---
title: Agentic Health Coach Medgemma
emoji: 💬
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.33.1
app_file: app.py
pinned: true
tags:
  - agent-demo-track
license: mit
short_description: agentic medGemma health coach with vllm.
---

[Youtube explainer (7 mins)](https://youtu.be/NwTKnTHfZAg)
Nb. Modal backend is turned off since completion of hackathon.
Host your own Modal LLM endpoint by referring to the .py files.

# MedGemma Agent: AI-Powered Medical Assistant

## 🏥 Overview

MedGemma Agent is an advanced AI-powered medical assistant that provides accessible and accurate medical information to patients and non-medical professionals. Built on top of Google's MedGemma model, this application combines state-of-the-art medical language understanding with multimodal capabilities to deliver clear, concise, and reliable medical insights.

## ✨ Key Features

- **Multimodal Understanding**: Process both text queries and medical images
- **Real-time Responses**: Stream responses for an interactive experience
- **Wikipedia Integration**: Access to verified medical information
- **User-friendly Interface**: Clean, modern UI with example queries
- **Secure API**: Protected endpoints with API key authentication

## 🚀 Technical Implementation

### Backend Architecture

The application is built using:
- **Modal**: For serverless deployment and GPU acceleration
- **FastAPI**: For robust API endpoints
- **VLLM**: For efficient model inference
- **MedGemma-4B**: Fine-tuned medical language model
- **Wikipedia API**: For additional medical context

### Key Components

1. **Model Deployment**
   - Utilizes Modal's GPU-accelerated containers
   - Implements efficient model loading with VLLM
   - Supports bfloat16 precision for optimal performance

2. **API Layer**
   - Streaming responses for real-time interaction
   - Secure API key authentication
   - Base64 image processing for multimodal inputs

3. **Frontend Interface**
   - Built with Gradio for seamless user interaction
   - Custom CSS theming for professional appearance
   - Example queries for common medical scenarios

## 🛠️ Usage

1. **Text Queries**
   - Ask medical questions in natural language
   - Get clear, patient-friendly explanations
   - Example: "What are the symptoms of a stroke?"

2. **Image Analysis**
   - Upload medical images for analysis
   - Get AI-powered insights about the image
   - Supports common medical image formats

## 🔒 Security

- API key authentication for all requests
- Secure image processing
- Protected model endpoints

## 🏗️ Technical Stack

- **Backend**: Modal, FastAPI, VLLM
- **Frontend**: Gradio
- **Model**: MedGemma-4B (unsloth/medgemma-4b-it-unsloth-bnb-4bit)
- **Additional Tools**: Wikipedia API for medical context

## 🎯 Performance

- Optimized for low latency responses
- GPU-accelerated inference
- Efficient memory utilization with 4-bit quantization
- Maximum context length of 8192 tokens

## 🤝 Contributing

We welcome contributions! Please feel free to submit issues and pull requests.

## 📝 License

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

---

Built with ❤️ for the Hugging Face Spaces Hackathon.