Flask Frontend Application

This is a Flask-based frontend application designed to serve static files and provide various API endpoints for integration with OpenAI's ChatGPT, Groq's API, and a custom Knowledge Graph (KG) API. The application runs on port 8001 and integrates seamlessly with a backend environment.

Features

• Serves static files including index.html, CSS, and JS from the static folder.
• Provides multiple API endpoints:
  • /chatgpt: Interacts with OpenAI's ChatGPT API.
  • /groq: Integrates with Groq's Llama3 API for generating responses.
  • /kg-graph: Returns a simulated Knowledge Graph response.
  • /custom-response: Provides a simulated custom response for testing.

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Prerequisites

1. Conda Environment: Ensure you are using the same Conda environment as the backend for consistency. Activate the environment using:

   conda activate <backend_environment_name>

   Replace <backend_environment_name> with the name of your backend environment.

2. Python Dependencies: Install the required dependencies in the activated environment:

   pip install flask openai groq

3. API Keys:

   • Set up your OpenAI API key: Replace <open_ai_key> in the code with your actual OpenAI API key.
   • Set up your Groq API key: Replace <groq_key> in the code with your actual Groq API key.

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Running the Flask Application

1. Navigate to the Application Directory: Ensure you are in the directory containing the app.py file.

2. Run the Flask App: Start the application by running:

   python app.py

3. Access the Application: Open a browser and navigate to:

   http://127.0.0.1:8001
   

   The app serves the index.html file and provides access to the APIs.

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API Endpoints

1. ChatGPT Endpoint

• URL: /chatgpt
• Method: POST
• Payload:

  {
      "question": "<Your question here>"
  }

• Response:

  {
      "response": "<ChatGPT's response>"
  }

2. Groq Endpoint

• URL: /groq
• Method: POST
• Payload:

  {
      "question": "<Your question here>"
  }

• Response:

  {
      "response": "<Groq's response>"
  }

3. KG Graph Endpoint

• URL: /kg-graph
• Method: GET
• Response:

  {
      "kg_graph_text": "This is the simulated KG Graph output."
  }

4. Custom Response Endpoint

• URL: /custom-response
• Method: POST
• Payload:

  {
      "question": "<Your question here>"
  }

• Response:

  {
      "response": "Response: <Your question>. This is a simulated custom response..."
  }

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Development Notes

• Debugging: The app runs in debug mode by default. If deploying to production, set debug=False.
• Static Folder: Place your static files (e.g., HTML, CSS, JS) in the static folder.
• Port: The app runs on port 8001. Update the app.run call in app.py if you need to use a different port.