Update app.py

app.py

@@ -88,7 +88,8 @@ demo = gr.Interface(
     inputs=[
         gr.Radio(["Yes", "No"], label="View GradCAM images?"),
         gr.Number(label="Number of GradCAM images to view", default=5, max=10),
-        gr.Textbox(label="Layer name for GradCAM visualization", default="layer4", lines=1),
+        # gr.Textbox(label="Layer name for GradCAM visualization", default="layer4", lines=1),
+        gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?"),
         gr.Slider(minimum=0.1, maximum=1.0, step=0.1, default=0.5, label="Opacity"),
         gr.Radio(["Yes", "No"], label="View misclassified images?"),
         gr.Number(label="Number of misclassified images to view", default=5, min=1, max=10),
@@ -96,7 +97,7 @@ demo = gr.Interface(
         gr.Button(label="Submit", type="boolean"),
         # gr.Image(shape=(32, 32), label="Input Image"),
         # gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"),
-        gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")
+        
         
     ],
     outputs = [gr.Label(num_top_classes=3), gr.Image(shape=(32, 32), label="Output").style(width=128, height=128)],
@@ -107,7 +108,7 @@ demo = gr.Interface(
 # Callback function for the Gradio interface
 def gradio_callback(view_gradcam, num_gradcam_images, layer_name, opacity,
                     view_misclassified, num_misclassified_images,
-                        input_img,submit,layer_name):
+                        input_img,submit):
     confidence = inference_confidences(input_img, transparency = 0.5, target_layer_number = -1)
     visualization = inference_visualization(input_img, transparency = 0.5, target_layer_number = -1)
     return confidence, visualization