AushadhiAI - Intelligent Prescription Analysis

🔍 AushadhiAI

Decode Prescriptions, Understand Medications

🔍 Overview

AushadhiAI is an advanced AI-powered solution that decodes doctors' handwritten prescriptions using computer vision and machine learning. The application extracts medication information and presents it in an easy-to-understand format, bridging the knowledge gap between healthcare providers and patients

✨ Key Features

• Multi-format Image Processing: Support for JPEG, PNG, and HEIC (iPhone) image formats
• Advanced OCR: Extract text from handwritten prescriptions using Azure Computer Vision
• Medication Identification: Automatically identify medications using ML-based text analysis
• Cross-platform Compatibility: Works across devices including iPhones and Android phones
• Responsive Design: Optimized interface for both desktop and mobile use
• Fault Tolerance: Robust fallback mechanisms when cloud services are unavailable

🏗️ Architecture

AushadhiAI employs a modern, scalable architecture:

• Frontend: HTML5, CSS3, JavaScript with responsive design principles
• Backend API: FastAPI (Python) providing RESTful endpoints for image analysis
• Cloud Vision: Azure Computer Vision API for OCR processing
• Medication Service: Custom NLP algorithms for medication identification
• Container Deployment: Docker containers for consistent development and production environments
• Cloud Infrastructure: AWS EC2 and ECR for reliable, scalable hosting

🚀 Deployment Options

Local Development

1. Clone the repository:

   git clone https://github.com/harryhome1/INTRO-AushadhiAI.git
   cd INTRO-AushadhiAI

2. Install dependencies:

   pip install -r requirements.txt

3. Configure Azure credentials: Create a backend/config.py file with your Azure credentials:

   # Azure Vision Configuration
   AZURE_VISION_ENDPOINT = "your_endpoint_here"
   AZURE_VISION_KEY = "your_key_here"
   
   # CORS Configuration
   ALLOWED_ORIGINS = ["http://localhost:8000", "http://127.0.0.1:8000"]
   
   # Set to False to use simulated results for development
   USE_AZURE_OCR = True

4. Run the backend server:

   cd backend
   uvicorn app:app --host 0.0.0.0 --port 8007 --reload

5. Serve the frontend (in a new terminal):

   # From project root
   python -m http.server 8000

6. Access the application: Open your browser and navigate to http://localhost:8000

Production Deployment (AWS)

AushadhiAI is designed for containerized deployment on AWS:

1. Build Docker image:

   docker build -t aushadhi-backend:latest .

2. Push to Amazon ECR:

   aws ecr get-login-password --region us-east-1 | docker login --username AWS --password-stdin 905418100386.dkr.ecr.us-east-1.amazonaws.com
   docker tag aushadhi-backend:latest 905418100386.dkr.ecr.us-east-1.amazonaws.com/aushadhi-backend:latest
   docker push 905418100386.dkr.ecr.us-east-1.amazonaws.com/aushadhi-backend:latest

3. Deploy on EC2:

   docker pull 905418100386.dkr.ecr.us-east-1.amazonaws.com/aushadhi-backend:latest
   docker run -d --name aushadhi-backend -p 8007:8007 905418100386.dkr.ecr.us-east-1.amazonaws.com/aushadhi-backend:latest

4. Configure NGINX (recommended for production):

   server {
       listen 80;
       server_name aiapi.yourdomain.com;
       
       location / {
           proxy_pass http://localhost:8007;
           proxy_set_header Host $host;
           proxy_set_header X-Real-IP $remote_addr;
       }
   }

📱 iPhone Support

AushadhiAI includes special handling for iPhone image formats:

1. HEIC Processing: Native support for Apple's High Efficiency Image Format
2. Format Detection: Automatic identification of image types
3. Conversion Pipeline: Fallback conversion when needed

Note for iPhone users: For best results, you can set your iPhone to capture in "Most Compatible" format:

• Go to Settings > Camera > Formats
• Select "Most Compatible" instead of "High Efficiency"

🛠️ Troubleshooting

Common Issues

1. Backend Connection Failed

   • Ensure the backend server is running on port 8007
   • Check CORS settings if accessing from a different domain
   • Verify network connectivity between frontend and backend

2. Image Processing Errors

   • Ensure the image is clear and well-lit
   • Check that the image size is below 5MB
   • For iPhone users, try using JPEG format if HEIC processing fails

3. No Medications Detected

   • Verify that the prescription image is legible
   • Ensure the handwriting is clear
   • Try using the sample prescription for testing

Health Check

To verify the backend is functioning properly:

curl https://aiapi.yourdomain.com/api/health

Expected response:

{"status":"healthy","services":{"ocr":"active","medication_db":"active","api":"active"}}

📄 API Documentation

Endpoint	Method	Description
/api/health	GET	Check system health
/api/analyze	POST	Analyze prescription image

Example analysis request:

curl -X POST https://aiapi.yourdomain.com/api/analyze \
  -F "[email protected]"

🔮 Future Development

• Enhanced Medication Database: Expanding coverage to more medications
• Dosage Recognition: Identifying dosage instructions from prescriptions
• Drug Interaction Warnings: Alert users to potential medication interactions
• Patient Profiles: Save prescription history and medication tracking
• Multilingual Support: Add support for prescriptions in multiple languages

🤝 Contributing

Contributions are welcome! Please follow these steps:

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

📜 License

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

👨‍💻 Team

• Nikhil Mishra - Hariom Shukla