Update app.py

app.py

@@ -11,8 +11,12 @@ class_names = ["pizza", "steak", "sushi"]
 
 effnetb2, effnetb2_transform = create_effnetb2_model(num_classes=len(class_names))
 
-effnetb2.load_state_dict(torch.load("pretrained_effnetb2_pizza_steak_sushi_20_percent.pt"),
-                         map_location=torch.device("cpu")) # Load model to the CPU
+state_dict = torch.load(
+    "pretrained_effnetb2_pizza_steak_sushi_20_percent.pt",
+    map_location=torch.device("cpu")  # Load model to the CPU
+)
+
+effnetb2.load_state_dict(state_dict)
 
 def predict(img) -> Tuple[Dict, float]:
     start_time = timer()