feat: integrate PluginStore and MinIO repository into model manager activities

- Added PluginStore integration for model management.
- Replaced StorageRepository with MinIORepository in Activities, Cleanup, and Training classes.
- Updated training logic to handle validation files and improved data management.
- Enhanced configuration for MinIO and PluginStore in connectors.
- Removed deprecated model repository and storage repository files.
- Updated environment variable handling for new configurations.

model_manager/utils/models/train_model_result.py

@@ -2,7 +2,6 @@ from dataclasses import dataclass
 
 import pandas as pd
 
-from model_manager.sientia.models import DataPreprocessor, LinearRegressionModel
 from model_manager.utils.models.train_model_params import TrainModelParams
 
 
@@ -12,18 +11,15 @@ class TrainModelResult:
     A data container for storing the results of a machine learning training process.
 
     This dataclass encapsulates all outputs from the training pipeline, including
-    the trained model, datasets, evaluation metrics, and paths to generated artifacts.
+    the prepared datasets, evaluation metrics, and paths to generated artifacts.
     It is used to pass results between activities in the training workflow.
 
     Attributes:
         params (TrainModelParams): The parameters used to train the model.
-        process_data (DataPreprocessor): The data preprocessor object used to process the input data.
         x_train (pd.DataFrame): The training dataset features.
         x_test (pd.DataFrame): The testing dataset features.
         y_train (pd.DataFrame): The training dataset target values.
         y_test (pd.DataFrame): The testing dataset target values.
-        regr (LinearRegressionModel): The trained linear regression model.
-        scaler_dict (dict): A dictionary containing the scalers used to scale the features and target values.
         y_pred (pd.Series | None): The predicted target values for the testing dataset. Default is None.
         y_train_pred (pd.Series | None): The predicted target values for the training dataset. Default is None.
         mse_val (float | None): The Mean Squared Error (MSE) of the predictions. Default is None.
@@ -35,17 +31,13 @@ class TrainModelResult:
         report_path (str | None): The path to the generated HTML report file. Default is None.
         train_data_path (str | None): The path to the training dataset CSV file. Default is None.
         test_data_path (str | None): The path to the testing dataset CSV file. Default is None.
-        run_dir (str | None): The path to the run directory containing all artifacts. Default is None.
     """
 
     params: TrainModelParams
-    process_data: DataPreprocessor
     x_train: pd.DataFrame
     x_test: pd.DataFrame
     y_train: pd.Series
     y_test: pd.Series
-    regr: LinearRegressionModel
-    scaler_dict: dict
     y_pred: pd.Series | None = None
     y_train_pred: pd.Series | None = None
     mse_val: float | None = None
@@ -57,4 +49,3 @@ class TrainModelResult:
     report_path: str | None = None
     train_data_path: str | None = None
     test_data_path: str | None = None
-    run_dir: str | None = None