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/utils/models/train_model_result.py

@@ -1,4 +1,5 @@
 from dataclasses import dataclass
+from typing import Any
 
 import pandas as pd
 
@@ -50,3 +51,9 @@ class TrainModelResult:
     test_data_path: str | None = None
 
     run_dir: str | None = None
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Convert TrainModelResult to a dictionary.
+        """
+        return self.__dict__

model_manager/utils/repository/data_manager_repository.py

@@ -15,8 +15,10 @@ and this repository focuses solely on preparing data structures for them.
 
 from datetime import datetime
 from io import BytesIO
+import json
 from os import makedirs, path
 from typing import Any
+from shutil import rmtree
 
 import numpy as np
 import pandas as pd
@@ -433,3 +435,24 @@ class DataManagerRepository(SientiaMonitoring):
                 json.dump(data.equation, f, indent=2, ensure_ascii=False)
 
         return data
+
+    def cleanup_run_directory(self, run_dir: str, metadata: dict[str, Any] | None = None) -> None:
+        """
+        Clean up temporary run directory after model training.
+
+        This activity deletes the temporary directory created during model training
+        and artifact generation. It implements idempotent cleanup to handle cases
+        where the directory may have already been deleted.
+
+        Args:
+            run_dir (str): Path to the run directory to delete
+        """
+        if not run_dir:
+            self.info('No run directory specified, skipping cleanup')
+            return
+
+        if path.exists(run_dir):
+            rmtree(run_dir)
+            self.info(f'Run directory deleted successfully: {run_dir}')
+        else:
+            self.info(f'Run directory already deleted: {run_dir}')