LLM-Chatbot-with-FastAPI-and-Streamlit

This project empowers you to generate creative text content using the power of large language models (LLMs). It leverages the strengths of FastAPI for building a robust API server and Streamlit for crafting a user-friendly web interface.

Key Technologies:

• FastAPI: A high-performance, Python-based framework for building APIs with ease.
• Langchain: A library for simplifying work with LLMs and managing complex chatbot interactions.
• Streamlit: A framework that enables the creation of interactive web applications in a streamlined manner.
• OpenAI API (Optional): A powerful option for accessing advanced language models from OpenAI (requires a paid API key).
• Groq Langchain Chat (Optional): A convenient way to utilize Groq's cloud-based collection of open-source models for fast and efficient processing.
• Ollama LLM (Locally Downloaded): A free and open-source LLM (Llama 3 in this case) that can be run locally on your machine.
• LangServe: A library that simplifies deployment of LangChain runnables and chains as REST APIs.
• Prompt Templates: Two templates are defined for generating essays and poems.
• Routes: Routes for essay and poem generation are added to the FastAPI application.
• Server Execution: The FastAPI application is run using uvicorn.

1. Environment Variables (.env):

• Create a file named .env in your project's root directory.
• This file stores sensitive information like API keys that you don't want to commit to version control.
• Include the following lines, replacing placeholders with your actual values:

OPENAI_API_KEY=your_openai_api_key  # Optional for OpenAI access
GROQ_API_KEY=your_groq_api_key     # Optional for Groq cloud

• Important: Never share your API keys publicly.

2. Running the Application:

There are two main components to run: the FastAPI server and the Streamlit web interface.

2.1. Running the FastAPI Server (app.py):

1. Make sure you have Python and the required libraries installed (pip install fastapi langchain langchain-openai langchain-groq langchain-community uvicorn dotenv).

2. Load the environment variables:

   source .env  # Linux/macOS
   .env\Scripts\activate  # Windows (if using virtual environment)

3. Start the server:

   python app.py  # Replace port if needed

   This will run the FastAPI server, listening on port 8000 (or your chosen port) and ready to receive requests from the Streamlit application.

2.2. Running the Streamlit Web Interface (client.py):

1. In a separate terminal session, navigate to your project's directory.

2. Start the Streamlit app:

   streamlit run client.py

   This will launch the Streamlit interface in your web browser, typically at http://localhost:8080 (the default port for Streamlit).

Basic Workflow:

1. Run the FastAPI server: Follow the steps in section 2.1 to start the server.
2. Run the Streamlit web interface: Follow the steps in section 2.2 to launch the Streamlit app in your browser.
3. Interact with the Streamlit app:
   • Select between generating an essay or a poem.
   • Enter a topic for the desired content.
   • Click the appropriate button to trigger the request to the FastAPI server.
   • The generated essay or poem will be displayed in the Streamlit interface.

Additional Notes:

• By default, the Streamlit application is configured to use the Groq API for essays and Ollama LLM for poems.
• To leverage OpenAI, you'll need to uncomment the corresponding code sections in app.py and provide the necessary API keys in the .env file.
• Remember to replace placeholders with your actual API keys and adjust port numbers if needed.
• For detailed information about configuration options and advanced usage, refer to the documentation for FastAPI, Langchain, and Streamlit.