SIENTIAPDE-1214

SIENTIAPDE-1214: Refactor MLFlow and model repository methods to use model_config dictionary

- Updated MLFlow class methods to accept model_config instead of model_retention for improved flexibility.
- Modified model_repository methods to handle model_config, extracting necessary parameters for transformation and prediction.
- Adjusted predictions_batch and prediction_process workflows to utilize model_config for better configuration management.
- Commented out the previous sientia-mlops-library dependency in requirements.txt for clarity.

laborious/utils/repository/model_repository.py

@@ -25,7 +25,7 @@ class MLFlowRepository():
                                           username=username, password=password,
                                           logger=logger)
 
-    def transform(self, model_name: str, data: pd.DataFrame, model_retention: int):
+    def transform(self, model_name: str, data: pd.DataFrame, model_config: dict) -> dict:
         """
         Transform data using a model.
 
@@ -39,11 +39,19 @@ class MLFlowRepository():
         """
 
         try:
+            model_retention = model_config.get('model_retention', 0)
+            flavor = model_config.get('transform_flavor', 'sklearn')
+            compressed = model_config.get('is_compressed', False)
+            retention_target = model_config.get('retention_target', 'model')
+            transform_keyword = model_config.get(
+                'transform_function_keyword', 'predict')
 
             return {
                 'success': True,
                 'content': self.model_serving.get_cached_transform(
-                    model_name, data, model_retention).to_dict()
+                    model_name, data, model_retention, flavor,
+                    compressed, retention_target, transform_keyword
+                ).to_dict()
             }
 
         except Exception as e:
@@ -55,7 +63,7 @@ class MLFlowRepository():
                 }
             }
 
-    def predict(self, model_name: str, data: pd.DataFrame, model_retention: int):
+    def predict(self, model_name: str, data: pd.DataFrame, model_config: dict) -> dict:
         """
         Predict data using a model.
 
@@ -68,11 +76,17 @@ class MLFlowRepository():
             - dict: A dictionary containing the predicted data.
         """
         try:
+            model_retention = model_config.get('retention_minutes', 0)
+            flavor = model_config.get('predict_flavor', 'pyfunc')
+            compressed = model_config.get('compressed', False)
+            retention_target = model_config.get('retention_target', 'model')
 
             input_index = data.index
             start_time = datetime.now()
             data = self.model_serving.get_cached_predict(
-                model_name, data, model_retention)
+                model_name, data, model_retention, flavor,
+                compressed, retention_target
+            )
 
             end_time = datetime.now()
             data = pd.DataFrame(data, columns=['prediction'])