Update model.py

model.py

@@ -1,47 +1,3 @@
-# import torch
-# from monai.networks.nets import DynUNet
-# import os
-
-# def load_model(model_path="best_model_large_data.pth", device="cpu"):
-#     """Load DynUNet model with weights"""
-#     try:
-#         model = DynUNet(
-#             spatial_dims=2,
-#             in_channels=1,
-#             out_channels=1,
-#             kernel_size=[3, 3, 3, 3, 3],
-#             strides=[1, 2, 2, 2, 2],
-#             upsample_kernel_size=[2, 2, 2, 2],
-#             filters=[32, 64, 128, 256, 512],
-#             norm_name="INSTANCE",
-#             res_block=True,
-#             deep_supervision=False,
-#         )
-        
-#         state_dict = torch.load(model_path, map_location=device)
-#         model.load_state_dict(state_dict)
-#         model.to(device)
-#         model.eval()
-#         return model
-
-#     except Exception as e:
-#         print(f"❌ Model initialization failed: {e}")
-#         raise
-
-# def predict_mask(model, image_tensor):
-#     """Predict segmentation mask with sigmoid activation."""
-#     try:
-#         if image_tensor.dim() != 4 or image_tensor.shape[1] != 1:
-#             raise ValueError(f"Input tensor must be [1, 1, H, W]. Got {image_tensor.shape}")
-        
-#         with torch.no_grad():
-#             return torch.sigmoid(model(image_tensor))
-            
-#     except Exception as e:
-#         print(f"Prediction failed: {e}")
-#         raise
-
-
 import torch
 from monai.networks.nets import DynUNet
 import os