Update README.md

README.md

@@ -1,261 +1,272 @@
-# Lab Report Analysis API
-
-A FastAPI-based web service for analyzing lab report images using AI. This service accepts lab report images and provides structured medical analysis with key findings, interpretations, and health insights.
-
-## Features
-
-- 🖼️ Image upload support (JPG, PNG, BMP, TIFF, WEBP)
-- 🔍 AI-powered lab report analysis
-- 📊 Structured response with summary, key findings, and interpretation
-- 🌐 RESTful API with automatic documentation
-- 🧪 Built-in test client and web interface
-- ⚡ Async processing for better performance
-
-## Project Structure
-
-```
-Lab_analysis/
-├── main.py                     # FastAPI application
-├── lab_analyzer.py            # Core analysis logic
-├── models.py                  # Pydantic models
-├── test_client.py             # API test client
-├── index.html                 # Web interface
-├── requirements.txt           # Dependencies
-├── Lab_report_analysis.py     # Original script
-└── README.md                  # This file
-```
-
-## Installation
-
-1. **Clone or navigate to the project directory:**
-
-   ```bash
-   cd "e:\E-JUST Assignments\Projects\HealthCare\Lab_analysis"
-   ```
-
-2. **Create a virtual environment (recommended):**
-
-   ```bash
-   python -m venv venv
-   venv\Scripts\activate  # On Windows
-   ```
-
-3. **Install dependencies:**
-   ```bash
-   pip install -r requirements.txt
-   ```
-
-## Running the API
-
-### Method 1: Using Python directly
-
-```bash
-python main.py
-```
-
-### Method 2: Using Uvicorn
-
-```bash
-uvicorn main:app --host 0.0.0.0 --port 8000 --reload
-```
-
-The API will be available at:
-
-- **API Endpoints**: http://localhost:8000
-- **Interactive Docs**: http://localhost:8000/docs
-- **ReDoc**: http://localhost:8000/redoc
-
-## API Endpoints
-
-### Health Check
-
-- **GET** `/health`
-- Returns service status
-
-### Analyze Lab Report (File Upload)
-
-- **POST** `/analyze`
-- Upload an image file for analysis
-- Accepts: `multipart/form-data` with `file` field
-
-### Analyze Lab Report (Base64)
-
-- **POST** `/analyze-base64`
-- Send base64 encoded image for analysis
-- Accepts: JSON with `image` field containing base64 string
-
-## Usage Examples
-
-### Using cURL
-
-1. **Health check:**
-
-   ```bash
-   curl http://localhost:8000/health
-   ```
-
-2. **Analyze image file:**
-
-   ```bash
-   curl -X POST "http://localhost:8000/analyze" \
-        -H "accept: application/json" \
-        -H "Content-Type: multipart/form-data" \
-        -F "file=@your_lab_report.jpg"
-   ```
-
-3. **Analyze base64 image:**
-   ```bash
-   curl -X POST "http://localhost:8000/analyze-base64" \
-        -H "Content-Type: application/json" \
-        -d '{"image": "your_base64_encoded_image_here"}'
-   ```
-
-### Using Python Test Client
-
-```python
-from test_client import LabReportAPIClient
-
-client = LabReportAPIClient()
-
-# Health check
-health = client.health_check()
-print(health)
-
-# Analyze image
-result = client.analyze_image_file("path/to/your/lab_report.jpg")
-print(result)
-```
-
-### Using the Web Interface
-
-1. Start the API server
-2. Open `index.html` in your web browser
-3. Drag and drop or select a lab report image
-4. Click "Analyze Report" to get results
-
-## Response Format
-
-Successful analysis returns:
-
-```json
-{
-  "success": true,
-  "filename": "lab_report.jpg",
-  "analysis": {
-    "error": false,
-    "summary": "Brief summary of the lab report",
-    "key_findings": ["Finding 1", "Finding 2", "Finding 3"],
-    "interpretation": "Medical interpretation",
-    "note": "Disclaimer about medical advice",
-    "raw_response": "Complete AI response"
-  }
-}
-```
-
-## Configuration
-
-### Environment Variables
-
-You can set these environment variables to customize the behavior:
-
-- `API_HOST`: Host to bind to (default: "0.0.0.0")
-- `API_PORT`: Port to bind to (default: 8000)
-- `HF_API_KEY`: Hugging Face API key (currently hardcoded in `lab_analyzer.py`)
-
-### Updating API Key
-
-To use your own Hugging Face API key, modify the `lab_analyzer.py` file:
-
-```python
-self.client = InferenceClient(
-    provider="nebius",
-    api_key="your_api_key_here",  # Replace with your API key
-)
-```
-
-## Development
-
-### Running in Development Mode
-
-```bash
-uvicorn main:app --reload --host 0.0.0.0 --port 8000
-```
-
-### Testing
-
-Run the test client:
-
-```bash
-python test_client.py
-```
-
-### Adding New Features
-
-1. Add new endpoints to `main.py`
-2. Update models in `models.py` if needed
-3. Extend the analyzer in `lab_analyzer.py`
-4. Update documentation
-
-## Deployment
-
-### Docker (Optional)
-
-Create a `Dockerfile`:
-
-```dockerfile
-FROM python:3.9-slim
-
-WORKDIR /app
-COPY requirements.txt .
-RUN pip install -r requirements.txt
-
-COPY . .
-
-EXPOSE 8000
-CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
-```
-
-Build and run:
-
-```bash
-docker build -t lab-analysis-api .
-docker run -p 8000:8000 lab-analysis-api
-```
-
-### Production Considerations
-
-- Use environment variables for API keys
-- Set up proper CORS origins
-- Add rate limiting
-- Use HTTPS
-- Add authentication if needed
-- Set up logging and monitoring
-
-## Troubleshooting
-
-### Common Issues
-
-1. **Import errors**: Make sure all dependencies are installed
-2. **Port conflicts**: Change the port in the uvicorn command
-3. **API key issues**: Verify your Hugging Face API key is valid
-4. **Image format errors**: Ensure images are in supported formats
-
-### Logs
-
-The application logs important events. Check console output for debugging information.
-
-## License
-
-This project is for educational purposes. Please ensure you have proper licenses for any AI models used.
-
-## Contributing
-
-1. Fork the repository
-2. Create a feature branch
-3. Make your changes
-4. Test thoroughly
-5. Submit a pull request
-
----
-
-**Note**: This analysis is for educational purposes only and should not replace professional medical advice.
+---
+title: GP-Tea Lab Analysis
+emoji: 🧪
+colorFrom: blue
+colorTo: green
+sdk: docker
+app_port: 7860
+pinned: false
+license: apache-2.0
+---
+
+# GP-Tea Lab Analysis Service
+
+A FastAPI-based web service for analyzing lab report images using AI. This service accepts lab report images and provides structured medical analysis with key findings, interpretations, and health insights.
+
+## Features
+
+- 🖼️ Image upload support (JPG, PNG, BMP, TIFF, WEBP)
+- 🔍 AI-powered lab report analysis
+- 📊 Structured response with summary, key findings, and interpretation
+- 🌐 RESTful API with automatic documentation
+- 🧪 Built-in test client and web interface
+- ⚡ Async processing for better performance
+
+## Project Structure
+
+```
+Lab_analysis/
+├── main.py                     # FastAPI application
+├── lab_analyzer.py            # Core analysis logic
+├── models.py                  # Pydantic models
+├── test_client.py             # API test client
+├── index.html                 # Web interface
+├── requirements.txt           # Dependencies
+├── Lab_report_analysis.py     # Original script
+└── README.md                  # This file
+```
+
+## Installation
+
+1. **Clone or navigate to the project directory:**
+
+   ```bash
+   cd "e:\E-JUST Assignments\Projects\HealthCare\Lab_analysis"
+   ```
+
+2. **Create a virtual environment (recommended):**
+
+   ```bash
+   python -m venv venv
+   venv\Scripts\activate  # On Windows
+   ```
+
+3. **Install dependencies:**
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+## Running the API
+
+### Method 1: Using Python directly
+
+```bash
+python main.py
+```
+
+### Method 2: Using Uvicorn
+
+```bash
+uvicorn main:app --host 0.0.0.0 --port 8000 --reload
+```
+
+The API will be available at:
+
+- **API Endpoints**: http://localhost:8000
+- **Interactive Docs**: http://localhost:8000/docs
+- **ReDoc**: http://localhost:8000/redoc
+
+## API Endpoints
+
+### Health Check
+
+- **GET** `/health`
+- Returns service status
+
+### Analyze Lab Report (File Upload)
+
+- **POST** `/analyze`
+- Upload an image file for analysis
+- Accepts: `multipart/form-data` with `file` field
+
+### Analyze Lab Report (Base64)
+
+- **POST** `/analyze-base64`
+- Send base64 encoded image for analysis
+- Accepts: JSON with `image` field containing base64 string
+
+## Usage Examples
+
+### Using cURL
+
+1. **Health check:**
+
+   ```bash
+   curl http://localhost:8000/health
+   ```
+
+2. **Analyze image file:**
+
+   ```bash
+   curl -X POST "http://localhost:8000/analyze" \
+        -H "accept: application/json" \
+        -H "Content-Type: multipart/form-data" \
+        -F "file=@your_lab_report.jpg"
+   ```
+
+3. **Analyze base64 image:**
+   ```bash
+   curl -X POST "http://localhost:8000/analyze-base64" \
+        -H "Content-Type: application/json" \
+        -d '{"image": "your_base64_encoded_image_here"}'
+   ```
+
+### Using Python Test Client
+
+```python
+from test_client import LabReportAPIClient
+
+client = LabReportAPIClient()
+
+# Health check
+health = client.health_check()
+print(health)
+
+# Analyze image
+result = client.analyze_image_file("path/to/your/lab_report.jpg")
+print(result)
+```
+
+### Using the Web Interface
+
+1. Start the API server
+2. Open `index.html` in your web browser
+3. Drag and drop or select a lab report image
+4. Click "Analyze Report" to get results
+
+## Response Format
+
+Successful analysis returns:
+
+```json
+{
+  "success": true,
+  "filename": "lab_report.jpg",
+  "analysis": {
+    "error": false,
+    "summary": "Brief summary of the lab report",
+    "key_findings": ["Finding 1", "Finding 2", "Finding 3"],
+    "interpretation": "Medical interpretation",
+    "note": "Disclaimer about medical advice",
+    "raw_response": "Complete AI response"
+  }
+}
+```
+
+## Configuration
+
+### Environment Variables
+
+You can set these environment variables to customize the behavior:
+
+- `API_HOST`: Host to bind to (default: "0.0.0.0")
+- `API_PORT`: Port to bind to (default: 8000)
+- `HF_API_KEY`: Hugging Face API key (currently hardcoded in `lab_analyzer.py`)
+
+### Updating API Key
+
+To use your own Hugging Face API key, modify the `lab_analyzer.py` file:
+
+```python
+self.client = InferenceClient(
+    provider="nebius",
+    api_key="your_api_key_here",  # Replace with your API key
+)
+```
+
+## Development
+
+### Running in Development Mode
+
+```bash
+uvicorn main:app --reload --host 0.0.0.0 --port 8000
+```
+
+### Testing
+
+Run the test client:
+
+```bash
+python test_client.py
+```
+
+### Adding New Features
+
+1. Add new endpoints to `main.py`
+2. Update models in `models.py` if needed
+3. Extend the analyzer in `lab_analyzer.py`
+4. Update documentation
+
+## Deployment
+
+### Docker (Optional)
+
+Create a `Dockerfile`:
+
+```dockerfile
+FROM python:3.9-slim
+
+WORKDIR /app
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+
+COPY . .
+
+EXPOSE 8000
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8000"]
+```
+
+Build and run:
+
+```bash
+docker build -t lab-analysis-api .
+docker run -p 8000:8000 lab-analysis-api
+```
+
+### Production Considerations
+
+- Use environment variables for API keys
+- Set up proper CORS origins
+- Add rate limiting
+- Use HTTPS
+- Add authentication if needed
+- Set up logging and monitoring
+
+## Troubleshooting
+
+### Common Issues
+
+1. **Import errors**: Make sure all dependencies are installed
+2. **Port conflicts**: Change the port in the uvicorn command
+3. **API key issues**: Verify your Hugging Face API key is valid
+4. **Image format errors**: Ensure images are in supported formats
+
+### Logs
+
+The application logs important events. Check console output for debugging information.
+
+## License
+
+This project is for educational purposes. Please ensure you have proper licenses for any AI models used.
+
+## Contributing
+
+1. Fork the repository
+2. Create a feature branch
+3. Make your changes
+4. Test thoroughly
+5. Submit a pull request
+
+---
+
+**Note**: This analysis is for educational purposes only and should not replace professional medical advice.