: "\ea61"; +} +.icon-xing-with-circle:before { + content: "\ea62"; +} +.icon-xing:before { + content: "\ea63"; +} +.icon-yelp:before { + content: "\ea64"; +} +.icon-dribbble-with-circle:before { + content: "\ea65"; +} +.icon-dribbble2:before { + content: "\ea66"; +} +.icon-facebook-with-circle:before { + content: "\ea67"; +} +.icon-facebook2:before { + content: "\ea68"; +} +.icon-flickr-with-circle:before { + content: "\ea69"; +} +.icon-flickr:before { + content: "\ea6a"; +} +.icon-github-with-circle:before { + content: "\ea6b"; +} +.icon-github:before { + content: "\ea6c"; +} +.icon-google-with-circle:before { + content: "\ea6d"; +} +.icon-google:before { + content: "\ea6e"; +} +.icon-instagram-with-circle:before { + content: "\ea6f"; +} +.icon-instagram:before { + content: "\ea70"; +} +.icon-lastfm-with-circle:before { + content: "\ea71"; +} +.icon-lastfm:before { + content: "\ea72"; +} +.icon-linkedin-with-circle:before { + content: "\ea73"; +} +.icon-linkedin2:before { + content: "\ea74"; +} +.icon-pinterest-with-circle:before { + content: "\ea75"; +} +.icon-pinterest:before { + content: "\ea76"; +} +.icon-rdio:before { + content: "\ea77"; +} +.icon-stumbleupon-with-circle:before { + content: "\ea78"; +} 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"\eb59"; +} +.icon-stats-bars:before { + content: "\eb5a"; +} +.icon-stats-bars2:before { + content: "\eb5b"; +} +.icon-trophy4:before { + content: "\eb5c"; +} +.icon-gift2:before { + content: "\eb5d"; +} +.icon-glass:before { + content: "\eb5e"; +} +.icon-glass2:before { + content: "\eb5f"; +} +.icon-mug:before { + content: "\eb60"; +} +.icon-spoon-knife:before { + content: "\eb61"; +} +.icon-leaf2:before { + content: "\eb62"; +} +.icon-rocket2:before { + content: "\eb63"; +} +.icon-meter:before { + content: "\eb64"; +} +.icon-meter2:before { + content: "\eb65"; +} +.icon-hammer2:before { + content: "\eb66"; +} +.icon-fire:before { + content: "\eb67"; +} +.icon-lab:before { + content: "\eb68"; +} +.icon-magnet2:before { + content: "\eb69"; +} +.icon-bin:before { + content: "\eb6a"; +} +.icon-bin2:before { + content: "\eb6b"; +} +.icon-briefcase3:before { + content: "\eb6c"; +} +.icon-airplane:before { + content: "\eb6d"; +} +.icon-truck:before { + content: "\eb6e"; +} 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#aaa;border-top-right-radius:4px;border-bottom-right-radius:4px;height:26px;position:absolute;top:1px;right:1px;width:20px;background-image:-webkit-linear-gradient(top, #eee 50%, #ccc 100%);background-image:-o-linear-gradient(top, #eee 50%, #ccc 100%);background-image:linear-gradient(to bottom, #eee 50%, #ccc 100%);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#FFEEEEEE', endColorstr='#FFCCCCCC', GradientType=0)}.select2-container--classic .select2-selection--single .select2-selection__arrow b{border-color:#888 transparent transparent transparent;border-style:solid;border-width:5px 4px 0 4px;height:0;left:50%;margin-left:-4px;margin-top:-2px;position:absolute;top:50%;width:0}.select2-container--classic[dir="rtl"] .select2-selection--single .select2-selection__clear{float:left}.select2-container--classic[dir="rtl"] .select2-selection--single .select2-selection__arrow{border:none;border-right:1px solid #aaa;border-radius:0;border-top-left-radius:4px;border-bottom-left-radius:4px;left:1px;right:auto}.select2-container--classic.select2-container--open .select2-selection--single{border:1px solid #5897fb}.select2-container--classic.select2-container--open .select2-selection--single .select2-selection__arrow{background:transparent;border:none}.select2-container--classic.select2-container--open .select2-selection--single .select2-selection__arrow b{border-color:transparent transparent #888 transparent;border-width:0 4px 5px 4px}.select2-container--classic.select2-container--open.select2-container--above .select2-selection--single{border-top:none;border-top-left-radius:0;border-top-right-radius:0;background-image:-webkit-linear-gradient(top, #fff 0%, #eee 50%);background-image:-o-linear-gradient(top, #fff 0%, #eee 50%);background-image:linear-gradient(to bottom, #fff 0%, #eee 50%);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#FFFFFFFF', endColorstr='#FFEEEEEE', GradientType=0)}.select2-container--classic.select2-container--open.select2-container--below .select2-selection--single{border-bottom:none;border-bottom-left-radius:0;border-bottom-right-radius:0;background-image:-webkit-linear-gradient(top, #eee 50%, #fff 100%);background-image:-o-linear-gradient(top, #eee 50%, #fff 100%);background-image:linear-gradient(to bottom, #eee 50%, #fff 100%);background-repeat:repeat-x;filter:progid:DXImageTransform.Microsoft.gradient(startColorstr='#FFEEEEEE', endColorstr='#FFFFFFFF', GradientType=0)}.select2-container--classic .select2-selection--multiple{background-color:white;border:1px solid #aaa;border-radius:4px;cursor:text;outline:0}.select2-container--classic .select2-selection--multiple:focus{border:1px solid #5897fb}.select2-container--classic .select2-selection--multiple .select2-selection__rendered{list-style:none;margin:0;padding:0 5px}.select2-container--classic .select2-selection--multiple .select2-selection__clear{display:none}.select2-container--classic .select2-selection--multiple .select2-selection__choice{background-color:#e4e4e4;border:1px solid #aaa;border-radius:4px;cursor:default;float:left;margin-right:5px;margin-top:5px;padding:0 5px}.select2-container--classic .select2-selection--multiple .select2-selection__choice__remove{color:#888;cursor:pointer;display:inline-block;font-weight:bold;margin-right:2px}.select2-container--classic .select2-selection--multiple .select2-selection__choice__remove:hover{color:#555}.select2-container--classic[dir="rtl"] .select2-selection--multiple .select2-selection__choice{float:right;margin-left:5px;margin-right:auto}.select2-container--classic[dir="rtl"] .select2-selection--multiple .select2-selection__choice__remove{margin-left:2px;margin-right:auto}.select2-container--classic.select2-container--open .select2-selection--multiple{border:1px solid #5897fb}.select2-container--classic.select2-container--open.select2-container--above .select2-selection--multiple{border-top:none;border-top-left-radius:0;border-top-right-radius:0}.select2-container--classic.select2-container--open.select2-container--below .select2-selection--multiple{border-bottom:none;border-bottom-left-radius:0;border-bottom-right-radius:0}.select2-container--classic .select2-search--dropdown .select2-search__field{border:1px solid #aaa;outline:0}.select2-container--classic .select2-search--inline .select2-search__field{outline:0;box-shadow:none}.select2-container--classic .select2-dropdown{background-color:#fff;border:1px solid transparent}.select2-container--classic .select2-dropdown--above{border-bottom:none}.select2-container--classic .select2-dropdown--below{border-top:none}.select2-container--classic .select2-results>.select2-results__options{max-height:200px;overflow-y:auto}.select2-container--classic .select2-results__option[role=group]{padding:0}.select2-container--classic .select2-results__option[aria-disabled=true]{color:grey}.select2-container--classic .select2-results__option--highlighted[aria-selected]{background-color:#3875d7;color:#fff}.select2-container--classic .select2-results__group{cursor:default;display:block;padding:6px}.select2-container--classic.select2-container--open .select2-dropdown{border-color:#5897fb} diff --git a/web/css/style.css b/web/css/style.css new file mode 100644 index 0000000..135ec93 --- /dev/null +++ b/web/css/style.css @@ -0,0 +1,1882 @@ +@font-face { + font-family: "icomoon"; + src: url("../fonts/icomoon/icomoon.eot?srf3rx"); + src: url("../fonts/icomoon/icomoon.eot?srf3rx#iefix") + format("embedded-opentype"), + url("../fonts/icomoon/icomoon.ttf?srf3rx") format("truetype"), + url("../fonts/icomoon/icomoon.woff?srf3rx") format("woff"), + url("../fonts/icomoon/icomoon.svg?srf3rx#icomoon") format("svg"); + font-weight: normal; + font-style: normal; +} +/* ======================================================= +* +* Template Style +* Edit this section +* +* ======================================================= */ +body { + font-family: "Open Sans", Arial, sans-serif; + line-height: 1.6; + font-size: 16px; + background: #fff; + color: #848484; + font-weight: 300; + overflow-x: hidden; +} + +body.fh5co-offcanvas { + overflow: hidden; +} + +a { + color: #ff5722; + -webkit-transition: 0.5s, ease; + -o-transition: 0.5s, ease; + transition: 0.5s, ease; +} + +a:hover { + text-decoration: none !important; + color: #ff5722 !important; +} + +p, +ul, +ol { + margin-bottom: 1.5em; + font-size: 16px; + color: #000; + font-family: "Open Sans", Arial, sans-serif; +} + +h1, +h2, +h3, +h4, +h5, +h6 { + color: #5a5a5a; + font-family: "Open Sans", Arial, sans-serif; + font-weight: 400; + margin: 0 0 30px 0; +} + +figure { + margin-bottom: 2em; +} + +::-webkit-selection { + color: #fcfcfc; + background: #ff5722; +} + +::-moz-selection { + color: #fcfcfc; + background: #ff5722; +} + +::selection { + color: #fcfcfc; + background: #ff5722; +} + +#fh5co-header { + position: relative; + margin-bottom: 0; + z-index: 9999999; +} + +.stuck { + position: fixed !important; + top: 0 !important; + -webkit-box-shadow: 0 2px 2px rgba(0, 0, 0, 0.05); + -moz-box-shadow: 0 2px 2px rgba(0, 0, 0, 0.05); + -ms-box-shadow: 0 2px 2px rgba(0, 0, 0, 0.05); + -o-box-shadow: 0 2px 2px rgba(0, 0, 0, 0.05); + box-shadow: 0 2px 2px rgba(0, 0, 0, 0.05); +} + +.header-top { + padding: 10px 0 0; + background: #fff; +} + +/* .header-top a img { + height: 4rem; +} */ + +.header-top h2 { + color: #000; +} + +.header-top .fh5co-link a { + margin-left: 10px; + font-size: 13px; + color: rgba(255, 255, 255, 0.7); +} + +.header-top .fh5co-link a:first-child { + margin-left: 0; +} + +.header-top .fh5co-link a:hover { + color: white !important; +} + +@media screen and (max-width: 768px) { + .header-top .fh5co-link { + text-align: center; + } +} + +.header-top .fh5co-social a { + color: rgba(255, 255, 255, 0.7); + font-size: 14px; + margin-right: 15px; + position: relative; + -webkit-transition: 0.5s, ease-in-out; + -o-transition: 0.5s, ease-in-out; + transition: 0.5s, ease-in-out; +} + +.header-top .fh5co-social a:last-child { + margin-right: 0; +} + +.header-top .fh5co-social a:hover, +.header-top .fh5co-social a:focus, +.header-top .fh5co-social a:active { + text-decoration: none; + color: white !important; +} + +.header-top .fh5co-social a:hover i, +.header-top .fh5co-social a:focus i, +.header-top .fh5co-social a:active i { + -webkit-transform: scale(1.2); + -moz-transform: scale(1.2); + -ms-transform: scale(1.2); + -o-transform: scale(1.2); + transform: scale(1.2); +} + +@media screen and (max-width: 768px) { + .header-top .fh5co-social { + text-align: center !important; + } +} + +.fh5co-cover { + background-image: url(/images/restaurant.png); +} + +.fh5co-cover, +.fh5co-hero { + position: relative; + height: 490px; +} + +@media screen and (max-width: 768px) { + .fh5co-cover, + .fh5co-hero { + height: 700px; + } +} + +.fh5co-cover.fh5co-hero-2, +.fh5co-hero.fh5co-hero-2 { + height: 600px; +} + +.fh5co-cover.fh5co-hero-2 .fh5co-overlay, +.fh5co-hero.fh5co-hero-2 .fh5co-overlay { + position: absolute !important; + width: 100%; + top: 0; + left: 0; + bottom: 0; + right: 0; + z-index: 1; + background: rgba(0, 0, 0, 0.2); +} + +.fh5co-overlay { + position: absolute !important; + width: 100%; + top: 0; + left: 0; + bottom: 0; + right: 0; + z-index: 1; + background: rgba(66, 64, 64, 0.9); + background: rgba(0, 0, 0, 0.2); +} + +.fh5co-cover { + background-size: cover; + position: relative; +} + +@media screen and (max-width: 768px) { + .fh5co-cover { + heifght: inherit; + padding: 3em 0; + } +} + +.fh5co-cover .btn { + padding: 1em 2em; + opacity: 0.9; +} + +.fh5co-cover.fh5co-cover_2 { + height: 600px; +} + +@media screen and (max-width: 768px) { + .fh5co-cover.fh5co-cover_2 { + heifght: inherit; + padding: 3em 0; + } +} + +.fh5co-cover .desc { + top: 60%; + position: absolute; + width: 100%; + /* margin-top: -80px; */ + z-index: 2; + color: #fff; + padding-left: 20px; +} + +@media screen and (max-width: 768px) { + .fh5co-cover .desc { + padding-left: 15px; + padding-right: 15px; + } +} + +.fh5co-cover .desc p { + color: #fff; + /* text-transform: uppercase; */ + font-size: 20px; + /* margin-bottom: 10px; */ + /* font-weight: 300 !important; */ +} + +.fh5co-cover .desc h2 strong { + font-weight: 700; +} + +@media screen and (max-width: 768px) { + .fh5co-cover .desc p { + font-size: 40px; + } +} + +.fh5co-cover .desc span { + display: block; + margin-bottom: 30px; + font-size: 28px; + letter-spacing: 1px; +} + +.fh5co-cover .desc span a { + color: #fff; +} + +.fh5co-cover .desc span a:hover, +.fh5co-cover .desc span a:focus { + color: #fff !important; +} + +.fh5co-cover .desc span a:hover.btn, +.fh5co-cover .desc span a:focus.btn { + border: 2px solid #fff !important; + background: transparent !important; +} + +.fh5co-cover .desc span a.fh5co-site-name { + padding-bottom: 2px; + border-bottom: 1px solid rgba(255, 255, 255, 0.7); +} + +/* Superfish Override Menu */ +.sf-menu { + margin: 7px 0 0 0 !important; +} + +.sf-menu { + float: right; +} + +.sf-menu ul { + box-shadow: none; + border: transparent; + min-width: 12em; + *width: 12em; +} + +.sf-menu a { + color: rgba(0, 0, 0, 0.5); + padding: 0.75em 1em !important; + font-weight: normal; + border-left: none; + border-top: none; + border-top: none; + text-decoration: none; + zoom: 1; + font-size: 16px; + border-bottom: none !important; +} + +.sf-menu a:hover, +.sf-menu a:focus { + color: #ff5722 !important; +} + +.sf-menu li.active a:hover, +.sf-menu li.active a:focus { + color: #ff5722; +} + +.sf-menu li, +.sf-menu ul li, +.sf-menu ul ul li, +.sf-menu li:hover, +.sf-menu li.sfHover { + background: transparent; +} + +.sf-menu ul li a, +.sf-menu ul ul li a { + text-transform: none; + padding: 0.2em 1em !important; + letter-spacing: 1px; + font-size: 14px !important; +} + +.sf-menu li:hover a, +.sf-menu li.sfHover a, +.sf-menu ul li:hover a, +.sf-menu ul li.sfHover a, +.sf-menu li.active a { + color: #ff5722; +} + +.sf-menu ul li:hover, +.sf-menu ul li.sfHover { + background: transparent; +} + +.sf-menu ul li { + background: transparent; +} + +.sf-arrows .sf-with-ul { + padding-right: 2.5em; + *padding-right: 1em; +} + +.sf-arrows .sf-with-ul:after { + content: ""; + position: absolute; + top: 50%; + right: 1em; + margin-top: -3px; + height: 0; + width: 0; + border: 5px solid transparent; + border-top-color: #ccc; +} + +.sf-arrows > li > .sf-with-ul:focus:after, +.sf-arrows > li:hover > .sf-with-ul:after, +.sf-arrows > .sfHover > .sf-with-ul:after { + border-top-color: #ccc; +} + +.sf-arrows ul .sf-with-ul:after { + margin-top: -5px; + margin-right: -3px; + border-color: transparent; + border-left-color: #ccc; +} + +.sf-arrows ul li > .sf-with-ul:focus:after, +.sf-arrows ul li:hover > .sf-with-ul:after, +.sf-arrows ul .sfHover > .sf-with-ul:after { + border-left-color: #ccc; +} + +#fh5co-header-section { + padding: 0; + position: relative; + left: 0; + width: 100%; + z-index: 1022; + background: #fff; +} + +#fh5co-header-section h1 { + margin: 10px 0 10px 0; +} + +.nav-header { + position: relative; + float: left; + width: 100%; +} + +#fh5co-logo { + position: relative; + z-index: 2; + font-size: 22px; + text-transform: uppercase; + margin-top: 10px; + font-weight: 700; + float: left; +} + +#fh5co-logo span { + font-weight: 700; +} + +#fh5co-logo a img { + height: 4rem; +} + +@media screen and (max-width: 768px) { + #fh5co-logo { + width: 100%; + float: none; + text-align: center; + } + + #fh5co-logo a img { + height: 5rem; + } +} + +#fh5co-logo a { + color: #ff5722; +} + +#fh5co-logo a:hover, +#fh5co-logo a:focus { + color: #ff5722 !important; +} + +#fh5co-menu-wrap { + position: relative; + z-index: 2; + display: block; + float: right; + margin-top: 15px; +} + +#fh5co-menu-wrap .sf-menu a { + padding: 1em 0.75em; +} + +#fh5co-primary-menu > li > ul li.active > a { + color: #ff5722 !important; +} + +#fh5co-primary-menu > li > .sf-with-ul:after { + border: none !important; + font-family: "icomoon"; + speak: none; + font-style: normal; + font-weight: normal; + font-variant: normal; + text-transform: none; + line-height: 1; + /* Better Font Rendering =========== */ + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + position: absolute; + float: right; + margin-right: -0px; + top: 50%; + font-size: 12px; + margin-top: -7px; + content: "\e922"; + color: rgba(0, 0, 0, 0.5); +} + +#fh5co-primary-menu > li > ul li { + position: relative; +} + +#fh5co-primary-menu > li > ul li > .sf-with-ul:after { + border: none !important; + font-family: "icomoon"; + speak: none; + font-style: normal; + font-weight: normal; + font-variant: normal; + text-transform: none; + line-height: 1; + /* Better Font Rendering =========== */ + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + position: absolute; + float: right; + margin-right: 10px; + top: 20px; + font-size: 12px; + content: "\e924"; + color: rgba(0, 0, 0, 0.5); +} + +#fh5co-primary-menu .fh5co-sub-menu { + padding: 10px 7px 10px; + background: #fff; + left: 0; + -webkit-border-radius: 5px; + -moz-border-radius: 5px; + -ms-border-radius: 5px; + border-radius: 5px; + -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.15); + -moz-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.15); + -ms-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.15); + -o-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.15); + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.15); +} + +#fh5co-primary-menu .fh5co-sub-menu > li > .fh5co-sub-menu { + left: 100%; +} + +#fh5co-primary-menu .fh5co-sub-menu:before { + position: absolute; + top: -9px; + left: 20px; + width: 0; + height: 0; + content: ""; +} + +#fh5co-primary-menu .fh5co-sub-menu:after { + position: absolute; + top: -8px; + left: 21px; + width: 0; + height: 0; + border-right: 8px solid transparent; + border-bottom: 8px solid #fff; + border-bottom: 8px solid white; + border-left: 8px solid transparent; + content: ""; +} + +#fh5co-primary-menu .fh5co-sub-menu .fh5co-sub-menu:before { + top: 6px; + left: 100%; +} + +#fh5co-primary-menu .fh5co-sub-menu .fh5co-sub-menu:after { + top: 7px; + right: 100%; + border: none !important; +} + +.site-header.has-image #primary-menu .sub-menu { + border-color: #ebebeb; + -webkit-box-shadow: 0 5px 15px 4px rgba(0, 0, 0, 0.09); + -moz-box-shadow: 0 5px 15px 4px rgba(0, 0, 0, 0.09); + -ms-box-shadow: 0 5px 15px 4px rgba(0, 0, 0, 0.09); + -o-box-shadow: 0 5px 15px 4px rgba(0, 0, 0, 0.09); + box-shadow: 0 5px 15px 4px rgba(0, 0, 0, 0.09); +} + +.site-header.has-image #primary-menu .sub-menu:before { + display: none; +} + +#fh5co-primary-menu .fh5co-sub-menu a { + letter-spacing: 0; + padding: 0 15px; + font-size: 18px; + line-height: 34px; + color: #575757 !important; + text-transform: none; + background: none; +} + +#fh5co-primary-menu .fh5co-sub-menu a:hover { + color: #ff5722 !important; +} + +#fh5co-work-section, +#fh5co-services-section, +#fh5co-content-section, +#fh5co-work-section_2, +#fh5co-blog-section, +#fh5co-features { + padding: 7em 0; +} + +@media screen and (max-width: 768px) { + #fh5co-work-section, + #fh5co-services-section, + #fh5co-content-section, + #fh5co-work-section_2, + #fh5co-blog-section, + #fh5co-features { + padding: 4em 0; + } +} + +.fh5co-section-gray { + background: #fff; +} + +.heading-section { + padding-bottom: 1em; + margin-bottom: 5em; +} + +.heading-section:after { + position: absolute; + content: ""; + bottom: 0; + left: 50%; + width: 100px; + margin-left: -50px; + height: 2px; + background: #ff5722; +} + +.heading-section h3 { + font-size: 40px; + font-weight: 300; + position: relative; +} + +.heading-section p { + font-size: 20px; +} + +.feature-text h3 { + text-transform: uppercase; + font-size: 14px; + margin-bottom: 10px; + font-weight: 700; + color: #000; + letter-spacing: 0.2em; +} + +.feature-text h3 .number { + margin-right: 5px; + color: #ff5722; + display: -moz-inline-stack; + display: inline-block; + zoom: 1; + *display: inline; +} + +.feature-text p { + font-size: 16px; +} + +.services { + background: #ff5722; + background: #fff; + padding: 40px 20px 20px 20px; + margin-bottom: 30px; + border: 1px solid rgba(0, 0, 0, 0.18); + -webkit-transition: 0.3s; + -o-transition: 0.3s; + transition: 0.3s; +} + +.services:hover, +.services:focus, +.services:active { + -webkit-box-shadow: 0px 10px 20px -12px rgba(0, 0, 0, 0.18); + -moz-box-shadow: 0px 10px 20px -12px rgba(0, 0, 0, 0.18); + box-shadow: 0px 10px 20px -12px rgba(0, 0, 0, 0.18); +} + +.services span { + margin-bottom: 30px; + display: block; + width: 100%; +} + +.services span i { + font-size: 40px; + color: #ff5722 !important; +} + +.services h3 { + font-size: 20px; + font-weight: 400; + color: #ff5722; +} + +.services p { + font-size: 16px; + color: gray; +} + +.feature-left { + float: left; + width: 100%; + margin-bottom: 30px; + position: relative; +} + +.feature-left .icon { + float: left; + text-align: center; + width: 15%; +} + +@media screen and (max-width: 992px) { + .feature-left .icon { + width: 10%; + } +} + +@media screen and (max-width: 768px) { + .feature-left .icon { + width: 15%; + } +} + +.feature-left .icon i { + display: table-cell; + vertical-align: middle; + font-size: 40px; + color: #ff5722; +} + +.feature-left .feature-copy { + float: right; + width: 80%; +} + +@media screen and (max-width: 992px) { + .feature-left .feature-copy { + width: 90%; + } +} + +@media screen and (max-width: 768px) { + .feature-left .feature-copy { + width: 85%; + } +} + +@media screen and (max-width: 480px) { + .feature-left .feature-copy { + width: 80%; + } +} + +.feature-left .feature-copy h3 { + font-size: 18px; + color: #1a1a1a; + margin-bottom: 10px; +} + +.feature-left .feature-copy p { + font-size: 16px; + line-height: 1.8; +} + +#fh5co-portfolio { + padding: 7em 0 0; + /* min-height: 500px; */ +} + +@media screen and (max-width: 768px) { + #fh5co-portfolio { + padding: 3em 0 0; + } +} + +#fh5co-portfolio-list { + flex-wrap: wrap; + -webkit-flex-wrap: wrap; + -moz-flex-wrap: wrap; + display: -webkit-box; + display: -moz-box; + display: -ms-flexbox; + display: -webkit-flex; + display: flex; + position: relative; + float: left; + padding: 0; + margin: 0; + width: 100%; +} + +#fh5co-portfolio-list li { + display: block; + padding: 0; + margin: 0 0 10px 1%; + list-style: none; + min-height: 400px; + background-position: center center; + background-size: cover; + background-repeat: no-repeat; + float: left; + clear: left; + position: relative; +} + +@media screen and (max-width: 480px) { + #fh5co-portfolio-list li { + margin-left: 0; + } +} + +#fh5co-portfolio-list li a { + min-height: 400px; + padding: 2em; + position: relative; + width: 100%; + display: block; +} + +#fh5co-portfolio-list li a:before { + position: absolute; + top: 0; + left: 0; + right: 0; + bottom: 0; + content: ""; + z-index: 80; + -webkit-transition: 0.5s; + -o-transition: 0.5s; + transition: 0.5s; + background: transparent; +} + +#fh5co-portfolio-list li a:hover:before { + background: rgba(0, 0, 0, 0.5); +} + +#fh5co-portfolio-list li a .case-studies-summary { + width: auto; + bottom: 2em; + left: 2em; + right: 2em; + position: absolute; + z-index: 100; +} + +@media screen and (max-width: 768px) { + #fh5co-portfolio-list li a .case-studies-summary { + bottom: 1em; + left: 1em; + right: 1em; + } +} + +#fh5co-portfolio-list li a .case-studies-summary span { + text-transform: uppercase; + letter-spacing: 2px; + font-size: 13px; + color: rgba(255, 255, 255, 0.7); +} + +#fh5co-portfolio-list li a .case-studies-summary h2 { + color: #fff; + margin-bottom: 0; +} + +@media screen and (max-width: 768px) { + #fh5co-portfolio-list li a .case-studies-summary h2 { + font-size: 20px; + } +} + +#fh5co-portfolio-list li.two-third { + width: 65.6%; +} + +@media screen and (max-width: 768px) { + #fh5co-portfolio-list li.two-third { + width: 49%; + } +} + +@media screen and (max-width: 480px) { + #fh5co-portfolio-list li.two-third { + width: 100%; + } +} + +#fh5co-portfolio-list li.one-third { + width: 32.3%; +} + +@media screen and (max-width: 768px) { + #fh5co-portfolio-list li.one-third { + width: 49%; + } +} + +@media screen and (max-width: 480px) { + #fh5co-portfolio-list li.one-third { + width: 100%; + } +} + +#fh5co-portfolio-list li.one-half { + width: 49%; +} + +@media screen and (max-width: 480px) { + #fh5co-portfolio-list li.one-half { + width: 100%; + } +} + +#fh5co-features { + padding: 4em 0; +} + +#fh5co-features .feature-left { + margin-bottom: 0; +} + +@media screen and (max-width: 992px) { + #fh5co-features .feature-left { + margin-bottom: 30px; + } +} + +#fh5co-features .feature-left .icon i { + color: #d9d9d9 !important; +} + +#fh5co-features .feature-left .icon { + float: left; + text-align: center; + width: 15%; +} + +@media screen and (max-width: 992px) { + #fh5co-features .feature-left .icon { + width: 10%; + } +} + +@media screen and (max-width: 768px) { + #fh5co-features .feature-left .icon { + width: 15%; + } +} + +#fh5co-features .feature-left .feature-copy { + float: right; + width: 80%; +} + +@media screen and (max-width: 992px) { + #fh5co-features .feature-left .feature-copy { + width: 90%; + } +} + +@media screen and (max-width: 768px) { + #fh5co-features .feature-left .feature-copy { + width: 85%; + } +} + +@media screen and (max-width: 480px) { + #fh5co-features .feature-left .feature-copy { + width: 80%; + } +} + +#fh5co-features .feature-left h3 { + text-transform: uppercase; + font-size: 14px; + font-weight: 700; +} + +#fh5co-features .feature-left p { + font-size: 15px; +} + +#fh5co-features .feature-left p:last-child { + margin-bottom: 0; +} + +#fh5co-feature-product { + padding: 1em 0 0; +} + +#fh5co-feature-product p { + line-height: 2; +} + +#fh5co-services, +#fh5co-about { + padding: 7em 0; +} + +@media screen and (max-width: 768px) { + #fh5co-services, + #fh5co-about, + #fh5co-contact { + padding: 4em 0 1em; + } +} + +#fh5co-contact { + padding: 4em 0 1em; +} + +#fh5co-services .fh5co-number { + font-size: 30px; + color: #ff5722; + position: relative; + padding-top: 10px; + font-style: italic; + display: -moz-inline-stack; + display: inline-block; + zoom: 1; + *display: inline; +} + +#fh5co-services .fh5co-number:before { + position: absolute; + content: ""; + width: 100%; + height: 3px; + top: 0; + left: 0; + background: #ff5722; +} + +#fh5co-services .fh5co-icon { + margin-bottom: 30px; + margin-top: 50px; + position: relative; + display: -moz-inline-stack; + display: inline-block; + zoom: 1; + *display: inline; +} + +@media screen and (max-width: 768px) { + #fh5co-services .fh5co-icon { + margin-top: 0px; + float: none; + } +} + +#fh5co-services .fh5co-icon i { + font-size: 200px; + color: #ff5722; +} + +@media screen and (max-width: 768px) { + #fh5co-services .fh5co-icon i { + font-size: 100px; + } +} + +#fh5co-contact .contact-info { + margin: 0; + padding: 0; +} + +#fh5co-contact .contact-info li { + list-style: none; + margin: 0 0 20px 0; + position: relative; + padding-left: 40px; +} + +#fh5co-contact .contact-info li i { + position: absolute; + top: 0.3em; + left: 0; + font-size: 22px; + color: rgba(0, 0, 0, 0.3); +} + +#fh5co-contact .contact-info li a { + font-weight: 400 !important; + color: #000; +} + +#fh5co-contact .form-control { + -webkit-box-shadow: none; + -moz-box-shadow: none; + -ms-box-shadow: none; + -o-box-shadow: none; + box-shadow: none; + border: none; + border: 1px solid rgba(0, 0, 0, 0.1); + font-size: 18px; + font-weight: 400; + -webkit-border-radius: 0px; + -moz-border-radius: 0px; + -ms-border-radius: 0px; + border-radius: 0px; +} + +#fh5co-contact .form-control:focus, +#fh5co-contact .form-control:active { + border-bottom: 1px solid rgba(0, 0, 0, 0.5); +} + +#fh5co-contact input[type="text"] { + height: 50px; +} + +#fh5co-contact .form-group { + margin-bottom: 30px; +} + +.fh5co-testimonial, +.fh5co-team { + position: relative; + background: #fff; + padding: 4em 0; + border-radius: 5px; +} + +@media screen and (max-width: 768px) { + .fh5co-testimonial, + .fh5co-team { + border-right: 0; + margin-bottom: 80px; + } +} + +.fh5co-testimonial h2, +.fh5co-team h2 { + font-size: 16px; + letter-spacing: 2px; + text-transform: uppercase; + font-weight: bold; + margin-bottom: 50px; +} + +.fh5co-testimonial figure, +.fh5co-team figure { + position: absolute; + top: -50px; + left: 50%; + margin-left: -65px; + background-size: cover; +} + +.fh5co-testimonial figure img, +.fh5co-team figure img { + width: 130px; + height: 130px; + border: 10px solid #fff; + background-size: cover; + -webkit-border-radius: 50%; + -moz-border-radius: 50%; + -ms-border-radius: 50%; + border-radius: 50%; +} + +.fh5co-testimonial > div, +.fh5co-team > div { + padding: 30px 30px 0 30px; +} + +.fh5co-testimonial > div h3, +.fh5co-team > div h3 { + margin-bottom: 0; + font-size: 20px; + color: #ff5722; +} + +.fh5co-testimonial > div .position, +.fh5co-team > div .position { + color: #d9d9d9; + margin-bottom: 30px; +} + +.fh5co-testimonial > div, +.fh5co-testimonial blockquote, +.fh5co-team > div, +.fh5co-team blockquote { + border: none; + margin-bottom: 20px; + padding-top: 30px; +} + +.fh5co-testimonial > div p, +.fh5co-testimonial blockquote p, +.fh5co-team > div p, +.fh5co-team blockquote p { + font-size: 18px; + line-height: 1.6em; + color: #555555; +} + +.fh5co-testimonial span, +.fh5co-team span { + font-style: italic; + color: #b3b3b3; +} + +.fh5co-blog { + margin-bottom: 60px; +} + +.fh5co-blog a img { + width: 100%; +} + +@media screen and (max-width: 768px) { + .fh5co-blog { + width: 100%; + } +} + +.fh5co-blog .blog-text { + margin-bottom: 30px; + position: relative; + background: #fff; + width: 90%; + padding: 30px; + float: right; + margin-top: -5em; + border-top-left-radius: 30px; + border-bottom-right-radius: 30px; + -webkit-box-shadow: 0px 10px 20px -12px rgba(0, 0, 0, 0.18); + -moz-box-shadow: 0px 10px 20px -12px rgba(0, 0, 0, 0.18); + box-shadow: 0px 10px 20px -12px rgba(0, 0, 0, 0.18); +} + +.fh5co-blog .blog-text span { + display: inline-block; + margin-bottom: 20px; +} + +.fh5co-blog .blog-text span.posted_by { + font-style: italic; + color: rgba(0, 0, 0, 0.3); + font-size: 18px; +} + +.fh5co-blog .blog-text span.comment { + float: right; +} + +.fh5co-blog .blog-text span.comment a { + color: rgba(0, 0, 0, 0.3); +} + +.fh5co-blog .blog-text span.comment a i { + color: #ff5722; + padding-left: 7px; +} + +.fh5co-blog .blog-text h3 { + font-size: 20px; + margin-bottom: 20px; + font-weight: 300; + line-height: 1.5; +} + +.fh5co-blog .blog-text h3 a { + color: rgba(0, 0, 0, 0.6); +} + +#fh5co-start-project-section { + padding: 7em 0; + background: #f64e8b; +} + +#fh5co-start-project-section h2 { + color: white; + font-size: 60px; + text-transform: uppercase; + font-weight: 300; +} + +@media screen and (max-width: 768px) { + #fh5co-start-project-section h2 { + font-size: 30px; + } +} + +#fh5co-start-project-section a:hover, +#fh5co-start-project-section a:focus, +#fh5co-start-project-section a:active { + color: white !important; +} + +#map { + width: 100%; + height: 500px; + position: relative; +} + +@media screen and (max-width: 768px) { + #map { + height: 200px; + } +} + +.fh5co-social-icons > a { + padding: 4px 10px; + color: #ff5722; +} + +.fh5co-social-icons > a i { + font-size: 26px; +} + +.fh5co-social-icons > a:hover, +.fh5co-social-icons > a:focus, +.fh5co-social-icons > a:active { + text-decoration: none; + color: #5a5a5a; +} + +#footer { + background: #25408f; + padding: 30px 0 5px; +} + +#footer p { + color: white; + font-size: 14px; +} + +#footer h2 { + color: white; +} + +#footer p a { + color: rgba(255, 255, 255, 0.5); +} + +#footer p a:hover, +#footer p a:active, +#footer p a:focus { + color: rgba(255, 255, 255, 0.9) !important; +} + +.btn { + -webkit-transition: 0.3s; + -o-transition: 0.3s; + transition: 0.3s; +} + +.btn { + text-transform: uppercase; + letter-spacing: 2px; + -webkit-transition: 0.3s; + -o-transition: 0.3s; + transition: 0.3s; +} + +.btn.btn-primary { + background: #ef232c; + color: #fff; + border: none !important; + border: 2px solid transparent !important; +} + +.btn.btn-primary:hover, +.btn.btn-primary:active, +.btn.btn-primary:focus { + box-shadow: none; + background: #ff5722; + color: #fff !important; +} + +.btn:hover, +.btn:active, +.btn:focus { + background: #393e46 !important; + color: #fff; + outline: none !important; +} + +.btn.btn-default:hover, +.btn.btn-default:focus, +.btn.btn-default:active { + border-color: transparent; +} + +.js .animate-box { + opacity: 0; +} + +#fh5co-wrapper { + overflow-x: hidden; + position: relative; +} + +.fh5co-offcanvas { + overflow: hidden; +} + +.fh5co-offcanvas #fh5co-wrapper { + overflow: hidden !important; +} + +#fh5co-page { + position: relative; + -webkit-transition: 0.5s; + -o-transition: 0.5s; + transition: 0.5s; +} + +.fh5co-offcanvas #fh5co-page { + -moz-transform: translateX(-240px); + -webkit-transform: translateX(-240px); + -ms-transform: translateX(-240px); + -o-transform: translateX(-240px); + transform: translateX(-240px); +} + +@media screen and (max-width: 768px) { + #fh5co-menu-wrap { + display: none; + } +} + +#offcanvas-menu { + position: absolute; + top: 0; + bottom: 0; + z-index: 99; + background: #222; + right: -240px; + width: 240px; + padding: 20px; + overflow-y: scroll !important; + display: none; +} + +@media screen and (max-width: 768px) { + #offcanvas-menu { + display: block; + } +} + +#offcanvas-menu ul { + padding: 0; + margin: 0; +} + +#offcanvas-menu ul li { + padding: 0; + margin: 0 0 10px 0; + list-style: none; +} + +#offcanvas-menu ul li a { + font-size: 18px; + color: rgba(255, 255, 255, 0.4); +} + +#offcanvas-menu ul li a:hover { + color: #ff5722; +} + +#offcanvas-menu ul li > .fh5co-sub-menu { + margin-top: 9px; + padding-left: 20px; +} + +#offcanvas-menu ul li.active > a { + color: #ff5722; +} + +#offcanvas-menu ul .fh5co-sub-menu { + display: block !important; +} + +.fh5co-nav-toggle { + cursor: pointer; + text-decoration: none; +} + +.fh5co-nav-toggle.active i::before, +.fh5co-nav-toggle.active i::after { + background: #fff; +} + +.fh5co-nav-toggle.dark.active i::before, +.fh5co-nav-toggle.dark.active i::after { + background: #fff; +} + +.fh5co-nav-toggle:hover, +.fh5co-nav-toggle:focus, +.fh5co-nav-toggle:active { + outline: none; + border-bottom: none !important; +} + +.fh5co-nav-toggle i { + position: relative; + display: -moz-inline-stack; + display: inline-block; + zoom: 1; + *display: inline; + width: 30px; + height: 2px; + color: #fff; + font: bold 14px/0.4 Helvetica; + text-transform: uppercase; + text-indent: -55px; + background: #fff; + transition: all 0.2s ease-out; +} + +.fh5co-nav-toggle i::before, +.fh5co-nav-toggle i::after { + content: ""; + width: 30px; + height: 2px; + background: #fff; + position: absolute; + left: 0; + -webkit-transition: 0.2s; + -o-transition: 0.2s; + transition: 0.2s; +} + +.fh5co-nav-toggle.dark i { + position: relative; + color: #000; + background: #000; + transition: all 0.2s ease-out; +} + +.fh5co-nav-toggle.dark i::before, +.fh5co-nav-toggle.dark i::after { + background: #000; + -webkit-transition: 0.2s; + -o-transition: 0.2s; + transition: 0.2s; +} + +.fh5co-nav-toggle i::before { + top: -7px; +} + +.fh5co-nav-toggle i::after { + bottom: -7px; +} + +.fh5co-nav-toggle:hover i::before { + top: -10px; +} + +.fh5co-nav-toggle:hover i::after { + bottom: -10px; +} + +.fh5co-nav-toggle.active i { + background: transparent; +} + +.fh5co-nav-toggle.active i::before { + top: 0; + -webkit-transform: rotateZ(45deg); + -moz-transform: rotateZ(45deg); + -ms-transform: rotateZ(45deg); + -o-transform: rotateZ(45deg); + transform: rotateZ(45deg); +} + +.fh5co-nav-toggle.active i::after { + bottom: 0; + -webkit-transform: rotateZ(-45deg); + -moz-transform: rotateZ(-45deg); + -ms-transform: rotateZ(-45deg); + -o-transform: rotateZ(-45deg); + transform: rotateZ(-45deg); +} + +.fh5co-nav-toggle { + position: absolute; + top: 0px; + right: 0px; + z-index: 9999; + display: block; + margin: 0 auto; + display: none; + cursor: pointer; +} + +@media screen and (max-width: 768px) { + .fh5co-nav-toggle { + display: block; + top: 0px; + margin-top: 2em; + } +} + +@media screen and (max-width: 480px) { + .col-xxs-12 { + float: none; + width: 100%; + } +} + +.row-bottom-padded-lg { + padding-bottom: 7em; +} + +@media screen and (max-width: 768px) { + .row-bottom-padded-lg { + padding-bottom: 2em; + } +} + +.row-top-padded-lg { + padding-top: 7em; +} + +@media screen and (max-width: 768px) { + .row-top-padded-lg { + padding-top: 2em; + } +} + +.row-bottom-padded-md { + padding-bottom: 4em; +} + +@media screen and (max-width: 768px) { + .row-bottom-padded-md { + padding-bottom: 2em; + } +} + +.row-top-padded-md { + padding-top: 4em; +} + +@media screen and (max-width: 768px) { + .row-top-padded-md { + padding-top: 2em; + } +} + +.row-bottom-padded-sm { + padding-bottom: 2em; +} + +@media screen and (max-width: 768px) { + .row-bottom-padded-sm { + padding-bottom: 2em; + } +} + +.row-top-padded-sm { + padding-top: 2em; +} + +@media screen and (max-width: 768px) { + .row-top-padded-sm { + padding-top: 2em; + } +} + +.border-bottom { + border-bottom: 1px solid rgba(0, 0, 0, 0.06); +} + +.lead { + line-height: 1.8; +} + +.no-js #loader { + display: none; +} + +.js #loader { + display: block; + position: absolute; + left: 100px; + top: 0; +} + +.fh5co-loader { + position: fixed; + left: 0px; + top: 0px; + width: 100%; + height: 100%; + z-index: 9999; + background: url(images/hourglass.svg) center no-repeat #222222; +} + +.d-inline { + display: inline !important; +} + +/*# sourceMappingURL=style.css.map */ +.hide { + visibility: hidden; +} + +.facility { + margin-top: 10px; +} + +.inlinepadding { + display: inline; + padding-right: 15px; +} + +.inline { + display: inline; +} + +.listing { + max-width: 1200px; + margin: 10px auto; + border: 1px solid #ddd; + border-radius: 8px; + overflow: hidden; + background: #fff; +} + +.listing-item { + display: flex; + justify-content: space-between; + align-items: center; + padding: 15px; + border-bottom: 1px solid #ddd; +} + +.listing-item:last-child { + border-bottom: none; +} + +.details { + flex: 1; + padding-right: 15px; +} + +.name-rating { + display: flex; + justify-content: space-between; + align-items: center; + margin-bottom: 10px; +} + +.name { + font-weight: bold; + font-size: 1.2em; +} + +.rating { + font-size: 1.2em; + color: #ff9800; +} + +.stars { + color: #ff9800; +} + +.info { + font-size: 0.9em; + color: #555; +} + +.amenities { + font-size: 0.9em; + color: #555; +} + +.location { + text-align: right; + font-size: 0.9em; + color: #555; +} + +ul { + list-style-type: none; /* Removes the dot */ + padding: 0; /* Optional: Removes default padding */ + margin: 0; /* Optional: Removes default margin */ +} + +li { + margin-bottom: 5px; /* Optional: Adds spacing between list items */ +} + +.irs--flat .irs-from, +.irs--flat .irs-to, +.irs--flat .irs-single { + color: white; + background-color: #c40f2e; +} + +.irs--flat .irs-min, +.irs--flat .irs-max { + color: #bc0a0a; + background-color: #e1e4e9; +} + +.irs--flat .irs-bar { + background-color: #1890ff; +} + +.select2-container .select2-selection--single { + height: 42px; +} + +.select2-container .select2-selection--single .select2-selection__rendered { + padding: 10px 20px; +} + +.select2-container--default + .select2-selection--single + .select2-selection__rendered { + line-height: 25px; + font-size: 14px; +} + +#overlay { + position: fixed; + top: 0; + z-index: 100; + width: 100%; + height: 100%; + display: none; + background: rgba(0, 0, 0, 0.6); +} +.cv-spinner { + height: 100%; + display: flex; + justify-content: center; + align-items: center; +} +.spinner { + width: 40px; + height: 40px; + border: 4px #ddd solid; + border-top: 4px #2e93e6 solid; + border-radius: 50%; + animation: sp-anime 0.8s infinite linear; +} +@keyframes sp-anime { + 100% { + transform: rotate(360deg); + } +} +.is-hide { + display: none; +} diff --git a/web/css/superfish.css b/web/css/superfish.css new file mode 100644 index 0000000..2728054 --- /dev/null +++ b/web/css/superfish.css @@ -0,0 +1,114 @@ +/*** ESSENTIAL STYLES ***/ +.sf-menu, .sf-menu * { + margin: 0; + padding: 0; + list-style: none; +} +.sf-menu li { + position: relative; +} +.sf-menu ul { + position: absolute; + display: none; + top: 100%; + right: 0; + + z-index: 99; +} +.sf-menu > li { + float: left; +} +.sf-menu li:hover > ul, +.sf-menu li.sfHover > ul { + display: block; +} + +.sf-menu a { + display: block; + position: relative; +} +.sf-menu ul ul { + top: 0; + left: 100%; +} + + +/*** DEMO SKIN ***/ +.sf-menu { + float: left; + margin-bottom: 1em; +} +.sf-menu ul { + box-shadow: 2px 2px 6px rgba(0,0,0,.2); + min-width: 12em; /* allow long menu items to determine submenu width */ + *width: 12em; /* no auto sub width for IE7, see white-space comment below */ +} +.sf-menu a { + border-left: 1px solid #fff; + border-top: 1px solid #dFeEFF; /* fallback colour must use full shorthand */ + border-top: 1px solid rgba(255,255,255,.5); + padding: .75em 1em; + text-decoration: none; + zoom: 1; /* IE7 */ +} +.sf-menu a { + color: #13a; +} +.sf-menu li { + background: #BDD2FF; + white-space: nowrap; /* no need for Supersubs plugin */ + *white-space: normal; /* ...unless you support IE7 (let it wrap) */ + -webkit-transition: background .2s; + transition: background .2s; +} +.sf-menu ul li { + background: #AABDE6; +} +.sf-menu ul ul li { + background: #9AAEDB; +} +.sf-menu li:hover, +.sf-menu li.sfHover { + background: #CFDEFF; + /* only transition out, not in */ + -webkit-transition: none; + transition: none; +} + +/*** arrows (for all except IE7) **/ +.sf-arrows .sf-with-ul { + padding-right: 2.5em; + *padding-right: 1em; /* no CSS arrows for IE7 (lack pseudo-elements) */ +} +/* styling for both css and generated arrows */ +.sf-arrows .sf-with-ul:after { + content: ''; + position: absolute; + top: 50%; + right: 1em; + margin-top: -3px; + height: 0; + width: 0; + /* order of following 3 rules important for fallbacks to work */ + border: 5px solid transparent; + border-top-color: #dFeEFF; /* edit this to suit design (no rgba in IE8) */ + border-top-color: rgba(255,255,255,.5); +} +.sf-arrows > li > .sf-with-ul:focus:after, +.sf-arrows > li:hover > .sf-with-ul:after, +.sf-arrows > .sfHover > .sf-with-ul:after { + border-top-color: white; /* IE8 fallback colour */ +} +/* styling for right-facing arrows */ +.sf-arrows ul .sf-with-ul:after { + margin-top: -5px; + /*margin-right: -3px;*/ + border-color: transparent; + border-left-color: #dFeEFF; /* edit this to suit design (no rgba in IE8) */ + border-left-color: rgba(255,255,255,.5); +} +.sf-arrows ul li > .sf-with-ul:focus:after, +.sf-arrows ul li:hover > .sf-with-ul:after, +.sf-arrows ul .sfHover > .sf-with-ul:after { + border-left-color: white; +} diff --git a/web/css/sweetalert.css b/web/css/sweetalert.css new file mode 100644 index 0000000..0f36f17 --- /dev/null +++ b/web/css/sweetalert.css @@ -0,0 +1,1144 @@ +.swal2-popup.swal2-toast { + box-sizing: border-box; + grid-column: 1/4 !important; + grid-row: 1/4 !important; + grid-template-columns: min-content auto min-content; + padding: 1em; + overflow-y: hidden; + background: #fff; + box-shadow: 0 0 1px rgba(0, 0, 0, 0.075), 0 1px 2px rgba(0, 0, 0, 0.075), + 1px 2px 4px rgba(0, 0, 0, 0.075), 1px 3px 8px rgba(0, 0, 0, 0.075), + 2px 4px 16px rgba(0, 0, 0, 0.075); + pointer-events: all; +} +.swal2-popup.swal2-toast > * { + grid-column: 2; +} +.swal2-popup.swal2-toast .swal2-title { + margin: 0.5em 1em; + padding: 0; + font-size: 1em; + text-align: initial; +} +.swal2-popup.swal2-toast .swal2-loading { + justify-content: center; +} +.swal2-popup.swal2-toast .swal2-input { + height: 2em; + margin: 0.5em; + font-size: 1em; +} +.swal2-popup.swal2-toast .swal2-validation-message { + font-size: 1em; +} +.swal2-popup.swal2-toast .swal2-footer { + margin: 0.5em 0 0; + padding: 0.5em 0 0; + font-size: 0.8em; +} +.swal2-popup.swal2-toast .swal2-close { + grid-column: 3/3; + grid-row: 1/99; + align-self: center; + width: 0.8em; + height: 0.8em; + margin: 0; + font-size: 2em; +} +.swal2-popup.swal2-toast .swal2-html-container { + margin: 0.5em 1em; + padding: 0; + overflow: initial; + font-size: 1em; + text-align: initial; +} +.swal2-popup.swal2-toast .swal2-html-container:empty { + padding: 0; +} +.swal2-popup.swal2-toast .swal2-loader { + grid-column: 1; + grid-row: 1/99; + align-self: center; + width: 2em; + height: 2em; + margin: 0.25em; +} +.swal2-popup.swal2-toast .swal2-icon { + grid-column: 1; + grid-row: 1/99; + align-self: center; + width: 2em; + min-width: 2em; + height: 2em; + margin: 0 0.5em 0 0; +} +.swal2-popup.swal2-toast .swal2-icon .swal2-icon-content { + display: flex; + align-items: center; + font-size: 1.8em; + font-weight: bold; +} +.swal2-popup.swal2-toast .swal2-icon.swal2-success .swal2-success-ring { + width: 2em; + height: 2em; +} +.swal2-popup.swal2-toast .swal2-icon.swal2-error [class^="swal2-x-mark-line"] { + top: 0.875em; + width: 1.375em; +} +.swal2-popup.swal2-toast + .swal2-icon.swal2-error + [class^="swal2-x-mark-line"][class$="left"] { + left: 0.3125em; +} +.swal2-popup.swal2-toast + .swal2-icon.swal2-error + [class^="swal2-x-mark-line"][class$="right"] { + right: 0.3125em; +} +.swal2-popup.swal2-toast .swal2-actions { + justify-content: flex-start; + height: auto; + margin: 0; + margin-top: 0.5em; + padding: 0 0.5em; +} +.swal2-popup.swal2-toast .swal2-styled { + margin: 0.25em 0.5em; + padding: 0.4em 0.6em; + font-size: 1em; +} +.swal2-popup.swal2-toast .swal2-success { + border-color: #a5dc86; +} +.swal2-popup.swal2-toast .swal2-success [class^="swal2-success-circular-line"] { + position: absolute; + width: 1.6em; + height: 3em; + border-radius: 50%; +} +.swal2-popup.swal2-toast + .swal2-success + [class^="swal2-success-circular-line"][class$="left"] { + top: -0.8em; + left: -0.5em; + transform: rotate(-45deg); + transform-origin: 2em 2em; + border-radius: 4em 0 0 4em; +} +.swal2-popup.swal2-toast + .swal2-success + [class^="swal2-success-circular-line"][class$="right"] { + top: -0.25em; + left: 0.9375em; + transform-origin: 0 1.5em; + border-radius: 0 4em 4em 0; +} +.swal2-popup.swal2-toast .swal2-success .swal2-success-ring { + width: 2em; + height: 2em; +} +.swal2-popup.swal2-toast .swal2-success .swal2-success-fix { + top: 0; + left: 0.4375em; + width: 0.4375em; + height: 2.6875em; +} +.swal2-popup.swal2-toast .swal2-success [class^="swal2-success-line"] { + height: 0.3125em; +} +.swal2-popup.swal2-toast + .swal2-success + [class^="swal2-success-line"][class$="tip"] { + top: 1.125em; + left: 0.1875em; + width: 0.75em; +} +.swal2-popup.swal2-toast + .swal2-success + [class^="swal2-success-line"][class$="long"] { + top: 0.9375em; + right: 0.1875em; + width: 1.375em; +} +.swal2-popup.swal2-toast + .swal2-success.swal2-icon-show + .swal2-success-line-tip { + animation: swal2-toast-animate-success-line-tip 0.75s; +} +.swal2-popup.swal2-toast + .swal2-success.swal2-icon-show + .swal2-success-line-long { + animation: swal2-toast-animate-success-line-long 0.75s; +} +.swal2-popup.swal2-toast.swal2-show { + animation: swal2-toast-show 0.5s; +} +.swal2-popup.swal2-toast.swal2-hide { + animation: swal2-toast-hide 0.1s forwards; +} +div:where(.swal2-container) { + display: grid; + position: fixed; + z-index: 1060; + inset: 0; + box-sizing: border-box; + grid-template-areas: "top-start top top-end" "center-start center center-end" "bottom-start bottom-center bottom-end"; + grid-template-rows: minmax(min-content, auto) minmax(min-content, auto) minmax( + min-content, + auto + ); + height: 100%; + padding: 0.625em; + overflow-x: hidden; + transition: background-color 0.1s; + -webkit-overflow-scrolling: touch; +} +div:where(.swal2-container).swal2-backdrop-show, +div:where(.swal2-container).swal2-noanimation { + background: rgba(0, 0, 0, 0.4); +} +div:where(.swal2-container).swal2-backdrop-hide { + background: rgba(0, 0, 0, 0) !important; +} +div:where(.swal2-container).swal2-top-start, +div:where(.swal2-container).swal2-center-start, +div:where(.swal2-container).swal2-bottom-start { + grid-template-columns: minmax(0, 1fr) auto auto; +} +div:where(.swal2-container).swal2-top, +div:where(.swal2-container).swal2-center, +div:where(.swal2-container).swal2-bottom { + grid-template-columns: auto minmax(0, 1fr) auto; +} +div:where(.swal2-container).swal2-top-end, +div:where(.swal2-container).swal2-center-end, +div:where(.swal2-container).swal2-bottom-end { + grid-template-columns: auto auto minmax(0, 1fr); +} +div:where(.swal2-container).swal2-top-start > .swal2-popup { + align-self: start; +} +div:where(.swal2-container).swal2-top > .swal2-popup { + grid-column: 2; + place-self: start center; +} +div:where(.swal2-container).swal2-top-end > .swal2-popup, +div:where(.swal2-container).swal2-top-right > .swal2-popup { + grid-column: 3; + place-self: start end; +} +div:where(.swal2-container).swal2-center-start > .swal2-popup, +div:where(.swal2-container).swal2-center-left > .swal2-popup { + grid-row: 2; + align-self: center; +} +div:where(.swal2-container).swal2-center > .swal2-popup { + grid-column: 2; + grid-row: 2; + place-self: center center; +} +div:where(.swal2-container).swal2-center-end > .swal2-popup, +div:where(.swal2-container).swal2-center-right > .swal2-popup { + grid-column: 3; + grid-row: 2; + place-self: center end; +} +div:where(.swal2-container).swal2-bottom-start > .swal2-popup, +div:where(.swal2-container).swal2-bottom-left > .swal2-popup { + grid-column: 1; + grid-row: 3; + align-self: end; +} +div:where(.swal2-container).swal2-bottom > .swal2-popup { + grid-column: 2; + grid-row: 3; + place-self: end center; +} +div:where(.swal2-container).swal2-bottom-end > .swal2-popup, +div:where(.swal2-container).swal2-bottom-right > .swal2-popup { + grid-column: 3; + grid-row: 3; + place-self: end end; +} +div:where(.swal2-container).swal2-grow-row > .swal2-popup, +div:where(.swal2-container).swal2-grow-fullscreen > .swal2-popup { + grid-column: 1/4; + width: 100%; +} +div:where(.swal2-container).swal2-grow-column > .swal2-popup, +div:where(.swal2-container).swal2-grow-fullscreen > .swal2-popup { + grid-row: 1/4; + align-self: stretch; +} +div:where(.swal2-container).swal2-no-transition { + transition: none !important; +} +div:where(.swal2-container) div:where(.swal2-popup) { + display: none; + position: relative; + box-sizing: border-box; + grid-template-columns: minmax(0, 100%); + width: 32em; + max-width: 100%; + padding: 0 0 1.25em; + border: none; + border-radius: 5px; + background: #fff; + color: hsl(0, 0%, 33%); + font-family: inherit; + font-size: 1rem; +} +div:where(.swal2-container) div:where(.swal2-popup):focus { + outline: none; +} +div:where(.swal2-container) div:where(.swal2-popup).swal2-loading { + overflow-y: hidden; +} +div:where(.swal2-container) h2:where(.swal2-title) { + position: relative; + max-width: 100%; + margin: 0; + padding: 0.8em 1em 0; + color: inherit; + font-size: 1.875em; + font-weight: 600; + text-align: center; + text-transform: none; + word-wrap: break-word; +} +div:where(.swal2-container) div:where(.swal2-actions) { + display: flex; + z-index: 1; + box-sizing: border-box; + flex-wrap: wrap; + align-items: center; + justify-content: center; + width: auto; + margin: 1.25em auto 0; + padding: 0; +} +div:where(.swal2-container) + div:where(.swal2-actions):not(.swal2-loading) + .swal2-styled[disabled] { + opacity: 0.4; +} +div:where(.swal2-container) + div:where(.swal2-actions):not(.swal2-loading) + .swal2-styled:hover { + background-image: linear-gradient(rgba(0, 0, 0, 0.1), rgba(0, 0, 0, 0.1)); +} +div:where(.swal2-container) + div:where(.swal2-actions):not(.swal2-loading) + .swal2-styled:active { + background-image: linear-gradient(rgba(0, 0, 0, 0.2), rgba(0, 0, 0, 0.2)); +} +div:where(.swal2-container) div:where(.swal2-loader) { + display: none; + align-items: center; + justify-content: center; + width: 2.2em; + height: 2.2em; + margin: 0 1.875em; + animation: swal2-rotate-loading 1.5s linear 0s infinite normal; + border-width: 0.25em; + border-style: solid; + border-radius: 100%; + border-color: #2778c4 rgba(0, 0, 0, 0) #2778c4 rgba(0, 0, 0, 0); +} +div:where(.swal2-container) button:where(.swal2-styled) { + margin: 0.3125em; + padding: 0.625em 1.1em; + transition: box-shadow 0.1s; + box-shadow: 0 0 0 3px rgba(0, 0, 0, 0); + font-weight: 500; +} +div:where(.swal2-container) button:where(.swal2-styled):not([disabled]) { + cursor: pointer; +} +div:where(.swal2-container) button:where(.swal2-styled):where(.swal2-confirm) { + border: 0; + border-radius: 0.25em; + background: initial; + background-color: #7066e0; + color: #fff; + font-size: 1em; +} +div:where(.swal2-container) + button:where(.swal2-styled):where(.swal2-confirm):focus-visible { + box-shadow: 0 0 0 3px rgba(112, 102, 224, 0.5); +} +div:where(.swal2-container) button:where(.swal2-styled):where(.swal2-deny) { + border: 0; + border-radius: 0.25em; + background: initial; + background-color: #dc3741; + color: #fff; + font-size: 1em; +} +div:where(.swal2-container) + button:where(.swal2-styled):where(.swal2-deny):focus-visible { + box-shadow: 0 0 0 3px rgba(220, 55, 65, 0.5); +} +div:where(.swal2-container) button:where(.swal2-styled):where(.swal2-cancel) { + border: 0; + border-radius: 0.25em; + background: initial; + background-color: #6e7881; + color: #fff; + font-size: 1em; +} +div:where(.swal2-container) + button:where(.swal2-styled):where(.swal2-cancel):focus-visible { + box-shadow: 0 0 0 3px rgba(110, 120, 129, 0.5); +} +div:where(.swal2-container) + button:where(.swal2-styled).swal2-default-outline:focus-visible { + box-shadow: 0 0 0 3px rgba(100, 150, 200, 0.5); +} +div:where(.swal2-container) button:where(.swal2-styled):focus-visible { + outline: none; +} +div:where(.swal2-container) button:where(.swal2-styled)::-moz-focus-inner { + border: 0; +} +div:where(.swal2-container) div:where(.swal2-footer) { + margin: 1em 0 0; + padding: 1em 1em 0; + border-top: 1px solid #eee; + color: inherit; + font-size: 1em; + text-align: center; +} +div:where(.swal2-container) .swal2-timer-progress-bar-container { + position: absolute; + right: 0; + bottom: 0; + left: 0; + grid-column: auto !important; + overflow: hidden; + border-bottom-right-radius: 5px; + border-bottom-left-radius: 5px; +} +div:where(.swal2-container) div:where(.swal2-timer-progress-bar) { + width: 100%; + height: 0.25em; + background: rgba(0, 0, 0, 0.2); +} +div:where(.swal2-container) img:where(.swal2-image) { + max-width: 100%; + margin: 2em auto 1em; +} +div:where(.swal2-container) button:where(.swal2-close) { + z-index: 2; + align-items: center; + justify-content: center; + width: 1.2em; + height: 1.2em; + margin-top: 0; + margin-right: 0; + margin-bottom: -1.2em; + padding: 0; + overflow: hidden; + transition: color 0.1s, box-shadow 0.1s; + border: none; + border-radius: 5px; + background: rgba(0, 0, 0, 0); + color: #ccc; + font-family: monospace; + font-size: 2.5em; + cursor: pointer; + justify-self: end; +} +div:where(.swal2-container) button:where(.swal2-close):hover { + transform: none; + background: rgba(0, 0, 0, 0); + color: #f27474; +} +div:where(.swal2-container) button:where(.swal2-close):focus-visible { + outline: none; + box-shadow: inset 0 0 0 3px rgba(100, 150, 200, 0.5); +} +div:where(.swal2-container) button:where(.swal2-close)::-moz-focus-inner { + border: 0; +} +div:where(.swal2-container) .swal2-html-container { + z-index: 1; + justify-content: center; + margin: 0; + padding: 1em 1.6em 0.3em; + overflow: auto; + color: inherit; + font-size: 1.125em; + font-weight: normal; + line-height: normal; + text-align: center; + word-wrap: break-word; + word-break: break-word; +} +div:where(.swal2-container) input:where(.swal2-input), +div:where(.swal2-container) input:where(.swal2-file), +div:where(.swal2-container) textarea:where(.swal2-textarea), +div:where(.swal2-container) select:where(.swal2-select), +div:where(.swal2-container) div:where(.swal2-radio), +div:where(.swal2-container) label:where(.swal2-checkbox) { + margin: 1em 2em 3px; +} +div:where(.swal2-container) input:where(.swal2-input), +div:where(.swal2-container) input:where(.swal2-file), +div:where(.swal2-container) textarea:where(.swal2-textarea) { + box-sizing: border-box; + width: auto; + transition: border-color 0.1s, box-shadow 0.1s; + border: 1px solid hsl(0, 0%, 85%); + border-radius: 0.1875em; + background: rgba(0, 0, 0, 0); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.06), 0 0 0 3px rgba(0, 0, 0, 0); + color: inherit; + font-size: 1.125em; +} +div:where(.swal2-container) input:where(.swal2-input).swal2-inputerror, +div:where(.swal2-container) input:where(.swal2-file).swal2-inputerror, +div:where(.swal2-container) textarea:where(.swal2-textarea).swal2-inputerror { + border-color: #f27474 !important; + box-shadow: 0 0 2px #f27474 !important; +} +div:where(.swal2-container) input:where(.swal2-input):focus, +div:where(.swal2-container) input:where(.swal2-file):focus, +div:where(.swal2-container) textarea:where(.swal2-textarea):focus { + border: 1px solid #b4dbed; + outline: none; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.06), + 0 0 0 3px rgba(100, 150, 200, 0.5); +} +div:where(.swal2-container) input:where(.swal2-input)::placeholder, +div:where(.swal2-container) input:where(.swal2-file)::placeholder, +div:where(.swal2-container) textarea:where(.swal2-textarea)::placeholder { + color: #ccc; +} +div:where(.swal2-container) .swal2-range { + margin: 1em 2em 3px; + background: #fff; +} +div:where(.swal2-container) .swal2-range input { + width: 80%; +} +div:where(.swal2-container) .swal2-range output { + width: 20%; + color: inherit; + font-weight: 600; + text-align: center; +} +div:where(.swal2-container) .swal2-range input, +div:where(.swal2-container) .swal2-range output { + height: 2.625em; + padding: 0; + font-size: 1.125em; + line-height: 2.625em; +} +div:where(.swal2-container) .swal2-input { + height: 2.625em; + padding: 0 0.75em; +} +div:where(.swal2-container) .swal2-file { + width: 75%; + margin-right: auto; + margin-left: auto; + background: rgba(0, 0, 0, 0); + font-size: 1.125em; +} +div:where(.swal2-container) .swal2-textarea { + height: 6.75em; + padding: 0.75em; +} +div:where(.swal2-container) .swal2-select { + min-width: 50%; + max-width: 100%; + padding: 0.375em 0.625em; + background: rgba(0, 0, 0, 0); + color: inherit; + font-size: 1.125em; +} +div:where(.swal2-container) .swal2-radio, +div:where(.swal2-container) .swal2-checkbox { + align-items: center; + justify-content: center; + background: #fff; + color: inherit; +} +div:where(.swal2-container) .swal2-radio label, +div:where(.swal2-container) .swal2-checkbox label { + margin: 0 0.6em; + font-size: 1.125em; +} +div:where(.swal2-container) .swal2-radio input, +div:where(.swal2-container) .swal2-checkbox input { + flex-shrink: 0; + margin: 0 0.4em; +} +div:where(.swal2-container) label:where(.swal2-input-label) { + display: flex; + justify-content: center; + margin: 1em auto 0; +} +div:where(.swal2-container) div:where(.swal2-validation-message) { + align-items: center; + justify-content: center; + margin: 1em 0 0; + padding: 0.625em; + overflow: hidden; + background: hsl(0, 0%, 94%); + color: #666; + font-size: 1em; + font-weight: 300; +} +div:where(.swal2-container) div:where(.swal2-validation-message)::before { + content: "!"; + display: inline-block; + width: 1.5em; + min-width: 1.5em; + height: 1.5em; + margin: 0 0.625em; + border-radius: 50%; + background-color: #f27474; + color: #fff; + font-weight: 600; + line-height: 1.5em; + text-align: center; +} +div:where(.swal2-container) .swal2-progress-steps { + flex-wrap: wrap; + align-items: center; + max-width: 100%; + margin: 1.25em auto; + padding: 0; + background: rgba(0, 0, 0, 0); + font-weight: 600; +} +div:where(.swal2-container) .swal2-progress-steps li { + display: inline-block; + position: relative; +} +div:where(.swal2-container) .swal2-progress-steps .swal2-progress-step { + z-index: 20; + flex-shrink: 0; + width: 2em; + height: 2em; + border-radius: 2em; + background: #2778c4; + color: #fff; + line-height: 2em; + text-align: center; +} +div:where(.swal2-container) + .swal2-progress-steps + .swal2-progress-step.swal2-active-progress-step { + background: #2778c4; +} +div:where(.swal2-container) + .swal2-progress-steps + .swal2-progress-step.swal2-active-progress-step + ~ .swal2-progress-step { + background: #add8e6; + color: #fff; +} +div:where(.swal2-container) + .swal2-progress-steps + .swal2-progress-step.swal2-active-progress-step + ~ .swal2-progress-step-line { + background: #add8e6; +} +div:where(.swal2-container) .swal2-progress-steps .swal2-progress-step-line { + z-index: 10; + flex-shrink: 0; + width: 2.5em; + height: 0.4em; + margin: 0 -1px; + background: #2778c4; +} +div:where(.swal2-icon) { + position: relative; + box-sizing: content-box; + justify-content: center; + width: 5em; + height: 5em; + margin: 2.5em auto 0.6em; + border: 0.25em solid rgba(0, 0, 0, 0); + border-radius: 50%; + border-color: #000; + font-family: inherit; + line-height: 5em; + cursor: default; + user-select: none; +} +div:where(.swal2-icon) .swal2-icon-content { + display: flex; + align-items: center; + font-size: 3.75em; +} +div:where(.swal2-icon).swal2-error { + border-color: #f27474; + color: #f27474; +} +div:where(.swal2-icon).swal2-error .swal2-x-mark { + position: relative; + flex-grow: 1; +} +div:where(.swal2-icon).swal2-error [class^="swal2-x-mark-line"] { + display: block; + position: absolute; + top: 2.3125em; + width: 2.9375em; + height: 0.3125em; + border-radius: 0.125em; + background-color: #f27474; +} +div:where(.swal2-icon).swal2-error [class^="swal2-x-mark-line"][class$="left"] { + left: 1.0625em; + transform: rotate(45deg); +} +div:where(.swal2-icon).swal2-error + [class^="swal2-x-mark-line"][class$="right"] { + right: 1em; + transform: rotate(-45deg); +} +div:where(.swal2-icon).swal2-error.swal2-icon-show { + animation: swal2-animate-error-icon 0.5s; +} +div:where(.swal2-icon).swal2-error.swal2-icon-show .swal2-x-mark { + animation: swal2-animate-error-x-mark 0.5s; +} +div:where(.swal2-icon).swal2-warning { + border-color: rgb(249.95234375, 205.965625, 167.74765625); + color: #f8bb86; +} +div:where(.swal2-icon).swal2-warning.swal2-icon-show { + animation: swal2-animate-error-icon 0.5s; +} +div:where(.swal2-icon).swal2-warning.swal2-icon-show .swal2-icon-content { + animation: swal2-animate-i-mark 0.5s; +} +div:where(.swal2-icon).swal2-info { + border-color: rgb(156.7033492823, 224.2822966507, 246.2966507177); + color: #3fc3ee; +} +div:where(.swal2-icon).swal2-info.swal2-icon-show { + animation: swal2-animate-error-icon 0.5s; +} +div:where(.swal2-icon).swal2-info.swal2-icon-show .swal2-icon-content { + animation: swal2-animate-i-mark 0.8s; +} +div:where(.swal2-icon).swal2-question { + border-color: rgb(200.8064516129, 217.9677419355, 225.1935483871); + color: #87adbd; +} +div:where(.swal2-icon).swal2-question.swal2-icon-show { + animation: swal2-animate-error-icon 0.5s; +} +div:where(.swal2-icon).swal2-question.swal2-icon-show .swal2-icon-content { + animation: swal2-animate-question-mark 0.8s; +} +div:where(.swal2-icon).swal2-success { + border-color: #a5dc86; + color: #a5dc86; +} +div:where(.swal2-icon).swal2-success [class^="swal2-success-circular-line"] { + position: absolute; + width: 3.75em; + height: 7.5em; + border-radius: 50%; +} +div:where(.swal2-icon).swal2-success + [class^="swal2-success-circular-line"][class$="left"] { + top: -0.4375em; + left: -2.0635em; + transform: rotate(-45deg); + transform-origin: 3.75em 3.75em; + border-radius: 7.5em 0 0 7.5em; +} +div:where(.swal2-icon).swal2-success + [class^="swal2-success-circular-line"][class$="right"] { + top: -0.6875em; + left: 1.875em; + transform: rotate(-45deg); + transform-origin: 0 3.75em; + border-radius: 0 7.5em 7.5em 0; +} +div:where(.swal2-icon).swal2-success .swal2-success-ring { + position: absolute; + z-index: 2; + top: -0.25em; + left: -0.25em; + box-sizing: content-box; + width: 100%; + height: 100%; + border: 0.25em solid rgba(165, 220, 134, 0.3); + border-radius: 50%; +} +div:where(.swal2-icon).swal2-success .swal2-success-fix { + position: absolute; + z-index: 1; + top: 0.5em; + left: 1.625em; + width: 0.4375em; + height: 5.625em; + transform: rotate(-45deg); +} +div:where(.swal2-icon).swal2-success [class^="swal2-success-line"] { + display: block; + position: absolute; + z-index: 2; + height: 0.3125em; + border-radius: 0.125em; + background-color: #a5dc86; +} +div:where(.swal2-icon).swal2-success + [class^="swal2-success-line"][class$="tip"] { + top: 2.875em; + left: 0.8125em; + width: 1.5625em; + transform: rotate(45deg); +} +div:where(.swal2-icon).swal2-success + [class^="swal2-success-line"][class$="long"] { + top: 2.375em; + right: 0.5em; + width: 2.9375em; + transform: rotate(-45deg); +} +div:where(.swal2-icon).swal2-success.swal2-icon-show .swal2-success-line-tip { + animation: swal2-animate-success-line-tip 0.75s; +} +div:where(.swal2-icon).swal2-success.swal2-icon-show .swal2-success-line-long { + animation: swal2-animate-success-line-long 0.75s; +} +div:where(.swal2-icon).swal2-success.swal2-icon-show + .swal2-success-circular-line-right { + animation: swal2-rotate-success-circular-line 4.25s ease-in; +} +[class^="swal2"] { + -webkit-tap-highlight-color: rgba(0, 0, 0, 0); +} +.swal2-show { + animation: swal2-show 0.3s; +} +.swal2-hide { + animation: swal2-hide 0.15s forwards; +} +.swal2-noanimation { + transition: none; +} +.swal2-scrollbar-measure { + position: absolute; + top: -9999px; + width: 50px; + height: 50px; + overflow: scroll; +} +.swal2-rtl .swal2-close { + margin-right: initial; + margin-left: 0; +} +.swal2-rtl .swal2-timer-progress-bar { + right: 0; + left: auto; +} +@keyframes swal2-toast-show { + 0% { + transform: translateY(-0.625em) rotateZ(2deg); + } + 33% { + transform: translateY(0) rotateZ(-2deg); + } + 66% { + transform: translateY(0.3125em) rotateZ(2deg); + } + 100% { + transform: translateY(0) rotateZ(0deg); + } +} +@keyframes swal2-toast-hide { + 100% { + transform: rotateZ(1deg); + opacity: 0; + } +} +@keyframes swal2-toast-animate-success-line-tip { + 0% { + top: 0.5625em; + left: 0.0625em; + width: 0; + } + 54% { + top: 0.125em; + left: 0.125em; + width: 0; + } + 70% { + top: 0.625em; + left: -0.25em; + width: 1.625em; + } + 84% { + top: 1.0625em; + left: 0.75em; + width: 0.5em; + } + 100% { + top: 1.125em; + left: 0.1875em; + width: 0.75em; + } +} +@keyframes swal2-toast-animate-success-line-long { + 0% { + top: 1.625em; + right: 1.375em; + width: 0; + } + 65% { + top: 1.25em; + right: 0.9375em; + width: 0; + } + 84% { + top: 0.9375em; + right: 0; + width: 1.125em; + } + 100% { + top: 0.9375em; + right: 0.1875em; + width: 1.375em; + } +} +@keyframes swal2-show { + 0% { + transform: scale(0.7); + } + 45% { + transform: scale(1.05); + } + 80% { + transform: scale(0.95); + } + 100% { + transform: scale(1); + } +} +@keyframes swal2-hide { + 0% { + transform: scale(1); + opacity: 1; + } + 100% { + transform: scale(0.5); + opacity: 0; + } +} +@keyframes swal2-animate-success-line-tip { + 0% { + top: 1.1875em; + left: 0.0625em; + width: 0; + } + 54% { + top: 1.0625em; + left: 0.125em; + width: 0; + } + 70% { + top: 2.1875em; + left: -0.375em; + width: 3.125em; + } + 84% { + top: 3em; + left: 1.3125em; + width: 1.0625em; + } + 100% { + top: 2.8125em; + left: 0.8125em; + width: 1.5625em; + } +} +@keyframes swal2-animate-success-line-long { + 0% { + top: 3.375em; + right: 2.875em; + width: 0; + } + 65% { + top: 3.375em; + right: 2.875em; + width: 0; + } + 84% { + top: 2.1875em; + right: 0; + width: 3.4375em; + } + 100% { + top: 2.375em; + right: 0.5em; + width: 2.9375em; + } +} +@keyframes swal2-rotate-success-circular-line { + 0% { + transform: rotate(-45deg); + } + 5% { + transform: rotate(-45deg); + } + 12% { + transform: rotate(-405deg); + } + 100% { + transform: rotate(-405deg); + } +} +@keyframes swal2-animate-error-x-mark { + 0% { + margin-top: 1.625em; + transform: scale(0.4); + opacity: 0; + } + 50% { + margin-top: 1.625em; + transform: scale(0.4); + opacity: 0; + } + 80% { + margin-top: -0.375em; + transform: scale(1.15); + } + 100% { + margin-top: 0; + transform: scale(1); + opacity: 1; + } +} +@keyframes swal2-animate-error-icon { + 0% { + transform: rotateX(100deg); + opacity: 0; + } + 100% { + transform: rotateX(0deg); + opacity: 1; + } +} +@keyframes swal2-rotate-loading { + 0% { + transform: rotate(0deg); + } + 100% { + transform: rotate(360deg); + } +} +@keyframes swal2-animate-question-mark { + 0% { + transform: rotateY(-360deg); + } + 100% { + transform: rotateY(0); + } +} +@keyframes swal2-animate-i-mark { + 0% { + transform: rotateZ(45deg); + opacity: 0; + } + 25% { + transform: rotateZ(-25deg); + opacity: 0.4; + } + 50% { + transform: rotateZ(15deg); + opacity: 0.8; + } + 75% { + transform: rotateZ(-5deg); + opacity: 1; + } + 100% { + transform: rotateX(0); + opacity: 1; + } +} +body.swal2-shown:not(.swal2-no-backdrop, .swal2-toast-shown) { + overflow: hidden; +} +body.swal2-height-auto { + height: auto !important; +} +body.swal2-no-backdrop .swal2-container { + background-color: rgba(0, 0, 0, 0) !important; + pointer-events: none; +} +body.swal2-no-backdrop .swal2-container .swal2-popup { + pointer-events: all; +} +body.swal2-no-backdrop .swal2-container .swal2-modal { + box-shadow: 0 0 10px rgba(0, 0, 0, 0.4); +} +@media print { + body.swal2-shown:not(.swal2-no-backdrop, .swal2-toast-shown) { + overflow-y: scroll !important; + } + body.swal2-shown:not(.swal2-no-backdrop, .swal2-toast-shown) + > [aria-hidden="true"] { + display: none; + } + body.swal2-shown:not(.swal2-no-backdrop, .swal2-toast-shown) + .swal2-container { + position: static !important; + } +} +body.swal2-toast-shown .swal2-container { + box-sizing: border-box; + width: 360px; + max-width: 100%; + background-color: rgba(0, 0, 0, 0); + pointer-events: none; +} +body.swal2-toast-shown .swal2-container.swal2-top { + inset: 0 auto auto 50%; + transform: translateX(-50%); +} +body.swal2-toast-shown .swal2-container.swal2-top-end, +body.swal2-toast-shown .swal2-container.swal2-top-right { + inset: 0 0 auto auto; +} +body.swal2-toast-shown .swal2-container.swal2-top-start, +body.swal2-toast-shown .swal2-container.swal2-top-left { + inset: 0 auto auto 0; +} +body.swal2-toast-shown .swal2-container.swal2-center-start, +body.swal2-toast-shown .swal2-container.swal2-center-left { + inset: 50% auto auto 0; + transform: translateY(-50%); +} +body.swal2-toast-shown .swal2-container.swal2-center { + inset: 50% auto auto 50%; + transform: translate(-50%, -50%); +} +body.swal2-toast-shown .swal2-container.swal2-center-end, +body.swal2-toast-shown .swal2-container.swal2-center-right { + inset: 50% 0 auto auto; + transform: translateY(-50%); +} +body.swal2-toast-shown .swal2-container.swal2-bottom-start, +body.swal2-toast-shown .swal2-container.swal2-bottom-left { + inset: auto auto 0 0; +} +body.swal2-toast-shown .swal2-container.swal2-bottom { + inset: auto auto 0 50%; + transform: translateX(-50%); +} +body.swal2-toast-shown .swal2-container.swal2-bottom-end, +body.swal2-toast-shown .swal2-container.swal2-bottom-right { + inset: auto 0 0 auto; +} diff --git a/web/fonts/bootstrap/glyphicons-halflings-regular.eot b/web/fonts/bootstrap/glyphicons-halflings-regular.eot new file mode 100644 index 0000000..b93a495 Binary files 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a/web/fonts/bootstrap/glyphicons-halflings-regular.ttf b/web/fonts/bootstrap/glyphicons-halflings-regular.ttf new file mode 100644 index 0000000..1413fc6 Binary files /dev/null and b/web/fonts/bootstrap/glyphicons-halflings-regular.ttf differ diff --git a/web/fonts/bootstrap/glyphicons-halflings-regular.woff b/web/fonts/bootstrap/glyphicons-halflings-regular.woff new file mode 100644 index 0000000..9e61285 Binary files /dev/null and b/web/fonts/bootstrap/glyphicons-halflings-regular.woff differ diff --git a/web/fonts/bootstrap/glyphicons-halflings-regular.woff2 b/web/fonts/bootstrap/glyphicons-halflings-regular.woff2 new file mode 100644 index 0000000..64539b5 Binary files /dev/null and b/web/fonts/bootstrap/glyphicons-halflings-regular.woff2 differ diff --git a/web/fonts/icomoon/icomoon.eot b/web/fonts/icomoon/icomoon.eot new file mode 100644 index 0000000..bdfa901 Binary files /dev/null and b/web/fonts/icomoon/icomoon.eot differ diff --git a/web/fonts/icomoon/icomoon.svg b/web/fonts/icomoon/icomoon.svg new file mode 100644 index 0000000..2cbc8f0 --- /dev/null +++ b/web/fonts/icomoon/icomoon.svg @@ -0,0 +1,1046 @@ + + + +Generated by IcoMoon + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ A machine learning model that recommends restaurants + based on key factors like location, industry trends, + customer sentiment, and competition, providing data-backed + recommendations to users on the best place to + visit. +

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+ + + + + + + + + + + + + + + + + + + + + + + diff --git a/web/js/app.js b/web/js/app.js new file mode 100644 index 0000000..0bea07a --- /dev/null +++ b/web/js/app.js @@ -0,0 +1,230 @@ +$(function () { + api_url = "http://127.0.0.1:5000/"; + //api_url = "https://bizrecapi.onrender.com"; + predict_api = api_url + "/recommend"; + location_api = api_url + "/location"; + cuizine_api = api_url + "/cuizine"; + settings_url = api_url + "/settings"; + + $(document).ajaxSend(function () { + $("#overlay").fadeIn(300); + }); + + $(".select2").select2(); + + $("#rating").ionRangeSlider({ + postfix: " Star", + min: 1, + max: 5, + from: 1, + onChange: function (obj) { + $("#txtrating").val(obj.from); + }, + onStart: function (obj) { + $("#txtrating").val(obj.from); + }, + }); + + $("#result").hide(); + $(".home").hide(); + + fetchData(); + + function fetchData() { + try { + //call location api + $.ajax({ + url: settings_url, + type: "GET", + success: function (response) { + // Process the response data + console.log(response); + loc_data = response.location; + cui_data = response.cuizine; + var location = document.getElementById("location"); + var cuzine = document.getElementById("cuzine"); + + //remove all options + var opt = document.createElement("option"); + opt.innerHTML = "Select Location"; + opt.value = ""; + location.innerHTML = ""; + location.appendChild(opt); + for (var i = 0; i < loc_data.length; i++) { + var opt = document.createElement("option"); + opt.value = loc_data[i]; + opt.innerHTML = loc_data[i]; + location.appendChild(opt); + } + + var opt = document.createElement("option"); + opt.innerHTML = "Select Cuizine"; + cuzine.innerHTML = ""; + opt.value = ""; + cuzine.appendChild(opt); + for (var i = 0; i < cui_data.length; i++) { + var opt = document.createElement("option"); + opt.value = cui_data[i]; + opt.innerHTML = cui_data[i]; + cuzine.appendChild(opt); + } + + setTimeout(function () { + $("#overlay").fadeOut(300); + }, 500); + }, + error: function (error) { + console.error("Error fetching data:", error); + Swal.fire({ + text: "Error fetching data", + icon: "error", + }); + + setTimeout(function () { + $("#overlay").fadeOut(300); + }, 500); + }, + }); + } catch (e) { + console.log(e); + Swal.fire({ + text: "Error loading the system", + icon: "error", + }); + + setTimeout(function () { + $("#overlay").fadeOut(300); + }, 500); + } + } + + submit.addEventListener("click", function () { + //get value of dropdowm control + var rating = document.getElementById("txtrating"); + var cuzine = document.getElementById("cuzine"); + var city = document.getElementById("location"); + var wifi = document.getElementById("wifi").checked; + var parking = document.getElementById("parking").checked; + + console.log(wifi); + console.log(parking); + + var rest_rating = rating.value; + var rest_cuzine = cuzine.value; + var location = city.value; + var need_wifi = wifi == true ? "Yes" : "No"; + var need_parking = parking == true ? "Yes" : "No"; + + var formErr = false; + + //check if value is empty + if (location == "" || rest_cuzine == "" || rest_rating == "") { + formErr = true; + } + + if (formErr) { + Swal.fire({ + text: "Please select value for required field(s)", + icon: "info", + }); + return false; + } + + $(".home").hide(); + $("#result").show(); + + const data = { + user_location: location, + min_rating: rest_rating, + need_parking: need_parking, + need_wifi: need_wifi, + cuisine_type: rest_cuzine, + }; + console.log(data); + $("#result").html(""); + + $.ajax({ + url: predict_api, + type: "POST", + contentType: "application/json", // Adjust as needed + data: JSON.stringify(data), + success: function (response) { + // Process the response data + console.log(response.data); + var data = response.data; + var html = ""; + for (var i = 0; i < data.length; i++) { + topiclist = ""; + topiclist = ""; + for (var j = 0; j < data[i].topic_list.length; j++) { + per = data[i].topic_percentage_list[j]; + count = data[i].topic_count_list[j]; + topic = data[i].topic_list[j]; + topiclist += + "
  • " + + topic + + " : " + + count + + " (" + + per.toFixed(2) + + "%)
    • " + + ""; + } + + html += + '
    ' + + '' + + data[i].bus_name + + "" + + '' + + data[i].rating + + '★★★★★ (' + + data[i].total_reviews + + " Reviews)" + + '
    Restaurant
    ' + + '
    Business Address: ' + + data[i].address + + "
    " + + '
    ' + + topiclist + + "
    " + + "
    "; + } + $("#result").html(html); + + setTimeout(function () { + $("#overlay").fadeOut(300); + }, 500); + }, + error: function (error) { + console.error("Error fetching data:", error); + Swal.fire({ + text: "Error fetching data", + icon: "error", + }); + + setTimeout(function () { + $("#overlay").fadeOut(300); + }, 500); + }, + }); + }); + + reset.addEventListener("click", function () { + //get value of dropdowm control + $("#result").hide(); + $(".home").hide(); + // $(".home").show(); + }); + + // Function to save data to local storage + function saveDataToLocalStorage(data) { + localStorage.setItem("appData", JSON.stringify(data)); + } + + // Function to retrieve data from local storage + function getDataFromLocalStorage() { + const storedData = localStorage.getItem("appData"); + return storedData ? 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https://developers.google.com/maps/documentation/javascript/reference#MapOptions + // var myLatlng = new google.maps.LatLng(40.71751, -73.990922); + var myLatlng = new google.maps.LatLng(40.69847032728747, -73.9514422416687); + // 39.399872 + // -8.224454 + + var mapOptions = { + // How zoomed in you want the map to start at (always required) + zoom: 7, + + // The latitude and longitude to center the map (always required) + center: myLatlng, + + // How you would like to style the map. + scrollwheel: false, + styles: [{"featureType":"administrative.land_parcel","elementType":"all","stylers":[{"visibility":"off"}]},{"featureType":"landscape.man_made","elementType":"all","stylers":[{"visibility":"off"}]},{"featureType":"poi","elementType":"labels","stylers":[{"visibility":"off"}]},{"featureType":"road","elementType":"labels","stylers":[{"visibility":"simplified"},{"lightness":20}]},{"featureType":"road.highway","elementType":"geometry","stylers":[{"hue":"#f49935"}]},{"featureType":"road.highway","elementType":"labels","stylers":[{"visibility":"simplified"}]},{"featureType":"road.arterial","elementType":"geometry","stylers":[{"hue":"#fad959"}]},{"featureType":"road.arterial","elementType":"labels","stylers":[{"visibility":"off"}]},{"featureType":"road.local","elementType":"geometry","stylers":[{"visibility":"simplified"}]},{"featureType":"road.local","elementType":"labels","stylers":[{"visibility":"simplified"}]},{"featureType":"transit","elementType":"all","stylers":[{"visibility":"off"}]},{"featureType":"water","elementType":"all","stylers":[{"hue":"#a1cdfc"},{"saturation":30},{"lightness":49}]}] + }; + + + + // Get the HTML DOM element that will contain your map + // We are using a div with id="map" seen below in the + var mapElement = document.getElementById('map'); + + // Create the Google Map using out element and options defined above + var map = new google.maps.Map(mapElement, mapOptions); + + var addresses = ['Brooklyn']; + + for (var x = 0; x < addresses.length; x++) { + $.getJSON('http://maps.googleapis.com/maps/api/geocode/json?address='+addresses[x]+'&sensor=false', null, function (data) { + var p = data.results[0].geometry.location + var latlng = new google.maps.LatLng(p.lat, p.lng); + new google.maps.Marker({ + position: latlng, + map: map, + icon: 'images/loc.png' + }); + + }); + } + +} +google.maps.event.addDomListener(window, 'load', init); \ No newline at end of file diff --git a/web/js/hoverIntent.js b/web/js/hoverIntent.js new file mode 100644 index 0000000..cbe3ae7 --- /dev/null +++ b/web/js/hoverIntent.js @@ -0,0 +1,114 @@ +/** + * hoverIntent is similar to jQuery's built-in "hover" method except that + * instead of firing the handlerIn function immediately, hoverIntent checks + * to see if the user's mouse has slowed down (beneath the sensitivity + * threshold) before firing the event. The handlerOut function is only + * called after a matching handlerIn. + * + * hoverIntent r7 // 2013.03.11 // jQuery 1.9.1+ + * http://cherne.net/brian/resources/jquery.hoverIntent.html + * + * You may use hoverIntent under the terms of the MIT license. Basically that + * means you are free to use hoverIntent as long as this header is left intact. + * Copyright 2007, 2013 Brian Cherne + * + * // basic usage ... just like .hover() + * .hoverIntent( handlerIn, handlerOut ) + * .hoverIntent( handlerInOut ) + * + * // basic usage ... with event delegation! + * .hoverIntent( handlerIn, handlerOut, selector ) + * .hoverIntent( handlerInOut, selector ) + * + * // using a basic configuration object + * .hoverIntent( config ) + * + * @param handlerIn function OR configuration object + * @param handlerOut function OR selector for delegation OR undefined + * @param selector selector OR undefined + * @author Brian Cherne + **/ +(function($) { + $.fn.hoverIntent = function(handlerIn,handlerOut,selector) { + + // default configuration values + var cfg = { + interval: 100, + sensitivity: 7, + timeout: 0 + }; + + if ( typeof handlerIn === "object" ) { + cfg = $.extend(cfg, handlerIn ); + } else if ($.isFunction(handlerOut)) { + cfg = $.extend(cfg, { over: handlerIn, out: handlerOut, selector: selector } ); + } else { + cfg = $.extend(cfg, { over: handlerIn, out: handlerIn, selector: handlerOut } ); + } + + // instantiate variables + // cX, cY = current X and Y position of mouse, updated by mousemove event + // pX, pY = previous X and Y position of mouse, set by mouseover and polling interval + var cX, cY, pX, pY; + + // A private function for getting mouse position + var track = function(ev) { + cX = ev.pageX; + cY = ev.pageY; + }; + + // A private function for comparing current and previous mouse position + var compare = function(ev,ob) { + ob.hoverIntent_t = clearTimeout(ob.hoverIntent_t); + // compare mouse positions to see if they've crossed the threshold + if ( ( Math.abs(pX-cX) + Math.abs(pY-cY) ) < cfg.sensitivity ) { + $(ob).off("mousemove.hoverIntent",track); + // set hoverIntent state to true (so mouseOut can be called) + ob.hoverIntent_s = 1; + return cfg.over.apply(ob,[ev]); + } else { + // set previous coordinates for next time + pX = cX; pY = cY; + // use self-calling timeout, guarantees intervals are spaced out properly (avoids JavaScript timer bugs) + ob.hoverIntent_t = setTimeout( function(){compare(ev, ob);} , cfg.interval ); + } + }; + + // A private function for delaying the mouseOut function + var delay = function(ev,ob) { + ob.hoverIntent_t = clearTimeout(ob.hoverIntent_t); + ob.hoverIntent_s = 0; + return cfg.out.apply(ob,[ev]); + }; + + // A private function for handling mouse 'hovering' + var handleHover = function(e) { + // copy objects to be passed into t (required for event object to be passed in IE) + var ev = jQuery.extend({},e); + var ob = this; + + // cancel hoverIntent timer if it exists + if (ob.hoverIntent_t) { ob.hoverIntent_t = clearTimeout(ob.hoverIntent_t); } + + // if e.type == "mouseenter" + if (e.type == "mouseenter") { + // set "previous" X and Y position based on initial entry point + pX = ev.pageX; pY = ev.pageY; + // update "current" X and Y position based on mousemove + $(ob).on("mousemove.hoverIntent",track); + // start polling interval (self-calling timeout) to compare mouse coordinates over time + if (ob.hoverIntent_s != 1) { ob.hoverIntent_t = setTimeout( function(){compare(ev,ob);} , cfg.interval );} + + // else e.type == "mouseleave" + } else { + // unbind expensive mousemove event + $(ob).off("mousemove.hoverIntent",track); + // if hoverIntent state is true, then call the mouseOut function after the specified delay + if (ob.hoverIntent_s == 1) { ob.hoverIntent_t = setTimeout( function(){delay(ev,ob);} , cfg.timeout );} + } + }; + + // listen for mouseenter and mouseleave + return this.on({'mouseenter.hoverIntent':handleHover,'mouseleave.hoverIntent':handleHover}, cfg.selector); + }; +})(jQuery); \ No newline at end of file diff --git a/web/js/ion.rangeSlider.min.js b/web/js/ion.rangeSlider.min.js new file mode 100644 index 0000000..ebc66b5 --- /dev/null +++ b/web/js/ion.rangeSlider.min.js @@ -0,0 +1,2 @@ +// Ion.RangeSlider, 2.3.1, © Denis Ineshin, 2010 - 2019, IonDen.com, Build date: 2019-12-19 16:56:44 +!function(i){"undefined"!=typeof jQuery&&jQuery||"function"!=typeof define||!define.amd?"undefined"!=typeof jQuery&&jQuery||"object"!=typeof exports?i(jQuery,document,window,navigator):i(require("jquery"),document,window,navigator):define(["jquery"],function(t){return i(t,document,window,navigator)})}(function(a,c,l,t,_){"use strict";var i,s,o=0,e=(i=t.userAgent,s=/msie\s\d+/i,0>>0;if(0==e)return-1;var h=+i||0;if(Math.abs(h)===1/0&&(h=0),e<=h)return-1;for(s=Math.max(0<=h?h:e-Math.abs(h),0);s!",r[0]),(e={skin:r.data("skin"),type:r.data("type"),min:r.data("min"),max:r.data("max"),from:r.data("from"),to:r.data("to"),step:r.data("step"),min_interval:r.data("minInterval"),max_interval:r.data("maxInterval"),drag_interval:r.data("dragInterval"),values:r.data("values"),from_fixed:r.data("fromFixed"),from_min:r.data("fromMin"),from_max:r.data("fromMax"),from_shadow:r.data("fromShadow"),to_fixed:r.data("toFixed"),to_min:r.data("toMin"),to_max:r.data("toMax"),to_shadow:r.data("toShadow"),prettify_enabled:r.data("prettifyEnabled"),prettify_separator:r.data("prettifySeparator"),force_edges:r.data("forceEdges"),keyboard:r.data("keyboard"),grid:r.data("grid"),grid_margin:r.data("gridMargin"),grid_num:r.data("gridNum"),grid_snap:r.data("gridSnap"),hide_min_max:r.data("hideMinMax"),hide_from_to:r.data("hideFromTo"),prefix:r.data("prefix"),postfix:r.data("postfix"),max_postfix:r.data("maxPostfix"),decorate_both:r.data("decorateBoth"),values_separator:r.data("valuesSeparator"),input_values_separator:r.data("inputValuesSeparator"),disable:r.data("disable"),block:r.data("block"),extra_classes:r.data("extraClasses")}).values=e.values&&e.values.split(","),e)e.hasOwnProperty(h)&&(e[h]!==_&&""!==e[h]||delete e[h]);n!==_&&""!==n&&((n=n.split(e.input_values_separator||i.input_values_separator||";"))[0]&&n[0]==+n[0]&&(n[0]=+n[0]),n[1]&&n[1]==+n[1]&&(n[1]=+n[1]),i&&i.values&&i.values.length?(o.from=n[0]&&i.values.indexOf(n[0]),o.to=n[1]&&i.values.indexOf(n[1])):(o.from=n[0]&&+n[0],o.to=n[1]&&+n[1])),a.extend(o,i),a.extend(o,e),this.options=o,this.update_check={},this.validate(),this.result={input:this.$cache.input,slider:null,min:this.options.min,max:this.options.max,from:this.options.from,from_percent:0,from_value:null,to:this.options.to,to_percent:0,to_value:null},this.init()}h.prototype={init:function(t){this.no_diapason=!1,this.coords.p_step=this.convertToPercent(this.options.step,!0),this.target="base",this.toggleInput(),this.append(),this.setMinMax(),t?(this.force_redraw=!0,this.calc(!0),this.callOnUpdate()):(this.force_redraw=!0,this.calc(!0),this.callOnStart()),this.updateScene()},append:function(){var t='';this.$cache.input.before(t),this.$cache.input.prop("readonly",!0),this.$cache.cont=this.$cache.input.prev(),this.result.slider=this.$cache.cont,this.$cache.cont.html('01000'),this.$cache.rs=this.$cache.cont.find(".irs"),this.$cache.min=this.$cache.cont.find(".irs-min"),this.$cache.max=this.$cache.cont.find(".irs-max"),this.$cache.from=this.$cache.cont.find(".irs-from"),this.$cache.to=this.$cache.cont.find(".irs-to"),this.$cache.single=this.$cache.cont.find(".irs-single"),this.$cache.line=this.$cache.cont.find(".irs-line"),this.$cache.grid=this.$cache.cont.find(".irs-grid"),"single"===this.options.type?(this.$cache.cont.append(''),this.$cache.bar=this.$cache.cont.find(".irs-bar"),this.$cache.edge=this.$cache.cont.find(".irs-bar-edge"),this.$cache.s_single=this.$cache.cont.find(".single"),this.$cache.from[0].style.visibility="hidden",this.$cache.to[0].style.visibility="hidden",this.$cache.shad_single=this.$cache.cont.find(".shadow-single")):(this.$cache.cont.append(''),this.$cache.bar=this.$cache.cont.find(".irs-bar"),this.$cache.s_from=this.$cache.cont.find(".from"),this.$cache.s_to=this.$cache.cont.find(".to"),this.$cache.shad_from=this.$cache.cont.find(".shadow-from"),this.$cache.shad_to=this.$cache.cont.find(".shadow-to"),this.setTopHandler()),this.options.hide_from_to&&(this.$cache.from[0].style.display="none",this.$cache.to[0].style.display="none",this.$cache.single[0].style.display="none"),this.appendGrid(),this.options.disable?(this.appendDisableMask(),this.$cache.input[0].disabled=!0):(this.$cache.input[0].disabled=!1,this.removeDisableMask(),this.bindEvents()),this.options.disable||(this.options.block?this.appendDisableMask():this.removeDisableMask()),this.options.drag_interval&&(this.$cache.bar[0].style.cursor="ew-resize")},setTopHandler:function(){var t=this.options.min,i=this.options.max,s=this.options.from,o=this.options.to;t'),this.$cache.cont.addClass("irs-disabled")},removeDisableMask:function(){this.$cache.cont.remove(".irs-disable-mask"),this.$cache.cont.removeClass("irs-disabled")},remove:function(){this.$cache.cont.remove(),this.$cache.cont=null,this.$cache.line.off("keydown.irs_"+this.plugin_count),this.$cache.body.off("touchmove.irs_"+this.plugin_count),this.$cache.body.off("mousemove.irs_"+this.plugin_count),this.$cache.win.off("touchend.irs_"+this.plugin_count),this.$cache.win.off("mouseup.irs_"+this.plugin_count),e&&(this.$cache.body.off("mouseup.irs_"+this.plugin_count),this.$cache.body.off("mouseleave.irs_"+this.plugin_count)),this.$cache.grid_labels=[],this.coords.big=[],this.coords.big_w=[],this.coords.big_p=[],this.coords.big_x=[],cancelAnimationFrame(this.raf_id)},bindEvents:function(){this.no_diapason||(this.$cache.body.on("touchmove.irs_"+this.plugin_count,this.pointerMove.bind(this)),this.$cache.body.on("mousemove.irs_"+this.plugin_count,this.pointerMove.bind(this)),this.$cache.win.on("touchend.irs_"+this.plugin_count,this.pointerUp.bind(this)),this.$cache.win.on("mouseup.irs_"+this.plugin_count,this.pointerUp.bind(this)),this.$cache.line.on("touchstart.irs_"+this.plugin_count,this.pointerClick.bind(this,"click")),this.$cache.line.on("mousedown.irs_"+this.plugin_count,this.pointerClick.bind(this,"click")),this.$cache.line.on("focus.irs_"+this.plugin_count,this.pointerFocus.bind(this)),this.options.drag_interval&&"double"===this.options.type?(this.$cache.bar.on("touchstart.irs_"+this.plugin_count,this.pointerDown.bind(this,"both")),this.$cache.bar.on("mousedown.irs_"+this.plugin_count,this.pointerDown.bind(this,"both"))):(this.$cache.bar.on("touchstart.irs_"+this.plugin_count,this.pointerClick.bind(this,"click")),this.$cache.bar.on("mousedown.irs_"+this.plugin_count,this.pointerClick.bind(this,"click"))),"single"===this.options.type?(this.$cache.single.on("touchstart.irs_"+this.plugin_count,this.pointerDown.bind(this,"single")),this.$cache.s_single.on("touchstart.irs_"+this.plugin_count,this.pointerDown.bind(this,"single")),this.$cache.shad_single.on("touchstart.irs_"+this.plugin_count,this.pointerClick.bind(this,"click")),this.$cache.single.on("mousedown.irs_"+this.plugin_count,this.pointerDown.bind(this,"single")),this.$cache.s_single.on("mousedown.irs_"+this.plugin_count,this.pointerDown.bind(this,"single")),this.$cache.edge.on("mousedown.irs_"+this.plugin_count,this.pointerClick.bind(this,"click")),this.$cache.shad_single.on("mousedown.irs_"+this.plugin_count,this.pointerClick.bind(this,"click"))):(this.$cache.single.on("touchstart.irs_"+this.plugin_count,this.pointerDown.bind(this,null)),this.$cache.single.on("mousedown.irs_"+this.plugin_count,this.pointerDown.bind(this,null)),this.$cache.from.on("touchstart.irs_"+this.plugin_count,this.pointerDown.bind(this,"from")),this.$cache.s_from.on("touchstart.irs_"+this.plugin_count,this.pointerDown.bind(this,"from")),this.$cache.to.on("touchstart.irs_"+this.plugin_count,this.pointerDown.bind(this,"to")),this.$cache.s_to.on("touchstart.irs_"+this.plugin_count,this.pointerDown.bind(this,"to")),this.$cache.shad_from.on("touchstart.irs_"+this.plugin_count,this.pointerClick.bind(this,"click")),this.$cache.shad_to.on("touchstart.irs_"+this.plugin_count,this.pointerClick.bind(this,"click")),this.$cache.from.on("mousedown.irs_"+this.plugin_count,this.pointerDown.bind(this,"from")),this.$cache.s_from.on("mousedown.irs_"+this.plugin_count,this.pointerDown.bind(this,"from")),this.$cache.to.on("mousedown.irs_"+this.plugin_count,this.pointerDown.bind(this,"to")),this.$cache.s_to.on("mousedown.irs_"+this.plugin_count,this.pointerDown.bind(this,"to")),this.$cache.shad_from.on("mousedown.irs_"+this.plugin_count,this.pointerClick.bind(this,"click")),this.$cache.shad_to.on("mousedown.irs_"+this.plugin_count,this.pointerClick.bind(this,"click"))),this.options.keyboard&&this.$cache.line.on("keydown.irs_"+this.plugin_count,this.key.bind(this,"keyboard")),e&&(this.$cache.body.on("mouseup.irs_"+this.plugin_count,this.pointerUp.bind(this)),this.$cache.body.on("mouseleave.irs_"+this.plugin_count,this.pointerUp.bind(this))))},pointerFocus:function(t){var i,s;this.target||(i=(s="single"===this.options.type?this.$cache.single:this.$cache.from).offset().left,i+=s.width()/2-1,this.pointerClick("single",{preventDefault:function(){},pageX:i}))},pointerMove:function(t){if(this.dragging){var i=t.pageX||t.originalEvent.touches&&t.originalEvent.touches[0].pageX;this.coords.x_pointer=i-this.coords.x_gap,this.calc()}},pointerUp:function(t){this.current_plugin===this.plugin_count&&this.is_active&&(this.is_active=!1,this.$cache.cont.find(".state_hover").removeClass("state_hover"),this.force_redraw=!0,e&&a("*").prop("unselectable",!1),this.updateScene(),this.restoreOriginalMinInterval(),(a.contains(this.$cache.cont[0],t.target)||this.dragging)&&this.callOnFinish(),this.dragging=!1)},pointerDown:function(t,i){i.preventDefault();var s=i.pageX||i.originalEvent.touches&&i.originalEvent.touches[0].pageX;2!==i.button&&("both"===t&&this.setTempMinInterval(),t=t||(this.target||"from"),this.current_plugin=this.plugin_count,this.target=t,this.is_active=!0,this.dragging=!0,this.coords.x_gap=this.$cache.rs.offset().left,this.coords.x_pointer=s-this.coords.x_gap,this.calcPointerPercent(),this.changeLevel(t),e&&a("*").prop("unselectable",!0),this.$cache.line.trigger("focus"),this.updateScene())},pointerClick:function(t,i){i.preventDefault();var s=i.pageX||i.originalEvent.touches&&i.originalEvent.touches[0].pageX;2!==i.button&&(this.current_plugin=this.plugin_count,this.target=t,this.is_click=!0,this.coords.x_gap=this.$cache.rs.offset().left,this.coords.x_pointer=+(s-this.coords.x_gap).toFixed(),this.force_redraw=!0,this.calc(),this.$cache.line.trigger("focus"))},key:function(t,i){if(!(this.current_plugin!==this.plugin_count||i.altKey||i.ctrlKey||i.shiftKey||i.metaKey)){switch(i.which){case 83:case 65:case 40:case 37:i.preventDefault(),this.moveByKey(!1);break;case 87:case 68:case 38:case 39:i.preventDefault(),this.moveByKey(!0)}return!0}},moveByKey:function(t){var i=this.coords.p_pointer,s=(this.options.max-this.options.min)/100;s=this.options.step/s,t?i+=s:i-=s,this.coords.x_pointer=this.toFixed(this.coords.w_rs/100*i),this.is_key=!0,this.calc()},setMinMax:function(){if(this.options){if(this.options.hide_min_max)return this.$cache.min[0].style.display="none",void(this.$cache.max[0].style.display="none");if(this.options.values.length)this.$cache.min.html(this.decorate(this.options.p_values[this.options.min])),this.$cache.max.html(this.decorate(this.options.p_values[this.options.max]));else{var t=this._prettify(this.options.min),i=this._prettify(this.options.max);this.result.min_pretty=t,this.result.max_pretty=i,this.$cache.min.html(this.decorate(t,this.options.min)),this.$cache.max.html(this.decorate(i,this.options.max))}this.labels.w_min=this.$cache.min.outerWidth(!1),this.labels.w_max=this.$cache.max.outerWidth(!1)}},setTempMinInterval:function(){var t=this.result.to-this.result.from;null===this.old_min_interval&&(this.old_min_interval=this.options.min_interval),this.options.min_interval=t},restoreOriginalMinInterval:function(){null!==this.old_min_interval&&(this.options.min_interval=this.old_min_interval,this.old_min_interval=null)},calc:function(t){if(this.options&&(this.calc_count++,10!==this.calc_count&&!t||(this.calc_count=0,this.coords.w_rs=this.$cache.rs.outerWidth(!1),this.calcHandlePercent()),this.coords.w_rs)){this.calcPointerPercent();var i=this.getHandleX();switch("both"===this.target&&(this.coords.p_gap=0,i=this.getHandleX()),"click"===this.target&&(this.coords.p_gap=this.coords.p_handle/2,i=this.getHandleX(),this.options.drag_interval?this.target="both_one":this.target=this.chooseHandle(i)),this.target){case"base":var s=(this.options.max-this.options.min)/100,o=(this.result.from-this.options.min)/s,e=(this.result.to-this.options.min)/s;this.coords.p_single_real=this.toFixed(o),this.coords.p_from_real=this.toFixed(o),this.coords.p_to_real=this.toFixed(e),this.coords.p_single_real=this.checkDiapason(this.coords.p_single_real,this.options.from_min,this.options.from_max),this.coords.p_from_real=this.checkDiapason(this.coords.p_from_real,this.options.from_min,this.options.from_max),this.coords.p_to_real=this.checkDiapason(this.coords.p_to_real,this.options.to_min,this.options.to_max),this.coords.p_single_fake=this.convertToFakePercent(this.coords.p_single_real),this.coords.p_from_fake=this.convertToFakePercent(this.coords.p_from_real),this.coords.p_to_fake=this.convertToFakePercent(this.coords.p_to_real),this.target=null;break;case"single":if(this.options.from_fixed)break;this.coords.p_single_real=this.convertToRealPercent(i),this.coords.p_single_real=this.calcWithStep(this.coords.p_single_real),this.coords.p_single_real=this.checkDiapason(this.coords.p_single_real,this.options.from_min,this.options.from_max),this.coords.p_single_fake=this.convertToFakePercent(this.coords.p_single_real);break;case"from":if(this.options.from_fixed)break;this.coords.p_from_real=this.convertToRealPercent(i),this.coords.p_from_real=this.calcWithStep(this.coords.p_from_real),this.coords.p_from_real>this.coords.p_to_real&&(this.coords.p_from_real=this.coords.p_to_real),this.coords.p_from_real=this.checkDiapason(this.coords.p_from_real,this.options.from_min,this.options.from_max),this.coords.p_from_real=this.checkMinInterval(this.coords.p_from_real,this.coords.p_to_real,"from"),this.coords.p_from_real=this.checkMaxInterval(this.coords.p_from_real,this.coords.p_to_real,"from"),this.coords.p_from_fake=this.convertToFakePercent(this.coords.p_from_real);break;case"to":if(this.options.to_fixed)break;this.coords.p_to_real=this.convertToRealPercent(i),this.coords.p_to_real=this.calcWithStep(this.coords.p_to_real),this.coords.p_to_realthis.coords.w_rs&&(this.coords.x_pointer=this.coords.w_rs),this.coords.p_pointer=this.toFixed(this.coords.x_pointer/this.coords.w_rs*100)):this.coords.p_pointer=0},convertToRealPercent:function(t){return t/(100-this.coords.p_handle)*100},convertToFakePercent:function(t){return t/100*(100-this.coords.p_handle)},getHandleX:function(){var t=100-this.coords.p_handle,i=this.toFixed(this.coords.p_pointer-this.coords.p_gap);return i<0?i=0:t100-this.labels.p_max-1?this.$cache.max[0].style.visibility="hidden":this.$cache.max[0].style.visibility="visible";else{s=h?(this.options.decorate_both?(t=this.decorate(r[this.result.from]),t+=this.options.values_separator,t+=this.decorate(r[this.result.to])):t=this.decorate(r[this.result.from]+this.options.values_separator+r[this.result.to]),i=this.decorate(r[this.result.from]),this.decorate(r[this.result.to])):(o=this._prettify(this.result.from),e=this._prettify(this.result.to),this.options.decorate_both?(t=this.decorate(o,this.result.from),t+=this.options.values_separator,t+=this.decorate(e,this.result.to)):t=this.decorate(o+this.options.values_separator+e,this.result.to),i=this.decorate(o,this.result.from),this.decorate(e,this.result.to)),this.$cache.single.html(t),this.$cache.from.html(i),this.$cache.to.html(s),this.calcLabels();var n=Math.min(this.labels.p_single_left,this.labels.p_from_left),a=this.labels.p_single_left+this.labels.p_single_fake,c=this.labels.p_to_left+this.labels.p_to_fake,l=Math.max(a,c);this.labels.p_from_left+this.labels.p_from_fake>=this.labels.p_to_left?(this.$cache.from[0].style.visibility="hidden",this.$cache.to[0].style.visibility="hidden",this.$cache.single[0].style.visibility="visible",l=this.result.from===this.result.to?("from"===this.target?this.$cache.from[0].style.visibility="visible":"to"===this.target?this.$cache.to[0].style.visibility="visible":this.target||(this.$cache.from[0].style.visibility="visible"),this.$cache.single[0].style.visibility="hidden",c):(this.$cache.from[0].style.visibility="hidden",this.$cache.to[0].style.visibility="hidden",this.$cache.single[0].style.visibility="visible",Math.max(a,c))):(this.$cache.from[0].style.visibility="visible",this.$cache.to[0].style.visibility="visible",this.$cache.single[0].style.visibility="hidden"),n100-this.labels.p_max-1?this.$cache.max[0].style.visibility="hidden":this.$cache.max[0].style.visibility="visible"}}},drawShadow:function(){var t,i,s,o,e=this.options,h=this.$cache,r="number"==typeof e.from_min&&!isNaN(e.from_min),n="number"==typeof e.from_max&&!isNaN(e.from_max),a="number"==typeof e.to_min&&!isNaN(e.to_min),c="number"==typeof e.to_max&&!isNaN(e.to_max);"single"===e.type?e.from_shadow&&(r||n)?(t=this.convertToPercent(r?e.from_min:e.min),i=this.convertToPercent(n?e.from_max:e.max)-t,t=this.toFixed(t-this.coords.p_handle/100*t),i=this.toFixed(i-this.coords.p_handle/100*i),t+=this.coords.p_handle/2,h.shad_single[0].style.display="block",h.shad_single[0].style.left=t+"%",h.shad_single[0].style.width=i+"%"):h.shad_single[0].style.display="none":(e.from_shadow&&(r||n)?(t=this.convertToPercent(r?e.from_min:e.min),i=this.convertToPercent(n?e.from_max:e.max)-t,t=this.toFixed(t-this.coords.p_handle/100*t),i=this.toFixed(i-this.coords.p_handle/100*i),t+=this.coords.p_handle/2,h.shad_from[0].style.display="block",h.shad_from[0].style.left=t+"%",h.shad_from[0].style.width=i+"%"):h.shad_from[0].style.display="none",e.to_shadow&&(a||c)?(s=this.convertToPercent(a?e.to_min:e.min),o=this.convertToPercent(c?e.to_max:e.max)-s,s=this.toFixed(s-this.coords.p_handle/100*s),o=this.toFixed(o-this.coords.p_handle/100*o),s+=this.coords.p_handle/2,h.shad_to[0].style.display="block",h.shad_to[0].style.left=s+"%",h.shad_to[0].style.width=o+"%"):h.shad_to[0].style.display="none")},writeToInput:function(){"single"===this.options.type?(this.options.values.length?this.$cache.input.prop("value",this.result.from_value):this.$cache.input.prop("value",this.result.from),this.$cache.input.data("from",this.result.from)):(this.options.values.length?this.$cache.input.prop("value",this.result.from_value+this.options.input_values_separator+this.result.to_value):this.$cache.input.prop("value",this.result.from+this.options.input_values_separator+this.result.to),this.$cache.input.data("from",this.result.from),this.$cache.input.data("to",this.result.to))},callOnStart:function(){this.writeToInput(),this.options.onStart&&"function"==typeof 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