Update app.py

app.py

@@ -74,6 +74,15 @@ def inference_visualization(input_img, transparency = 0.5, target_layer_number =
     visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
     return visualization
 
+# 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):
+    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
+
+
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "Gradio interface to infer on ResNet18 model, and get GradCAM results"
 examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]]
@@ -85,7 +94,7 @@ demo = gr.Interface(
     # title = title,
     # description = description,
     # examples = examples,
-    fn=None,  # We'll add the function later after defining all functions,  # We'll add the function later after defining all functions
+    fn=gradio_callback,  # We'll add the function later after defining all functions,  # We'll add the function later after defining all functions
     inputs=[
         gr.Radio(["Yes", "No"], label="View GradCAM images?"),
         gr.Number(label="Number of GradCAM images to view", default=5, max=10),
@@ -106,15 +115,8 @@ demo = gr.Interface(
     live=True
 )
 
-# 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):
-    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
 
 
 # Set the callback function to the Gradio interface
-demo.fn = gradio_callback
+# demo.fn = gradio_callback
 demo.launch()