Update README.md

README.md

@@ -1,111 +1,111 @@
----
-title: Medical Image Analysis Tool
-emoji: 🏥
-colorFrom: blue
-colorTo: green
-sdk: gradio
-sdk_version: "4.0.0"
-app_file: app.py
-pinned: false
-license: mit
----
-
-# 🏥 Medical Image Analysis Tool
-
-An AI-powered medical image analysis application using advanced detection models and large language models for medical image interpretation.
-
-## Features
-
-- **Advanced Object Detection**: Uses RF-DETR (Real-time Fine-grained Detection Transformer) for precise object detection
-- **Medical AI Analysis**: Integrates MedGemma, a specialized medical vision-language model
-- **Interactive Interface**: Built with Gradio for easy web-based interaction
-- **Configurable Thresholds**: Adjustable confidence thresholds for detection sensitivity
-- **Model Size Selection**: Choose between MedGemma 4B (faster) or 27B (more accurate) models
-- **GPU Acceleration**: Optimized for GPU usage when available with 4-bit quantization
-- **Automatic Model Downloads**: Models download automatically from Hugging Face Hub
-
-## Models Used
-
-- **RF-DETR Medium**: State-of-the-art object detection model
-- **MedGemma 4B/27B**: Medical-specialized vision-language models for analysis and descriptions
-  - 4B model: Faster inference, lower memory usage
-  - 27B model: Higher accuracy, requires more resources
-
-## Usage
-
-1. **Upload Image**: Click on the image upload area or drag and drop a medical image
-2. **Adjust Settings**:
-   - Use the confidence threshold slider to control detection sensitivity
-   - Select model size (4B for speed, 27B for accuracy)
-3. **Analyze**: Click "Analyze Image" to run the AI analysis
-4. **View Results**: See the annotated image with detected objects and AI-generated descriptions
-
-## Installation & Setup
-
-This application is designed to run on Hugging Face Spaces. The following files are required:
-
-- `app.py` - Main application file (optimized for Spaces)
-- `requirements.txt` - Python dependencies
-- `packages.txt` - System packages
-- `README.md` - This documentation
-
-## Model Loading
-
-**RF-DETR Model:**
-- Upload your trained `rf-detr-medium.pth` file to the Space
-- The application will automatically find and load it
-
-**MedGemma Models:**
-- Models download automatically from Hugging Face Hub on first use
-- No manual installation required
-- Choose between 4B (faster) or 27B (more accurate) models
-
-## Space Configuration
-
-For optimal performance, configure your Space settings:
-- **Hardware**: GPU (T4 minimum, A100 recommended for 27B models)
-- **Storage**: Enable persistent storage for model caching
-- **Timeout**: 30+ minutes for large model downloads
-
-## Technical Details
-
-- **Framework**: PyTorch + Transformers
-- **Interface**: Gradio
-- **Computer Vision**: OpenCV, PIL, Supervision
-- **Hardware**: Optimized for both CPU and GPU inference
-
-## Performance Tips
-
-- **Model Selection**: Use MedGemma 4B for faster processing or 27B for higher accuracy
-- **Confidence Thresholds**: Higher values reduce false positives but may miss subtle findings
-- **GPU Acceleration**: The application automatically uses GPU acceleration when available
-- **Memory Optimization**: Uses 4-bit quantization to reduce memory usage
-- **Model Caching**: Models are cached after first load for faster subsequent analyses
-
-## Limitations
-
-- Requires significant computational resources for optimal performance
-- Best suited for medical imaging applications
-- Results should be verified by qualified medical professionals
-
-## Development
-
-To run locally:
-
-```bash
-pip install -r requirements.txt
-python app.py
-```
-
-**Note**: For local development, you'll need to:
-1. Install the RF-DETR package or ensure it's available
-2. Place your `rf-detr-medium.pth` file in the project directory
-3. Models will download automatically on first run
-
-## License
-
-This project is for research and educational purposes. Medical applications should be developed and validated according to appropriate regulatory standards.
-
-## Support
-
-For issues or questions, please refer to the Hugging Face Space documentation or create an issue in the project repository.
+---
+title: Medical Image Analysis Tool
+emoji: 🏥
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+license: mit
+---
+
+# 🏥 Medical Image Analysis Tool
+
+An AI-powered medical image analysis application using advanced detection models and large language models for medical image interpretation.
+
+## Features
+
+- **Advanced Object Detection**: Uses RF-DETR (Real-time Fine-grained Detection Transformer) for precise object detection
+- **Medical AI Analysis**: Integrates MedGemma, a specialized medical vision-language model
+- **Interactive Interface**: Built with Gradio for easy web-based interaction
+- **Configurable Thresholds**: Adjustable confidence thresholds for detection sensitivity
+- **Model Size Selection**: Choose between MedGemma 4B (faster) or 27B (more accurate) models
+- **GPU Acceleration**: Optimized for GPU usage when available with 4-bit quantization
+- **Automatic Model Downloads**: Models download automatically from Hugging Face Hub
+
+## Models Used
+
+- **RF-DETR Medium**: State-of-the-art object detection model
+- **MedGemma 4B/27B**: Medical-specialized vision-language models for analysis and descriptions
+  - 4B model: Faster inference, lower memory usage
+  - 27B model: Higher accuracy, requires more resources
+
+## Usage
+
+1. **Upload Image**: Click on the image upload area or drag and drop a medical image
+2. **Adjust Settings**:
+   - Use the confidence threshold slider to control detection sensitivity
+   - Select model size (4B for speed, 27B for accuracy)
+3. **Analyze**: Click "Analyze Image" to run the AI analysis
+4. **View Results**: See the annotated image with detected objects and AI-generated descriptions
+
+## Installation & Setup
+
+This application is designed to run on Hugging Face Spaces. The following files are required:
+
+- `app.py` - Main application file (optimized for Spaces)
+- `requirements.txt` - Python dependencies
+- `packages.txt` - System packages
+- `README.md` - This documentation
+
+## Model Loading
+
+**RF-DETR Model:**
+- Upload your trained `rf-detr-medium.pth` file to the Space
+- The application will automatically find and load it
+
+**MedGemma Models:**
+- Models download automatically from Hugging Face Hub on first use
+- No manual installation required
+- Choose between 4B (faster) or 27B (more accurate) models
+
+## Space Configuration
+
+For optimal performance, configure your Space settings:
+- **Hardware**: GPU (T4 minimum, A100 recommended for 27B models)
+- **Storage**: Enable persistent storage for model caching
+- **Timeout**: 30+ minutes for large model downloads
+
+## Technical Details
+
+- **Framework**: PyTorch + Transformers
+- **Interface**: Gradio
+- **Computer Vision**: OpenCV, PIL, Supervision
+- **Hardware**: Optimized for both CPU and GPU inference
+
+## Performance Tips
+
+- **Model Selection**: Use MedGemma 4B for faster processing or 27B for higher accuracy
+- **Confidence Thresholds**: Higher values reduce false positives but may miss subtle findings
+- **GPU Acceleration**: The application automatically uses GPU acceleration when available
+- **Memory Optimization**: Uses 4-bit quantization to reduce memory usage
+- **Model Caching**: Models are cached after first load for faster subsequent analyses
+
+## Limitations
+
+- Requires significant computational resources for optimal performance
+- Best suited for medical imaging applications
+- Results should be verified by qualified medical professionals
+
+## Development
+
+To run locally:
+
+```bash
+pip install -r requirements.txt
+python app.py
+```
+
+**Note**: For local development, you'll need to:
+1. Install the RF-DETR package or ensure it's available
+2. Place your `rf-detr-medium.pth` file in the project directory
+3. Models will download automatically on first run
+
+## License
+
+This project is for research and educational purposes. Medical applications should be developed and validated according to appropriate regulatory standards.
+
+## Support
+
+For issues or questions, please refer to the Hugging Face Space documentation or create an issue in the project repository.