Update app.py

app.py

@@ -5,10 +5,6 @@ import torch
 torch.jit.script = lambda f: f
 # torch.autocast = lambda device_type, dtype: torch.autocast(device_type, torch.float)
 
-from t2v_metrics import VQAScore, list_all_vqascore_models
-
-print(list_all_vqascore_models())
-
 # Initialize the model only once
 # if torch.cuda.is_available(): 
 # model_pipe = VQAScore(model="clip-flant5-xl", device="cpu")  # our recommended scoring model
@@ -16,6 +12,11 @@ print("Model initialized!")
 
 @spaces.GPU
 def generate(model_name, image, text):
+    
+    from t2v_metrics import VQAScore, list_all_vqascore_models
+    
+    print(list_all_vqascore_models())
+    
     # print("Model_name:", model_name)
     print("Image:", image)
     print("Text:", text)