SIENTIAPDE-1248: Integrate MinIO for object storage and add related configurations

This commit introduces MinIO integration for object storage within the Model Manager system. It includes:

- Added MinIO activity class for file operations (fetch, delete).
- Updated Activities orchestrator to include MinIO activities.
- Added MinIO configuration builder to utils/connectors_config.py.
- Added environment variables for MinIO configuration in .env.example.
- Added boto3 and botocore dependencies to requirements.txt.
- Added unit tests for MinIO activities.

tests/laborious/utils/repository/test_model_repository.py

@@ -303,13 +303,22 @@ def test_create_model_experiment(set_experiment, sklearn, mlflow_repository):
     )
 
 
+@patch('model_manager.utils.repository.model_repository.path.exists')
+@patch('model_manager.utils.repository.model_repository.remove')
 @patch('model_manager.utils.repository.model_repository.mlflow.start_run')
 @patch('model_manager.utils.repository.model_repository.mlflow.log_param')
 @patch('model_manager.utils.repository.model_repository.mlflow.sklearn.log_model')
 @patch('model_manager.utils.repository.model_repository.mlflow.log_artifact')
-def test_perform_model_retrain(log_artifact, log_model, log_param, start_run, mlflow_repository):
+def test_perform_model_retrain(
+    log_artifact, log_model, log_param, start_run, mock_remove, mock_path_exists, mlflow_repository
+):
+    # Create mock models with attributes to test the for loops (lines 268-274)
     prediction_model_mock = MagicMock()
+    prediction_model_mock.__dict__ = {'model': 'pred_model', 'param1': 'value1', 'param2': 'value2'}
+
     data_model_mock = MagicMock()
+    data_model_mock.__dict__ = {'model': 'data_model', 'param3': 'value3', 'param4': 'value4'}
+
     experiment = 'test'
     model_name = 'test'
     data = MagicMock()
@@ -317,6 +326,7 @@ def test_perform_model_retrain(log_artifact, log_model, log_param, start_run, ml
     mlflow_repository.get_next_run_name = MagicMock(return_value='test-1')
     run = MagicMock()
     start_run.__enter__.return_value = run
+    mock_path_exists.return_value = True
 
     output = mlflow_repository.perform_model_retrain(
         prediction_model_mock, data_model_mock, experiment, model_name, data
@@ -338,12 +348,58 @@ def test_perform_model_retrain(log_artifact, log_model, log_param, start_run, ml
 
     log_artifact.assert_called_once_with('temp/raw_data_test.csv')
 
+    # Verify that model attributes were logged (excluding 'model' key)
     log_param.assert_has_calls(
         [
+            call('param1', 'value1'),  # from prediction_model
+            call('param2', 'value2'),  # from prediction_model
+            call('param3', 'value3'),  # from data_model
+            call('param4', 'value4'),  # from data_model
             call('retrain', True),
-        ]
+        ],
+        any_order=True,
     )
 
+    # Verify temp file cleanup
+    mock_path_exists.assert_called_once_with('temp/raw_data_test.csv')
+    mock_remove.assert_called_once_with('temp/raw_data_test.csv')
+
+    assert output == ('Model retrained successfully', experiment)
+
+
+@patch('model_manager.utils.repository.model_repository.path.exists')
+@patch('model_manager.utils.repository.model_repository.remove')
+@patch('model_manager.utils.repository.model_repository.mlflow.start_run')
+@patch('model_manager.utils.repository.model_repository.mlflow.log_param')
+@patch('model_manager.utils.repository.model_repository.mlflow.sklearn.log_model')
+@patch('model_manager.utils.repository.model_repository.mlflow.log_artifact')
+def test_perform_model_retrain_file_not_exists(
+    log_artifact, log_model, log_param, start_run, mock_remove, mock_path_exists, mlflow_repository
+):
+    """Test perform_model_retrain when temp file doesn't exist (line 291->294 branch)."""
+    prediction_model_mock = MagicMock()
+    prediction_model_mock.__dict__ = {'model': 'pred_model'}
+
+    data_model_mock = MagicMock()
+    data_model_mock.__dict__ = {'model': 'data_model'}
+
+    experiment = 'test'
+    model_name = 'test'
+    data = MagicMock()
+
+    mlflow_repository.get_next_run_name = MagicMock(return_value='test-1')
+    run = MagicMock()
+    start_run.__enter__.return_value = run
+    mock_path_exists.return_value = False  # File doesn't exist
+
+    output = mlflow_repository.perform_model_retrain(
+        prediction_model_mock, data_model_mock, experiment, model_name, data
+    )
+
+    # Verify temp file cleanup was checked but not executed
+    mock_path_exists.assert_called_once_with('temp/raw_data_test.csv')
+    mock_remove.assert_not_called()  # Should not be called when file doesn't exist
+
     assert output == ('Model retrained successfully', experiment)