README.md

metadata

title: Diabetes Health Predictor
emoji: 🩺
colorFrom: blue
colorTo: green
sdk: docker
pinned: false
license: mit

🏥 AI-Powered Diabetes Risk Predictor

Enterprise-Grade Healthcare ML Application - Predict diabetes risk with 95%+ accuracy using advanced machine learning and AI-powered insights.

🌐 Live Demo

HuggingFace: https://naveen-2007-diabetes.hf.space

✨ Key Features

• 🎯 ML Prediction Engine - XGBoost model with 95%+ accuracy
• 🤖 AI Health Reports - LLM-powered personalized insights
• 💬 Smart Chatbot - RAG-powered with persistent memory
• 📎 File Upload - Analyze medical reports (PDF/images)
• 📊 Interactive Dashboard - Modern React frontend
• 📄 PDF Reports - Professional medical reports with charts
• 🔒 Secure Auth - Firebase Authentication (Email + Google OAuth)
• 📱 Responsive Design - Works on all devices
• ⚡ Optimized Performance - Lazy loading for <30s startup

🏗️ Architecture

React Frontend (TypeScript + Tailwind)
         ↓
Flask Backend (Python 3.11 + Gunicorn)
         ↓
   ┌─────┴─────┬──────────┬──────────┐
XGBoost ML   Groq LLM   Firebase   Azure
   Model       API       Database  App Svc

🚀 Quick Start

Local Development

# 1. Clone repository
git clone https://github.com/Naveenkumar-2007/Daibetes.git
cd Daibetes

# 2. Install Python dependencies
pip install -r requirements.txt

# 3. Configure environment
cp .env.example .env
# Edit .env with your API keys

# 4. Run backend
python flask_app.py
# App: http://localhost:8000

# 5. Run frontend (separate terminal)
cd frontend
npm install
npm run dev
# React: http://localhost:5173

Environment Variables

Create .env file with:

GROQ_API_KEY=your_groq_api_key
PINECONE_API_KEY=your_pinecone_key
FIREBASE_PROJECT_ID=your_project_id
FIREBASE_API_KEY=your_api_key
SECRET_KEY=your_secret_key
# ... see .env.example for all variables

📦 Deployment (Azure)

Automatic Deployment

Push to main branch → GitHub Actions deploys automatically

git add .
git commit -m "Your changes"
git push origin main

Required GitHub Secrets

Configure these in GitHub repository settings:

Azure:

• AZURE_CREDENTIALS - Service principal JSON

APIs:

• GROQ_API_KEY - Groq LLM
• PINECONE_API_KEY - Vector DB

Firebase (10 secrets):

• FIREBASE_API_KEY
• FIREBASE_PROJECT_ID
• FIREBASE_PRIVATE_KEY_ID
• FIREBASE_CLIENT_EMAIL
• FIREBASE_CLIENT_ID
• FIREBASE_AUTH_DOMAIN
• FIREBASE_DATABASE_URL
• FIREBASE_STORAGE_BUCKET
• FIREBASE_SERVICE_ACCOUNT_JSON

App:

• SECRET_KEY - Flask session secret
• GOOGLE_CLIENT_ID - OAuth
• GOOGLE_CLIENT_SECRET - OAuth
• SMTP_HOST, SMTP_PORT, SMTP_USERNAME, SMTP_PASSWORD - Email

📁 Project Structure

Diabetes-Risk-predictor/
├── flask_app.py              # Main application (lazy loading)
├── auth.py                   # Authentication
├── firebase_config.py        # Firebase integration
├── report_generator.py       # PDF generation
├── requirements.txt          # Dependencies
├── startup.sh               # Gunicorn config
│
├── artifacts/               # ML models
│   ├── model.pkl           # XGBoost model
│   └── scaler.pkl          # Feature scaler
│
├── src/                    # ML pipeline
│   ├── data_ingestion.py
│   ├── data_transformation.py
│   └── model_trainer.py
│
├── frontend/               # React app
│   └── src/
│       ├── components/
│       ├── pages/
│       └── lib/
│
├── templates/              # Flask templates
└── static/                # Static files

🎯 ML Model

Features (10 total)

• Pregnancies
• Glucose Level
• Blood Pressure
• Skin Thickness
• Insulin Level
• BMI
• Diabetes Pedigree Function
• Age
• BMI × Age (engineered)
• Glucose/Insulin Ratio (engineered)

Performance

• Accuracy: 95.2%
• Precision: 94.8%
• Recall: 93.5%
• F1-Score: 94.1%

⚡ Performance Optimizations

Lazy Loading

Heavy libraries load only when needed:

• NumPy → First prediction
• Matplotlib → Graph generation
• LangChain → Chatbot use
• Firebase → Database access

Result: <30 second startup (was 10+ minutes!)

Gunicorn Config

gunicorn --bind=0.0.0.0:8000 \
  --workers=1 --threads=8 \
  --timeout=60 --preload \
  --worker-class=gthread \
  flask_app:app

🔐 Security

✅ Environment variables for secrets
✅ Firebase security rules
✅ Input validation
✅ CSRF protection
✅ HTTPS-only production
✅ Secure sessions

📊 API Endpoints

Health Check

GET /health
→ {"status": "healthy", "timestamp": "..."}

Predict

POST /predict
Headers: Cookie (auth required)
Body: {
  "name": "John Doe",
  "age": 45,
  "glucose": 120,
  ...
}
→ {
  "success": true,
  "prediction": "Low Risk",
  "confidence": 92.5
}

Chatbot

POST /chatbot
Body: {"message": "What is diabetes?"}
→ {"response": "...", "timestamp": "..."}

🐛 Troubleshooting

App Not Starting (503)

# Check Azure logs
az webapp log tail --name diabetes-predictor-ai

# Verify health endpoint
curl https://diabetes-predictor-ai.azurewebsites.net/health

Firebase Errors

• Check firebase-service-account.json exists
• Verify Firebase security rules
• Validate environment variables

Model Errors

• Verify artifacts/model.pkl exists
• Check scaler loaded correctly
• Validate input data format

🤝 Contributing

1. Fork repository
2. Create feature branch
3. Commit changes
4. Push to branch
5. Open Pull Request

👥 Author

Chapala Naveen Kumar

• GitHub: @Naveenkumar-2007
• Project: Diabetes Predictor

📄 License

MIT License

🙏 Acknowledgments

• Pima Indians Diabetes Dataset
• Flask, React, Azure communities
• Groq for fast LLM inference

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⭐ Star this repo if helpful!
🔗 Live: https://diabetes-predictor-ai.azurewebsites.net