Update app.py

app.py

@@ -12,7 +12,7 @@ with fs.open(efficientnet_model_path, 'rb') as f:
 
 # Load the EfficientNet model onto the CPU
 efficientnet_model_file = io.BytesIO(efficientnet_model_content)
-model = torch.load(efficientnet_model_file, map_location=torch.device('cpu'))
+efficientnet_model = torch.load(efficientnet_model_file, map_location=torch.device('cpu'))
 
 # Authenticate and download your custom model from Hugging Face
 custom_model_path = 'dhhd255/efficient_net_parkinsons/best_model.pth'
@@ -21,7 +21,13 @@ with fs.open(custom_model_path, 'rb') as f:
 
 # Load your custom model onto the CPU
 custom_model_file = io.BytesIO(custom_model_content)
-model.load_state_dict(torch.load(custom_model_file, map_location=torch.device('cpu')))
+custom_model_state_dict = torch.load(custom_model_file, map_location=torch.device('cpu'))
+
+# Create a new instance of your model
+model = MyModel()
+
+# Load your custom model into the new instance
+model.load_state_dict(custom_model_state_dict)
 model.eval()
 
 # Define a function that takes an image as input and uses the model for inference