README.md

metadata

title: Lab Report Analysis API
emoji: 🏥
colorFrom: blue
colorTo: green
sdk: docker
app_port: 7860
pinned: false
license: apache-2.0
short_description: AI-powered lab report analysis API using Google AI Studio

🏥 Lab Report Analysis API

A powerful FastAPI-based web service for analyzing medical lab reports using Google AI Studio's Gemini model. Provides both a beautiful web interface and REST API endpoints for programmatic access.

✨ Features

• 📸 Image Analysis: Upload lab report images in various formats (JPG, PNG, TIFF, etc.)
• 🤖 AI-Powered: Uses Google's Gemini 2.0 Flash model for accurate analysis
• 📊 Structured Output: Provides organized summary, key findings, and interpretations
• ⚡ Fast Processing: Quick analysis with real-time results
• 🔒 Secure: Images are processed securely and not stored
• 🌐 Web Interface: Beautiful HTML interface for manual testing
• 📱 Flutter Compatible: Full REST API support for mobile apps

🚀 How to Use

Web Interface

1. Visit the main page
2. Upload Image: Click on the upload area and select your lab report image
3. Analyze: Click the "Analyze Report" button
4. Review Results: Get structured analysis with summary, key findings, and interpretations

API Endpoints

• POST /analyze - Upload file analysis
• POST /analyze-base64 - Base64 image analysis
• POST /api/analyze-lab - Flutter-friendly file upload
• POST /api/analyze-lab-base64 - Flutter-friendly base64 analysis
• GET /health - Health check endpoint
• GET /docs - Interactive API documentation
• GET /redoc - ReDoc documentation

🛠️ Technology Stack

• Backend: FastAPI for high-performance API
• Frontend: Custom HTML/CSS/JavaScript interface
• AI Model: Google AI Studio (Gemini 2.0 Flash)
• Image Processing: PIL for image handling
• Deployment: Docker containerization for Hugging Face Spaces

📱 Flutter Integration

Perfect for Flutter apps! All endpoints return consistent JSON responses:

// File upload
POST /api/analyze-lab
Content-Type: multipart/form-data

// Base64 upload  
POST /api/analyze-lab-base64
Content-Type: application/json
{
  "image": "base64_encoded_image_string"
}

// Response format
{
  "success": true,
  "filename": "lab_report.jpg",
  "analysis": {
    "error": false,
    "summary": "Analysis summary",
    "key_findings": ["Finding 1", "Finding 2"],
    "interpretation": "Medical interpretation",
    "note": "Disclaimer about medical advice",
    "raw_response": "Full AI response"
  }
}

⚠️ Important Disclaimer

This tool is for educational and informational purposes only. It should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always consult with qualified healthcare professionals for medical concerns.

🏗️ Local Development

Prerequisites

• Python 3.11+
• Google AI Studio API key

Installation

1. Clone the repository
2. Install dependencies:

   pip install -r requirements.txt
   

3. Set your API key:

   export GOOGLE_AI_STUDIO_API_KEY=your_api_key_here
   

4. Run the application:

   uvicorn app:app --host 0.0.0.0 --port 7860 --reload
   

Docker Development

# Build the image
docker build -t lab-analysis-api .

# Run the container
docker run -p 7860:7860 -e GOOGLE_AI_STUDIO_API_KEY=your_key lab-analysis-api

📁 Project Structure

├── app.py                 # Main FastAPI application with web interface
├── lab_analyzer.py       # Core analysis logic
├── models.py            # Data models
├── requirements.txt     # Python dependencies
├── Dockerfile          # Container configuration
└── README.md           # Documentation

🎯 Endpoints Overview

Method	Endpoint	Description	Use Case
GET	/	Web interface	Manual testing
POST	/analyze	File upload analysis	General API usage
POST	/analyze-base64	Base64 analysis	Web applications
POST	/api/analyze-lab	Flutter file upload	Mobile apps
POST	/api/analyze-lab-base64	Flutter base64	Mobile apps
GET	/health	Health check	Monitoring
GET	/docs	OpenAPI docs	API reference

🙏 Acknowledgments

• Google AI Studio for providing the Gemini API
• Hugging Face for Spaces hosting
• FastAPI for the excellent web framework