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HealthGuard AI: Your Smart Health Companion

Hi! 👋 I built HealthGuard AI to make health monitoring more personal and actionable. It's not just another health tracker - it's like having a smart friend who understands your health patterns and gives you meaningful advice.

What Makes This Special?

I wanted to solve a common problem: most health apps just show numbers, but don't tell you what they mean for you. HealthGuard AI uses some cool tech (like Pathway for real-time processing and RAG for smart recommendations) to give you insights that actually make sense.

Key features I'm excited about:

  • Real-time health monitoring that adapts to your data
  • Smart recommendations that combine medical knowledge with your current stats
  • A clean, simple interface that shows you what matters

Quick Demo

Watch how it works →

Getting Started

  1. Clone and set up:

    git clone https://github.com/yourusername/healthguard_ai.git
cd healthguard_ai
python -m venv venv
venv\Scripts\activate  # On Windows
pip install -r requirements.txt

  2. Set up your environment:

    • Copy .env.example to .env
    • Add your OpenAI API key

  3. Run it:

    # Start the backend
python -m uvicorn src.api.metrics_service:app --reload --port 7000

# Fire up the dashboard
python -m streamlit run src/ui/app.py

How It Works

I built this using three main components:

  1. Real-Time Processing

    • Uses Pathway to handle your health data as it comes in
    • Instantly detects important changes in your metrics
    • Updates recommendations on the fly

  2. Smart Insights

    • Combines your current health data with medical guidelines
    • Uses RAG (Retrieval-Augmented Generation) to give relevant advice
    • Learns from a curated database of health knowledge

  3. User Interface

    • Built with Streamlit for a clean, responsive experience
    • Shows your health status at a glance
    • Makes complex health data easy to understand

Tech Stack

  • Backend: Python with FastAPI
  • Frontend: Streamlit
  • Data Processing: Pathway
  • AI/ML: OpenAI, RAG pipeline
  • Deployment: Docker support for easy hosting

Deployment

I've included deployment guides for AWS, Azure, and GCP in DEPLOYMENT.md. Pick your favorite cloud provider and follow along!

Contributing

Got ideas? Found a bug? Want to make it better? I'd love your help! Just:

  1. Fork it
  2. Create your feature branch (git checkout -b cool-new-feature)
  3. Commit your changes (git commit -am 'Added something awesome')
  4. Push to the branch (git push origin cool-new-feature)
  5. Open a Pull Request

What's Next?

I'm working on some exciting additions:

  • Mobile app integration
  • More health metrics support
  • Advanced trend analysis
  • Integration with popular health devices

Questions?

Feel free to open an issue or reach out if you have questions or ideas!