UI_IMPLEMENTATION.md

# 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*