feat: enhance training model functionality and reporting

- Added `evidently` to requirements for improved model evaluation.
- Introduced `TrainModelResult` class with a `to_dict` method for better result handling.
- Updated `train_model` method to return a comprehensive training result, including run details.
- Enhanced `cleanup_run_directory` method in `DataManagerRepository` for improved resource management.
- Adjusted type hints in `TrainModel` for clarity and consistency.

model_manager/workflows/train_model.py

@@ -71,7 +71,7 @@ class TrainModel:
     """
 
     @workflow.run
-    async def run(self, input_data: dict[str, Any]) -> dict[str, str | None]:
+    async def run(self, input_data: dict[str, Any]) -> dict[str, str | None] | None:
         """
         Execute the complete model training workflow.
 
@@ -112,7 +112,7 @@ class TrainModel:
         )
 
         training_succeeded = False
-        train_result: dict[str, str | None]
+        train_result: dict[str, str | None] | None = None
 
         try:
             train_result = await self._train_model(
@@ -123,10 +123,11 @@ class TrainModel:
             training_succeeded = True
         finally:
             try:
-                await self._cleanup_resources(
-                    run_dir=train_result.get('run_dir'),
-                    metadata=metadata,
-                )
+                if train_result is not None:
+                    await self._cleanup_resources(
+                        run_dir=train_result.get('run_dir'),
+                        metadata=metadata,
+                    )
             except Exception:
                 if training_succeeded:
                     raise
@@ -290,7 +291,7 @@ class TrainModel:
 
     async def _cleanup_resources(
         self,
-        run_dir: str,
+        run_dir: str | None,
         metadata: dict[str, Any],
     ) -> None:
         """
@@ -302,6 +303,9 @@ class TrainModel:
             run_dir: Temporary directory to remove
             metadata: Workflow execution metadata
         """
+        if run_dir is None:
+            return
+
         await workflow.execute_activity_method(
             Activities.cleanup_resources,
             {