refactor: remove MinIO repository references and streamline cleanup process

- Eliminated MinIO repository dependencies from the `Activities`, `Cleanup`, and `Training` classes.
- Updated the `cleanup_resources` method to focus on removing local temporary directories instead of handling MinIO file deletions.
- Adjusted the `TrainModel` class to pass the run directory for cleanup, enhancing resource management in the training workflow.

model_manager/workflows/train_model.py

@@ -124,10 +124,7 @@ class TrainModel:
         finally:
             try:
                 await self._cleanup_resources(
-                    experiment_run_id=experiment_run_id,
-                    bucket_name=train_params.bucket_name,
-                    file_name=train_params.file_name,
-                    val_file_name=train_params.val_file_name,
+                    run_dir=train_result.get('run_dir'),
                     metadata=metadata,
                 )
             except Exception:
@@ -278,22 +275,13 @@ class TrainModel:
 
             return train_result
         except Exception as e:
-            # Mapear flags -> status
-            # False/False: erro no treino
-            # True/False: erro ao salvar (MLflow)
-            # False/True: estado inconsistente, tratar como erro de treino
-            # True/True: não deveria cair aqui; tratar como erro genérico de treino
-            status = ExperimentStatus.TRAINING_ERROR
-
-            if isinstance(e, ModelTrainingError) and (e.model_trained and not e.model_saved):
-                status = ExperimentStatus.TRACKING_SEND_ERROR
 
             try:
                 await self._update_experiment_run(
                     metadata=metadata,
                     experiment_run_id=experiment_run_id,
                     update_type=UpdateType.STATUS_WITH_ERROR,
-                    status=status,
+                    status=ExperimentStatus.TRAINING_ERROR,
                     error_message=self._extract_error_message(e),
                 )
             except Exception:
@@ -302,54 +290,27 @@ class TrainModel:
 
     async def _cleanup_resources(
         self,
-        experiment_run_id: int,
-        bucket_name: str,
-        file_name: str,
-        val_file_name: str | None,
+        run_dir: str,
         metadata: dict[str, Any],
     ) -> None:
         """
         Cleanup resources.
 
-        This method deletes the training file from MinIO. On success, updates
-        DB status to FILE_DELETED.
-        On error, updates DB status to FILE_DELETE_ERROR.
+        This method removes the temporary run directory via activity.
 
         Args:
-            experiment_run_id: Validated experiment run ID
+            run_dir: Temporary directory to remove
             metadata: Workflow execution metadata
         """
-        try:
-            await workflow.execute_activity_method(
-                Activities.cleanup_resources,
-                {
-                    **metadata,
-                    'bucket_name': bucket_name,
-                    'file_name': file_name,
-                    'val_file_name': val_file_name,
-                },
-                retry_policy=network_retry_policy,
-                start_to_close_timeout=timedelta(seconds=TIMEOUT_DELETE_FILE),
-            )
-
-            await self._update_experiment_run(
-                metadata=metadata,
-                experiment_run_id=experiment_run_id,
-                update_type=UpdateType.STATUS,
-                status=ExperimentStatus.FILE_DELETED,
-            )
-        except Exception as e:
-            try:
-                await self._update_experiment_run(
-                    metadata=metadata,
-                    experiment_run_id=experiment_run_id,
-                    update_type=UpdateType.STATUS_WITH_ERROR,
-                    status=ExperimentStatus.FILE_DELETE_ERROR,
-                    error_message=self._extract_error_message(e),
-                )
-            except Exception:
-                pass
-            raise
+        await workflow.execute_activity_method(
+            Activities.cleanup_resources,
+            {
+                **metadata,
+                'run_dir': run_dir,
+            },
+            retry_policy=network_retry_policy,
+            start_to_close_timeout=timedelta(seconds=TIMEOUT_DELETE_FILE),
+        )
 
     async def _update_experiment_run(
         self,