The pyfunc.add_to_model() function you mentioned is used to add the Python Function flavor to the model, which is different from changing the primary flavor of the logged model. That's why changing its parameter didn't solve the issue.
If you need to maintain compatibility with the existing AutoML pipeline, you might consider logging the model twice: once with the scikit-learn flavor for the pipeline, and once with the LightGBM flavor for accessing LightGBM-specific features.
Remember to test these changes thoroughly, as they may affect how the model is used in production environments that expect the scikit-learn flavor.
