Update app.py

app.py

@@ -31,12 +31,13 @@ show_gradcam_checkbox = gr.inputs.Checkbox(default=False, label="Show GradCAM Ou
 
 # Slider for adjusting the opacity of the GradCAM overlay
 opacity_slider = gr.inputs.Slider(minimum=0, maximum=1, default=0.7,step=0.1,    label="GradCAM Opacity")
-
+layer_options = ['layer1', 'layer2', 'layer3']
+layer_input = gr.inputs.Dropdown(layer_options,label="Select a Layer",default="layer3")
 gr.Interface(
-    fn=lambda image, num_of_output_classes,show_misclassified, num_images, show_gradcam, opacity: [main_inference(num_of_output_classes,classes,model,image),
+    fn=lambda image, num_of_output_classes,show_misclassified, num_images, show_gradcam, opacity,layer: [main_inference(num_of_output_classes,classes,model,image),
                                                                               get_misclassified_images(show_misclassified, num_images) if show_misclassified else None,
-                                                                              get_gradcam(model,image, opacity) if show_gradcam else None],
-    inputs=[input_component, num_of_output_classes,show_misclassified_checkbox, num_images_input, show_gradcam_checkbox, opacity_slider],
+                                                                              get_gradcam(model,image, opacity,layer) if show_gradcam else None],
+    inputs=[input_component, num_of_output_classes,show_misclassified_checkbox, num_images_input, show_gradcam_checkbox, opacity_slider,layer_input],
     outputs=[gr.outputs.Label(), gr.Image(shape=(500, 500)), gr.Image(shape=(500, 500))],
     title="CIFAR10 Trained on Custom Residual CNN Architecture",
     examples=[