mostafa-atef21/Epilepsy-Management-AI-Agent

🧠 Epilepsy Management AI Agent

AI-powered medical device recommendation system for epilepsy management using semantic search, safety filtering, and evidence-based ranking.

🎯 What This App Does

This Streamlit application helps clinicians and patients explore epilepsy management devices based on patient-specific descriptions. It uses:

• —Semantic Search: ChromaDB + Sentence Transformers for intelligent device matching
• —Safety Rules: Configurable filters for invasiveness and regulatory approvals (FDA/CE)
• —Interactive UI: Multi-page interface with device catalog, admin tools, and recommendations

✨ Features

• —📝 Patient Input → Smart Recommendations: Describe patient condition and receive personalized device suggestions
• —🔍 Semantic Search: Uses all-MiniLM-L6-v2 embeddings for intelligent text matching
• —🛡️ Safety Filters: Toggle invasive device filtering and requirement for specific approvals
• —📚 Device Catalog: Browse and search the complete knowledge base
• —🛠️ Admin Tools: Upload CSV data and rebuild vector index
• —📊 Detailed Results: View device specifications, approvals, indications, and source links

🚀 How to Use

1. 1.Enter Patient Description: Describe the patient's epilepsy characteristics (seizure type, frequency, age, lifestyle needs)
2. 2.Configure Filters:
3. 3.Toggle "Avoid invasive devices" on/off
4. 4.Specify required approvals (e.g., "FDA, CE")
5. 5.Adjust top K results (1-10)
6. 6.Generate Recommendations: Click the button to get AI-filtered device suggestions
7. 7.Explore Results: View device details, click source links, and browse the full catalog

📋 Example Query

"16-year-old with focal aware seizures 2–3 times/week, mostly nocturnal; wants non-invasive detection and caregiver alerts."

🏗️ Architecture

Patient Input → Semantic Search (ChromaDB) 
             → Safety Rules Filtering 
             → Ranked Recommendations 
             → Interactive Display

Technologies

• —Streamlit: Web UI framework
• —ChromaDB: Vector database for semantic search
• —Sentence Transformers: Text embeddings (all-MiniLM-L6-v2)
• —Pandas: Data manipulation
• —Python 3.11: Runtime environment

📁 Project Structure

Epilepsy-Management-AI-Agent/
├── src/
│   └── app.py              # Main Streamlit application
├── pages/
│   ├── 1_📚_Device_Catalog.py    # Browse all devices
│   ├── 2_🛠️_Admin_ETL.py        # CSV upload & index rebuild
│   └── 3_ℹ️_About.py            # Project information
├── data/
│   └── devices.csv          # Device catalog database
├── utils/
│   ├── embeddings.py        # ChromaDB integration
│   └── rules.py            # Safety rule filtering
├── requirements.txt         # Python dependencies
├── Dockerfile              # Container configuration
└── README.md              # This file

⚙️ Deployment

This app is containerized using Docker and deployed on Hugging Face Spaces.

Local Development

# Build and run
docker build -t epilepsy-ai .
docker run -p 7860:7860 epilepsy-ai

# Or with Streamlit directly
pip install -r requirements.txt
streamlit run src/app.py

📊 Data Model

The device catalog (data/devices.csv) includes:

• —device_id: Unique identifier
• —name: Device name
• —category: Device type (wearable, implant, etc.)
• —indication: Use case
• —invasiveness: Invasive/Non-invasive
• —approvals: Regulatory status (FDA, CE, etc.)
• —summary: Description text
• —source_url: Reference link
• —last_seen: Timestamp

⚠️ Important Notice

This tool provides informational recommendations only and is not a substitute for professional medical advice. Always consult a licensed clinician for diagnosis and treatment decisions.

🔧 Configuration

• —ChromaDB: Persists in .chroma/ directory (rebuilds automatically on HF Spaces)
• —Model: sentence-transformers/all-MiniLM-L6-v2 (~80MB, downloads on first run)
• —Port: 7860 (Hugging Face default)

📝 License

Apache 2.0

👥 Contributing

Feel free to submit issues or improvements to the device catalog or codebase.

Built with ❤️ for epilepsy management and healthcare innovation.