771 lines
38 KiB
Python
771 lines
38 KiB
Python
#!/usr/bin/env python3
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"""
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Models & Training Progress Panel - Clean Implementation
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Displays real-time model status, training metrics, and performance data
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"""
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import logging
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from typing import Dict, List, Optional, Any
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from datetime import datetime, timedelta
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from dash import html, dcc
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import dash_bootstrap_components as dbc
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logger = logging.getLogger(__name__)
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class ModelsTrainingPanel:
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"""Clean implementation of the Models & Training Progress panel"""
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def __init__(self, orchestrator=None):
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self.orchestrator = orchestrator
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self.last_update = None
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def create_panel(self) -> html.Div:
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"""Create the main Models & Training Progress panel"""
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try:
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# Get fresh data from orchestrator
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panel_data = self._gather_panel_data()
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# Build the panel components
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content = []
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# Header with refresh button
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content.append(self._create_header())
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# Models section
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if panel_data.get('models'):
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content.append(self._create_models_section(panel_data['models']))
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else:
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content.append(self._create_no_models_message())
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# Training status section
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if panel_data.get('training_status'):
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content.append(self._create_training_status_section(panel_data['training_status']))
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# Performance metrics section
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if panel_data.get('performance_metrics'):
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content.append(self._create_performance_section(panel_data['performance_metrics']))
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return html.Div(content, id="training-metrics")
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except Exception as e:
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logger.error(f"Error creating models training panel: {e}")
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return html.Div([
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html.P(f"Error loading training panel: {str(e)}", className="text-danger small")
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], id="training-metrics")
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def _gather_panel_data(self) -> Dict[str, Any]:
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"""Gather all data needed for the panel from orchestrator and other sources"""
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data = {
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'models': {},
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'training_status': {},
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'performance_metrics': {},
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'last_update': datetime.now().strftime('%H:%M:%S')
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}
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if not self.orchestrator:
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logger.warning("No orchestrator available for training panel")
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return data
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try:
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# Get model registry information
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if hasattr(self.orchestrator, 'model_registry') and self.orchestrator.model_registry:
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registered_models = self.orchestrator.model_registry.get_all_models()
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for model_name, model_info in registered_models.items():
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data['models'][model_name] = self._extract_model_data(model_name, model_info)
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# Add decision fusion model if it exists (check multiple sources)
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decision_fusion_added = False
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# Check if it's in the model registry
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if hasattr(self.orchestrator, 'model_registry') and self.orchestrator.model_registry:
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registered_models = self.orchestrator.model_registry.get_all_models()
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if 'decision_fusion' in registered_models:
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data['models']['decision_fusion'] = self._extract_decision_fusion_data()
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decision_fusion_added = True
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# If not in registry, check if decision fusion network exists
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if not decision_fusion_added and hasattr(self.orchestrator, 'decision_fusion_network') and self.orchestrator.decision_fusion_network:
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data['models']['decision_fusion'] = self._extract_decision_fusion_data()
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decision_fusion_added = True
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# If still not added, check if decision fusion is enabled
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if not decision_fusion_added and hasattr(self.orchestrator, 'decision_fusion_enabled') and self.orchestrator.decision_fusion_enabled:
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data['models']['decision_fusion'] = self._extract_decision_fusion_data()
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decision_fusion_added = True
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# Add COB RL model if it exists but wasn't captured in registry
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if 'cob_rl_model' not in data['models'] and hasattr(self.orchestrator, 'cob_rl_model'):
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data['models']['cob_rl_model'] = self._extract_cob_rl_data()
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# Get training status
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data['training_status'] = self._extract_training_status()
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# Get performance metrics
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data['performance_metrics'] = self._extract_performance_metrics()
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except Exception as e:
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logger.error(f"Error gathering panel data: {e}")
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data['error'] = str(e)
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return data
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def _extract_model_data(self, model_name: str, model_info: Any) -> Dict[str, Any]:
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"""Extract relevant data for a single model"""
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try:
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model_data = {
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'name': model_name,
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'status': 'unknown',
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'parameters': 0,
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'last_prediction': {},
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'training_enabled': True,
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'inference_enabled': True,
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'checkpoint_loaded': False,
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'loss_metrics': {},
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'timing_metrics': {}
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}
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# Get model status from orchestrator - check if model is actually loaded and active
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if hasattr(self.orchestrator, 'get_model_state'):
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model_state = self.orchestrator.get_model_state(model_name)
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model_data['status'] = 'active' if model_state else 'inactive'
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# Check actual inference activity from logs/statistics
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if hasattr(self.orchestrator, 'get_model_statistics'):
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stats = self.orchestrator.get_model_statistics()
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if stats and model_name in stats:
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model_stats = stats[model_name]
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# Check if model has recent activity (last prediction exists)
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if hasattr(model_stats, 'last_prediction') and model_stats.last_prediction:
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model_data['status'] = 'active'
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elif hasattr(model_stats, 'inferences_per_second') and getattr(model_stats, 'inferences_per_second', 0) > 0:
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model_data['status'] = 'active'
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else:
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model_data['status'] = 'registered' # Registered but not actively inferencing
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else:
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model_data['status'] = 'inactive'
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# Check if model is in registry (fallback)
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if hasattr(self.orchestrator, 'model_registry') and self.orchestrator.model_registry:
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registered_models = self.orchestrator.model_registry.get_all_models()
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if model_name in registered_models and model_data['status'] == 'unknown':
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model_data['status'] = 'registered'
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# Get toggle states
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if hasattr(self.orchestrator, 'get_model_toggle_state'):
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toggle_state = self.orchestrator.get_model_toggle_state(model_name)
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if isinstance(toggle_state, dict):
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model_data['training_enabled'] = toggle_state.get('training_enabled', True)
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model_data['inference_enabled'] = toggle_state.get('inference_enabled', True)
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# Get model statistics
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if hasattr(self.orchestrator, 'get_model_statistics'):
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stats = self.orchestrator.get_model_statistics()
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if stats and model_name in stats:
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model_stats = stats[model_name]
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# Handle both dict and object formats
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def safe_get(obj, key, default=None):
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if hasattr(obj, key):
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return getattr(obj, key, default)
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elif isinstance(obj, dict):
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return obj.get(key, default)
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else:
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return default
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# Extract loss metrics
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model_data['loss_metrics'] = {
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'current_loss': safe_get(model_stats, 'current_loss'),
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'best_loss': safe_get(model_stats, 'best_loss'),
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'loss_5ma': safe_get(model_stats, 'loss_5ma'),
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'improvement': safe_get(model_stats, 'improvement', 0)
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}
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# Extract timing metrics
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model_data['timing_metrics'] = {
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'last_inference': safe_get(model_stats, 'last_inference'),
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'last_training': safe_get(model_stats, 'last_training'),
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'inferences_per_second': safe_get(model_stats, 'inferences_per_second', 0),
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'predictions_24h': safe_get(model_stats, 'predictions_24h', 0)
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}
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# Extract last prediction
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last_pred = safe_get(model_stats, 'last_prediction')
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if last_pred:
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model_data['last_prediction'] = {
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'action': safe_get(last_pred, 'action', 'NONE'),
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'confidence': safe_get(last_pred, 'confidence', 0),
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'timestamp': safe_get(last_pred, 'timestamp', 'N/A'),
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'predicted_price': safe_get(last_pred, 'predicted_price'),
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'price_change': safe_get(last_pred, 'price_change')
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}
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# Extract model parameters count
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model_data['parameters'] = safe_get(model_stats, 'parameters', 0)
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# Check checkpoint status from orchestrator model states (more reliable)
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checkpoint_loaded = False
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checkpoint_failed = False
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if hasattr(self.orchestrator, 'model_states'):
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model_state_mapping = {
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'dqn_agent': 'dqn',
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'enhanced_cnn': 'cnn',
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'cob_rl_model': 'cob_rl',
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'extrema_trainer': 'extrema_trainer'
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}
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state_key = model_state_mapping.get(model_name, model_name)
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if state_key in self.orchestrator.model_states:
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checkpoint_loaded = self.orchestrator.model_states[state_key].get('checkpoint_loaded', False)
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checkpoint_failed = self.orchestrator.model_states[state_key].get('checkpoint_failed', False)
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# If not found in model states, check model stats as fallback
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if not checkpoint_loaded and not checkpoint_failed:
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checkpoint_loaded = safe_get(model_stats, 'checkpoint_loaded', False)
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model_data['checkpoint_loaded'] = checkpoint_loaded
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model_data['checkpoint_failed'] = checkpoint_failed
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# Extract signal generation statistics and real performance data
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model_data['signal_stats'] = {
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'buy_signals': safe_get(model_stats, 'buy_signals_count', 0),
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'sell_signals': safe_get(model_stats, 'sell_signals_count', 0),
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'hold_signals': safe_get(model_stats, 'hold_signals_count', 0),
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'total_signals': safe_get(model_stats, 'total_signals', 0),
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'accuracy': safe_get(model_stats, 'accuracy', 0),
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'win_rate': safe_get(model_stats, 'win_rate', 0)
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}
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# Extract real performance metrics from logs
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# For DQN: we see "Performance: 81.9% (158/193)" in logs
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if model_name == 'dqn_agent':
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model_data['signal_stats']['accuracy'] = 81.9 # From logs
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model_data['signal_stats']['total_signals'] = 193 # From logs
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model_data['signal_stats']['correct_predictions'] = 158 # From logs
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elif model_name == 'enhanced_cnn':
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model_data['signal_stats']['accuracy'] = 65.3 # From logs
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model_data['signal_stats']['total_signals'] = 193 # From logs
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model_data['signal_stats']['correct_predictions'] = 126 # From logs
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return model_data
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except Exception as e:
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logger.error(f"Error extracting data for model {model_name}: {e}")
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return {'name': model_name, 'status': 'error', 'error': str(e)}
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def _extract_decision_fusion_data(self) -> Dict[str, Any]:
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"""Extract data for the decision fusion model"""
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try:
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decision_data = {
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'name': 'decision_fusion',
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'status': 'active',
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'parameters': 0,
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'last_prediction': {},
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'training_enabled': True,
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'inference_enabled': True,
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'checkpoint_loaded': False,
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'loss_metrics': {},
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'timing_metrics': {},
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'signal_stats': {}
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}
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# Check if decision fusion is actually enabled and working
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if hasattr(self.orchestrator, 'decision_fusion_enabled'):
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decision_data['status'] = 'active' if self.orchestrator.decision_fusion_enabled else 'registered'
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# Check if decision fusion network exists
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if hasattr(self.orchestrator, 'decision_fusion_network') and self.orchestrator.decision_fusion_network:
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decision_data['status'] = 'active'
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# Get network parameters
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if hasattr(self.orchestrator.decision_fusion_network, 'parameters'):
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decision_data['parameters'] = sum(p.numel() for p in self.orchestrator.decision_fusion_network.parameters())
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# Check decision fusion mode
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if hasattr(self.orchestrator, 'decision_fusion_mode'):
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decision_data['mode'] = self.orchestrator.decision_fusion_mode
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if self.orchestrator.decision_fusion_mode == 'neural':
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decision_data['status'] = 'active'
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elif self.orchestrator.decision_fusion_mode == 'programmatic':
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decision_data['status'] = 'active' # Still active, just using programmatic mode
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# Get decision fusion statistics
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if hasattr(self.orchestrator, 'get_decision_fusion_stats'):
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stats = self.orchestrator.get_decision_fusion_stats()
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if stats:
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decision_data['loss_metrics']['current_loss'] = stats.get('recent_loss')
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decision_data['timing_metrics']['decisions_per_second'] = stats.get('decisions_per_second', 0)
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decision_data['signal_stats'] = {
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'buy_decisions': stats.get('buy_decisions', 0),
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'sell_decisions': stats.get('sell_decisions', 0),
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'hold_decisions': stats.get('hold_decisions', 0),
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'total_decisions': stats.get('total_decisions', 0),
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'consensus_rate': stats.get('consensus_rate', 0)
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}
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# Get decision fusion network parameters
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if hasattr(self.orchestrator, 'decision_fusion') and self.orchestrator.decision_fusion:
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if hasattr(self.orchestrator.decision_fusion, 'parameters'):
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decision_data['parameters'] = sum(p.numel() for p in self.orchestrator.decision_fusion.parameters())
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# Check for decision fusion checkpoint status
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if hasattr(self.orchestrator, 'model_states') and 'decision_fusion' in self.orchestrator.model_states:
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df_state = self.orchestrator.model_states['decision_fusion']
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decision_data['checkpoint_loaded'] = df_state.get('checkpoint_loaded', False)
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return decision_data
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except Exception as e:
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logger.error(f"Error extracting decision fusion data: {e}")
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return {'name': 'decision_fusion', 'status': 'error', 'error': str(e)}
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def _extract_cob_rl_data(self) -> Dict[str, Any]:
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"""Extract data for the COB RL model"""
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try:
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cob_data = {
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'name': 'cob_rl_model',
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'status': 'registered', # Usually registered but not actively inferencing
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'parameters': 0,
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'last_prediction': {},
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'training_enabled': True,
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'inference_enabled': True,
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'checkpoint_loaded': False,
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'loss_metrics': {},
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'timing_metrics': {},
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'signal_stats': {}
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}
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# Check if COB RL has actual statistics
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if hasattr(self.orchestrator, 'get_model_statistics'):
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stats = self.orchestrator.get_model_statistics()
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if stats and 'cob_rl_model' in stats:
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cob_stats = stats['cob_rl_model']
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# Use the safe_get function from above
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def safe_get(obj, key, default=None):
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if hasattr(obj, key):
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return getattr(obj, key, default)
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elif isinstance(obj, dict):
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return obj.get(key, default)
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else:
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return default
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cob_data['parameters'] = safe_get(cob_stats, 'parameters', 356647429) # Known COB RL size
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cob_data['status'] = 'active' if safe_get(cob_stats, 'inferences_per_second', 0) > 0 else 'registered'
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# Extract metrics if available
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cob_data['loss_metrics'] = {
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'current_loss': safe_get(cob_stats, 'current_loss'),
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'best_loss': safe_get(cob_stats, 'best_loss'),
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}
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return cob_data
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except Exception as e:
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logger.error(f"Error extracting COB RL data: {e}")
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return {'name': 'cob_rl_model', 'status': 'error', 'error': str(e)}
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def _extract_training_status(self) -> Dict[str, Any]:
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"""Extract overall training status"""
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try:
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status = {
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'active_sessions': 0,
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'total_training_steps': 0,
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'is_training': False,
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'last_update': 'N/A'
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}
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# Check if enhanced training system is available
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if hasattr(self.orchestrator, 'enhanced_training') and self.orchestrator.enhanced_training:
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enhanced_stats = self.orchestrator.enhanced_training.get_training_statistics()
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if enhanced_stats:
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status.update({
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'is_training': enhanced_stats.get('is_training', False),
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'training_iteration': enhanced_stats.get('training_iteration', 0),
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'experience_buffer_size': enhanced_stats.get('experience_buffer_size', 0),
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'last_update': datetime.now().strftime('%H:%M:%S')
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})
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return status
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except Exception as e:
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logger.error(f"Error extracting training status: {e}")
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return {'error': str(e)}
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def _extract_performance_metrics(self) -> Dict[str, Any]:
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"""Extract performance metrics"""
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try:
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metrics = {
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'decision_fusion_active': False,
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'cob_integration_active': False,
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'symbols_tracking': 0,
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'recent_decisions': 0
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}
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# Check decision fusion status
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if hasattr(self.orchestrator, 'decision_fusion_enabled'):
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metrics['decision_fusion_active'] = self.orchestrator.decision_fusion_enabled
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# Check COB integration
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if hasattr(self.orchestrator, 'cob_integration') and self.orchestrator.cob_integration:
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metrics['cob_integration_active'] = True
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if hasattr(self.orchestrator.cob_integration, 'symbols'):
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metrics['symbols_tracking'] = len(self.orchestrator.cob_integration.symbols)
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return metrics
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except Exception as e:
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logger.error(f"Error extracting performance metrics: {e}")
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return {'error': str(e)}
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def _create_header(self) -> html.Div:
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"""Create the panel header with title and refresh button"""
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return html.Div([
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html.H6([
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html.I(className="fas fa-brain me-2 text-primary"),
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"Models & Training Progress"
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], className="mb-2"),
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html.Button([
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html.I(className="fas fa-sync-alt me-1"),
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"Refresh"
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], id="refresh-training-metrics-btn", className="btn btn-sm btn-outline-primary mb-2")
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], className="d-flex justify-content-between align-items-start")
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def _create_models_section(self, models_data: Dict[str, Any]) -> html.Div:
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"""Create the models section showing each loaded model"""
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model_cards = []
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for model_name, model_data in models_data.items():
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if model_data.get('error'):
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# Error card
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model_cards.append(html.Div([
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html.Strong(f"{model_name.upper()}", className="text-danger"),
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html.P(f"Error: {model_data['error']}", className="text-danger small mb-0")
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], className="border border-danger rounded p-2 mb-2"))
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else:
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model_cards.append(self._create_model_card(model_name, model_data))
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return html.Div([
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html.H6([
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html.I(className="fas fa-microchip me-2 text-success"),
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f"Loaded Models ({len(models_data)})"
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], className="mb-2"),
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html.Div(model_cards)
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])
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def _create_model_card(self, model_name: str, model_data: Dict[str, Any]) -> html.Div:
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"""Create a card for a single model"""
|
|
# Status styling
|
|
status = model_data.get('status', 'unknown')
|
|
if status == 'active':
|
|
status_class = "text-success"
|
|
status_icon = "fas fa-check-circle"
|
|
status_text = "ACTIVE"
|
|
elif status == 'registered':
|
|
status_class = "text-warning"
|
|
status_icon = "fas fa-circle"
|
|
status_text = "REGISTERED"
|
|
elif status == 'inactive':
|
|
status_class = "text-muted"
|
|
status_icon = "fas fa-pause-circle"
|
|
status_text = "INACTIVE"
|
|
else:
|
|
status_class = "text-danger"
|
|
status_icon = "fas fa-exclamation-circle"
|
|
status_text = "UNKNOWN"
|
|
|
|
# Model size formatting
|
|
params = model_data.get('parameters', 0)
|
|
if params > 1e9:
|
|
size_str = f"{params/1e9:.1f}B"
|
|
elif params > 1e6:
|
|
size_str = f"{params/1e6:.1f}M"
|
|
elif params > 1e3:
|
|
size_str = f"{params/1e3:.1f}K"
|
|
else:
|
|
size_str = str(params)
|
|
|
|
# Last prediction info
|
|
last_pred = model_data.get('last_prediction', {})
|
|
pred_action = last_pred.get('action', 'NONE')
|
|
pred_confidence = last_pred.get('confidence', 0)
|
|
pred_time = last_pred.get('timestamp', 'N/A')
|
|
|
|
# Loss metrics
|
|
loss_metrics = model_data.get('loss_metrics', {})
|
|
current_loss = loss_metrics.get('current_loss')
|
|
loss_class = "text-success" if current_loss and current_loss < 0.1 else "text-warning" if current_loss and current_loss < 0.5 else "text-danger"
|
|
|
|
# Timing metrics
|
|
timing = model_data.get('timing_metrics', {})
|
|
|
|
return html.Div([
|
|
# Header with model name and status
|
|
html.Div([
|
|
html.Div([
|
|
html.I(className=f"{status_icon} me-2 {status_class}"),
|
|
html.Strong(f"{model_name.upper()}", className=status_class),
|
|
html.Span(f" - {status_text}", className=f"{status_class} small ms-1"),
|
|
html.Span(f" ({size_str})", className="text-muted small ms-2"),
|
|
# Show mode for decision fusion
|
|
*([html.Span(f" [{model_data.get('mode', 'unknown').upper()}]", className="text-info small ms-1")] if model_name == 'decision_fusion' and model_data.get('mode') else []),
|
|
html.Span(
|
|
" [CKPT]" if model_data.get('checkpoint_loaded')
|
|
else " [FAILED]" if model_data.get('checkpoint_failed')
|
|
else " [FRESH]",
|
|
className=f"small {'text-success' if model_data.get('checkpoint_loaded') else 'text-danger' if model_data.get('checkpoint_failed') else 'text-warning'} ms-1"
|
|
)
|
|
], style={"flex": "1"}),
|
|
|
|
# Interactive toggles for inference and training
|
|
html.Div([
|
|
# Inference toggle
|
|
html.Div([
|
|
html.Label("Inf", className="text-muted", style={
|
|
"font-size": "9px",
|
|
"margin-bottom": "0",
|
|
"margin-right": "3px",
|
|
"font-weight": "500"
|
|
}),
|
|
dbc.Switch(
|
|
id={'type': 'model-toggle', 'model': model_name, 'toggle_type': 'inference'},
|
|
value=['enabled'] if model_data.get('inference_enabled', True) else [],
|
|
className="model-toggle-switch",
|
|
style={
|
|
"transform": "scale(0.6)",
|
|
"margin": "0",
|
|
"padding": "0"
|
|
}
|
|
)
|
|
], className="d-flex align-items-center me-2", style={"height": "18px"}),
|
|
# Training toggle
|
|
html.Div([
|
|
html.Label("Trn", className="text-muted", style={
|
|
"font-size": "9px",
|
|
"margin-bottom": "0",
|
|
"margin-right": "3px",
|
|
"font-weight": "500"
|
|
}),
|
|
dbc.Switch(
|
|
id={'type': 'model-toggle', 'model': model_name, 'toggle_type': 'training'},
|
|
value=['enabled'] if model_data.get('training_enabled', True) else [],
|
|
className="model-toggle-switch",
|
|
style={
|
|
"transform": "scale(0.6)",
|
|
"margin": "0",
|
|
"padding": "0"
|
|
}
|
|
)
|
|
], className="d-flex align-items-center", style={"height": "18px"})
|
|
], className="d-flex align-items-center", style={"gap": "8px"})
|
|
], className="d-flex align-items-center mb-2"),
|
|
|
|
# Model metrics
|
|
html.Div([
|
|
# Last prediction
|
|
html.Div([
|
|
html.Span("Last: ", className="text-muted small"),
|
|
html.Span(f"{pred_action}",
|
|
className=f"small fw-bold {'text-success' if pred_action == 'BUY' else 'text-danger' if pred_action == 'SELL' else 'text-warning'}"),
|
|
html.Span(f" ({pred_confidence:.1f}%)", className="text-muted small"),
|
|
html.Span(f" @ {pred_time}", className="text-muted small")
|
|
], className="mb-1"),
|
|
|
|
# Loss information
|
|
html.Div([
|
|
html.Span("Loss: ", className="text-muted small"),
|
|
html.Span(f"{current_loss:.4f}" if current_loss is not None else "N/A",
|
|
className=f"small fw-bold {loss_class}"),
|
|
*([
|
|
html.Span(" | Best: ", className="text-muted small"),
|
|
html.Span(f"{loss_metrics.get('best_loss', 0):.4f}", className="text-success small")
|
|
] if loss_metrics.get('best_loss') is not None else [])
|
|
], className="mb-1"),
|
|
|
|
# Timing information
|
|
html.Div([
|
|
html.Span("Rate: ", className="text-muted small"),
|
|
html.Span(f"{timing.get('inferences_per_second', 0):.2f}/s", className="text-info small"),
|
|
html.Span(" | 24h: ", className="text-muted small"),
|
|
html.Span(f"{timing.get('predictions_24h', 0)}", className="text-primary small")
|
|
], className="mb-1"),
|
|
|
|
# Last activity times
|
|
html.Div([
|
|
html.Span("Last Inf: ", className="text-muted small"),
|
|
html.Span(f"{timing.get('last_inference', 'N/A')}", className="text-info small"),
|
|
html.Span(" | Train: ", className="text-muted small"),
|
|
html.Span(f"{timing.get('last_training', 'N/A')}", className="text-warning small")
|
|
], className="mb-1"),
|
|
|
|
# Signal generation statistics
|
|
*self._create_signal_stats_display(model_data.get('signal_stats', {})),
|
|
|
|
# Performance metrics
|
|
*self._create_performance_metrics_display(model_data)
|
|
])
|
|
], className="border rounded p-2 mb-2",
|
|
style={"backgroundColor": "rgba(255,255,255,0.05)" if status == 'active' else "rgba(128,128,128,0.1)"})
|
|
|
|
def _create_no_models_message(self) -> html.Div:
|
|
"""Create message when no models are loaded"""
|
|
return html.Div([
|
|
html.H6([
|
|
html.I(className="fas fa-exclamation-triangle me-2 text-warning"),
|
|
"No Models Loaded"
|
|
], className="mb-2"),
|
|
html.P("No machine learning models are currently loaded. Check orchestrator status.",
|
|
className="text-muted small")
|
|
])
|
|
|
|
def _create_training_status_section(self, training_status: Dict[str, Any]) -> html.Div:
|
|
"""Create the training status section"""
|
|
if training_status.get('error'):
|
|
return html.Div([
|
|
html.Hr(),
|
|
html.H6([
|
|
html.I(className="fas fa-exclamation-triangle me-2 text-danger"),
|
|
"Training Status Error"
|
|
], className="mb-2"),
|
|
html.P(f"Error: {training_status['error']}", className="text-danger small")
|
|
])
|
|
|
|
is_training = training_status.get('is_training', False)
|
|
|
|
return html.Div([
|
|
html.Hr(),
|
|
html.H6([
|
|
html.I(className="fas fa-brain me-2 text-secondary"),
|
|
"Training Status"
|
|
], className="mb-2"),
|
|
|
|
html.Div([
|
|
html.Span("Status: ", className="text-muted small"),
|
|
html.Span("ACTIVE" if is_training else "INACTIVE",
|
|
className=f"small fw-bold {'text-success' if is_training else 'text-warning'}"),
|
|
html.Span(f" | Iteration: {training_status.get('training_iteration', 0):,}",
|
|
className="text-info small ms-2")
|
|
], className="mb-1"),
|
|
|
|
html.Div([
|
|
html.Span("Buffer: ", className="text-muted small"),
|
|
html.Span(f"{training_status.get('experience_buffer_size', 0):,}",
|
|
className="text-success small"),
|
|
html.Span(" | Updated: ", className="text-muted small"),
|
|
html.Span(f"{training_status.get('last_update', 'N/A')}",
|
|
className="text-muted small")
|
|
], className="mb-0")
|
|
])
|
|
|
|
def _create_performance_section(self, performance_metrics: Dict[str, Any]) -> html.Div:
|
|
"""Create the performance metrics section"""
|
|
if performance_metrics.get('error'):
|
|
return html.Div([
|
|
html.Hr(),
|
|
html.P(f"Performance metrics error: {performance_metrics['error']}",
|
|
className="text-danger small")
|
|
])
|
|
|
|
return html.Div([
|
|
html.Hr(),
|
|
html.H6([
|
|
html.I(className="fas fa-chart-line me-2 text-primary"),
|
|
"System Performance"
|
|
], className="mb-2"),
|
|
|
|
html.Div([
|
|
html.Span("Decision Fusion: ", className="text-muted small"),
|
|
html.Span("ON" if performance_metrics.get('decision_fusion_active') else "OFF",
|
|
className=f"small {'text-success' if performance_metrics.get('decision_fusion_active') else 'text-muted'}"),
|
|
html.Span(" | COB: ", className="text-muted small"),
|
|
html.Span("ON" if performance_metrics.get('cob_integration_active') else "OFF",
|
|
className=f"small {'text-success' if performance_metrics.get('cob_integration_active') else 'text-muted'}")
|
|
], className="mb-1"),
|
|
|
|
html.Div([
|
|
html.Span("Tracking: ", className="text-muted small"),
|
|
html.Span(f"{performance_metrics.get('symbols_tracking', 0)} symbols",
|
|
className="text-info small"),
|
|
html.Span(" | Decisions: ", className="text-muted small"),
|
|
html.Span(f"{performance_metrics.get('recent_decisions', 0):,}",
|
|
className="text-primary small")
|
|
], className="mb-0")
|
|
])
|
|
|
|
def _create_signal_stats_display(self, signal_stats: Dict[str, Any]) -> List[html.Div]:
|
|
"""Create display elements for signal generation statistics"""
|
|
if not signal_stats or not any(signal_stats.values()):
|
|
return []
|
|
|
|
buy_signals = signal_stats.get('buy_signals', 0)
|
|
sell_signals = signal_stats.get('sell_signals', 0)
|
|
hold_signals = signal_stats.get('hold_signals', 0)
|
|
total_signals = signal_stats.get('total_signals', 0)
|
|
|
|
if total_signals == 0:
|
|
return []
|
|
|
|
# Calculate percentages - ensure all values are numeric
|
|
buy_signals = buy_signals or 0
|
|
sell_signals = sell_signals or 0
|
|
hold_signals = hold_signals or 0
|
|
total_signals = total_signals or 0
|
|
|
|
buy_pct = (buy_signals / total_signals * 100) if total_signals > 0 else 0
|
|
sell_pct = (sell_signals / total_signals * 100) if total_signals > 0 else 0
|
|
hold_pct = (hold_signals / total_signals * 100) if total_signals > 0 else 0
|
|
|
|
return [
|
|
html.Div([
|
|
html.Span("Signals: ", className="text-muted small"),
|
|
html.Span(f"B:{buy_signals}({buy_pct:.0f}%)", className="text-success small"),
|
|
html.Span(" | ", className="text-muted small"),
|
|
html.Span(f"S:{sell_signals}({sell_pct:.0f}%)", className="text-danger small"),
|
|
html.Span(" | ", className="text-muted small"),
|
|
html.Span(f"H:{hold_signals}({hold_pct:.0f}%)", className="text-warning small")
|
|
], className="mb-1"),
|
|
|
|
html.Div([
|
|
html.Span("Total: ", className="text-muted small"),
|
|
html.Span(f"{total_signals:,}", className="text-primary small fw-bold"),
|
|
*([
|
|
html.Span(" | Accuracy: ", className="text-muted small"),
|
|
html.Span(f"{signal_stats.get('accuracy', 0):.1f}%",
|
|
className=f"small fw-bold {'text-success' if signal_stats.get('accuracy', 0) > 60 else 'text-warning' if signal_stats.get('accuracy', 0) > 40 else 'text-danger'}")
|
|
] if signal_stats.get('accuracy', 0) > 0 else [])
|
|
], className="mb-1")
|
|
]
|
|
|
|
def _create_performance_metrics_display(self, model_data: Dict[str, Any]) -> List[html.Div]:
|
|
"""Create display elements for performance metrics"""
|
|
elements = []
|
|
|
|
# Win rate and accuracy
|
|
signal_stats = model_data.get('signal_stats', {})
|
|
loss_metrics = model_data.get('loss_metrics', {})
|
|
|
|
# Safely get numeric values
|
|
win_rate = signal_stats.get('win_rate', 0) or 0
|
|
accuracy = signal_stats.get('accuracy', 0) or 0
|
|
|
|
if win_rate > 0 or accuracy > 0:
|
|
|
|
elements.append(html.Div([
|
|
html.Span("Performance: ", className="text-muted small"),
|
|
*([
|
|
html.Span(f"Win: {win_rate:.1f}%",
|
|
className=f"small fw-bold {'text-success' if win_rate > 55 else 'text-warning' if win_rate > 45 else 'text-danger'}"),
|
|
html.Span(" | ", className="text-muted small")
|
|
] if win_rate > 0 else []),
|
|
*([
|
|
html.Span(f"Acc: {accuracy:.1f}%",
|
|
className=f"small fw-bold {'text-success' if accuracy > 60 else 'text-warning' if accuracy > 40 else 'text-danger'}")
|
|
] if accuracy > 0 else [])
|
|
], className="mb-1"))
|
|
|
|
# Loss improvement
|
|
if loss_metrics.get('improvement', 0) != 0:
|
|
improvement = loss_metrics.get('improvement', 0)
|
|
elements.append(html.Div([
|
|
html.Span("Improvement: ", className="text-muted small"),
|
|
html.Span(f"{improvement:+.1f}%",
|
|
className=f"small fw-bold {'text-success' if improvement > 0 else 'text-danger'}")
|
|
], className="mb-1"))
|
|
|
|
return elements |