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!