# from tensorflow.keras.applications import VGG19, EfficientNetB0, DenseNet121
# from tensorflow.keras.models import Model
# from tensorflow.keras.layers import Dense, Flatten, GlobalAveragePooling2D, Input

# def create_vgg19_model():
#     base_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
#     x = Flatten()(base_model.output)
#     x = Dense(128, activation='relu')(x)
#     output = Dense(2, activation='softmax')(x)
#     model = Model(inputs=base_model.input, outputs=output)
#     return model

# def create_efficientnet_model():
#     base_model = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
#     x = GlobalAveragePooling2D()(base_model.output)
#     x = Dense(128, activation='relu')(x)
#     output = Dense(2, activation='softmax')(x)
#     model = Model(inputs=base_model.input, outputs=output)
#     return model

# def create_densenet_model():
#     base_model = DenseNet121(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
#     x = GlobalAveragePooling2D()(base_model.output)
#     x = Dense(128, activation='relu')(x)
#     output = Dense(2, activation='softmax')(x)
#     model = Model(inputs=base_model.input, outputs=output)
#     return model


# 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():
    base_model = VGG19(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
    # Use last convolutional layer directly
    model = Model(inputs=base_model.input, outputs=base_model.get_layer("block5_conv4").output)
    return model