device fix , TZ fix

2025-07-27 22:13:28 +03:00
parent 9e1684f9f8
commit 368c49df50
6 changed files with 194 additions and 163 deletions
--- a/web/clean_dashboard.py
+++ b/web/clean_dashboard.py
@ -5851,20 +5851,76 @@ class CleanTradingDashboard:
            
            logger.debug(f"Base data input created successfully for {symbol}")
            
-            # Make prediction using CNN adapter
-            model_output = self.cnn_adapter.predict(base_data_input)
-            
-            # Convert to dictionary for dashboard use
-            prediction = {
-                'action': model_output.predictions.get('action', 'HOLD'),
-                'confidence': model_output.confidence,
-                'buy_probability': model_output.predictions.get('buy_probability', 0.0),
-                'sell_probability': model_output.predictions.get('sell_probability', 0.0),
-                'hold_probability': model_output.predictions.get('hold_probability', 0.0),
-                'timestamp': model_output.timestamp,
-                'hidden_states': model_output.hidden_states,
-                'metadata': model_output.metadata
-            }
+            # Make prediction using CNN model directly (EnhancedCNN uses act method)
+            if hasattr(self.cnn_adapter, 'act'):
+                # Use the act method for EnhancedCNN
+                features = base_data_input.get_feature_vector()
+                
+                # Convert to tensor and ensure proper device placement
+                import torch
+                device = next(self.cnn_adapter.parameters()).device
+                features_tensor = torch.tensor(features, dtype=torch.float32, device=device)
+                
+                # Ensure batch dimension
+                if features_tensor.dim() == 1:
+                    features_tensor = features_tensor.unsqueeze(0)
+                
+                # Set model to evaluation mode
+                self.cnn_adapter.eval()
+                
+                # Get prediction from CNN model
+                with torch.no_grad():
+                    q_values, extrema_pred, price_pred, features_refined, advanced_pred = self.cnn_adapter(features_tensor)
+                    
+                    # Convert to probabilities using softmax
+                    action_probs = torch.softmax(q_values, dim=1)
+                    action_idx = torch.argmax(action_probs, dim=1).item()
+                    confidence = float(action_probs[0, action_idx].item())
+                    
+                    # Map action index to action string
+                    actions = ['BUY', 'SELL', 'HOLD']
+                    action = actions[action_idx]
+                    
+                    # Create probabilities dictionary
+                    probabilities = {
+                        'BUY': float(action_probs[0, 0].item()),
+                        'SELL': float(action_probs[0, 1].item()),
+                        'HOLD': float(action_probs[0, 2].item())
+                    }
+                    
+                    # Extract price predictions if available
+                    price_prediction = None
+                    if price_pred is not None:
+                        price_prediction = price_pred.squeeze(0).cpu().numpy().tolist()
+                    
+                    prediction = {
+                        'action': action,
+                        'confidence': confidence,
+                        'buy_probability': probabilities['BUY'],
+                        'sell_probability': probabilities['SELL'],
+                        'hold_probability': probabilities['HOLD'],
+                        'timestamp': datetime.now(),
+                        'hidden_states': features_refined.squeeze(0).cpu().numpy().tolist() if features_refined is not None else None,
+                        'metadata': {
+                            'price_prediction': price_prediction,
+                            'extrema_prediction': extrema_pred.squeeze(0).cpu().numpy().tolist() if extrema_pred is not None else None
+                        }
+                    }
+            else:
+                # Fallback for other CNN models that might have predict method
+                model_output = self.cnn_adapter.predict(base_data_input)
+                
+                # Convert to dictionary for dashboard use
+                prediction = {
+                    'action': model_output.predictions.get('action', 'HOLD'),
+                    'confidence': model_output.confidence,
+                    'buy_probability': model_output.predictions.get('buy_probability', 0.0),
+                    'sell_probability': model_output.predictions.get('sell_probability', 0.0),
+                    'hold_probability': model_output.predictions.get('hold_probability', 0.0),
+                    'timestamp': model_output.timestamp,
+                    'hidden_states': model_output.hidden_states,
+                    'metadata': model_output.metadata
+                }
            
            logger.debug(f"CNN prediction for {symbol}: {prediction['action']} ({prediction['confidence']:.3f})")
            return prediction