SIENTIAPDE-1243: Refactor and enhance model manager activities and workflows

This commit includes several changes:

- Reorganized imports and class inheritance in activities.py, gates.py and mlflow.py for better readability and maintainability.
- Improved error handling and logging in gates.py and mlflow.py.
- Added input validation and filtering in gates.py to ensure data quality.
- Enhanced prediction formatting and storage policy management in gates.py.
- Updated metrics.py to use consistent naming conventions and labels.
- Refactored connectors_config.py to use type hints and improve code clarity.
- Updated conditional and MLFlow filters for better data quality checks.
- Improved model repository logic for retraining and updating models.
- Enhanced worker.py to include SDK metrics and improved error handling.
- Refactored workflows for better modularity and error handling.
- Updated tests to reflect the changes and improve test coverage.

model_manager/metrics.py

@@ -22,36 +22,36 @@ Metric Labels:
 - pipeline_name: Name of the prediction pipeline
 """
 
-from prometheus_client import Gauge, Counter, Histogram
+from prometheus_client import Counter, Gauge, Histogram
 
 # Application health metric
 APP_UP = Gauge(
-    "app_up",
-    "Indicates if the application is running (1) or shutting down (0)",
-    ["pod_id"],
+    'app_up',
+    'Indicates if the application is running (1) or shutting down (0)',
+    ['pod_id'],
 )
 
 # Core labels used across multiple metrics
-CORE_LABELS = ["pod_id", "model_name", "pipeline_name"]
+CORE_LABELS = ['pod_id', 'model_name', 'pipeline_name']
 
 # Prediction operation metrics
 PREDICTIONS_WRITTEN_COUNT = Counter(
-    "model_manager_predictions_written_count",
-    "Number of predictions written to the database table predictions",
+    'model_manager_predictions_written_count',
+    'Number of predictions written to the database table predictions',
     CORE_LABELS,
 )
 
 # Prediction quality metrics
 PREDICTION_CONFIDENCE_MONITOR = Gauge(
-    "model_manager_prediction_confidence_monitor",
-    "Current confidence of each prediction",
+    'model_manager_prediction_confidence_monitor',
+    'Current confidence of each prediction',
     CORE_LABELS,
 )
 
 # Performance monitoring metrics
 PREDICTION_RESPONSE_TIME_MONITOR = Histogram(
-    "model_manager_prediction_response_time_monitor",
-    "Current response time of each prediction",
+    'model_manager_prediction_response_time_monitor',
+    'Current response time of each prediction',
     CORE_LABELS,
-    buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0]
+    buckets=[0.01, 0.05, 0.1, 0.2, 0.5, 1.0, 2.0, 5.0, 10.0],
 )