SAM0123-320: fix: call pyfunc data model predict with wrapper signature

When a data model's `fit` returns the refit object instead of a DataFrame,
`fit_models` called `predict(data)` sklearn-style. A pyfunc wrapper's
`predict` takes `(context, model_input)`, so Courier Stratified retrain
failed with "missing 1 required positional argument: 'model_input'".

Mirror `get_prediction_data`: pass `{}` as context when the transform
flavor is pyfunc.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>

laborious/utils/repository/model_repository.py

@@ -1341,6 +1341,9 @@ class MLFlowRepository(SientiaMonitoring):
 
         if not isinstance(treated_data_candidate, pd.DataFrame):
             data_model = treated_data_candidate
+            if transform_flavor == 'pyfunc':
+                treated_data = data_model.predict({}, data)
+            else:
                 treated_data = data_model.predict(data)
         else:
             treated_data = treated_data_candidate

tests/laborious/utils/repository/test_model_repository.py

@@ -1043,6 +1043,38 @@ async def test_fit_models_not_df_target_name_none_and_not_in_model(
     }
 
 
+@patch('laborious.utils.repository.model_repository.pd.merge')
+@patch('laborious.utils.repository.model_repository.isinstance')
+@pytest.mark.asyncio
+async def test_fit_models_pyfunc_transform_predicts_with_wrapper_signature(
+    isinstance_mock, pd_merge, mlflow_repository
+):
+    """A pyfunc data model refit returns the wrapper, whose `predict` takes `(context, model_input)`."""
+    isinstance_mock.return_value = False
+
+    data_model = MagicMock()
+    mlflow_repository.download_model = AsyncMock(
+        side_effect=[(data_model, 'artifact_path'), (MagicMock(), 'artifact_path')],
+    )
+    mlflow_repository.detect_and_parse_datetime_index = MagicMock()
+    mlflow_repository.get_prediction_data = MagicMock(return_value=DataFrame())
+
+    data = MagicMock()
+
+    await mlflow_repository.fit_models(
+        'model_name',
+        data,
+        'latest_production_id',
+        metadata['metadata'],
+        'pyfunc',
+        False,
+        'pyfunc',
+        'target',
+    )
+
+    data_model.fit.return_value.predict.assert_called_once_with({}, data)
+
+
 @patch('laborious.utils.repository.model_repository.pd.merge')
 @patch('laborious.utils.repository.model_repository.isinstance')
 @pytest.mark.asyncio