SIENTIAPDE-1243: Remove OPC server integration and add code quality tools.

This commit removes the OPC server integration from the Model Manager, including related activities, repositories, metrics, and configuration. It also adds code quality tools such as Ruff (linting/formatting), mypy (type checking), and Bandit (security analysis) along with a validation script and CI/CD integration for automated code validation. The README has been updated to reflect these changes.

model_manager/workflows/predictions_batch.py

@@ -58,7 +58,7 @@ class PredictionsBatch():
                     - mlflow_predict_filters (dict, optional): MLFlow prediction filters
                     - model_retention (int, optional): Model retention period in minutes
                     - path_priority (list[str]): Decision path priority configuration
-                    - opc_output_config (dict, optional): OPC server export configuration
+
                     - datetime_columns (list[str], optional): Columns to treat as datetime
 
         Returns:
@@ -115,7 +115,7 @@ class PredictionsBatch():
             }),
             'model_config': input_data.get('model_config', {}),
             'path_priority': input_data.get('path_priority', ['STOP', 'CONTINUE', 'REPEAT']),
-            'opc_output_config': input_data.get('opc_output_config', {}),
+
             'prediction_store_policy': input_data.get(
                 'prediction_store_policy', 'lts:1')
         }

model_manager/workflows/sub_workflows/format_and_export_prediction.py

@@ -14,9 +14,9 @@ class FormatAndExportPrediction():
     Data formatting and export workflow for prediction results.
 
     This workflow handles the final stages of the prediction pipeline, including
-    data formatting, database persistence, OPC server export, and metrics recording.
+    data formatting, database persistence, and metrics recording.
     It implements flexible formatting based on prediction quality and provides
-    comprehensive export capabilities to multiple destinations.
+    comprehensive export capabilities.
 
     The workflow supports two main prediction paths:
     1. Normal Prediction: Formats and exports successful prediction results
@@ -24,7 +24,6 @@ class FormatAndExportPrediction():
 
     Export Destinations:
     - PostgreSQL Database: Persistent storage with timestamp conversion
-    - OPC Servers: Real-time industrial system integration
     - Prometheus Metrics: Performance monitoring and operational visibility
     """
 
@@ -36,14 +35,12 @@ class FormatAndExportPrediction():
         This method orchestrates the complete data export process by:
         1. Determining the appropriate formatting strategy based on path_flag
         2. Formatting prediction data according to quality and requirements
-        3. Exporting data to OPC servers for real-time industrial access
-        4. Persisting data to PostgreSQL database with comprehensive metadata
-        5. Recording performance metrics for operational monitoring
+        3. Persisting data to PostgreSQL database with comprehensive metadata
+        4. Recording performance metrics for operational monitoring
 
         The method implements flexible formatting strategies:
         - Normal predictions: Full data formatting with confidence scores
         - Error predictions: Default formatting with error indicators
-        - Comprehensive export: Multi-destination data distribution
 
         Args:
             input_data: Complete configuration for the export workflow
@@ -58,7 +55,6 @@ class FormatAndExportPrediction():
                     - comment (str): Operational comment or error description
                     - schema (str): Database schema for data storage
                     - table_name (str): Target table for data persistence
-                    - opc_output_config (dict[str, Any]): OPC server export configuration
                     - prediction_store_policy (str, optional): Data retention policy
 
         Returns:
@@ -100,18 +96,6 @@ class FormatAndExportPrediction():
                 start_to_close_timeout=timedelta(seconds=60)
             )
 
-        # write to opc
-        prediction = await workflow.execute_activity_method(
-            Activities.write_opc_data,
-            {
-                **metadata,
-                'opc_output_config': input_data['opc_output_config'],
-                'data': prediction
-            },
-            retry_policy=retry_policy,
-            start_to_close_timeout=timedelta(seconds=60)
-        )
-
         # write to postgres
         await workflow.execute_activity_method(
             Activities.export_data_to_postgres,

model_manager/workflows/sub_workflows/prediction_process.py

@@ -64,7 +64,7 @@ class PredictionProcess():
                     - mlflow_predict_filters (dict): MLFlow prediction filters
                     - model_retention (int): Model retention period in minutes
                     - path_priority (list[str]): Decision path priority configuration
-                    - opc_output_config (dict): OPC server export configuration
+
 
         Returns:
             None: The workflow completes successfully when export workflow finishes
@@ -210,7 +210,6 @@ class PredictionProcess():
                 'model_id': model_id,
                 'model_name': model_name,
                 'model_config': model_config,
-                'opc_output_config': input_data['opc_output_config'],
                 'schema': input_data['schema'],
                 'table_name': input_data['table_name'],
                 'comment': comment,
@@ -287,7 +286,6 @@ class PredictionProcess():
                     'schema': schema,
                     'table_name': table_name,
                     'comment': comment,
-                    'opc_output_config': input_data['opc_output_config'],
                     'prediction_store_policy': input_data['prediction_store_policy']
                 }
             )