Update app.py

app.py

@@ -25,13 +25,12 @@ with open(custom_model_file, 'wb') as f:
     f.write(custom_model_content)
 
 # Load the EfficientNet model onto the CPU
-model = torch.load(efficientnet_model_file)
+model = torch.load(efficientnet_model_file, map_location=torch.device('cpu'))
 
 # Load your custom model onto the CPU
 model.load_state_dict(torch.load(custom_model_file, map_location=torch.device('cpu')))
 model.eval()
 
-
 # Define a function that takes an image as input and uses the model for inference
 def image_classifier(image):
     # Preprocess the input image