Update app.py

app.py

@@ -52,11 +52,24 @@ def inference(audio_file: str, model_name: str, vocals: bool, drums: bool, bass:
 
     # Use apply_model as a standalone function
     try:
-        sources = apply_model(separator, wav.to(device), device=device)
+        result = apply_model(separator, wav.to(device), device=device)
+        yield None, stream_log(f"Model application result type: {type(result)}")
+        yield None, stream_log(f"Model application result shape: {result.shape if hasattr(result, 'shape') else 'N/A'}")
+        
+        if isinstance(result, tuple) and len(result) == 2:
+            sources, _ = result
+        elif isinstance(result, torch.Tensor):
+            sources = result
+        else:
+            raise ValueError(f"Unexpected result type from apply_model: {type(result)}")
+        
+        yield None, stream_log(f"Sources shape: {sources.shape}")
     except ValueError as e:
         yield None, stream_log(f"Error applying model: {str(e)}")
         yield None, stream_log(f"Separator sources: {separator.sources}")
         yield None, stream_log(f"WAV shape: {wav.shape}")
+        yield None, stream_log(f"Separator model: {separator.__class__.__name__}")
+        yield None, stream_log(f"Separator config: {separator.config}")
         raise gr.Error(f"Failed to apply model: {str(e)}. This might be due to incompatible audio format or model configuration.")
     except Exception as e:
         yield None, stream_log(f"Unexpected error applying model: {str(e)}")