DEPLOYMENT.md

Deployment Instructions

Deploying to Hugging Face Spaces

Prerequisites

• A Hugging Face account (free)
• Git installed locally

Steps

1. Create a new Space on Hugging Face:

   • Go to https://huggingface.co/spaces
   • Click "Create new Space"
   • Choose a name (e.g., "ai-text-assistant")
   • Select "Gradio" as the SDK
   • Choose visibility (Public or Private)
   • Click "Create Space"

2. Clone your Space repository:

   git clone https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME
   cd YOUR_SPACE_NAME
   

3. Copy the application files: Copy these files from this project to your Space repository:

   • app.py
   • requirements.txt
   • README.md
   • .gitignore (optional)

4. Commit and push:

   git add .
   git commit -m "Initial commit: AI Text Assistant"
   git push
   

5. Wait for deployment:

   • Hugging Face Spaces will automatically detect the changes
   • The build process will install dependencies and start the app
   • This may take 5-10 minutes for the first deployment
   • You can watch the build logs in the Space's "Logs" tab

6. Access your app:

   • Once deployed, your app will be available at:
   • https://huggingface.co/spaces/YOUR_USERNAME/YOUR_SPACE_NAME

Local Testing

To test locally before deploying:

# Install dependencies
pip install -r requirements.txt

# Run the app
python app.py

The app will be available at http://127.0.0.1:7860

Configuration Options

Hardware

For better performance, you can upgrade your Space's hardware:

• Go to Space Settings → Hardware
• Options include CPU (free), GPU T4 (small fee), GPU A10G, etc.
• The app works on CPU but will be faster with GPU

Environment Variables

You can set these in Space Settings → Variables:

• TRANSFORMERS_CACHE: Custom cache directory for models
• HF_HOME: Hugging Face home directory

Troubleshooting

Build fails with memory errors:

• The models are relatively small, but if you encounter issues:
• Upgrade to a better hardware tier
• Or consider using Hugging Face Inference API instead

App starts slowly:

• The first run downloads models (~1GB for Qwen, ~1.6GB for BART)
• Subsequent runs will use cached models
• Model loading takes 30-60 seconds on CPU

Token alternatives not showing:

• Make sure you hover over the generated words
• The tooltip appears on hover with a slight delay
• Try different browsers if issues persist

Performance Notes

• First Load: Slow due to model downloads
• Model Loading: 30-60 seconds on CPU, 5-10 seconds on GPU
• Generation Speed:
  • Qwen (0.5B): ~10-20 tokens/sec on CPU, ~100+ tokens/sec on GPU
  • BART-large: ~5-10 tokens/sec on CPU, ~50+ tokens/sec on GPU

Support

For issues or questions:

• Check Hugging Face Spaces documentation: https://huggingface.co/docs/hub/spaces
• Open an issue on the repository
• Contact: Your email/contact info