Update app.py

app.py

@@ -132,7 +132,7 @@ if len(file_upload) == 1:
             model = EfficientNetBN(
                 "efficientnet-b0", spatial_dims=3, in_channels=1, num_classes=3)
             model.load_state_dict(torch.load(
-                'MCEBNfold1.pth', map_location='cpu'))
+                'MCEBNfold5.pth', map_location='cpu'))
             model.eval()
             prediction = model(test_images.unsqueeze(1))
             pred = prediction.argmax(dim=1).item()
@@ -161,7 +161,7 @@ if len(file_upload) == 1:
             with st.spinner('Please wait...verifying the model output with another model'):
                 model_verify = monai.networks.nets.DenseNet264(spatial_dims=3, in_channels=1, out_channels=2)
                 model_verify.load_state_dict(torch.load(
-                    'F3DENSENET264ADvsCNbest_metric_model_classification3d_dict.pth', map_location='cpu'))
+                    'DENSENET264ADvsCNbest_metric_model_classification3d_dict.pth', map_location='cpu'))
                 model_verify.eval()
                 prediction_verify = model_verify(test_images.unsqueeze(1))
                 pred_verify = prediction_verify.argmax(dim=1).item()
@@ -310,7 +310,7 @@ if len(file_upload) > 1:
             model = EfficientNetBN(
                 "efficientnet-b0", spatial_dims=3, in_channels=1, num_classes=3)
             model.load_state_dict(torch.load(
-                'MCEBNfold1.pth', map_location='cpu'))
+                'MCEBNfold5.pth', map_location='cpu'))
             model.eval()
             prediction = model(test_images.unsqueeze(1))
             pred = prediction.argmax(dim=1).item() 
@@ -340,7 +340,7 @@ if len(file_upload) > 1:
             with st.spinner('Please wait...verifying the model output with another model'):
                 model_verify = monai.networks.nets.DenseNet264(spatial_dims=3, in_channels=1, out_channels=2)
                 model_verify.load_state_dict(torch.load(
-                    'F3DENSENET264ADvsCNbest_metric_model_classification3d_dict.pth', map_location='cpu'))
+                    'DENSENET264ADvsCNbest_metric_model_classification3d_dict.pth', map_location='cpu'))
                 model_verify.eval()
                 prediction_verify = model_verify(test_images.unsqueeze(1))
                 pred_verify = prediction_verify.argmax(dim=1).item()