added num_top_classes as Input

app.py

@@ -13,7 +13,7 @@ model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')),
 classes = ('plane', 'car', 'bird', 'cat', 'deer',
            'dog', 'frog', 'horse', 'ship', 'truck')
 
-def inference(input_img, transparency = 0.5, target_layer_number = -1):
+def inference(input_img, num_top_classes, transparency = 0.5, target_layer_number = -1):
     transform = transforms.ToTensor()
     org_img = input_img
     input_img = transform(input_img)
@@ -42,7 +42,10 @@ examples = [["airplane.png", 0.5, -1],["bird.jpeg", 0.5, -1], ["car.jpeg", 0.5,
 
 demo = gr.Interface(
     inference, 
-    inputs = [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?")], 
+    inputs = [gr.Image(shape=(32, 32), label="Input Image"),
+              gr.Slider(0, 10, value = 0.5, label="Number of Top Classes"),
+              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": "128px", "height": "128px"})],
     title = title,
     description = description,