Update app.py

app.py

@@ -28,17 +28,7 @@ model.eval()
 
 def predict(im):
     
-    def list_shape(lst):
-        if not isinstance(lst, list):
-            return []
-        if not lst:
-            return [0]
-        shapes = [list_shape(sublist) for sublist in lst]
-        max_shape = max(shapes, key=len)
-        return [len(lst)] + [max(dimensions[i] if i < len(dimensions) else 0 for dimensions in shapes) for i in range(len(max_shape))]
-
-    
-    print(list_shape(im))
+    print(type(im))
 
     
     x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.