test

app.py

@@ -47,13 +47,21 @@ topt = A.Compose([
 labels = ['good', 'ill']
 
 def predict(inp):
+  target_layers = [model.norm_pre]
+  cam = GradCAM(model=model,
+       target_layers=target_layers)
   img = ho_trans_center(image = inp)['image']
   img = topt(image = img)['image']
   img = img.unsqueeze(0)
   with torch.no_grad():
     prediction = model(img).softmax(1).numpy()
     confidences = {labels[i]: float(prediction[0][i]) for i in range(2)}    
-  return confidences, inp
+  grad = cam(input_tensor=img,
+              targets=None,
+              # eigen_smooth=args.eigen_smooth,
+              # aug_smooth=args.aug_smooth
+        )
+  return confidences, grad[0, :]
 
 import gradio as gr