refactor(logging): replace logger.info with agent_logger.log for weight optimization messages

agents/ensemble_weights.py

@@ -3,11 +3,13 @@ import torch
 from utils.registry import MODEL_REGISTRY # Import MODEL_REGISTRY
 from utils.agent_logger import AgentLogger
 
+
 logger = logging.getLogger(__name__)
 
 class ContextualWeightOverrideAgent:
     def __init__(self):
-        logger.info("Initializing ContextualWeightOverrideAgent.")
+        agent_logger = AgentLogger()
+        agent_logger.log("weight_optimization", "info", "Initializing ContextualWeightOverrideAgent.")
         self.context_overrides = {
             # Example: when image is outdoor, model_X is penalized, model_Y is boosted
             "outdoor": {
@@ -26,7 +28,7 @@ class ContextualWeightOverrideAgent:
         }
 
     def get_overrides(self, context_tags: list[str]) -> dict:
-        logger.info(f"Getting weight overrides for context tags: {context_tags}")
+        agent_logger.log("weight_optimization", "info", f"Getting weight overrides for context tags: {context_tags}")
         combined_overrides = {}
         for tag in context_tags:
             if tag in self.context_overrides:
@@ -34,10 +36,11 @@ class ContextualWeightOverrideAgent:
                     # If a model appears in multiple contexts, we can decide how to combine (e.g., multiply, average, take max)
                     # For now, let's just take the last one if there are conflicts, or multiply for simple cumulative effect.
                     combined_overrides[model_id] = combined_overrides.get(model_id, 1.0) * multiplier
-        logger.info(f"Combined context overrides: {combined_overrides}")
+        agent_logger.log("weight_optimization", "info", f"Combined context overrides: {combined_overrides}")
         return combined_overrides
 
 
+
 class ModelWeightManager:
     def __init__(self, strongest_model_id: str = None):
         agent_logger = AgentLogger()