# Medical Q&A Bot - UI Implementation Summary

## 📦 What Was Added

### New Files Created:

1. **`app.py`** - Main Gradio web interface (RECOMMENDED)
   - Clean, modern UI with gradient header
   - Dual-view mode (formatted HTML + JSON)
   - Real-time classification and retrieval
   - Example queries built-in
   - Automatic API generation

2. **`app_streamlit.py`** - Alternative Streamlit interface
   - Interactive sidebar with settings
   - Card-based result display
   - Progress bars for confidence scores
   - More customizable styling options

3. **`QUICKSTART.md`** - Step-by-step setup guide
   - Installation instructions
   - How to launch both UIs
   - Troubleshooting tips
   - Demo tips for presentations

4. **`UI_README.md`** - Comprehensive documentation
   - Feature descriptions
   - Configuration options
   - Advanced usage
   - API information

5. **`setup_ui.py`** - Automated setup script
   - Installs all dependencies
   - Checks for required data files
   - Verifies setup completeness

### Modified Files:

1. **`requirements.txt`** - Added:
   - `gradio` - Main UI framework
   - `streamlit` - Alternative UI framework

## 🎯 Key Features

### Classification Display
- ✅ Shows query type (Medical/Administrative/Other)
- ✅ Confidence scores with visual indicators
- ✅ Color-coded results

### Document Retrieval
- ✅ Retrieves top N relevant documents (1-50)
- ✅ Shows BM25, Dense, and RRF scores
- ✅ Displays document preview with full metadata
- ✅ Optional reranker for better accuracy

### User Experience
- ✅ Clean, professional design
- ✅ Example queries for easy testing
- ✅ Formatted and raw JSON views
- ✅ Responsive layout
- ✅ Real-time processing

## 🚀 Quick Start (TL;DR)

```powershell
# 1. Install dependencies
pip install -r requirements.txt

# 2. Build data (if needed)
python -m adapters.build_corpora

# 3. Launch UI (choose one)
python app.py              # Gradio (recommended)
streamlit run app_streamlit.py  # Streamlit (alternative)
```

## 🌟 Which UI to Use?

### Use Gradio (`app.py`) if you want:
- Quick setup and deployment
- Clean, minimal interface
- Easy sharing (public links)
- Automatic REST API
- Better for demos and presentations

### Use Streamlit (`app_streamlit.py`) if you want:
- More interactive controls
- Sidebar with settings
- More customization options
- Data-science focused interface

**Recommendation:** Start with **Gradio** - it's simpler and looks very professional!

## 📊 How It Works

```
User Input → Gradio/Streamlit Interface
    ↓
Classifier (classify query as medical/admin/other)
    ↓
If Medical → Retriever (BM25 + Dense + RRF)
    ↓
Optional Reranker (cross-encoder)
    ↓
Display Results (formatted cards + JSON)
```

## 🎓 For Your Presentation

### Demo Flow:
1. **Open the interface** - Show the clean design
2. **Enter a medical query** - e.g., "I have a rash on my hands"
3. **Show classification** - Highlight confidence scores
4. **Display results** - Show retrieved medical documents
5. **Try administrative query** - Show different handling
6. **Toggle settings** - Demonstrate reranker and result count
7. **Show JSON view** - For technical audience

### Key Talking Points:
- "We built a user-friendly web interface using Gradio"
- "The system classifies queries in real-time with confidence scores"
- "For medical queries, it retrieves relevant research from PubMed and Miriad"
- "Uses hybrid search combining BM25 and dense embeddings"
- "Optional reranker for improved accuracy"

## 🎨 Customization Options

### Change Theme/Colors
Edit the CSS in `app.py`:
```python
custom_css = """
.header {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
}
"""
```

### Add More Example Queries
Edit the `examples` list in `app.py`:
```python
gr.Examples(
    examples=[
        ["Your custom query here"],
    ],
    inputs=query_input,
)
```

### Change Port
```python
demo.launch(server_port=8080)  # Default is 7860
```

### Enable Public Sharing
```python
demo.launch(share=True)  # Creates 72-hour public link
```

## 🔧 Technical Details

### Gradio Version
- Framework: Gradio 4.x
- Features: Blocks API, custom CSS, examples, tabs
- Auto-generates REST API at `/docs`

### Streamlit Version
- Framework: Streamlit
- Features: Sidebar, caching, progress bars, metrics
- More suitable for data exploration

### Integration
- Uses existing classifier and retriever modules
- No changes to core logic
- Models loaded once and cached
- Async processing for better UX

## 📁 File Structure

```
health-query-classifier/
├── app.py                  # ← Main Gradio UI
├── app_streamlit.py        # ← Alternative Streamlit UI
├── setup_ui.py             # ← Setup script
├── QUICKSTART.md           # ← Quick start guide
├── UI_README.md            # ← Detailed documentation
├── requirements.txt        # ← Updated with gradio/streamlit
├── classifier/
│   ├── infer.py           # Used by UI
│   └── ...
├── retriever/
│   └── ...
└── team/
    ├── candidates.py      # Used by UI
    └── ...
```

## 🐛 Common Issues & Solutions

### "Module not found: gradio"
```powershell
pip install gradio
```

### "No corpora files found"
```powershell
python -m adapters.build_corpora
```

### Models take long to load
- This is normal on first run
- Models are cached after initial load
- Consider using smaller models for faster demo

### Port already in use
- Change port in `app.py` (line ~255)
- Or kill the process using that port

## 🌐 Deployment Options

### Local (Current Setup)
- Best for development and demos
- Access via localhost

### Hugging Face Spaces (Free)
- Free hosting for Gradio apps
- Easy to deploy
- Public URL

### Cloud Platforms
- AWS, Google Cloud, Azure
- More control and scalability
- Requires more setup

## 📈 Future Enhancements

Potential additions for future development:
- User authentication
- Query history
- Bookmarking results
- Export to PDF
- Multi-language support
- Voice input
- Mobile app
- Analytics dashboard

## 👥 Team Contributions

This UI implementation demonstrates:
- Full-stack development skills
- ML model integration
- User experience design
- Modern web frameworks
- Professional documentation

Perfect for showcasing in your group project presentation!

## 📞 Support

For issues or questions:
1. Check `QUICKSTART.md` for setup issues
2. Check `UI_README.md` for feature documentation
3. Review error messages carefully
4. Contact team members

---

## ✅ Ready to Present!

Your medical Q&A bot now has a professional web interface that:
- ✅ Looks modern and clean
- ✅ Is easy to use
- ✅ Demonstrates your ML capabilities
- ✅ Provides clear results
- ✅ Is well-documented
- ✅ Can be easily deployed

**Great work, team!** 🎉

---

*Created by: Tarak Jha, with contributions from the entire team*
*Team: David Gray • Tarak Jha • Sravani Segireddy • Riley Millikan • Kent R. Spillner*