From 47d63fddfb3c129fd3eead40f98cba6dac551a29 Mon Sep 17 00:00:00 2001
From: Dobromir Popov <dobromir.popov@gmail.com>
Date: Tue, 9 Sep 2025 03:59:06 +0300
Subject: [PATCH] dash fix wip

---
 web/clean_dashboard.py | 86 +++++++++++++++++++-----------------------
 1 file changed, 38 insertions(+), 48 deletions(-)

diff --git a/web/clean_dashboard.py b/web/clean_dashboard.py
index 19ed98f..4a24bec 100644
--- a/web/clean_dashboard.py
+++ b/web/clean_dashboard.py
@@ -1062,6 +1062,7 @@ class CleanTradingDashboard:
                 logger.error(f"Training status error: {e}")
                 return 'Error', 'badge bg-danger small'
 
+        # Simple prediction tracking callback to test registration
         @self.app.callback(
             [Output('total-predictions-count', 'children'),
              Output('active-models-count', 'children'),
@@ -1075,60 +1076,49 @@ class CleanTradingDashboard:
              Output('model-performance-chart', 'figure')],
             [Input('interval-component', 'n_intervals')]
         )
-        def update_prediction_tracking(n_intervals):
-            """Update prediction tracking with REAL inference stats and performance"""
+        def update_prediction_tracking_simple(n_intervals):
+            """Simple prediction tracking callback to test registration"""
             try:
-                # Get real model status and performance data
-                model_stats = self._get_real_model_performance_data()
-
-                # Calculate summary metrics
-                total_predictions = model_stats.get('total_predictions', 0)
-                active_models = model_stats.get('active_models', 0)
-                total_rewards = model_stats.get('total_rewards', 0.0)
-                recent_predictions = model_stats.get('recent_predictions', [])
-
-                # Calculate average confidence
-                avg_confidence = 0.0
-                if recent_predictions:
-                    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)
-
-                # Calculate trend information
-                predictions_trend = "↗️ Active" if total_predictions > 5 else "⏸️ Waiting"
-                models_status = "✅ Loaded" if active_models > 0 else "⚠️ Loading..."
-                confidence_trend = f"📈 {avg_confidence:.1%}" if avg_confidence > 0.5 else f"📉 {avg_confidence:.1%}"
-                rewards_trend = "💰 Profitable" if total_rewards > 0 else "📊 Learning"
-
-                # Create timeline chart with real prediction data
-                timeline_fig = self._create_prediction_timeline_chart(model_stats)
-
-                # Create performance chart with real model metrics
-                performance_fig = self._create_model_performance_chart(model_stats)
-
-                return (
-                    str(total_predictions),
-                    str(active_models),
-                    f"{avg_confidence:.1%}",
-                    f"{total_rewards:+.2f}",
-                    predictions_trend,
-                    models_status,
-                    confidence_trend,
-                    rewards_trend,
-                    timeline_fig,
-                    performance_fig
-                )
-
-            except Exception as e:
-                logger.error(f"Error updating prediction tracking: {e}")
-                # Return safe defaults
+                # Return basic static values for testing
                 empty_fig = {
                     'data': [],
                     'layout': {
+                        'title': 'Dashboard Initializing...',
                         'template': 'plotly_dark',
                         'height': 300,
                         'annotations': [{
-                            'text': f'Error loading data: {str(e)[:50]}...',
+                            'text': 'Loading model data...',
+                            'xref': 'paper', 'yref': 'paper',
+                            'x': 0.5, 'y': 0.5,
+                            'showarrow': False,
+                            'font': {'size': 16, 'color': 'gray'}
+                        }]
+                    }
+                }
+
+                return (
+                    "Loading...",
+                    "Checking...",
+                    "0.0%",
+                    "0.00",
+                    "⏳ Initializing",
+                    "🔄 Starting...",
+                    "⏸️ Waiting",
+                    "📊 Ready",
+                    empty_fig,
+                    empty_fig
+                )
+
+            except Exception as e:
+                logger.error(f"Error in simple prediction tracking: {e}")
+                empty_fig = {
+                    'data': [],
+                    'layout': {
+                        'title': 'Error',
+                        'template': 'plotly_dark',
+                        'height': 300,
+                        'annotations': [{
+                            'text': f'Error: {str(e)[:30]}...',
                             'xref': 'paper', 'yref': 'paper',
                             'x': 0.5, 'y': 0.5,
                             'showarrow': False,
@@ -1136,7 +1126,7 @@ class CleanTradingDashboard:
                         }]
                     }
                 }
-                return "0", "0", "0", "0.00", empty_fig, empty_fig
+                return "Error", "Error", "0.0%", "0.00", "❌ Error", "❌ Error", "❌ Error", "❌ Error", empty_fig, empty_fig
 
     def _get_real_model_performance_data(self) -> Dict[str, Any]:
         """Get real model performance data from orchestrator"""