Update app.py

app.py

@@ -20,6 +20,7 @@ classes = ('plane', 'car', 'bird', 'cat', 'deer',
 
 def inference(input_img, transparency = 0.5, target_layer_number = -1):
     transform = transforms.ToTensor()
+    org_img = input_img
     input_img = transform(input_img)
     input_img = input_img
     input_img = input_img.unsqueeze(0)
@@ -33,12 +34,12 @@ def inference(input_img, transparency = 0.5, target_layer_number = -1):
     img = inv_normalize(img)
     rgb_img = np.transpose(img, (1, 2, 0))
     rgb_img = rgb_img.numpy()
-    visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency)
+    visualization = show_cam_on_image(org_img/255, grayscale_cam, use_rgb=True, image_weight=transparency)
     return classes[prediction[0].item()], visualization
 
 title = "CIFAR10 trained on ResNet18 Model with GradCAM"
 description = "A simple Gradio interface to infer on ResNet model, and get GradCAM results"
-examples = [["cat.jpg", "dog.jpg"]]
+examples = [["cat.jpg", 0.5, -1], ["dog.jpg", 0.5, -1]]
 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?")],