Update app.py

app.py

@@ -10,6 +10,11 @@ import gradio as gr
 model = ResNet18()
 model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
 
+inv_normalize = transforms.Normalize(
+    mean=[-0.50/0.23, -0.50/0.23, -0.50/0.23],
+    std=[1/0.23, 1/0.23, 1/0.23]
+)
+
 def inference(input_img, transparency, target_layer_number):
     transform = transforms.ToTensor()
     input_img = transform(input_img)
@@ -28,5 +33,5 @@ def inference(input_img, transparency, target_layer_number):
     visualization = show_cam_on_image(rgb_img, grayscale_cam, use_rgb=True, image_weight=transparency)
     return classes[prediction[0].item()], visualization
 
-demo = gr.Interface(inference, [gr.Image(shape=(32, 32)), gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-5, -1, value = -2, label="Which Layer?")], ["text", gr.Image(shape=(32, 32)).style(width=128, height=128)])
+demo = gr.Interface(inference, [gr.Image(shape=(32, 32)), gr.Slider(0, 1, value = 0.5, label="Opacity of GradCAM"), gr.Slider(-5, -1, value = -2, step=1, label="Which Layer?")], ["text", gr.Image(shape=(32, 32)).style(width=128, height=128)])
 demo.launch()