Update app.py

app.py

@@ -10,16 +10,16 @@ import gradio as gr
 model = ResNet18()
 model.load_state_dict(torch.load("model.pth", map_location=torch.device('cpu')), strict=False)
 
-def inference(input_img, transparency):
+def inference(input_img, transparency, target_layer_number):
     transform = transforms.ToTensor()
     input_img = transform(input_img)
     input_img = input_img
     input_img = input_img.unsqueeze(0)
     outputs = model(input_img)
     _, prediction = torch.max(outputs, 1)
-    target_layers = [model.layer2[-2]]
+    target_layers = [model.layer2[target_layer_number]]
     cam = GradCAM(model=model, target_layers=target_layers, use_cuda=False)
-    grayscale_cam = cam(input_tensor=input_img, targets=targets)
+    grayscale_cam = cam(input_tensor=input_img, targets=None)
     grayscale_cam = grayscale_cam[0, :]
     img = input_img.squeeze(0)
     img = inv_normalize(img)
@@ -28,5 +28,5 @@ def inference(input_img, transparency):
     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)], ["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, label="Which Layer?")], ["text", gr.Image(shape=(32, 32)).style(width=128, height=128)])
 demo.launch()