Copy_of_crewai_jobprep_v1_012224.ipynb

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      "cell_type": "markdown",
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        "id": "view-in-github",
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        "<a href=\"https://colab.research.google.com/github/localecho/Python-GUI-examples/blob/master/Copy_of_crewai_jobprep_v1_012224.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
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      "cell_type": "code",
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        "id": "dVf-PqESLOob",
        "outputId": "5bde0d90-989a-498e-8917-a19a81f5f804"
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      "outputs": [
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          ]
        }
      ],
      "source": [
        "# Environment Setup\n",
        "!pip install crewai\n",
        "\n",
        "# Importing necessary libraries for CrewAI\n",
        "import os\n",
        "\n",
        "from crewai import Agent, Task, Crew, Process\n",
        "from langchain.agents import Tool\n",
        "from langchain.utilities import GoogleSerperAPIWrapper\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "# Setting up environment variables for API keys\n",
        "# Replace 'Your_Key' with your actual API keys\n",
        "os.environ[\"OPENAI_API_KEY\"] = \"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\"\n",
        "os.environ[\"SERPER_API_KEY\"] = \"4e153c0ec14a8c5726bfcd2a8f0ea30c8073cb92\"\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_context_and_question(topic):\n",
        "    # Define a template for context and question\n",
        "    context_template = f\"\"\"\n",
        "    [This is a background about {topic}. It includes key information,\n",
        "    historical aspects, recent developments, and crucial factors related to {topic}.]\n",
        "    \"\"\"\n",
        "\n",
        "    question_template = f\"\"\"\n",
        "    [What are the main challenges and opportunities associated with {topic},\n",
        "    considering the current trends and future prospects?]\n",
        "    \"\"\"\n",
        "\n",
        "    # Return the formatted context and question\n",
        "    return context_template, question_template\n",
        "\n",
        "# Example usageto\n",
        "topic = \"design a presonalized education planner website from soup to nuts. output is an execuative summary.\"  # Replace with your desired topic\n",
        "\n",
        "\"\"\"\n",
        "context, question = generate_context_and_question(topic)\n",
        "\n",
        "# Display the context and question\n",
        "print(\"Context:\\n\", context)\n",
        "print(\"\\nQuestion:\\n\", question)\n",
        "\"\"\"\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "Qy9J74ToL-E7",
        "outputId": "70a57450-49e2-43c1-cdbd-f6dc5bdaba24"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\ncontext, question = generate_context_and_question(topic)\\n\\n# Display the context and question\\nprint(\"Context:\\n\", context)\\nprint(\"\\nQuestion:\\n\", question)\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from transformers import BertTokenizer, BertModel\n",
        "from sklearn.metrics.pairwise import euclidean_distances\n",
        "from nltk.util import ngrams\n",
        "import torch\n",
        "\n",
        "def extract_themes_pro(context, question, num_themes=100, ngram_range=(1, 3)):\n",
        "    # Load pre-trained BERT model\n",
        "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "    model = BertModel.from_pretrained('bert-base-uncased')\n",
        "\n",
        "    # Combine context and question\n",
        "    context = ''\n",
        "    question = ''\n",
        "    #need to clean cell\n",
        "    text = #context + \" \" + question\n",
        "\n",
        "    # Generate n-grams\n",
        "    tokens = tokenizer.tokenize(text)\n",
        "    n_grams = list(ngrams(tokens, ngram_range[1], pad_left=True, pad_right=True))\n",
        "    n_grams = [' '.join(gram).strip() for gram in n_grams if None not in gram]\n",
        "\n",
        "    # Generate embeddings\n",
        "    embeddings = []\n",
        "    for gram in n_grams:\n",
        "        inputs = tokenizer(gram, return_tensors='pt')\n",
        "        outputs = model(**inputs)\n",
        "        embeddings.append(outputs.last_hidden_state.mean(dim=1).detach().numpy())\n",
        "\n",
        "    embeddings = np.vstack(embeddings)\n",
        "\n",
        "    # Calculate distances\n",
        "    dist_matrix = euclidean_distances(embeddings)\n",
        "\n",
        "    # Extract themes based on the closest distances\n",
        "    themes = []\n",
        "    for idx, row in enumerate(dist_matrix):\n",
        "        closest_indices = np.argsort(row)[:num_themes]\n",
        "        closest_ngrams = [n_grams[i] for i in closest_indices]\n",
        "        themes.extend(closest_ngrams)\n",
        "\n",
        "    # Ensure uniqueness and return\n",
        "    unique_themes = list(set(themes))\n",
        "    return unique_themes[:num_themes]"
      ],
      "metadata": {
        "id": "xXai3dIgqz_g"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from transformers import BertTokenizer, BertModel\n",
        "import torch\n",
        "from sklearn.metrics.pairwise import euclidean_distances\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.tokenize import word_tokenize\n",
        "import nltk\n",
        "\n",
        "# Ensure NLTK resources are available\n",
        "nltk.download('punkt')\n",
        "nltk.download('stopwords')\n",
        "\n",
        "def extract_themes_pro_bert(context, question, num_themes=1000):\n",
        "    \"\"\"\n",
        "    Analyze the context and question to extract key themes using BERT embeddings and multi-dimensional Euclidean distances.\n",
        "\n",
        "    Args:\n",
        "    context (str): The context or background information.\n",
        "    question (str): The specific question or problem statement.\n",
        "    num_themes (int): Number of themes to extract.\n",
        "\n",
        "    Returns:\n",
        "    list: A list of the most prominent themes.\n",
        "    \"\"\"\n",
        "    # Initialize BERT tokenizer and model\n",
        "    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n",
        "    model = BertModel.from_pretrained('bert-base-uncased')\n",
        "\n",
        "    # Combine context and question\n",
        "    text = context + \" \" + question\n",
        "\n",
        "    # Tokenize and remove stopwords\n",
        "    tokens = word_tokenize(text)\n",
        "    stop_words = set(stopwords.words('english'))\n",
        "    filtered_tokens = [word.lower() for word in tokens if word.isalpha() and word.lower() not in stop_words]\n",
        "\n",
        "    # Convert tokens to BERT embeddings\n",
        "    inputs = tokenizer(filtered_tokens, return_tensors='pt', padding=True, truncation=True)\n",
        "    outputs = model(**inputs)\n",
        "    embeddings = outputs.last_hidden_state.mean(dim=1).detach().numpy()\n",
        "\n",
        "    # Calculate Euclidean distances between embeddings\n",
        "    dist_matrix = euclidean_distances(embeddings)\n",
        "\n",
        "    # Extract themes based on the closest distances\n",
        "    idx_closest = np.argsort(dist_matrix, axis=1)[:, :num_themes]\n",
        "    themes = [filtered_tokens[i] for i in idx_closest.flatten()]\n",
        "\n",
        "    # Ensure uniqueness and return\n",
        "    unique_themes = list(set(themes))\n",
        "    return unique_themes[:num_themes]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uf5k2hN5xXBN",
        "outputId": "1171fb9e-927c-4746-fbb6-bb5b7ca19ffc"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def match_job_titles(themes):\n",
        "    \"\"\"\n",
        "    Match extracted themes with appropriate job titles.\n",
        "\n",
        "    Args:\n",
        "    themes (list): List of themes extracted from text.\n",
        "\n",
        "    Returns:\n",
        "    list: A list of matched job titles.\n",
        "    \"\"\"\n",
        "    # Example job titles categorization (can be expanded based on the resources)\n",
        "    job_titles = {\n",
        "        \"marketing\": [\"Digital Marketing Director\", \"Senior Marketing Strategist\"],\n",
        "        \"cybersecurity\": [\"Security Analyst\", \"Information Security Manager\"],\n",
        "        \"data science\": [\"Data Scientist\", \"Data Analyst\"],\n",
        "        \"finance\": [\"Financial Analyst\", \"Corporate Finance Manager\"],\n",
        "        \"sales\": [\"Sales Manager\", \"Account Executive\"],\n",
        "        \"hr\": [\"HR Manager\", \"Talent Acquisition Specialist\"],\n",
        "        # Add more categories and titles as needed\n",
        "    }\n",
        "\n",
        "    matched_titles = []\n",
        "    for theme in themes:\n",
        "        # Example logic to match themes with job titles\n",
        "        # This can be refined based on the specific themes and available job titles\n",
        "        category = theme.lower()\n",
        "        if category in job_titles:\n",
        "            matched_titles.extend(job_titles[category])\n",
        "        else:\n",
        "            # Handling unmatched themes\n",
        "            matched_titles.append(\"Specialist in \" + theme.title())\n",
        "\n",
        "    return matched_titles\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "q3qQ1fxrXKlQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assemble the AI Crew\n",
        "from crewai import Agent, Task, Crew, Process\n",
        "import random\n",
        "\n",
        "# Function to analyze context and question for key themes\n",
        "\n",
        "# Extracting themes\n",
        "themes_bert = extract_themes_pro_bert(context, question)\n",
        "themes_pro =  extract_themes_pro(context, question)\n",
        "themes = themes_bert + themes_pro\n",
        "\n",
        "\n",
        "# Number of agents to create\n",
        "num_agents = 100\n",
        "\n",
        "# Creating a list to hold all agents\n",
        "agents = []\n",
        "# Loop to create each agent\n",
        "for i in range(num_agents):\n",
        "    theme_index = i % len(themes)\n",
        "    role = f'Agent with expertise in {themes[theme_index]}'\n",
        "    goal = f'Provide insights on {themes[theme_index]} related to the question'\n",
        "    backstory = f'Agent {i+1} specializing in {themes[theme_index]}.'\n",
        "\n",
        "    # Creating a wildcard agent with contrasting characteristics\n",
        "    if i == num_agents - 1:\n",
        "        role = 'Wildcard Agent with Diverse Perspective'\n",
        "        goal = 'Challenge the conventional thinking and provide alternative viewpoints'\n",
        "        backstory = 'An agent known for unconventional approaches and thinking outside the box.'\n",
        "\n",
        "    # Creating an agent\n",
        "    agent = Agent(\n",
        "        role=role,\n",
        "        goal=goal,\n",
        "        backstory=backstory,\n",
        "        verbose=True\n",
        "    )\n",
        "    agents.append(agent)\n",
        "\n",
        "# Displaying the created agents\n",
        "for agent in agents:\n",
        "    print(f'Agent Role: {agent.role}, Goal: {agent.goal}, Backstory: {agent.backstory}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        },
        "id": "eIWiASsgMKWI",
        "outputId": "61f9db0b-c08f-4452-b6e5-a56dc11bb46c"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'context' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-69a311b58120>\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Extracting themes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mthemes_bert\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_themes_pro_bert\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquestion\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      9\u001b[0m \u001b[0mthemes_pro\u001b[0m \u001b[0;34m=\u001b[0m  \u001b[0mextract_themes_pro\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mquestion\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mthemes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthemes_bert\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mthemes_pro\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'context' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assemble the AI Crew\n",
        "from crewai import Agent, Task, Crew, Process\n",
        "import random\n",
        "\n",
        "# Function to analyze context and question for key themes\n",
        "\n",
        "# Extracting themes\n",
        "themes_bert = extract_themes_pro_bert(context, question)\n",
        "themes_pro = extract_themes_pro(context, question)\n",
        "themes = themes_bert + themes_pro\n",
        "\n",
        "# Number of agents to create\n",
        "num_agents = 240\n",
        "\n",
        "# Creating a list to hold all agents\n",
        "agents = []\n",
        "# Loop to create each agent\n",
        "for i in range(num_agents):\n",
        "    theme_index = i % len(themes)\n",
        "    role = f'Agent with expertise in {themes[theme_index]}'\n",
        "    goal = f'Provide insights on {themes[theme_index]} related to the question'\n",
        "    backstory = f'Agent {i+1} specializing in {themes[theme_index]}.'\n",
        "\n",
        "    # Creating a wildcard agent with contrasting characteristics\n",
        "    if i == num_agents - 1:\n",
        "        role = 'Wildcard Agent with Diverse Perspective'\n",
        "        goal = 'Challenge the conventional thinking and provide alternative viewpoints'\n",
        "        backstory = 'An agent known for unconventional approaches and thinking outside the box.'\n",
        "\n",
        "    # Creating an agent\n",
        "    agent = Agent(\n",
        "        role=role,\n",
        "        goal=goal,\n",
        "        backstory=backstory,\n",
        "        verbose=True\n",
        "    )\n",
        "    agents.append(agent)\n",
        "\n",
        "# Displaying the created agents\n",
        "for agent in agents:\n",
        "    print(f'Agent Role: {agent.role}, Goal: {agent.goal}, Backstory: {agent.backstory}')\n"
      ],
      "metadata": {
        "id": "vXyD_Q46cqPT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Execute crew to perform the tasks\n",
        "\n",
        "# Define a task for the agents\n",
        "# This should be defined according to the specific problem at hand\n",
        "task = Task(description= question, agent=agents[0])\n",
        "\n",
        "# Create the crew with a sequential process\n",
        "crew = Crew(\n",
        "    agents=agents,\n",
        "    tasks=[task],\n",
        "    process=Process.sequential,  # Using sequential process, but can be adapted\n",
        "    verbose=True\n",
        ")\n",
        "\n"
      ],
      "metadata": {
        "id": "Ymg9kdIyMM5d"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "# Initialize metrics\n",
        "start_time = time.time()\n",
        "error_count = 0\n",
        "total_tasks = len(crew.tasks)  # Assuming 'crew' is your CrewAI instance\n",
        "\n",
        "# Execute CrewAI tasks\n",
        "try:\n",
        "    result = crew.kickoff()\n",
        "except Exception as e:\n",
        "    error_count += 1\n",
        "    print(f\"Error occurred: {e}\")\n",
        "\n",
        "# Calculate total time taken\n",
        "end_time = time.time()\n",
        "total_time = end_time - start_time\n",
        "\n",
        "# Output results\n",
        "print(f\"Total tasks: {total_tasks}\")\n",
        "print(f\"Errors: {error_count}\")\n",
        "print(f\"Total time taken: {total_time} seconds\")\n"
      ],
      "metadata": {
        "id": "WIOEjqr5Px9h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "i1uByeei-fRu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "# Initialize metrics\n",
        "start_time = time.time()\n",
        "error_count = 0\n",
        "total_tasks = len(crew.tasks)  # Assuming 'crew' is your CrewAI instance\n",
        "\n",
        "# Execute CrewAI tasks\n",
        "try:\n",
        "    result = crew.kickoff()\n",
        "except Exception as e:\n",
        "    error_count += 1\n",
        "    print(f\"Error occurred: {e}\")\n",
        "\n",
        "# Calculate total time taken\n",
        "end_time = time.time()\n",
        "total_time = end_time - start_time\n",
        "\n",
        "# Output results\n",
        "print(f\"Total tasks: {total_tasks}\")\n",
        "print(f\"Errors: {error_count}\")\n",
        "print(f\"Total time taken: {total_time} seconds\")\n"
      ],
      "metadata": {
        "id": "HspaX32w-iC8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "√"
      ],
      "metadata": {
        "id": "w8rzTopq-n-H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "he prospects of data science web applet plotly opportunities are vast. Data science is a rapidly growing field, and tools like Plotly are in demand for their ability to create interactive and visually appealing data visualizations. Here's a look at the future prospects:\n",
        "\n",
        "1. Increased Demand: The demand for data science and data visualization tools, like Plotly, is expected to increase. Companies are realizing the importance of data-driven decisions, which will lead to a rise in opportunities.\n",
        "\n",
        "2. Advanced Visualizations: As technology advances, we can expect to see more advanced, interactive, and dynamic visualizations. This will provide new opportunities for tools like Plotly to innovate and develop new features and products.\n",
        "\n",
        "3. Integration with AI: The integration of AI and machine learning with data visualization tools will be a big opportunity. This can lead to the creation of smarter and more insightful visualizations.\n",
        "\n",
        "4. Customization: There will be more opportunities for customization. Companies will want tools that can be tailored to their specific needs, creating opportunities for more specialized versions of tools like Plotly.\n",
        "\n",
        "5. Accessibility: There will be a growing need for tools that are user-friendly and accessible to people without a deep understanding of data science. This will create opportunities for tools that can make data science accessible to a wider audience.\n",
        "\n",
        "Challenges that may come along the way include staying up to date with rapidly advancing trends and technologies, maintaining the balance between user-friendliness and advanced features, and ensuring data security and privacy. However, these challenges also represent opportunities for growth and innovation.Do I need to use a tool? No\n",
        "Final Answer: To answer the question about the main challenges and opportunities associated with building a 30-day marketing strategy to determine the top 10 MVP data science web applet plotly opportunities, we have to consider the following:\n",
        "\n",
        "Challenges include time constraints, market research, identifying key metrics, limited resources, customer acquisition, competition, rapid technological advancements, and user-friendly design.\n",
        "\n",
        "Opportunities include fast feedback, market positioning, learning opportunities, potential partnerships, customer engagement, interactive data visualization, real-time data analysis, machine learning integration, customization, collaboration, mobile compatibility, user-friendly interface, scalability, and education and training.\n",
        "\n",
        "The major current market trends are the growing demand for data science and data visualization tools, the potential for integration with other platforms, and the untapped educational sector.\n",
        "\n",
        "Looking at the future prospects, there is a predicted increased demand for data science and visualization tools. Other prospects include the development of more advanced, interactive, and dynamic visualizations, integration with AI, more opportunities for customization, and a greater need for user-friendly and accessible tools.\n",
        "\n",
        "To determine the top 10 MVP data science web applet plotly opportunities, factors such as the size of the potential market, the level of competition, the potential for growth, and the needs and preferences of the target audience should be considered.\n",
        "\n",
        "> Finished chain.\n",
        "\n",
        "[DEBUG]: [Agent with expertise in main] Task output: To answer the question about the main challenges and opportunities associated with building a 30-day marketing strategy to determine the top 10 MVP data science web applet plotly opportunities, we have to consider the following:\n",
        "\n",
        "Challenges include time constraints, market research, identifying key metrics, limited resources, customer acquisition, competition, rapid technological advancements, and user-friendly design.\n",
        "\n",
        "Opportunities include fast feedback, market positioning, learning opportunities, potential partnerships, customer engagement, interactive data visualization, real-time data analysis, machine learning integration, customization, collaboration, mobile compatibility, user-friendly interface, scalability, and education and training.\n",
        "\n",
        "The major current market trends are the growinghe prospects of data science web applet plotly opportunities are vast. Data science is a rapidly growing field, and tools like Plotly are in demand for their ability to create interactive and visually appealing data visualizations. Here's a look at the future prospects:\n",
        "\n",
        "1. Increased Demand: The demand for data science and data visualization tools, like Plotly, is expected to increase. Companies are realizing the importance of data-driven decisions, which will lead to a rise in opportunities.\n",
        "\n",
        "2. Advanced Visualizations: As technology advances, we can expect to see more advanced, interactive, and dynamic visualizations. This will provide new opportunities for tools like Plotly to innovate and develop new features and products.\n",
        "\n",
        "3. Integration with AI: The integration of AI and machine learning with data visualization tools will be a big opportunity. This can lead to the creation of smarter and more insightful visualizations.\n",
        "\n",
        "4. Customization: There will be more opportunities for customization. Companies will want tools that can be tailored to their specific needs, creating opportunities for more specialized versions of tools like Plotly.\n",
        "\n",
        "5. Accessibility: There will be a growing need for tools that are user-friendly and accessible to people without a deep understanding of data science. This will create opportunities for tools that can make data science accessible to a wider audience.\n",
        "\n",
        "Challenges that may come along the way include staying up to date with rapidly advancing trends and technologies, maintaining the balance between user-friendliness and advanced features, and ensuring data security and privacy. However, these challenges also represent opportunities for growth and innovation.Do I need to use a tool? No\n",
        "Final Answer: To answer the question about the main challenges and opportunities associated with building a 30-day marketing strategy to determine the top 10 MVP data science web applet plotly opportunities, we have to consider the following:\n",
        "\n",
        "Challenges include time constraints, market research, identifying key metrics, limited resources, customer acquisition, competition, rapid technological advancements, and user-friendly design.\n",
        "\n",
        "Opportunities include fast feedback, market positioning, learning opportunities, potential partnerships, customer engagement, interactive data visualization, real-time data analysis, machine learning integration, customization, collaboration, mobile compatibility, user-friendly interface, scalability, and education and training.\n",
        "\n",
        "The major current market trends are the growing demand for data science and data visualization tools, the potential for integration with other platforms, and the untapped educational sector.\n",
        "\n",
        "Looking at the future prospects, there is a predicted increased demand for data science and visualization tools. Other prospects include the development of more advanced, interactive, and dynamic visualizations, integration with AI, more opportunities for customization, and a greater need for user-friendly and accessible tools.\n",
        "\n",
        "To determine the top 10 MVP data science web applet plotly opportunities, factors such as the size of the potential market, the level of competition, the potential for growth, and the needs and preferences of the target audience should be considered.\n",
        "\n",
        "> Finished chain.\n",
        "\n",
        "[DEBUG]: [Agent with expertise in main] Task output: To answer the question about the main challenges and opportunities associated with building a 30-day marketing strategy to determine the top 10 MVP data science web applet plotly opportunities, we have to consider the following:\n",
        "\n",
        "Challenges include time constraints, market research, identifying key metrics, limited resources, customer acquisition, competition, rapid technological advancements, and user-friendly design.\n",
        "\n",
        "Opportunities include fast feedback, market positioning, learning opportunities, potential partnerships, customer engagement, interactive data visualization, real-time data analysis, machine learning integration, customization, collaboration, mobile compatibility, user-friendly interface, scalability, and education and training.\n",
        "\n",
        "The major current market trends are the growing demand for data science and data visualization tools, the potential for integration with other platforms, and the untapped educational sector.\n",
        "\n",
        "Looking at the future prospects, there is a predicted increased demand for data science and visualization tools. Other prospects include the development of more advanced, interactive, and dynamic visualizations, integration with AI, more opportunities for customization, and a greater need for user-friendly and accessible tools.\n",
        "\n",
        "To determine the top 10 MVP data science web applet plotly opportunities, factors such as the size of the potential market, the level of competition, the potential for growth, and the needs and preferences of the target audience should be considered.\n",
        "\n",
        "\n",
        "Total tasks: 1\n",
        "Errors: 0\n",
        "Total time taken: 221.04770803451538 seconds demand for data science and data visualization tools, the potential for integration with other platforms, and the untapped educational sector.\n",
        "\n",
        "Looking at the future prospects, there is a predicted increased demand for data science and visualization tools. Other prospects include the development of more advanced, interactive, and dynamic visualizations, integration with AI, more opportunities for customization, and a greater need for user-friendly and accessible tools.\n",
        "\n",
        "To determine the top 10 MVP data science web applet plotly opportunities, factors such as the size of the potential market, the level of competition, the potential for growth, and the needs and preferences of the target audience should be considered.\n",
        "\n",
        "\n",
        "Total tasks: 1\n",
        "Errors: 0\n",
        "Total time taken: 221.04770803451538 seconds"
      ],
      "metadata": {
        "id": "0XMoO1Gn-6RE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "# Initialize metrics\n",
        "start_time = time.time()\n",
        "error_count = 0\n",
        "total_tasks = len(crew.tasks)  # Assuming 'crew' is your CrewAI instance\n",
        "\n",
        "# Execute CrewAI tasks\n",
        "try:\n",
        "    result = crew.kickoff()\n",
        "except Exception as e:\n",
        "    error_count += 1\n",
        "    print(f\"Error occurred: {e}\")\n",
        "\n",
        "# Calculate total time taken\n",
        "end_time = time.time()\n",
        "total_time = end_time - start_time\n",
        "\n",
        "# Output results\n",
        "print(f\"Total tasks: {total_tasks}\")\n",
        "print(f\"Errors: {error_count}\")\n",
        "print(f\"Total time taken: {total_time} seconds\")\n"
      ],
      "metadata": {
        "id": "8FBL8NGV-puO"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}