tentaive de correction de l'erreur

src/mlflow_integration/mlflow_decorator.py

@@ -44,6 +44,24 @@ class MLflowDecorator:
 
         return wrapper
 
+    def _get_params_for_trainer(self, trainer_instance) -> Dict[str, Any]:
+        """
+        Gets the relevant parameters from the trainer's config.
+        This replaces the singledispatch logic previously in parameter_logging.py
+        """
+        # In this specific project structure, all trainers seem to store
+        # the relevant hyperparameters directly in trainer_instance.config.model.params.
+        # If specific trainers needed different logic, we could add isinstance checks here.
+        # from trainers.cuml.svm_trainer import SvmTrainer # Example import if needed
+        # if isinstance(trainer_instance, SvmTrainer):
+        #    return specific_logic_for_svm(trainer_instance)
+        
+        # Default logic: return the model params from the config
+        if hasattr(trainer_instance, 'config') and hasattr(trainer_instance.config, 'model') and hasattr(trainer_instance.config.model, 'params'):
+             return trainer_instance.config.model.params
+        return {}
+
+
     def _start_run(self) -> None:
         """
         Démarre explicitement un run MLflow.

src/utilities/cuml_pyfunc_wrapper.py

@@ -9,11 +9,12 @@ import cudf
 import cupy as cp
 import pickle
 import os
+import mlflow
 
 from interfaces.vectorizer import Vectorizer
 
 
-class CuMLPyFuncWrapper:
+class CuMLPyFuncWrapper(mlflow.pyfunc.PythonModel):
     """
     Classe wrapper pour intégration de modèles cuML dans MLflow PyFunc,
     permettant le chargement et l'inférence.