Merge pull request #636 from Raghucharan16/chicken-disease-classifica…

…tion

Added complete project of chicken disease classification

Chicken_Disease_Classification/Dockerfile

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+FROM python:3.8-slim-buster
+
+RUN apt update -y && apt install awscli -y
+WORKDIR /app
+
+COPY . /app
+RUN pip install -r requirements.txt
+
+CMD ["python3", "app.py"]

Chicken_Disease_Classification/LICENSE

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+MIT License
+
+Copyright (c) 2023 BAPPY AHMED
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Chicken_Disease_Classification/README.md

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+# Chicken-Disease-Classification-Project
+
+
+## Workflows
+
+1. Update config.yaml
+2. Update secrets.yaml [Optional]
+3. Update params.yaml
+4. Update the entity
+5. Update the configuration manager in src config
+6. Update the components
+7. Update the pipeline 
+8. Update the main.py
+9. Update the dvc.yaml
+While Building the project, we have to follow these workflows, make sure we are updating everthing at every step.
+
+# How to run?
+### STEPS:
+
+Clone the repository
+
+```bash
+https://github.com/entbappy/Chicken-Disease-Classification--Project
+```
+### STEP 01- Create a conda environment after opening the repository
+
+```bash
+conda create -n cnncls python=3.8 -y
+```
+
+```bash
+conda activate cnncls
+```
+
+
+### STEP 02- install the requirements
+```bash
+pip install -r requirements.txt
+```
+
+
+```bash
+# Finally run the following command
+python app.py
+```
+
+Now,
+```bash
+open up you local host and port
+```
+
+
+### DVC cmd
+
+1. dvc init
+2. dvc repro
+3. dvc dag
+
+
+## MLflow
+
+[Documentation](https://mlflow.org/docs/latest/index.html)
+
+
+##### cmd
+- mlflow ui
+
+### dagshub
+[dagshub](https://dagshub.com/)
+
+MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/MLflow-DVC-Chicken-Disease-Classification.mlflow 
+MLFLOW_TRACKING_USERNAME=entbappy 
+MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0 
+python script.py
+
+Run this to export as env variables:
+
+```bash
+
+export MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/MLflow-DVC-Chicken-Disease-Classification.mlflow
+
+export MLFLOW_TRACKING_USERNAME=entbappy 
+
+export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0 
+
+```
+
+
+# AWS-CICD-Deployment-with-Github-Actions
+
+## 1. Login to AWS console.
+
+## 2. Create IAM user for deployment
+
+	#with specific access
+
+	1. EC2 access : It is virtual machine
+
+	2. ECR: Elastic Container registry to save your docker image in aws
+
+
+	#Description: About the deployment
+
+	1. Build docker image of the source code
+
+	2. Push your docker image to ECR
+
+	3. Launch Your EC2 
+
+	4. Pull Your image from ECR in EC2
+
+	5. Lauch your docker image in EC2
+
+	#Policy:
+
+	1. AmazonEC2ContainerRegistryFullAccess
+
+	2. AmazonEC2FullAccess
+
+
+## 3. Create ECR repo to store/save docker image
+    - Save the URI: 566373416292.dkr.ecr.us-east-1.amazonaws.com/chicken
+
+
+## 4. Create EC2 machine (Ubuntu) 
+
+## 5. Open EC2 and Install docker in EC2 Machine:
+
+
+	#optinal
+
+	sudo apt-get update -y
+
+	sudo apt-get upgrade
+
+	#required
+
+	curl -fsSL https://get.docker.com -o get-docker.sh
+
+	sudo sh get-docker.sh
+
+	sudo usermod -aG docker ubuntu
+
+	newgrp docker
+
+# 6. Configure EC2 as self-hosted runner:
+    setting>actions>runner>new self hosted runner> choose os> then run command one by one
+
+
+# 7. Setup github secrets:
+
+    AWS_ACCESS_KEY_ID=
+
+    AWS_SECRET_ACCESS_KEY=
+
+    AWS_REGION = us-east-1
+
+    AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com
+
+    ECR_REPOSITORY_NAME = simple-app
+
+
+
+
+# AZURE-CICD-Deployment-with-Github-Actions
+
+## Save pass:
+
+s3cEZKH5yytiVnJ3h+eI3qhhzf9q1vNwEi6+q+WGdd+ACRCZ7JD6
+
+
+## Run from terminal:
+
+docker build -t chickenapp.azurecr.io/chicken:latest .
+
+docker login chickenapp.azurecr.io
+
+docker push chickenapp.azurecr.io/chicken:latest
+
+
+## Deployment Steps:
+
+1. Build the Docker image of the Source Code
+2. Push the Docker image to Container Registry
+3. Launch the Web App Server in Azure 
+4. Pull the Docker image from the container registry to Web App server and run 
+
+
+
+## About MLflow & DVC
+
+MLflow
+
+ - Its Production Grade
+ - Trace all of your expriements
+ - Logging & taging your model
+
+
+DVC 
+
+ - Its very lite weight for POC only
+ - lite weight expriements tracker
+ - It can perform Orchestration (Creating Pipelines)

Chicken_Disease_Classification/app.py

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+from flask import Flask, request, jsonify, render_template
+import os
+from flask_cors import CORS, cross_origin
+from cnnClassifier.utils.common import decodeImage
+from cnnClassifier.pipeline.predict import PredictionPipeline
+
+
+os.putenv('LANG', 'en_US.UTF-8')
+os.putenv('LC_ALL', 'en_US.UTF-8')
+
+app = Flask(__name__)
+CORS(app)
+
+
+class ClientApp:
+    def __init__(self):
+        self.filename = "inputImage.jpg"
+        self.classifier = PredictionPipeline(self.filename)
+
+
+@app.route("/", methods=['GET'])
+@cross_origin()
+def home():
+    return render_template('index.html')
+
+
+@app.route("/train", methods=['GET','POST'])
+@cross_origin()
+def trainRoute():
+    os.system("python main.py")
+    return "Training done successfully!"
+
+
+
+@app.route("/predict", methods=['POST'])
+@cross_origin()
+def predictRoute():
+    image = request.json['image']
+    decodeImage(image, clApp.filename)
+    result = clApp.classifier.predict()
+    return jsonify(result)
+
+
+if __name__ == "__main__":
+    clApp = ClientApp()
+    # app.run(host='0.0.0.0', port=8080) #local host
+    # app.run(host='0.0.0.0', port=8080) #for AWS
+    app.run(host='0.0.0.0', port=80) #for AZURE
+

Chicken_Disease_Classification/config/config.yaml

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+artifacts_root: artifacts
+
+
+data_ingestion:
+  root_dir: artifacts/data_ingestion
+  source_URL: https://github.com/entbappy/Branching-tutorial/raw/master/Chicken-fecal-images.zip
+  local_data_file: artifacts/data_ingestion/data.zip
+  unzip_dir: artifacts/data_ingestion
+
+
+
+prepare_base_model:
+  root_dir: artifacts/prepare_base_model
+  base_model_path: artifacts/prepare_base_model/base_model.h5
+  updated_base_model_path: artifacts/prepare_base_model/base_model_updated.h5
+
+
+
+
+prepare_callbacks:
+  root_dir: artifacts/prepare_callbacks
+  tensorboard_root_log_dir: artifacts/prepare_callbacks/tensorboard_log_dir
+  checkpoint_model_filepath: artifacts/prepare_callbacks/checkpoint_dir/model.h5
+
+
+
+
+training:
+  root_dir: artifacts/training
+  trained_model_path: artifacts/training/model.h5