Update app.py

app.py

@@ -39,10 +39,8 @@ def resize_image_pil(image, new_width, new_height):
 
     return resized
 
-def get_num_top_classes(num_classes_input):
-    return int(num_classes_input)
 
-def inference(input_img, transparency=0.5, target_layer_number=-1, grad_cam_option="Yes", num_classes_input=3, num_top_classes=3):
+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)
     org_img = input_img
@@ -61,11 +59,12 @@ def inference(input_img, transparency=0.5, target_layer_number=-1, grad_cam_opti
     grayscale_cam = grayscale_cam[0, :]
     img = input_img.squeeze(0)
     img = inv_normalize(img)
+    print('Confidences ',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, num_top_classes
+        return classes[prediction[0].item()], visualization, confidences
     else:
-        return classes[prediction[0].item()], None, confidences, num_top_classes
+        return classes[prediction[0].item()], None, confidences
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
@@ -78,14 +77,12 @@ demo = gr.Interface(
         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?"),
-        gr.Number(1, 10, label="Number of Classes"),
-        gr.Number(1, 10, label="Number of Top Classes")
+        gr.Dropdown(["Yes", "No"], label="Want to see Grad Cam Images?")
     ], 
     outputs=[
         "text", 
         gr.Image(width=256, height=256, label="Output"),
-        gr.Label(num_top_classes=get_num_top_classes)
+        gr.Label(num_top_classes=3)
     ],
     title=title,
     description=description,