Update app.py

app.py

@@ -6,12 +6,13 @@ device=torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
 
 # Cargamos el learner
-def load(map_location='cpu', pickle_module=pickle, **pickle_load_args):
-    with open ('modelomemes.pkl',mode='rb') as file:
-        learn=torch.load(cloudpickle.load(file), map_location="cpu")
+#def load(map_location='cpu', pickle_module=pickle, **pickle_load_args):
+#    with open ('modelomemes.pkl',mode='rb') as file:
+#        learn=torch.load(cloudpickle.load(file), map_location="cpu")
     
-
-load()
+learn = Net()
+learn.load_state_dict(torch.load(weights_paths, map_location=device))
+learn.to(device)
 
 
 # Definimos una función que se encarga de llevar a cabo las predicciones