anotation system operational

ANNOTATE/core/training_simulator.py

@@ -62,16 +62,56 @@ class TrainingSimulator:
     def load_model(self, model_name: str):
         """Load model from orchestrator"""
         if model_name in self.model_cache:
+            logger.info(f"Using cached model: {model_name}")
             return self.model_cache[model_name]
         
         if not self.orchestrator:
             logger.error("Orchestrator not available")
             return None
         
-        # Get model from orchestrator
-        # This will be implemented when we integrate with actual models
-        logger.info(f"Loading model: {model_name}")
-        return None
+        try:
+            # Get model from orchestrator based on name
+            model = None
+            
+            if model_name == "StandardizedCNN" or model_name == "CNN":
+                model = self.orchestrator.cnn_model
+            elif model_name == "DQN":
+                model = self.orchestrator.rl_agent
+            elif model_name == "Transformer":
+                model = self.orchestrator.primary_transformer
+            elif model_name == "COB":
+                model = self.orchestrator.cob_rl_agent
+            
+            if model:
+                self.model_cache[model_name] = model
+                logger.info(f"Loaded model: {model_name}")
+                return model
+            else:
+                logger.warning(f"Model not found: {model_name}")
+                return None
+                
+        except Exception as e:
+            logger.error(f"Error loading model {model_name}: {e}")
+            return None
+    
+    def get_available_models(self) -> List[str]:
+        """Get list of available models from orchestrator"""
+        if not self.orchestrator:
+            return []
+        
+        available = []
+        
+        if self.orchestrator.cnn_model:
+            available.append("StandardizedCNN")
+        if self.orchestrator.rl_agent:
+            available.append("DQN")
+        if self.orchestrator.primary_transformer:
+            available.append("Transformer")
+        if self.orchestrator.cob_rl_agent:
+            available.append("COB")
+        
+        logger.info(f"Available models: {available}")
+        return available
     
     def start_training(self, model_name: str, test_cases: List[Dict]) -> str:
         """Start training session with test cases"""