Update models.py

models.py

@@ -27,20 +27,34 @@
 #     return model
 
 
-from tensorflow.keras.applications import VGG19, EfficientNetB0, DenseNet121
+# from tensorflow.keras.applications import VGG19, EfficientNetB0, DenseNet121
+# from tensorflow.keras.models import Model
+
+# def create_vgg19_model():
+#     base_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+#     model = Model(inputs=base_model.input, outputs=base_model.get_layer("block5_conv4").output)
+#     return model
+
+# def create_efficientnet_model():
+#     base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+#     model = Model(inputs=base_model.input, outputs=base_model.get_layer("top_conv").output)
+#     return model
+
+# def create_densenet_model():
+#     base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
+#     model = Model(inputs=base_model.input, outputs=base_model.get_layer("conv5_block16_concat").output)
+#     return model
+
+
+
+from tensorflow.keras.applications import VGG19
 from tensorflow.keras.models import Model
 
 def create_vgg19_model():
+    """
+    Loads the VGG19 model with ImageNet weights and removes the top classification layers.
+    The output will be the last convolutional layer's output (used for Grad-CAM).
+    """
     base_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-    model = Model(inputs=base_model.input, outputs=base_model.get_layer("block5_conv4").output)
-    return model
-
-def create_efficientnet_model():
-    base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-    model = Model(inputs=base_model.input, outputs=base_model.get_layer("top_conv").output)
-    return model
-
-def create_densenet_model():
-    base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-    model = Model(inputs=base_model.input, outputs=base_model.get_layer("conv5_block16_concat").output)
-    return model
+    return Model(inputs=base_model.input, outputs=base_model.output)
+