# Textilindo AI Assistant - Hugging Face Spaces Deployment Guide

## 🚀 Quick Setup for Hugging Face Spaces

### 1. Environment Variables Required

Set these environment variables in your Hugging Face Space settings:

```bash
# Required: Hugging Face API Key (use your secret variable name)
HUGGINGFAC_API_KEY_2=your_huggingface_api_key_here

# Optional: Default model (defaults to Llama 3.1 8B Instruct)
DEFAULT_MODEL=meta-llama/Llama-3.1-8B-Instruct

# Optional: Alternative models
# DEFAULT_MODEL=meta-llama/Llama-3.2-1B-Instruct
# DEFAULT_MODEL=meta-llama/Llama-3.2-3B-Instruct
# DEFAULT_MODEL=gpt2
# DEFAULT_MODEL=distilgpt2
```

### 2. Files Structure

Your Hugging Face Space should contain:

```
├── app.py                    # Main FastAPI application
├── Dockerfile               # Docker configuration
├── requirements.txt         # Python dependencies
├── README.md               # Space description
├── configs/
│   ├── system_prompt.md  # AI system prompt
│   └── training_config.yaml # Training configuration
├── data/
│   └── lora_dataset_*.jsonl  # Training datasets
└── templates/
    └── chat.html           # Chat interface (optional)
```

### 3. Deployment Steps

1. **Create a new Hugging Face Space:**
   - Go to https://huggingface.co/new-space
   - Choose "Docker" as the SDK
   - Name your space (e.g., "textilindo-ai-assistant")

2. **Upload files:**
   - Clone your space repository
   - Copy all files from this project
   - Commit and push to your space

3. **Set environment variables:**
   - Go to your space settings
   - Add the required environment variables
   - Make sure to set `HUGGINGFACE_API_KEY`

4. **Deploy:**
   - Your space will automatically build and deploy
   - Check the logs for any issues

### 4. API Endpoints

Once deployed, your space will have these endpoints:

- `GET /` - Main chat interface
- `POST /chat` - Chat API endpoint
- `GET /health` - Health check
- `GET /info` - Application information

### 5. Usage Examples

#### Chat API
```bash
curl -X POST "https://your-space-name.hf.space/chat" \
  -H "Content-Type: application/json" \
  -d '{"message": "dimana lokasi textilindo?"}'
```

#### Health Check
```bash
curl "https://your-space-name.hf.space/health"
```

### 6. Troubleshooting

#### Common Issues:

1. **"HUGGINGFACE_API_KEY not found"**
   - Make sure you've set the environment variable in your space settings
   - The app will use mock responses if no API key is provided

2. **Model loading errors**
   - Check if the model name is correct
   - Try using a lighter model like `meta-llama/Llama-3.2-1B-Instruct`

3. **Memory issues**
   - Hugging Face Spaces have limited memory
   - Use smaller models or reduce batch sizes

4. **Build failures**
   - Check the build logs in your space
   - Ensure all dependencies are in requirements.txt

### 7. Customization

#### Change the Model:
Update the `DEFAULT_MODEL` environment variable:
```bash
DEFAULT_MODEL=meta-llama/Llama-3.2-1B-Instruct
```

#### Update System Prompt:
Edit `configs/system_prompt.md` and redeploy.

#### Add More Training Data:
Add more JSONL files to the `data/` directory.

### 8. Performance Optimization

For better performance on Hugging Face Spaces:

1. **Use smaller models:**
   - `meta-llama/Llama-3.2-1B-Instruct` (1B parameters)
   - `microsoft/DialoGPT-medium` (355M parameters)

2. **Optimize system prompt:**
   - Keep it concise
   - Remove unnecessary instructions

3. **Monitor resource usage:**
   - Check the space logs
   - Use the `/health` endpoint

### 9. Security Notes

- Never commit API keys to your repository
- Use environment variables for sensitive data
- The app includes CORS middleware for web access
- All user inputs are logged (check logs for debugging)

### 10. Support

If you encounter issues:

1. Check the space logs
2. Verify environment variables
3. Test with the `/health` endpoint
4. Try the mock responses (without API key)

For more help, check the Hugging Face Spaces documentation:
https://huggingface.co/docs/hub/spaces