README.md

Boston Public School Selection Chatbot

This is a skeleton repo you can use to design a school choice chatbot. Feel free to change it however you'd like! The end goal: make the chatbot and upload it to a huggingface space. Instructions for doing so are below.

Setup

1. Install the required dependencies:

pip install -r requirements.txt

2. Get access to the LLaMA model:

   • Visit Hugging Face
   • Request access to the LLaMA 2 model
   • Once approved, log in to Hugging Face:

   huggingface-cli login
   

3. Run the chatbot:

python app.py

Deploying to Hugging Face

To deploy your chatbot as a free web interface using Hugging Face Spaces:

1. Create a Hugging Face Space:

   • Go to Hugging Face Spaces
   • Click "Create new Space"
   • Choose a name for your space (e.g., "boston-school-chatbot")
   • Select "Gradio" as the SDK
   • Choose "CPU" as the hardware (free tier)
   • Make it "Public" so others can use your chatbot

2. Prepare your files: Your repository should already have all needed files:

   6.so41-midterm/
   ├── README.md           # Description of your chatbot
   ├── app.py             # Your Gradio interface
   ├── requirements.txt   # Already set up with needed dependencies
   └── src/              # Your implementation files
   

3. Push your code to the Space:

   git init
   git add .
   git commit -m "Initial commit"
   git remote add origin https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
   git push -u origin main
   

4. Important Free Tier Considerations:

   • Use TinyLlama model (already configured in model.py)
   • Free CPU spaces have 2GB RAM limit
   • Responses might be slower than local testing
   • The interface might queue requests when multiple users access it

5. After Deployment:

   • Your chatbot will be available at: https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
   • Anyone can use it through their web browser
   • You can update the deployment anytime by pushing changes:

     git add .
     git commit -m "Update chatbot"
     git push
     

6. Troubleshooting:

   • Check the Space's logs if the chatbot isn't working
   • Make sure you're using TinyLlama model
   • Verify the chatbot works locally before deploying
   • Remember free tier has limited resources

Your chatbot should now be accessible to anyone through their web browser!

Repository Organization

boston-school-chatbot/
├── app.py              # Gradio web interface - implement the chat function
├── requirements.txt    # Python dependencies
├── chatbot_development.ipynb     # Notebook for developing and testing your chatbot
├── chatbot_conversation_example.txt     # Example conversation we might want to have with this chatbot
└── src/
    ├── model.py       # Model loading/saving (already implemented)
    └── chat.py        # SchoolChatbot class (implement this)

Key Files:

• app.py: Creates the web interface using Gradio. You only need to implement the chat function that generates responses.

• model.py: Handles loading and saving of LLaMA models. This is already implemented.

• chat.py: Contains the SchoolChatbot class where you'll implement:

  • format_prompt: Format user input into proper prompts
  • get_response: Generate responses using the model

• chatbot_development.ipynb: Jupyter notebook for:

  • Loading and testing your model
  • Experimenting with the chatbot
  • Trying different approaches
  • Testing responses before deployment

What You Need to Implement:

1. In chat.py:

   • Complete the SchoolChatbot class methods
   • Design how the chatbot formats prompts
   • Implement response generation

2. In app.py:

   • Implement the chat function to work with Gradio
   • The rest of the file is already set up

3. Use chatbot_development.ipynb to:

   • Develop and test your implementation
   • Try different approaches
   • Verify everything works before deployment