https://github.com/asmaaebraheem1/diabeticretionpathydetection
A Streamlit-based web application for detecting diabetic retinopathy from eye fundus images using a deep learning model from Hugging Face.
https://github.com/asmaaebraheem1/diabeticretionpathydetection
ai css detecting-diabetic html python streamlit
Last synced: about 2 months ago
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A Streamlit-based web application for detecting diabetic retinopathy from eye fundus images using a deep learning model from Hugging Face.
- Host: GitHub
- URL: https://github.com/asmaaebraheem1/diabeticretionpathydetection
- Owner: Asmaaebraheem1
- Created: 2025-06-21T16:51:42.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-21T17:08:29.000Z (about 1 year ago)
- Last Synced: 2025-06-21T18:22:32.597Z (about 1 year ago)
- Topics: ai, css, detecting-diabetic, html, python, streamlit
- Language: Python
- Homepage: https://huggingface.co/spaces/AsmaaElnagger/DiabeticRetionPathyDetection
- Size: 12.7 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Security: SECURITY.md
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README
# Diabetic Retinopathy Detection
[](https://huggingface.co/spaces/AsmaaElnagger/DiabeticRetionPathyDetection)
[](https://huggingface.co/Asmaa111/diabetic-eye)
A Streamlit-based web application for detecting diabetic retinopathy from eye fundus images using a deep learning model from Hugging Face.
## 🔍 Overview
This application uses a pre-trained deep learning model to classify eye fundus images into different stages of diabetic retinopathy. The model is hosted on Hugging Face and integrated into a user-friendly Streamlit interface.
## 🚀 Getting Started
### Using Docker (Recommended)
1. Build the Docker image:
```bash
docker build -t diabetic-retinopathy .
```
2. Run the container:
```bash
docker run -p 8501:8501 diabetic-retinopathy
```
3. Access the app at `http://localhost:8501`
### Without Docker
1. Install dependencies:
```bash
pip install -r requirements.txt
```
2. Download the model:
```bash
python download_model.py
```
3. Run the Streamlit app:
```bash
streamlit run src/streamlit_app.py
```
## 🧠 Model Information
- **Model Name:** diabetic-eye
- **Repository:** [Asmaa111/diabetic-eye](https://huggingface.co/Asmaa111/diabetic-eye)
- **Framework:** PyTorch
- **Input:** Eye fundus images (JPEG/PNG)
- **Output:** Classification into retinopathy stages
## 📂 Project Structure
```
DiabeticRetionPathyDetection/
├── .streamlit/ # Streamlit configuration
├── src/
│ └── streamlit_app.py # Main application code
├── Dockerfile # Docker configuration
├── download_model.py # Model download script
├── requirements.txt # Python dependencies
└── README.md # Project documentation
```
## 🌐 Live Demo
Try the live version hosted on Hugging Face Spaces:
[DiabeticRetionPathyDetection Demo](https://huggingface.co/spaces/AsmaaElnagger/DiabeticRetionPathyDetection)
## 📚 Resources
- [Streamlit Documentation](https://docs.streamlit.io)
- [Hugging Face Transformers](https://huggingface.co/docs/transformers/index)
- [Diabetic Retinopathy Detection Research](https://www.kaggle.com/c/diabetic-retinopathy-detection)