update dash with model performance

web/clean_dashboard.py

@@ -1064,132 +1064,388 @@ class CleanTradingDashboard:
 
         @self.app.callback(
             [Output('total-predictions-count', 'children'),
-             Output('pending-predictions-count', 'children'),
              Output('active-models-count', 'children'),
+             Output('avg-confidence', 'children'),
              Output('total-rewards-sum', 'children'),
+             Output('predictions-trend', 'children'),
+             Output('models-status', 'children'),
+             Output('confidence-trend', 'children'),
+             Output('rewards-trend', 'children'),
              Output('prediction-timeline-chart', 'figure'),
              Output('model-performance-chart', 'figure')],
             [Input('interval-component', 'n_intervals')]
         )
         def update_prediction_tracking(n_intervals):
-            """Update prediction tracking charts and metrics"""
+            """Update prediction tracking with REAL inference stats and performance"""
             try:
-                if (hasattr(self.orchestrator, 'enhanced_training_system') and 
+                # Get real model status and performance data
-                    self.orchestrator.enhanced_training_system):
+                model_stats = self._get_real_model_performance_data()
 
-                    # Get prediction data
+                # Calculate summary metrics
-                    stats = self.orchestrator.enhanced_training_system.get_model_performance_stats()
+                total_predictions = model_stats.get('total_predictions', 0)
-                    models = stats.get('models', [])
+                active_models = model_stats.get('active_models', 0)
-                    total_active = stats.get('total_active_predictions', 0)
+                total_rewards = model_stats.get('total_rewards', 0.0)
+                recent_predictions = model_stats.get('recent_predictions', [])
 
-                    # Calculate totals
+                # Calculate average confidence
-                    total_predictions = sum(m.get('total_predictions', 0) for m in models)
+                avg_confidence = 0.0
-                    total_rewards = sum(m.get('total_reward', 0) for m in models)
+                if recent_predictions:
-                    active_models = len(models)
+                    valid_confidences = [p.get('confidence', 0.0) for p in recent_predictions if p.get('confidence', 0.0) > 0]
+                    if valid_confidences:
+                        avg_confidence = sum(valid_confidences) / len(valid_confidences)
 
-                    # Create timeline chart (simplified)
+                # Calculate trend information
-                    timeline_fig = {
+                predictions_trend = "↗️ Active" if total_predictions > 5 else "⏸️ Waiting"
-                        'data': [],
+                models_status = "✅ Loaded" if active_models > 0 else "⚠️ Loading..."
-                        'layout': {
+                confidence_trend = f"📈 {avg_confidence:.1%}" if avg_confidence > 0.5 else f"📉 {avg_confidence:.1%}"
-                            'title': 'Recent Predictions Timeline',
+                rewards_trend = "💰 Profitable" if total_rewards > 0 else "📊 Learning"
-                            'xaxis': {'title': 'Time'},
-                            'yaxis': {'title': 'Confidence'},
-                            'template': 'plotly_dark',
-                            'height': 300,
-                            'showlegend': False
-                        }
-                    }
 
-                    # Add empty annotation if no data
+                # Create timeline chart with real prediction data
-                    if not models:
+                timeline_fig = self._create_prediction_timeline_chart(model_stats)
-                        timeline_fig['layout']['annotations'] = [{
-                            'text': 'No prediction data yet',
-                            'xref': 'paper', 'yref': 'paper',
-                            'x': 0.5, 'y': 0.5,
-                            'showarrow': False,
-                            'font': {'size': 16, 'color': 'gray'}
-                        }]
 
-                    # Create performance chart
+                # Create performance chart with real model metrics
-                    performance_fig = {
+                performance_fig = self._create_model_performance_chart(model_stats)
-                        'data': [],
-                        'layout': {
-                            'title': 'Model Performance',
-                            'template': 'plotly_dark',
-                            'height': 300,
-                            'showlegend': True
-                        }
-                    }
 
-                    if models:
+                return (
-                        model_names = [m.get('model_name', 'Unknown') for m in models]
+                    str(total_predictions),
-                        accuracies = [m.get('accuracy', 0) * 100 for m in models]
+                    str(active_models),
-                        rewards = [m.get('total_reward', 0) for m in models]
+                    f"{avg_confidence:.1%}",
-                        
+                    f"{total_rewards:+.2f}",
-                        # Add accuracy bars
+                    predictions_trend,
-                        performance_fig['data'].append({
+                    models_status,
-                            'x': model_names,
+                    confidence_trend,
-                            'y': accuracies,
+                    rewards_trend,
-                            'type': 'bar',
+                    timeline_fig,
-                            'name': 'Accuracy (%)',
+                    performance_fig
-                            'marker': {'color': 'lightblue'}
+                )
-                        })
-                        
-                        performance_fig['layout']['xaxis'] = {'title': 'Model'}
-                        performance_fig['layout']['yaxis'] = {'title': 'Accuracy (%)'}
-                    else:
-                        performance_fig['layout']['annotations'] = [{
-                            'text': 'No model data yet',
-                            'xref': 'paper', 'yref': 'paper',
-                            'x': 0.5, 'y': 0.5,
-                            'showarrow': False,
-                            'font': {'size': 16, 'color': 'gray'}
-                        }]
-                    
-                    return (
-                        str(total_predictions),
-                        str(total_active),
-                        str(active_models),
-                        f"{total_rewards:.1f}",
-                        timeline_fig,
-                        performance_fig
-                    )
-                else:
-                    # Training system not available
-                    empty_fig = {
-                        'data': [],
-                        'layout': {
-                            'template': 'plotly_dark',
-                            'height': 300,
-                            'annotations': [{
-                                'text': 'Training system not available',
-                                'xref': 'paper', 'yref': 'paper',
-                                'x': 0.5, 'y': 0.5,
-                                'showarrow': False,
-                                'font': {'size': 16, 'color': 'red'}
-                            }]
-                        }
-                    }
-                    
-                    return "N/A", "N/A", "N/A", "N/A", empty_fig, empty_fig
 
             except Exception as e:
                 logger.error(f"Error updating prediction tracking: {e}")
-                error_fig = {
+                # Return safe defaults
+                empty_fig = {
                     'data': [],
                     'layout': {
                         'template': 'plotly_dark',
                         'height': 300,
                         'annotations': [{
-                            'text': f'Error: {str(e)}',
+                            'text': f'Error loading data: {str(e)[:50]}...',
                             'xref': 'paper', 'yref': 'paper',
                             'x': 0.5, 'y': 0.5,
                             'showarrow': False,
-                            'font': {'size': 14, 'color': 'red'}
+                            'font': {'size': 12, 'color': 'red'}
                         }]
                     }
                 }
-                return "Error", "Error", "Error", "Error", error_fig, error_fig
+                return "0", "0", "0", "0.00", empty_fig, empty_fig
+
+    def _get_real_model_performance_data(self) -> Dict[str, Any]:
+        """Get real model performance data from orchestrator"""
+        try:
+            model_data = {
+                'total_predictions': 0,
+                'pending_predictions': 0,
+                'active_models': 0,
+                'total_rewards': 0.0,
+                'models': [],
+                'recent_predictions': []
+            }
+
+            if not self.orchestrator:
+                return model_data
+
+            # Get model states from orchestrator
+            model_states = getattr(self.orchestrator, 'model_states', {})
+
+            # Check each model type
+            for model_type in ['cnn', 'dqn', 'cob_rl']:
+                if model_type in model_states:
+                    state = model_states[model_type]
+                    is_loaded = state.get('checkpoint_loaded', False)
+
+                    if is_loaded:
+                        model_data['active_models'] += 1
+
+                    # Add model info (include all models, not just loaded ones)
+                    model_data['models'].append({
+                        'name': model_type.upper(),
+                        'status': 'LOADED' if is_loaded else 'FRESH',
+                        'current_loss': state.get('current_loss', 0.0),
+                        'best_loss': state.get('best_loss', None),
+                        'checkpoint_filename': state.get('checkpoint_filename', 'none'),
+                        'training_sessions': getattr(self.orchestrator, f'{model_type}_training_count', 0),
+                        'last_inference': getattr(self.orchestrator, f'{model_type}_last_inference', None),
+                        'inference_count': getattr(self.orchestrator, f'{model_type}_inference_count', 0)
+                    })
+
+            # Get recent predictions from our tracking
+            if hasattr(self, 'recent_decisions') and self.recent_decisions:
+                for decision in list(self.recent_decisions)[-20:]:  # Last 20 decisions
+                    model_data['recent_predictions'].append({
+                        'timestamp': decision.get('timestamp', datetime.now()),
+                        'action': decision.get('action', 'UNKNOWN'),
+                        'confidence': decision.get('confidence', 0.0),
+                        'reward': decision.get('reward', 0.0),
+                        'outcome': decision.get('outcome', 'pending')
+                    })
+
+            model_data['total_predictions'] = len(model_data['recent_predictions'])
+            model_data['pending_predictions'] = sum(1 for p in model_data['recent_predictions']
+                                                   if p.get('outcome') == 'pending')
+            model_data['total_rewards'] = sum(p.get('reward', 0.0) for p in model_data['recent_predictions'])
+
+            return model_data
+
+        except Exception as e:
+            logger.error(f"Error getting real model performance data: {e}")
+            return {
+                'total_predictions': 0,
+                'pending_predictions': 0,
+                'active_models': 0,
+                'total_rewards': 0.0,
+                'models': [],
+                'recent_predictions': []
+            }
+
+    def _create_prediction_timeline_chart(self, model_stats: Dict[str, Any]) -> Dict[str, Any]:
+        """Create prediction timeline chart with real data"""
+        try:
+            recent_predictions = model_stats.get('recent_predictions', [])
+
+            if not recent_predictions:
+                return {
+                    'data': [],
+                    'layout': {
+                        'title': 'Recent Predictions Timeline',
+                        'template': 'plotly_dark',
+                        'height': 300,
+                        'annotations': [{
+                            'text': 'No predictions yet',
+                            'xref': 'paper', 'yref': 'paper',
+                            'x': 0.5, 'y': 0.5,
+                            'showarrow': False,
+                            'font': {'size': 16, 'color': 'gray'}
+                        }]
+                    }
+                }
+
+            # Prepare data for timeline
+            timestamps = []
+            confidences = []
+            rewards = []
+            actions = []
+
+            for pred in recent_predictions[-50:]:  # Last 50 predictions
+                timestamps.append(pred.get('timestamp', datetime.now()))
+                confidences.append(pred.get('confidence', 0.0) * 100)  # Convert to percentage
+                rewards.append(pred.get('reward', 0.0))
+                actions.append(pred.get('action', 'UNKNOWN'))
+
+            # Create timeline chart
+            fig = {
+                'data': [
+                    {
+                        'x': timestamps,
+                        'y': confidences,
+                        'type': 'scatter',
+                        'mode': 'lines+markers',
+                        'name': 'Confidence (%)',
+                        'line': {'color': '#00ff88', 'width': 2},
+                        'marker': {'size': 6}
+                    },
+                    {
+                        'x': timestamps,
+                        'y': rewards,
+                        'type': 'bar',
+                        'name': 'Reward',
+                        'yaxis': 'y2',
+                        'marker': {'color': '#ff6b6b'}
+                    }
+                ],
+                'layout': {
+                    'title': 'Prediction Timeline (Last 50)',
+                    'template': 'plotly_dark',
+                    'height': 300,
+                    'xaxis': {
+                        'title': 'Time',
+                        'type': 'date'
+                    },
+                    'yaxis': {
+                        'title': 'Confidence (%)',
+                        'range': [0, 100]
+                    },
+                    'yaxis2': {
+                        'title': 'Reward',
+                        'overlaying': 'y',
+                        'side': 'right',
+                        'showgrid': False
+                    },
+                    'showlegend': True,
+                    'legend': {'x': 0, 'y': 1}
+                }
+            }
+
+            return fig
+
+        except Exception as e:
+            logger.error(f"Error creating prediction timeline chart: {e}")
+            return {
+                'data': [],
+                'layout': {
+                    'title': 'Prediction Timeline',
+                    'template': 'plotly_dark',
+                    'height': 300,
+                    'annotations': [{
+                        'text': f'Chart error: {str(e)[:30]}...',
+                        'xref': 'paper', 'yref': 'paper',
+                        'x': 0.5, 'y': 0.5,
+                        'showarrow': False,
+                        'font': {'size': 12, 'color': 'red'}
+                    }]
+                }
+            }
+
+    def _create_model_performance_chart(self, model_stats: Dict[str, Any]) -> Dict[str, Any]:
+        """Create model performance chart with real metrics"""
+        try:
+            models = model_stats.get('models', [])
+
+            if not models:
+                return {
+                    'data': [],
+                    'layout': {
+                        'title': 'Model Performance',
+                        'template': 'plotly_dark',
+                        'height': 300,
+                        'annotations': [{
+                            'text': 'No active models',
+                            'xref': 'paper', 'yref': 'paper',
+                            'x': 0.5, 'y': 0.5,
+                            'showarrow': False,
+                            'font': {'size': 16, 'color': 'gray'}
+                        }]
+                    }
+                }
+
+            # Prepare data for performance chart
+            model_names = []
+            current_losses = []
+            best_losses = []
+            training_sessions = []
+            inference_counts = []
+            statuses = []
+
+            for model in models:
+                model_names.append(model.get('name', 'Unknown'))
+                current_losses.append(model.get('current_loss', 0.0))
+                best_losses.append(model.get('best_loss', model.get('current_loss', 0.0)))
+                training_sessions.append(model.get('training_sessions', 0))
+                inference_counts.append(model.get('inference_count', 0))
+                statuses.append(model.get('status', 'Unknown'))
+
+            # Create comprehensive performance chart
+            fig = {
+                'data': [
+                    {
+                        'x': model_names,
+                        'y': current_losses,
+                        'type': 'bar',
+                        'name': 'Current Loss',
+                        'marker': {'color': '#ff6b6b'},
+                        'yaxis': 'y1'
+                    },
+                    {
+                        'x': model_names,
+                        'y': best_losses,
+                        'type': 'bar',
+                        'name': 'Best Loss',
+                        'marker': {'color': '#4ecdc4'},
+                        'yaxis': 'y1'
+                    },
+                    {
+                        'x': model_names,
+                        'y': training_sessions,
+                        'type': 'scatter',
+                        'mode': 'markers',
+                        'name': 'Training Sessions',
+                        'marker': {'color': '#ffd93d', 'size': 12},
+                        'yaxis': 'y2'
+                    },
+                    {
+                        'x': model_names,
+                        'y': inference_counts,
+                        'type': 'scatter',
+                        'mode': 'markers',
+                        'name': 'Inference Count',
+                        'marker': {'color': '#a8e6cf', 'size': 8},
+                        'yaxis': 'y2'
+                    }
+                ],
+                'layout': {
+                    'title': 'Real Model Performance & Activity',
+                    'template': 'plotly_dark',
+                    'height': 300,
+                    'xaxis': {
+                        'title': 'Model'
+                    },
+                    'yaxis': {
+                        'title': 'Loss',
+                        'side': 'left'
+                    },
+                    'yaxis2': {
+                        'title': 'Activity Count',
+                        'side': 'right',
+                        'overlaying': 'y',
+                        'showgrid': False
+                    },
+                    'showlegend': True,
+                    'legend': {'x': 0, 'y': 1}
+                }
+            }
+
+            # Add status annotations with more detail
+            annotations = []
+            for i, (name, status) in enumerate(zip(model_names, statuses)):
+                color = '#00ff88' if status == 'LOADED' else '#ff6b6b'
+                loss_text = f"{status}<br>Loss: {current_losses[i]:.4f}"
+                if training_sessions[i] > 0:
+                    loss_text += f"<br>Trained: {training_sessions[i]}x"
+                if inference_counts[i] > 0:
+                    loss_text += f"<br>Inferred: {inference_counts[i]}x"
+
+                annotations.append({
+                    'text': loss_text,
+                    'x': name,
+                    'y': max(current_losses[i] * 1.1, 0.01),
+                    'xref': 'x',
+                    'yref': 'y',
+                    'showarrow': False,
+                    'font': {'color': color, 'size': 8},
+                    'align': 'center'
+                })
+
+            fig['layout']['annotations'] = annotations
+
+            return fig
+
+        except Exception as e:
+            logger.error(f"Error creating model performance chart: {e}")
+            return {
+                'data': [],
+                'layout': {
+                    'title': 'Model Performance',
+                    'template': 'plotly_dark',
+                    'height': 300,
+                    'annotations': [{
+                        'text': f'Chart error: {str(e)[:30]}...',
+                        'xref': 'paper', 'yref': 'paper',
+                        'x': 0.5, 'y': 0.5,
+                        'showarrow': False,
+                        'font': {'size': 12, 'color': 'red'}
+                    }]
+                }
+            }
+
+            return "0", "0", "0.0%", "0.00", "❌ Error", "❌ Error", "❌ Error", "❌ Error", error_fig, error_fig
         
         @self.app.callback(
             [Output('eth-cob-content', 'children'),

web/layout_manager.py

@@ -37,33 +37,37 @@ class DashboardLayoutManager:
                         "🧠 Model Predictions & Performance Tracking"
                     ], className="text-light mb-3"),
                     
-                    # Summary cards row
+                    # Summary cards row - Enhanced with real metrics
                     html.Div([
                         html.Div([
                             html.Div([
                                 html.H6("0", id="total-predictions-count", className="mb-0 text-primary"),
-                                html.Small("Total Predictions", className="text-light")
+                                html.Small("Recent Signals", className="text-light"),
-                            ], className="card-body text-center p-2 bg-dark")
+                                html.Small("", id="predictions-trend", className="d-block text-xs text-muted")
-                        ], className="card col-md-3 mx-1 bg-dark border-secondary"),
-                        
-                        html.Div([
-                            html.Div([
-                                html.H6("0", id="pending-predictions-count", className="mb-0 text-warning"),
-                                html.Small("Pending Resolution", className="text-light")
                             ], className="card-body text-center p-2 bg-dark")
                         ], className="card col-md-3 mx-1 bg-dark border-secondary"),
 
                         html.Div([
                             html.Div([
                                 html.H6("0", id="active-models-count", className="mb-0 text-info"),
-                                html.Small("Active Models", className="text-light")
+                                html.Small("Loaded Models", className="text-light"),
+                                html.Small("", id="models-status", className="d-block text-xs text-success")
                             ], className="card-body text-center p-2 bg-dark")
                         ], className="card col-md-3 mx-1 bg-dark border-secondary"),
 
                         html.Div([
                             html.Div([
-                                html.H6("0.0", id="total-rewards-sum", className="mb-0 text-success"),
+                                html.H6("0.00", id="avg-confidence", className="mb-0 text-warning"),
-                                html.Small("Total Rewards", className="text-light")
+                                html.Small("Avg Confidence", className="text-light"),
+                                html.Small("", id="confidence-trend", className="d-block text-xs text-muted")
+                            ], className="card-body text-center p-2 bg-dark")
+                        ], className="card col-md-3 mx-1 bg-dark border-secondary"),
+
+                        html.Div([
+                            html.Div([
+                                html.H6("+0.00", id="total-rewards-sum", className="mb-0 text-success"),
+                                html.Small("Total Rewards", className="text-light"),
+                                html.Small("", id="rewards-trend", className="d-block text-xs text-muted")
                             ], className="card-body text-center p-2 bg-dark")
                         ], className="card col-md-3 mx-1 bg-dark border-secondary")
                     ], className="row mb-3"),