Update app.py

app.py

@@ -18,13 +18,7 @@ example_list = [["examples/" + example] for example in os.listdir("examples")]
 #     img_ = img_.add(torch.FloatTensor(MEAN).view(3, 1, 1)).detach().numpy()
 #     img_ = np.transpose(img_, (1, 2, 0))
 #     return img_ 
-inverse_transform = transforms.Compose([    transforms.Normalize(mean=[-0.5, -0.5, -0.5], std=[1/0.5, 1/0.5, 1/0.5]),
-    transforms.Normalize(mean=[-transform.mean[0]/transform.std[0],
-                               -transform.mean[1]/transform.std[1],
-                               -transform.mean[2]/transform.std[2]],
-                          std=[1/transform.std[0],
-                               1/transform.std[1],
-                               1/transform.std[2]]),
+inverse_transform = transforms.Compose([    transforms.Normalize(mean=[-1.0, -1.0, -1.0], std=[2.0, 2.0, 2.0]),
     transforms.ToPILImage()
 ])
 def predict(img):