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)

# 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:

custom_css = """
.header {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
}
"""

Add More Example Queries

Edit the examples list in app.py:

gr.Examples(
    examples=[
        ["Your custom query here"],
    ],
    inputs=query_input,
)

Change Port

demo.launch(server_port=8080)  # Default is 7860

Enable Public Sharing

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"

pip install gradio

"No corpora files found"

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