Use PyTorch inference mode

app.py

@@ -38,10 +38,10 @@ class App:
         if name in self._model_cache:
             return self._model_cache[name]
         model = torch.hub.load('baudm/parseq', name, pretrained=True).eval()
-        model.freeze()
         self._model_cache[name] = model
         return model
 
+    @torch.inference_mode()
     def __call__(self, model_name, image):
         if image is None:
             return '', []