Update app.py

app.py

@@ -27,7 +27,20 @@ model.load_state_dict(state_dict, strict=False)
 model.eval()
 
 def predict(im):
-    print (len(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))
+
+    
     x = torch.tensor(im, dtype=torch.float32).unsqueeze(0).unsqueeze(0) / 255.
 
     with torch.no_grad():