Puneeth-mail/Deep-Learning-Based-Prediction-System-for-Optimal-Embryo-Transfer-Timing- — reverse-engineered prompt

Build me a proof of concept Python app that can analyze an ultrasound image for endometrial receptivity.

I want a simple Streamlit screen where I can upload an ultrasound image, then the app shows the original image, the predicted endometrium segmentation mask, the estimated thickness in millimeters, and a clear result like Non Receptive, Pre Receptive, Receptive, or Hyper Thickened. Use the thickness rules from the README, with 1 mm treated as about 5 pixels.

Since the actual dataset and trained model aren’t included, set up the project so I can later train it on my own annotated images. Use a UNet++ style segmentation model with an EfficientNet B3 encoder in PyTorch if possible, and include training, validation, model saving, and inference scripts. If no trained model exists, the app should explain that and not crash.

Please keep it easy to run locally, include requirements, and add a short note that this is only for research or demo use, not medical diagnosis.