Hear attack prediction using eye retina

I need a working prototype that takes retina-fundus images, runs them through a deep-learning pipeline, and returns a clear heart-attack risk score—low, medium, or high—together with a confidence percentage. The core of the project is a convolutional model (CNN, RetinaNet, or whichever architecture proves most accurate after experimentation) trained on a suitably annotated dataset.

The finished solution must run end-to-end inside a web-based interface: the user drags a scan into the browser, the image is processed server-side (Python, TensorFlow or PyTorch, OpenCV as needed), and the prediction appears instantly on screen accompanied by a heat-map or attention overlay that highlights the retinal regions driving the decision.


For hand-off, please include:
• Clean, well-commented source code and model weights
• The web UI (HTML/CSS/JS or a lightweight framework such as Streamlit/FastAPI + React) ready to deploy on a standard cloud VM
• A short README covering environment setup, dataset preparation, and instructions for retraining or fine-tuning

Accuracy benchmarks aren’t fixed yet, but the model should outperform naive baselines and show sensible ROC/AUC on a held-out test set. I’ll supply or help locate retina datasets; advise if additional labeling is required. Continuous collaboration is expected until the tool is reproducible on my machine and running smoothly online.
Skills Required
JavaScript
Python
Machine Learning (ML)
MySQL
HTML5
Convolutional Neural Network