SIENTIAPDE-1273

Refactor data handling in various modules to ensure DataFrame consistency

- Replaced direct DataFrame instantiation with `ensure_dataframe` utility in Gates, MLFlow, OPC, and ModelMetrics classes to standardize data handling.
- Updated return types in several asynchronous methods to return DataFrames instead of dictionaries for improved usability.
- Adjusted data export processes in workflows to convert DataFrames to dictionaries with `to_dict(orient='records')` for compatibility with downstream systems.

laborious/worker/worker.py

@@ -48,6 +48,7 @@ with workflow.unsafe.imports_passed_through():
         build_opc_config,
         build_postgres_config,
     )
+    from laborious.utils.temporal_codec import create_dataframe_data_converter
     from laborious.workflows.minimal_retrain import MinimalRetrain
     from laborious.workflows.predictions_batch import PredictionsBatch
     from laborious.workflows.drift import Drift
@@ -132,10 +133,14 @@ async def main():
 
     logger.custom_info(f'Starting Temporal Client at {host}...', metadata)
 
+    # Create custom data converter with DataFrame support
+    data_converter = create_dataframe_data_converter()
+
     temporal_client = await client.Client.connect(
         target_host=host,
         namespace=os.getenv('TEMPORAL_NAMESPACE', 'laborious'),
         runtime=new_runtime,
+        data_converter=data_converter,
     )
 
     logger.custom_info('Starting Workers...', metadata)