refactor(weights): update strongest model ID and adjust queue concurrency limit

agents/ensemble_weights.py

@@ -38,8 +38,6 @@ class ContextualWeightOverrideAgent:
         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()
@@ -49,8 +47,8 @@ class ModelWeightManager:
         if num_models > 0:
             if strongest_model_id and strongest_model_id in MODEL_REGISTRY:
                 agent_logger.log("weight_optimization", "info", f"Designating '{strongest_model_id}' as the strongest model.")
-                # Assign a high weight to the strongest model (e.g., 50%)
-                strongest_weight_share = 0.5
+                # Assign a high weight to the strongest model (e.g., 40%)
+                strongest_weight_share = 0.4
                 self.base_weights = {strongest_model_id: strongest_weight_share}
                 remaining_models = [mid for mid in MODEL_REGISTRY.keys() if mid != strongest_model_id]
                 if remaining_models:
@@ -126,7 +124,7 @@ class ModelWeightManager:
         """Check if models agree on prediction"""
         agent_logger.log("weight_optimization", "info", "Checking for consensus among model predictions.")
         non_none_predictions = [p.get("Label") for p in predictions.values() if p is not None and isinstance(p, dict) and p.get("Label") is not None and p.get("Label") != "Error"]
-        agent_logger.log("weight_optimization", "debug", f"Non-none predictions for consensus check: {non_none_predictions}")
+        agent_logger.debug("weight_optimization", "info", f"Non-none predictions for consensus check: {non_none_predictions}")
         result = len(non_none_predictions) > 0 and len(set(non_none_predictions)) == 1
         agent_logger.log("weight_optimization", "info", f"Consensus detected: {result}")
         return result
@@ -135,7 +133,7 @@ class ModelWeightManager:
         """Check if models have conflicting predictions"""
         agent_logger.log("weight_optimization", "info", "Checking for conflicts among model predictions.")
         non_none_predictions = [p.get("Label") for p in predictions.values() if p is not None and isinstance(p, dict) and p.get("Label") is not None and p.get("Label") != "Error"]
-        agent_logger.log("weight_optimization", "debug", f"Non-none predictions for conflict check: {non_none_predictions}")
+        agent_logger.debug("weight_optimization", "info", f"Non-none predictions for conflict check: {non_none_predictions}")
         result = len(non_none_predictions) > 1 and len(set(non_none_predictions)) > 1
         agent_logger.log("weight_optimization", "info", f"Conflicts detected: {result}")
         return result
@@ -154,4 +152,4 @@ class ModelWeightManager:
                 return {} # No models registered
         normalized = {k: v/total for k, v in weights.items()}
         agent_logger.log("weight_optimization", "info", f"Weights normalized. Total sum: {sum(normalized.values()):.2f}")
-        return normalized
+        return normalized 

app.py

@@ -188,7 +188,7 @@ def full_prediction(img, confidence_threshold, rotate_degrees, noise_level, shar
         img = img.convert('RGB')
 
     monitor_agent = EnsembleMonitorAgent()
-    weight_manager = ModelWeightManager(strongest_model_id="simple_prediction")
+    weight_manager = ModelWeightManager(strongest_model_id="model_8")
     optimization_agent = WeightOptimizationAgent(weight_manager)
     health_agent = SystemHealthAgent()
     context_agent = ContextualIntelligenceAgent()
@@ -679,4 +679,4 @@ with gr.Blocks() as app:
     footer.render()
     
 
-app.queue(max_size=10, default_concurrency_limit=2).launch(mcp_server=True)
+app.queue(max_size=10, default_concurrency_limit=1).launch(mcp_server=True)