AI Healthcare System

Bridging Lab Results to Patient Understanding with AI

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🎯 About The Project

AI Healthcare System is a next-gen patient portal built for diagnostic centers. We wanted to solve a simple problem: Lab reports are confusing.

Most patients get a PDF full of numbers they don't understand. Our platform fixes this by combining:

1. Automated Screening: Immediate risk assessment for Diabetes, Heart Disease, and more.
2. AI Explanation: A "Medical Assistant" chat that explains the report in plain English (powered by Gemini Pro).

It's a full-stack solution—diagnostic centers get a dashboard to manage patients, and patients get a secure portal to understand their health.

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✨ Features

• For Patients:

  • 📄 Smart Reports: Upload a PDF and get an instant AI summary.
  • 🤖 Health Assistant: Chat with an AI that knows your medical history.
  • 🩺 Risk Screening: ML models check your vitals (Diabetes, Kidney, Liver, etc.).

• For Doctors & Clinics:

  • 🏥 Patient Dashboard: View all patient records in one place.
  • 📈 Trend Analysis: Visualize patient health metrics over time.
  • 🔐 Secure & compliant: Role-based access and data isolation.

🔬 Supported Screenings

We use trained ML models (XGBoost/RandomForest) to screen for:

• Diabetes: (Glucose, BMI, Insulin)
• Heart Disease: (Cholesterol, BP, ECG)
• Liver & Kidney Health
• Lung Cancer Risk

🤖 Under the Hood

• RAG Architecture: We use separate vector stores for each user to prevent data leakage.
• Vision AI: Gemini Pro Vision reads raw PDF reports so you don't have to type data manually.
• Security: Full JWT authentication and session management.

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🚀 Quick Start

Option 1: Docker (Recommended)

Spin up the entire stack with one command:

# Clone the repository
git clone https://github.com/pavanbadempet/AI-Healthcare-System.git
cd AI-Healthcare-System

# Configure environment
cp .env.example .env
# Edit .env and add your GOOGLE_API_KEY

# Launch all services
docker-compose up --build

Service	URL
App (Frontend)	http://localhost:8501
API Docs	http://localhost:8000/docs
MLflow UI	http://localhost:5000

Option 2: Local Development

Prerequisites: Python 3.10+, pip

# Install dependencies
# Install dependencies (Full Feature Set)
pip install -r requirements-full.txt

# OR for Lite Version (No PySpark/Heavy ML)
# pip install -r requirements.txt

# Start Backend (Terminal 1)
uvicorn backend.main:app --reload --port 8000

# Start Frontend (Terminal 2)
streamlit run frontend/main.py

Option 3: Quick Scripts (Windows)

# Run everything
.\scripts\runners\run_app.bat

# Run E2E tests
.\scripts\runners\run_e2e_tests.ps1

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🛠️ Technology Stack

Layer	Technology	Purpose
Frontend	Streamlit	Responsive UI & Data Visualization
Backend	FastAPI, Pydantic	REST API & Request Validation
ML/AI	XGBoost, Scikit-Learn	Disease Classification Models
GenAI	Gemini Pro, LangChain	Chat Assistant & RAG Pipeline
Vector DB	FAISS	Semantic Search & Memory
Database	SQLite	User Data & Chat History
DevOps	Docker, GitHub Actions	Containerization & CI/CD
Hosting	Streamlit Cloud, Render	Production Deployment

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🧪 Testing

# Run all tests with coverage
pytest tests/ --cov=backend --cov-report=term-missing

# Run specific test suites
pytest tests/unit/           # Unit tests
pytest tests/integration/    # Integration tests
pytest tests/e2e/            # End-to-end tests (requires running app)

CI/CD Pipeline

GitHub Actions automatically runs on every push:

• ✅ Unit & Integration Tests
• ✅ Code Coverage Reporting
• ✅ Placeholder Model Generation for CI

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📁 Project Structure

├── backend/                # FastAPI backend
│   ├── main.py             # API entrypoint
│   ├── prediction.py       # ML prediction logic
│   ├── agent.py            # AI chat agent
│   ├── rag.py              # RAG pipeline
│   ├── vision_service.py   # Lab report analyzer
│   └── *.pkl               # Trained ML models
├── frontend/               # Streamlit frontend
│   ├── main.py             # App entrypoint
│   ├── views/              # Page components
│   └── components/         # Reusable UI components
├── mlops/                  # MLOps pipeline
│   ├── data_ingestion.py   # Data loading
│   ├── data_processing.py  # Feature engineering
│   └── model_training.py   # Training scripts
├── tests/                  # Test suites
│   ├── unit/               # Unit tests
│   ├── integration/        # API integration tests
│   └── e2e/                # End-to-end tests
├── scripts/                # Utility scripts
├── docker-compose.yml      # Multi-container setup
└── render.yaml             # Render deployment config

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🌐 Deployment

Frontend (Streamlit Cloud)

1. Fork/Push to GitHub
2. Connect repository to Streamlit Cloud
3. Set BACKEND_URL environment variable

Backend (Render)

1. Connect repository to Render
2. Uses render.yaml for auto-configuration
3. Set required environment variables:
   • GOOGLE_API_KEY - Gemini API key
   • SECRET_KEY - JWT signing key

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🤝 Contributing

Contributions are welcome! Please check CONTRIBUTING.md for guidelines.

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

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