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5t4l1n/ai-eye-disease-detection

sourceHugging Faceupdated 1y agoView on Hugging Face

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AI Eye Disease Detection

A web-based application for detecting glaucoma severity from retinal images using a ResNet101-based deep learning model with Squeeze-and-Excitation (SE) blocks. The app features a Flask backend for image processing and a frontend for user interaction. The pre-trained model is hosted on Hugging Face.

—Project Overview
—Prerequisites
—Installation
—Project Structure
—Usage
—Running the Application
—Testing via API
—Using the Frontend
—Training the Model
—Troubleshooting
—Contributing

Project Overview

This application predicts glaucoma severity (Normal, Mild, Moderate, Severe) from retinal images by estimating the Cup-to-Disc Ratio (CDR) and providing confidence scores. The backend, served via Flask on port 5000, uses a pre-trained ResNet101 model. The frontend, served on port 8000, allows users to upload images and view results. The model is available at Hugging Face, and the dataset includes images in dataset/G1020/Images_Square/ (e.g., 237.jpg).

Prerequisites

—Python 3.10.16 (recommended for PyTorch compatibility)
—Git
—Dependencies listed in backend/requirements.txt
—GPU recommended for faster inference/training (CPU supported)
—Retinal images in dataset/G1020/Images_Square/ (e.g., 237.jpg)
—Pre-trained model from Hugging Face

Installation

1.Clone the Repository:

bash

   git clone https://github.com/Stalin-143/ai-eye-disease-detection.git
   cd ai-eye-disease-detection

1.Create Virtual Environment:

bash

   python3.10 -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate  # Windows
   pip install --upgrade pip

1.Install Dependencies:

bash

   cd backend
   pip install -r requirements.txt

If torch==2.0.1 fails, try:

bash

   pip install torch==2.0.1 torchvision==0.15.2

1.Download the Model:

bash

   mkdir -p backend/model
   wget https://huggingface.co/5t4l1n/ai-eye-disease-detection/resolve/main/model/best_glaucoma_model.pth -O backend/model/best_glaucoma_model.pth

1.Configure Environment Variables: Create backend/.env:

plaintext

   FLASK_ENV=development
   FLASK_DEBUG=True
   FLASK_HOST=0.0.0.0
   FLASK_PORT=5000
   MODEL_PATH=./backend/model/best_glaucoma_model.pth
   MAX_CONTENT_LENGTH=16777216
   ALLOWED_ORIGINS=http://localhost:8000

Update MODEL_PATH with the absolute path if needed (e.g., /home/stalin/Projects/ai-eye-disease-detection/backend/model/best_glaucoma_model.pth).

1.Clear Cache:

bash

   rm -rf __pycache__ backend/__pycache__ backend/app/__pycache__

Project Structure

ai-eye-disease-detection/
├── backend/
│   ├── app/
│   │   ├── main.py              # Flask app entry point
│   │   ├── routes/prediction.py # API endpoints for predictions
│   │   ├── utils/glaucoma_predictor.py # Model inference logic
│   ├── model/
│   │   ├── best_glaucoma_model.pth # Pre-trained model
│   ├── requirements.txt         # Backend dependencies
│   ├── .env                    # Environment variables
├── frontend/
│   ├── index.html              # Frontend UI
│   ├── script.js               # Frontend JavaScript
│   ├── styles.css              # Frontend styles
├── dataset/
│   ├── G1020/
│   │   ├── Images_Square/      # Retinal images (e.g., 237.jpg)
│   ├── training.ipynb          # Jupyter notebook for training
├── server.py                   # Runs backend and frontend
├── venv/                       # Virtual environment
├── backup.ipynb                # Backup notebook
├── models/                     # Additional models (optional)
├── README.md                   # Project documentation
├── requirements.txt            # Root-level dependencies (optional)
├── templates/                  # Flask templates (if used)

Usage

Running the Application

1.Start Servers:

bash

   cd ai-eye-disease-detection
   source venv/bin/activate
   python server.py

—Backend: http://localhost:5000
—Frontend: http://localhost:8000 Expected logs:

   INFO:__main__:Starting Flask backend on http://localhost:5000...
   Backend: DEBUG:__main__:✅ Glaucoma predictor initialized successfully!
   INFO:__main__:Starting frontend HTTP server on http://localhost:8000...
   INFO:__main__:Both servers started successfully!

1.Stop Servers: Press Ctrl+C.

Build Docker Image

Linux

bash

docker build -t ai-eye-disease-detection .

Windows (Command Prompt or PowerShell)

bash

docker build -t ai-eye-disease-detection .

Run Docker Container

Mount the dataset/ directory to access images.

Linux

bash

docker run -d \
  -p 5000:5000 \
  -p 8000:8000 \
  -v $(pwd)/dataset:/app/dataset \
  --name ai-eye-disease-container \
  ai-eye-disease-detection

Windows (PowerShell)

powershell

docker run -d `
  -p 5000:5000 `
  -p 8000:8000 `
  -v ${PWD}/dataset:/app/dataset `
  --name ai-eye-disease-container `
  ai-eye-disease-detection

Windows (Command Prompt)

cmd

docker run -d ^
  -p 5000:5000 ^
  -p 8000:8000 ^
  -v %CD%\dataset:/app/dataset ^
  --name ai-eye-disease-container ^
  ai-eye-disease-detection

Notes

—-v mounts the local dataset/ directory to /app/dataset in the container.
—Use backticks (`) in PowerShell or carets (^) in Command Prompt for line continuation.
—Ensure the dataset/ directory exists locally.

Verify Container

Linux/Windows

bash

docker ps
docker logs ai-eye-disease-container

Expected Logs

INFO:__main__:Starting Flask backend on http://localhost:5000...
Backend: DEBUG:__main__:✅ Glaucoma predictor initialized successfully!
INFO:__main__:Starting frontend HTTP server on http://localhost:8000...

Testing via API

1.Check Health:

bash

   curl http://localhost:5000/health

Expected:

json

   {"status":"healthy","model_loaded":true}

1.Get Model Info:

bash

   curl http://localhost:5000/api/model-info

Expected:

json

   {"success":true,"data":{"model_type":"GlaucomaSeverityModel","architecture":"ResNet101 with SE Blocks",...}}

1.Predict Glaucoma:

bash

   curl -F "image=@dataset/G1020/Images_Square/237.jpg" -F "return_probabilities=true" http://localhost:5000/api/predict

Expected:

json

   {"success":true,"data":{"cdr":0.45,"severity":"Normal","severity_description":"No significant glaucoma signs","confidence":0.95,"risk_level":"Low","probabilities":{"Normal":0.95,"Mild":0.03,"Moderate":0.01,"Severe":0.01}},"filename":"237.jpg"}

Using the Frontend

1.Open http://localhost:8000 in a browser.
2.Get Model Info: Click “Get Model Info” to view model details.
3.Predict Glaucoma:
4.Upload an image (e.g., 237.jpg).
5.Check “Show Probabilities” (optional).
6.Click “Predict” to view results.

Training the Model

1.Prepare Dataset:
2.Ensure dataset/G1020/Images_Square/ has labeled images.
3.Check labels in a CSV or directory structure (e.g., Normal/, Mild/).
4.Run:

bash

     ls dataset/G1020

1.Install Jupyter:

bash

   source venv/bin/activate
   pip install jupyter

1.Run training.ipynb:

bash

   jupyter notebook dataset/training.ipynb

—Update dataset path (e.g., dataset/G1020/Images_Square/).
—Configure model (ResNet101 with SE blocks).
—Adjust hyperparameters (e.g., learning rate, batch size).
—Example:

python

     import torch
     import torchvision
     from torch.utils.data import DataLoader
     from torchvision import transforms
     transform = transforms.Compose([
         transforms.Resize((224, 224)),
         transforms.ToTensor(),
         transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
     ])
     dataset = torchvision.datasets.ImageFolder('dataset/G1020/Images_Square/', transform=transform)
     loader = DataLoader(dataset, batch_size=32, shuffle=True)
     model = torchvision.models.resnet101(pretrained=True)
     model.fc = torch.nn.Linear(model.fc.in_features, 4)
     optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
     criterion = torch.nn.CrossEntropyLoss()

1.Save Model:

python

   torch.save(model.state_dict(), 'backend/model/new_glaucoma_model.pth')

1.Update .env: Set MODEL_PATH to the new model in backend/.env.

Troubleshooting

—Model Not Loaded:
—Check:

bash

    ls backend/model/

—Verify MODEL_PATH in backend/.env.
—Share logs.

—Prediction Fails:
—Run:

bash

    curl -F "image=@dataset/G1020/Images_Square/237.jpg" -F "return_probabilities=true" http://localhost:5000/api/predict

—Share output and logs.
—Check browser console (F12).

—Pip Install Fails:
—Share error. Try:

bash

    pip install torch==2.0.1 torchvision==0.15.2

Contributing

1.Fork: https://github.com/Stalin-143/ai-eye-disease-detection
2.Branch: git checkout -b feature/your-feature
3.Commit: git commit -m "Add your feature"
4.Push: git push origin feature/your-feature
5.Open a pull request.

AI Eye Disease Detection

Table of Contents

Project Overview

Prerequisites

Installation

Project Structure

Usage

Running the Application

Build Docker Image

Linux

Windows (Command Prompt or PowerShell)

Run Docker Container

Linux

Windows (PowerShell)

Windows (Command Prompt)

Notes

Verify Container

Linux/Windows

Expected Logs

Testing via API

Using the Frontend

Training the Model

Troubleshooting

Contributing