Running the Application

1. Start Servers:

   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!
   

   

2. Stop Servers: Press Ctrl+C.

Build Docker Image

Linux

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

Windows (Command Prompt or PowerShell)

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

Run Docker Container

Mount the dataset/ directory to access images.

Linux

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)

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)

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

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:

   curl http://localhost:5000/health
   

   Expected:

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

   

2. Get Model Info:

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

   Expected:

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

   

3. Predict Glaucoma:

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

   Expected:

   {"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:

   • Upload an image (e.g., 016.jpg).
   • Check “Show Probabilities” (optional).
   • Click “Predict” to view results.