added to give user to pass input whether they want to see grad cam output images or not

app.py

@@ -39,7 +39,7 @@ def resize_image_pil(image, new_width, new_height):
 
     return resized
 
-def inference(input_img, transparency = 0.5, target_layer_number = -1):
+def inference(input_img, transparency = 0.5, target_layer_number = -1, grad_cam_option = "Yes"):
     input_img = resize_image_pil(input_img, 32, 32)
     
     input_img = np.array(input_img)
@@ -61,8 +61,11 @@ def inference(input_img, transparency = 0.5, target_layer_number = -1):
     img = input_img.squeeze(0)
     img = inv_normalize(img)
     print(transparency)
-    visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
-    return classes[prediction[0].item()], visualization, confidences
+    if grad_cam_option == "Yes":
+        visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
+        return classes[prediction[0].item()], visualization, confidences
+    else:
+        return classes[prediction[0].item()], confidences
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
@@ -71,9 +74,10 @@ examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1], ["bird_1.jpg", 0.5, -1],
 demo = gr.Interface(
     inference, 
     inputs = [
-        gr.Image(width=256, height=256, label="Input Image"), gr.Slider
-        (0, 1, value = 0.5, label="Overall Opacity of Image"), 
-        gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?")
+        gr.Image(width=256, height=256, label="Input Image"), 
+        gr.Slider(0, 1, value = 0.5, label="Overall Opacity of Image"), 
+        gr.Slider(-2, -1, value = -2, step=1, label="Which Layer?"),
+        gr.Dropdown([“Yes”, “No”], label="Want to see Grad Cam Images?")
         ], 
     outputs = [
         "text",