remamed cm_config.yaml

app.py

@@ -61,7 +61,6 @@ def load_checkpoint(checkpoint_path: str, device: torch.device) -> Tuple[nn.Modu
     model.load_state_dict(checkpoint['state_dict'])
     model.to(device)
     model.eval()
-    # The idx_to_class is no longer needed as we hardcode labels
     return model, {}
 
 print("--- Loading all models into memory ---")
@@ -96,7 +95,6 @@ def predict(pil_image, model_name: str):
         output = model(image_tensor)
         prob = torch.sigmoid(output).item()
         
-    # Per user request: Class 0 is "clean", Class 1 is "messy"
     return {"clean": 1 - prob, "messy": prob}
 
 iface = gr.Interface(