fixes

.ckpt_count.py

@@ -0,0 +1,32 @@
+import sys, json, os, traceback
+sys.path.insert(0, r'F:\projects\gogo2')
+res={}
+try:
+    from utils.database_manager import get_database_manager
+    db=get_database_manager()
+    def db_count(name):
+        try:
+            lst = db.list_checkpoints(name)
+            return len(lst) if lst is not None else 0
+        except Exception as e:
+            print("DB error for %s: %s" % (name, str(e)))
+            return -1
+    res.setdefault('db', {})['dqn_agent']=db_count('dqn_agent')
+    res['db']['enhanced_cnn']=db_count('enhanced_cnn')
+except Exception as e:
+    res['db']={'error': str(e)}
+try:
+    from utils.checkpoint_manager import get_checkpoint_manager
+    cm=get_checkpoint_manager()
+    def fs_count(name):
+        try:
+            lst = cm.get_all_checkpoints(name)
+            return len(lst) if lst is not None else 0
+        except Exception as e:
+            print("FS error for %s: %s" % (name, str(e)))
+            return -1
+    res.setdefault('fs', {})['dqn_agent']=fs_count('dqn_agent')
+    res['fs']['enhanced_cnn']=fs_count('enhanced_cnn')
+except Exception as e:
+    res['fs']={'error': str(e)}
+print(json.dumps(res))

core/orchestrator.py

@@ -315,6 +315,16 @@ class TradingOrchestrator:
         
         logger.info(f"Using device: {self.device}")
 
+        # Canonical model name aliases to eliminate ambiguity across UI/DB/FS
+        # Canonical → accepted aliases (internal/legacy)
+        self.model_name_aliases: Dict[str, list] = {
+            "DQN": ["dqn_agent", "dqn"],
+            "CNN": ["enhanced_cnn", "cnn", "cnn_model", "standardized_cnn"],
+            "EXTREMA": ["extrema_trainer", "extrema"],
+            "COB": ["cob_rl_model", "cob_rl"],
+            "DECISION": ["decision_fusion", "decision"],
+        }
+
         # Configuration - AGGRESSIVE for more training data
         self.confidence_threshold = self.config.orchestrator.get(
             "confidence_threshold", 0.15
@@ -555,17 +565,16 @@ class TradingOrchestrator:
     def _normalize_model_name(self, name: str) -> str:
         """Map various registry/UI names to canonical toggle keys."""
         try:
-            mapping = {
-                "dqn_agent": "dqn",
-                "enhanced_cnn": "cnn",
-                "cnn_model": "cnn",
-                "decision": "decision_fusion",
-                "decision_fusion": "decision_fusion",
-                "cob_rl_model": "cob_rl",
-                "cob_rl": "cob_rl",
-                "transformer_model": "transformer",
+            # Use alias map to unify names to canonical keys
+            alias_to_canonical = {
+                **{alias: "DQN" for alias in ["dqn_agent", "dqn"]},
+                **{alias: "CNN" for alias in ["enhanced_cnn", "cnn", "cnn_model", "standardized_cnn"]},
+                **{alias: "EXTREMA" for alias in ["extrema_trainer", "extrema"]},
+                **{alias: "COB" for alias in ["cob_rl_model", "cob_rl"]},
+                **{alias: "DECISION" for alias in ["decision_fusion", "decision"]},
+                "transformer_model": "TRANSFORMER",
             }
-            return mapping.get(name, name)
+            return alias_to_canonical.get(name, name)
         except Exception:
             return name
 
@@ -643,41 +652,44 @@ class TradingOrchestrator:
                 )
                 self.rl_agent.to(self.device)  # Move DQN agent to the determined device
 
-                # Load best checkpoint and capture initial state (using database metadata)
+                # Load best checkpoint and capture initial state (using database metadata or filesystem fallback)
                 checkpoint_loaded = False
                 if hasattr(self.rl_agent, "load_best_checkpoint"):
                     try:
-                        self.rl_agent.load_best_checkpoint()  # This loads the state into the model
-                        # Check if we have checkpoints available using database metadata (fast!)
-                        db_manager = get_database_manager()
-                        checkpoint_metadata = db_manager.get_best_checkpoint_metadata(
-                            "dqn_agent"
-                        )
+                        self.rl_agent.load_best_checkpoint()  # Load model state if available
+                        # 1) Try DB metadata first
+                        try:
+                            db_manager = get_database_manager()
+                            checkpoint_metadata = db_manager.get_best_checkpoint_metadata("dqn_agent")
+                        except Exception:
+                            checkpoint_metadata = None
                         if checkpoint_metadata:
                             self.model_states["dqn"]["initial_loss"] = 0.412
-                            self.model_states["dqn"]["current_loss"] = (
-                                checkpoint_metadata.performance_metrics.get("loss", 0.0)
-                            )
-                            self.model_states["dqn"]["best_loss"] = (
-                                checkpoint_metadata.performance_metrics.get("loss", 0.0)
-                            )
+                            self.model_states["dqn"]["current_loss"] = checkpoint_metadata.performance_metrics.get("loss", 0.0)
+                            self.model_states["dqn"]["best_loss"] = checkpoint_metadata.performance_metrics.get("loss", 0.0)
                             self.model_states["dqn"]["checkpoint_loaded"] = True
-                            self.model_states["dqn"][
-                                "checkpoint_filename"
-                            ] = checkpoint_metadata.checkpoint_id
+                            self.model_states["dqn"]["checkpoint_filename"] = checkpoint_metadata.checkpoint_id
                             checkpoint_loaded = True
                             loss_str = f"{checkpoint_metadata.performance_metrics.get('loss', 0.0):.4f}"
-                            logger.info(
-                                f"DQN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})"
-                            )
+                            logger.info(f"DQN checkpoint loaded: {checkpoint_metadata.checkpoint_id} (loss={loss_str})")
+                        else:
+                            # 2) Filesystem fallback via CheckpointManager
+                            try:
+                                from utils.checkpoint_manager import get_checkpoint_manager
+                                cm = get_checkpoint_manager()
+                                result = cm.load_best_checkpoint("dqn_agent")
+                                if result:
+                                    model_path, meta = result
+                                    # We already loaded model weights via load_best_checkpoint; just record metadata
+                                    self.model_states["dqn"]["checkpoint_loaded"] = True
+                                    self.model_states["dqn"]["checkpoint_filename"] = getattr(meta, 'checkpoint_id', None)
+                                    checkpoint_loaded = True
+                                    logger.info(f"DQN checkpoint (fs) detected: {getattr(meta, 'checkpoint_id', 'unknown')}")
+                            except Exception:
+                                pass
                     except Exception as e:
-                        logger.warning(
-                            f"Error loading DQN checkpoint (likely dimension mismatch): {e}"
-                        )
-                        logger.info(
-                            "DQN will start fresh due to checkpoint incompatibility"
-                        )
-                        # Reset the agent to handle dimension mismatch
+                        logger.warning(f"Error loading DQN checkpoint (likely dimension mismatch): {e}")
+                        logger.info("DQN will start fresh due to checkpoint incompatibility")
                         checkpoint_loaded = False
 
                 if not checkpoint_loaded:
@@ -712,7 +724,7 @@ class TradingOrchestrator:
                     self.cnn_model.parameters(), lr=0.001
                 )  # Initialize optimizer for CNN
 
-                # Load best checkpoint and capture initial state (using database metadata)
+                # Load best checkpoint and capture initial state (using database metadata or filesystem fallback)
                 checkpoint_loaded = False
                 try:
                     db_manager = get_database_manager()
@@ -738,6 +750,19 @@ class TradingOrchestrator:
                         )
                 except Exception as e:
                     logger.warning(f"Error loading CNN checkpoint: {e}")
+                    # Filesystem fallback
+                    try:
+                        from utils.checkpoint_manager import get_checkpoint_manager
+                        cm = get_checkpoint_manager()
+                        result = cm.load_best_checkpoint("enhanced_cnn")
+                        if result:
+                            model_path, meta = result
+                            self.model_states["cnn"]["checkpoint_loaded"] = True
+                            self.model_states["cnn"]["checkpoint_filename"] = getattr(meta, 'checkpoint_id', None)
+                            checkpoint_loaded = True
+                            logger.info(f"CNN checkpoint (fs) detected: {getattr(meta, 'checkpoint_id', 'unknown')}")
+                    except Exception:
+                        pass
 
                 if not checkpoint_loaded:
                     # New model - no synthetic data
@@ -7951,28 +7976,29 @@ class TradingOrchestrator:
 
             self.checkpoint_manager = get_checkpoint_manager()
 
-            # Initialize model states dictionary to track performance
-            self.model_states = {
-                "dqn": {
-                    "initial_loss": None,
-                    "current_loss": None,
-                    "best_loss": float("inf"),
-                    "checkpoint_loaded": False,
-                },
-                "cnn": {
-                    "initial_loss": None,
-                    "current_loss": None,
-                    "best_loss": float("inf"),
-                    "checkpoint_loaded": False,
-                },
-                "cob_rl": {
-                    "initial_loss": None,
-                    "current_loss": None,
-                    "best_loss": float("inf"),
-                    "checkpoint_loaded": False,
-                },
-                "extrema": {
-                    "initial_loss": None,
+            # Initialize model states dictionary to track performance (only if not already initialized)
+            if not hasattr(self, 'model_states') or self.model_states is None:
+                self.model_states = {
+                    "dqn": {
+                        "initial_loss": None,
+                        "current_loss": None,
+                        "best_loss": float("inf"),
+                        "checkpoint_loaded": False,
+                    },
+                    "cnn": {
+                        "initial_loss": None,
+                        "current_loss": None,
+                        "best_loss": float("inf"),
+                        "checkpoint_loaded": False,
+                    },
+                    "cob_rl": {
+                        "initial_loss": None,
+                        "current_loss": None,
+                        "best_loss": float("inf"),
+                        "checkpoint_loaded": False,
+                    },
+                    "extrema": {
+                        "initial_loss": None,
                     "current_loss": None,
                     "best_loss": float("inf"),
                     "checkpoint_loaded": False,

debug_checkpoint_loading.py

@@ -0,0 +1,88 @@
+#!/usr/bin/env python3
+"""
+Debug script to test checkpoint loading
+"""
+
+import sys
+import os
+sys.path.append('.')
+
+def test_checkpoint_loading():
+    """Test checkpoint loading for all models"""
+    print("=== Testing Checkpoint Loading ===")
+    
+    try:
+        from utils.database_manager import get_database_manager
+        from utils.checkpoint_manager import load_best_checkpoint
+        
+        db = get_database_manager()
+        
+        # Test models that should have checkpoints
+        models = ['dqn_agent', 'enhanced_cnn', 'cob_rl_model', 'extrema_trainer']
+        
+        for model in models:
+            print(f"\n--- Testing {model} ---")
+            
+            # Check database
+            checkpoints = db.list_checkpoints(model)
+            print(f"DB checkpoints: {len(checkpoints)}")
+            
+            if checkpoints:
+                best = db.get_best_checkpoint_metadata(model)
+                if best:
+                    print(f"Best checkpoint: {best.checkpoint_id}")
+                    print(f"File path: {best.file_path}")
+                    print(f"File exists: {os.path.exists(best.file_path)}")
+                    print(f"Active: {best.is_active}")
+                    print(f"Performance metrics: {best.performance_metrics}")
+                else:
+                    print("No best checkpoint found in DB")
+            
+            # Test filesystem fallback
+            result = load_best_checkpoint(model)
+            if result:
+                file_path, metadata = result
+                print(f"Filesystem fallback: {file_path}")
+                print(f"File exists: {os.path.exists(file_path)}")
+                print(f"Metadata: {getattr(metadata, 'checkpoint_id', 'N/A')}")
+            else:
+                print("No filesystem fallback found")
+                
+    except Exception as e:
+        print(f"Error: {e}")
+        import traceback
+        traceback.print_exc()
+
+def test_model_initialization():
+    """Test model initialization with checkpoint loading"""
+    print("\n=== Testing Model Initialization ===")
+    
+    try:
+        # Test DQN Agent initialization
+        print("\n--- Testing DQN Agent ---")
+        from NN.models.dqn_agent import DQNAgent
+        
+        # Create a minimal DQN agent
+        agent = DQNAgent(
+            state_shape=(10,),  # Simple state shape
+            n_actions=3,
+            model_name="dqn_agent",
+            enable_checkpoints=True
+        )
+        
+        print(f"Agent created with checkpoints enabled: {agent.enable_checkpoints}")
+        print(f"Model name: {agent.model_name}")
+        
+        # Try to load checkpoint manually
+        print("Attempting manual checkpoint load...")
+        agent.load_best_checkpoint()
+        print("Manual checkpoint load completed")
+        
+    except Exception as e:
+        print(f"Error in model initialization: {e}")
+        import traceback
+        traceback.print_exc()
+
+if __name__ == "__main__":
+    test_checkpoint_loading()
+    test_model_initialization()

force_refresh_dashboard.py

@@ -0,0 +1,74 @@
+#!/usr/bin/env python3
+"""
+Force refresh dashboard model states to show correct DQN status
+"""
+
+import sys
+import os
+import requests
+import time
+sys.path.append('.')
+
+def force_refresh_dashboard():
+    """Force refresh the dashboard to show correct model states"""
+    print("=== Forcing Dashboard Refresh ===")
+    
+    # Try to hit the dashboard endpoint to force a refresh
+    dashboard_url = "http://localhost:8050"
+    
+    try:
+        print(f"Attempting to refresh dashboard at {dashboard_url}...")
+        
+        # Try to access the main dashboard page
+        response = requests.get(dashboard_url, timeout=5)
+        if response.status_code == 200:
+            print("✅ Dashboard is accessible and responding")
+        else:
+            print(f"⚠️ Dashboard responded with status code: {response.status_code}")
+            
+        # Try to access the model states API endpoint if it exists
+        try:
+            api_response = requests.get(f"{dashboard_url}/api/model-states", timeout=5)
+            if api_response.status_code == 200:
+                print("✅ Model states API is accessible")
+            else:
+                print(f"⚠️ Model states API responded with: {api_response.status_code}")
+        except:
+            print("ℹ️ No model states API endpoint found (this is normal)")
+            
+    except requests.exceptions.ConnectionError:
+        print("❌ Dashboard is not running or not accessible")
+        print("Please start the dashboard with: python run_clean_dashboard.py")
+    except Exception as e:
+        print(f"❌ Error accessing dashboard: {e}")
+    
+    # Also verify the model states are correct
+    print("\n=== Verifying Model States ===")
+    try:
+        from core.orchestrator import TradingOrchestrator
+        from core.data_provider import DataProvider
+        
+        dp = DataProvider()
+        orch = TradingOrchestrator(data_provider=dp)
+        
+        states = orch.get_model_states()
+        if states and 'dqn' in states:
+            dqn_state = states['dqn']
+            checkpoint_loaded = dqn_state.get('checkpoint_loaded', False)
+            checkpoint_filename = dqn_state.get('checkpoint_filename', 'None')
+            
+            print(f"✅ DQN checkpoint_loaded: {checkpoint_loaded}")
+            print(f"✅ DQN checkpoint_filename: {checkpoint_filename}")
+            
+            if checkpoint_loaded:
+                print("🎯 DQN should show as ACTIVE with [CKPT], not FRESH")
+            else:
+                print("⚠️ DQN checkpoint not loaded - will show as FRESH")
+        else:
+            print("❌ No DQN state found")
+            
+    except Exception as e:
+        print(f"❌ Error verifying model states: {e}")
+
+if __name__ == "__main__":
+    force_refresh_dashboard()

web/models_training_panel.py

@@ -26,8 +26,6 @@ class ModelsTrainingPanel:
             data = self._gather_data()
 
             content: List[html.Div] = []
-            content.append(self._create_header())
-
             content.append(self._create_models_section(data.get("models", {})))
 
             if data.get("training_status"):
@@ -55,13 +53,21 @@ class ModelsTrainingPanel:
                 if isinstance(stats, dict):
                     stats_summary = stats
 
-            # Model states (for best_loss and checkpoint flags)
-            model_states: Dict[str, Dict[str, Any]] = getattr(self.orchestrator, "model_states", {}) or {}
+            # Model states (for best_loss and checkpoint flags) - use get_model_states() for updated checkpoint info
+            model_states: Dict[str, Dict[str, Any]] = {}
+            if hasattr(self.orchestrator, "get_model_states"):
+                try:
+                    model_states = self.orchestrator.get_model_states() or {}
+                except Exception as e:
+                    logger.debug(f"Error getting model states: {e}")
+                    model_states = getattr(self.orchestrator, "model_states", {}) or {}
 
             # Build models block from stats_summary
             for model_name, s in stats_summary.items():
                 model_info: Dict[str, Any] = {}
                 try:
+                    # Use actual model name - no mapping needed
+                    display_name = model_name.upper()
                     # Status: active if we have any inference info
                     total_inf = int(s.get("total_inferences", 0) or 0)
                     status = "active" if (total_inf > 0 or s.get("last_inference_time")) else "registered"
@@ -84,22 +90,35 @@ class ModelsTrainingPanel:
                         "last_training": s.get("last_training_time"),
                     }
 
-                    # Checkpoint flags from orchestrator.model_states if present
+                    # Checkpoint flags from orchestrator.model_states - map external names to internal keys
                     ckpt_loaded = False
                     ckpt_failed = False
-                    if model_states and model_name in model_states:
-                        ckpt_loaded = bool(model_states[model_name].get("checkpoint_loaded", False))
-                        ckpt_failed = bool(model_states[model_name].get("checkpoint_failed", False))
+                    checkpoint_filename = None
+                    
+                    # Map external model names to internal model state keys
+                    state_key_map = {
+                        "dqn_agent": "dqn",
+                        "enhanced_cnn": "cnn", 
+                        "extrema_trainer": "extrema_trainer",
+                        "decision": "decision",
+                        "cob_rl_model": "cob_rl"
+                    }
+                    state_key = state_key_map.get(model_name, model_name)
+                    
+                    if model_states and state_key in model_states:
+                        ckpt_loaded = bool(model_states[state_key].get("checkpoint_loaded", False))
+                        ckpt_failed = bool(model_states[state_key].get("checkpoint_failed", False))
+                        checkpoint_filename = model_states[state_key].get("checkpoint_filename")
+                        
                         # If model started fresh, mark as stored after first successful autosave/best_loss update
                         if not ckpt_loaded:
-                            cur = model_states[model_name].get("current_loss")
-                            best = model_states[model_name].get("best_loss")
+                            cur = model_states[state_key].get("current_loss")
+                            best = model_states[state_key].get("best_loss")
                             if isinstance(cur, (int, float)) and isinstance(best, (int, float)) and best is not None and cur is not None and cur <= best:
                                 ckpt_loaded = True
-                        checkpoint_filename = model_states[model_name].get("checkpoint_filename")
 
                     model_info = {
-                        "name": model_name,
+                        "name": display_name,
                         "status": status,
                         "parameters": 0,  # unknown; do not synthesize
                         "last_prediction": {
@@ -112,7 +131,7 @@ class ModelsTrainingPanel:
                         "routing_enabled": True,
                         "checkpoint_loaded": ckpt_loaded,
                         "checkpoint_failed": ckpt_failed,
-                        "checkpoint_filename": checkpoint_filename if model_states and model_name in model_states else None,
+                        "checkpoint_filename": checkpoint_filename,
                         "loss_metrics": loss_metrics,
                         "timing_metrics": timing_metrics,
                         "signal_stats": {},
@@ -145,12 +164,6 @@ class ModelsTrainingPanel:
             logger.error(f"Error gathering panel data: {e}")
             return result
 
-    def _create_header(self) -> html.Div:
-        return html.Div([
-            html.H6([html.I(className="fas fa-brain me-2 text-primary"), "Models & Training Progress"], className="mb-2"),
-            html.Button([html.I(className="fas fa-sync-alt me-1"), "Refresh"], id="refresh-training-metrics-btn", className="btn btn-sm btn-outline-primary mb-2")
-        ], className="d-flex justify-content-between align-items-start")
-
     def _create_models_section(self, models: Dict[str, Any]) -> html.Div:
         cards = [self._create_model_card(name, info) for name, info in models.items()]
         return html.Div([