MedAssist - AI-Powered Medical Consultation Platform

An intelligent healthcare assistant platform powered by LLM

Features • Tech Stack • Getting Started • API Documentation • Contributing

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🎯 Overview

MedAssist is a full-stack medical consultation platform that leverages Google's Gemini AI to provide preliminary medical advice, symptom analysis, and AI-assisted medical image interpretation. Built with a modern React frontend and FastAPI backend, it offers a seamless experience for users seeking health information and medical insights.

⚠️ Disclaimer

This platform is designed for informational purposes only and should not replace professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare professionals for proper medical care.

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🌟 Features

Core Capabilities

• 💬 Medical Consultations: Get preliminary medical advice through conversational AI interaction
• 🔍 Symptom Analysis: Describe your symptoms and receive potential insights about your condition
• 📸 Medical Image Analysis: Upload X-rays, CT scans, MRI, ultrasound, and blood test images for AI-assisted analysis
• 📋 Patient Profiles: Track patient details including name, age, gender, and chief complaints
• 📄 PDF Report Generation: Generate professional medical reports and prescriptions with recommendations
• 🔄 Conversation History: Maintain context across multiple consultations for better AI responses
• ⚡ Real-time Responses: Fast, intelligent responses powered by Gemini 2.5 Flash model

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🛠️ Tech Stack

Backend

• Framework: FastAPI - Modern, fast web framework
• Language: Python 3.8+
• AI Models: Google Gemini 2.5 Flash (Chat & Vision)
• PDF Generation: ReportLab
• Image Processing: Pillow (PIL)
• APIs: FastAPI with CORS middleware

Frontend

• Library: React 19.1+
• Build Tool: Vite 7.1+
• Routing: React Router 7.9+
• Markdown Rendering: React Markdown 10.1+
• Styling: CSS3 with responsive design

Key Dependencies

• google-genai: Google Gemini AI integration
• python-dotenv: Environment variable management
• reportlab: PDF generation
• pillow: Image processing
• cors: Cross-Origin Resource Sharing middleware

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📋 System Architecture

┌─────────────────────────────────────────────────────────────┐
│                    Frontend (React + Vite)                  │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  Home • Chat Interface • Patient Forms • Reports    │   │
│  └──────────────────────────────────────────────────────┘   │
└────────────────┬────────────────────────────────────────────┘
                 │ HTTP/REST
┌────────────────▼────────────────────────────────────────────┐
│              Backend (FastAPI + Python)                     │
│  ┌──────────────────────────────────────────────────────┐   │
│  │  • Chat Endpoint (/api/chat)                        │   │
│  │  • Report Generation (/api/generate-report)         │   │
│  │  • X-ray Analysis (Gemini Vision)                   │   │
│  │  • PDF Generation & Download                        │   │
│  └──────────────────────────────────────────────────────┘   │
└────────────────┬────────────────────────────────────────────┘
                 │
         ┌───────▼────────┐
         │ Google Gemini  │
         │ AI (2.5 Flash) │
         └────────────────┘

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🚀 Getting Started

Prerequisites

• Python 3.8+ installed on your system
• Node.js 16+ and npm for frontend development
• Google Gemini API Key (Get it here)
• Git for version control

Backend Setup

1. Clone the Repository

git clone https://github.com/yourusername/MedAssist.git
cd MedAssist/back

2. Create Virtual Environment

# Windows
python -m venv venv
venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Dependencies

pip install fastapi uvicorn google-genai python-dotenv pillow reportlab python-multipart

4. Configure Environment

Create a .env file in the back directory:

GEMINI_API_KEY=your_gemini_api_key_here

5. Start the Backend Server

uvicorn main:app --reload --host 127.0.0.1 --port 8000

The API will be available at http://127.0.0.1:8000

Interactive API Documentation: Visit http://127.0.0.1:8000/docs (Swagger UI)

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Frontend Setup

1. Navigate to Frontend Directory

cd ../front

2. Install Dependencies

npm install

3. Start Development Server

npm run dev

The application will open at http://localhost:5173 (or specified port)

4. Build for Production

npm run build

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📡 API Documentation

Endpoints Overview

Method	Endpoint	Purpose
GET	/	Health check and API information
POST	/api/chat	Send messages and analyze medical images
POST	/api/generate-report	Generate professional PDF reports
GET	/api/status	System status check

POST /api/chat

Unified Chat Endpoint - Handle both text conversations and X-ray analysis

Request (Form Data):

{
  "message": "I have a persistent headache and fever",
  "conversation_history": "[{\"role\": \"user\", \"content\": \"...\"}, ...]",
  "xray_image": "<binary image file>"
}

Response:

{
  "response": "Based on your symptoms of headache and fever...",
  "xray_analysis": "The X-ray shows...",
  "timestamp": "2024-01-15T10:30:00.123456"
}

cURL Example:

curl -X POST "http://127.0.0.1:8000/api/chat" \
  -F "message=Analyze my X-ray image" \
  -F "conversation_history=[]" \
  -F "xray_image=@xray.jpg"

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POST /api/generate-report

PDF Report Generation - Create professional medical reports

Request (JSON):

{
  "patient_name": "John Doe",
  "patient_age": 35,
  "patient_gender": "Male",
  "symptoms": "Chest pain, shortness of breath",
  "diagnosis": "Suspected acute coronary syndrome",
  "xray_analysis": "Chest X-ray shows normal cardiac silhouette",
  "medications": [
    "Aspirin 500mg - twice daily for 5 days",
    "Atorvastatin 20mg - once daily"
  ],
  "instructions": "Rest for 48 hours, avoid strenuous activity, follow-up with cardiologist"
}

Response:

• Returns a PDF file for download with formatted medical report

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GET /api/status

System Status Check - Verify API and model initialization

Response:

{
  "message": "Medical Chatbot API",
  "status": "running",
  "version": "1.0.0",
  "models": {
    "gemini_client_initialized": true,
    "vision_model": "gemini-2.5-flash",
    "chat_model": "gemini-2.5-flash"
  }
}

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📁 Project Structure

MedAssist/
├── back/                          # Backend (FastAPI)
│   ├── main.py                   # Main API application
│   ├── requirements.txt           # Python dependencies
│   └── .env                       # Environment variables (create this)
│
├── front/                         # Frontend (React + Vite)
│   ├── src/
│   │   ├── components/           # Reusable React components
│   │   │   ├── ChatInterface.jsx
│   │   │   ├── ChatWindow.jsx
│   │   │   ├── MessageInput.jsx
│   │   │   ├── Navbar.jsx
│   │   │   └── PatientList.jsx
│   │   ├── pages/                # Page components
│   │   │   ├── Home.jsx
│   │   │   ├── Chat.jsx
│   │   │   └── About.jsx
│   │   ├── App.jsx
│   │   ├── main.jsx
│   │   └── index.css
│   ├── package.json
│   ├── vite.config.js
│   └── index.html
│
└── README.md                      # This file

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🎨 User Interface

Home Page

• Hero section with welcome message
• Feature cards highlighting capabilities
• Quick-start button to begin consultations

Chat Interface

• Patient information form (name, age, gender, symptoms)
• Real-time message display
• File upload for medical images
• Report type selection dropdown
• PDF report generation

Component Hierarchy

App
├── Home
├── Chat
│   └── ChatInterface
│       ├── ChatWindow
│       └── MessageInput
├── About
└── Navbar

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🔐 Security Considerations

Current Implementation

• CORS is enabled for all origins (*)
• Environment variables for API keys
• Input validation for file uploads

Production Recommendations

• 🔒 Restrict CORS to specific frontend domain
• 🔐 Implement authentication and authorization
• 🛡️ Add rate limiting to API endpoints
• 📝 Validate all user inputs on backend
• 🔑 Rotate API keys regularly
• 📊 Implement logging and monitoring

Important Notes

• Never commit .env files to version control
• Store sensitive API keys securely
• Use HTTPS in production
• Implement HIPAA compliance if handling real patient data

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🚦 Getting Help

Common Issues

Backend won't start

# Clear cache and reinstall
rm -r venv
python -m venv venv
pip install -r requirements.txt

API connection errors

• Verify backend is running: http://127.0.0.1:8000/docs
• Check vite proxy configuration in vite.config.js
• Ensure CORS middleware is properly configured

Gemini API errors

• Verify API key is correct in .env
• Check API key has proper permissions
• Ensure API is enabled in Google Cloud Console

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📚 Additional Resources

• FastAPI Documentation
• React Documentation
• Google Gemini API Guide
• Vite Documentation
• ReportLab Documentation

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🤝 Contributing

Contributions are welcome! Here's how to help:

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

Development Guidelines

• Follow PEP 8 for Python code
• Use React functional components with hooks
• Include comments for complex logic
• Test changes before submitting PR
• Update documentation as needed

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⚠️ Medical Disclaimer

MedAssist is NOT a substitute for professional medical advice. This platform:

• Provides general information only
• Should not be used for diagnosis or treatment decisions
• Cannot replace consultation with licensed healthcare providers
• Is designed for educational purposes

Always consult with qualified healthcare professionals for proper medical evaluation and treatment.

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👨‍💻 About

MedAssist is an AI-powered medical consultation assistant that demonstrates the capabilities of modern AI in healthcare informatics. Built with modern web technologies and powered by Google's Gemini AI.

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