suppress_callback_exceptions
This commit is contained in:
Dobromir Popov
@ -327,7 +327,7 @@ class CleanTradingDashboard:
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self.app = Dash(__name__, external_stylesheets=[
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'https://cdn.jsdelivr.net/npm/bootstrap@5.1.3/dist/css/bootstrap.min.css',
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'https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css'
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])
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], suppress_callback_exceptions=True)
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# Suppress Dash development mode logging
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self.app.enable_dev_tools(debug=False, dev_tools_silence_routes_logging=True)
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@ -864,14 +864,32 @@ class CleanTradingDashboard:
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available_models[model_name] = {'name': model_name, 'type': 'unknown'}
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logger.debug(f"Found {len(toggle_models)} models in toggle states")
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# Apply model name mapping to match orchestrator's internal mapping
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# This ensures component IDs match what the orchestrator expects
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mapped_models = {}
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model_mapping = {
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'dqn_agent': 'dqn',
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'enhanced_cnn': 'cnn',
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'extrema_trainer': 'extrema_trainer',
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'decision': 'decision_fusion',
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'cob_rl': 'cob_rl',
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'transformer': 'transformer'
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}
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for model_name, model_info in available_models.items():
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# Use mapped name if available, otherwise use original name
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mapped_name = model_mapping.get(model_name, model_name)
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mapped_models[mapped_name] = model_info
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logger.debug(f"Mapped model name: {model_name} -> {mapped_name}")
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# Fallback: Add known models if none found
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if not available_models:
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if not mapped_models:
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fallback_models = ['dqn', 'cnn', 'cob_rl', 'decision_fusion', 'transformer']
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for model_name in fallback_models:
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available_models[model_name] = {'name': model_name, 'type': 'fallback'}
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mapped_models[model_name] = {'name': model_name, 'type': 'fallback'}
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logger.warning(f"Using fallback models: {fallback_models}")
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return available_models
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return mapped_models
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except Exception as e:
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logger.error(f"Error getting available models: {e}")
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@ -916,13 +934,25 @@ class CleanTradingDashboard:
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enabled = bool(value and len(value) > 0) # Convert list to boolean
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if self.orchestrator:
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# Map component model name back to orchestrator's expected model name
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reverse_mapping = {
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'dqn': 'dqn_agent',
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'cnn': 'enhanced_cnn',
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'decision_fusion': 'decision',
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'extrema_trainer': 'extrema_trainer',
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'cob_rl': 'cob_rl',
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'transformer': 'transformer'
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}
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orchestrator_model_name = reverse_mapping.get(model_name, model_name)
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# Update orchestrator toggle state
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if toggle_type == 'inference':
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self.orchestrator.set_model_toggle_state(model_name, inference_enabled=enabled)
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self.orchestrator.set_model_toggle_state(orchestrator_model_name, inference_enabled=enabled)
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elif toggle_type == 'training':
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self.orchestrator.set_model_toggle_state(model_name, training_enabled=enabled)
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self.orchestrator.set_model_toggle_state(orchestrator_model_name, training_enabled=enabled)
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logger.info(f"Model {model_name} {toggle_type} toggle: {enabled}")
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logger.info(f"Model {model_name} ({orchestrator_model_name}) {toggle_type} toggle: {enabled}")
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# Update dashboard state variables for backward compatibility
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self._update_dashboard_state_variable(model_name, toggle_type, enabled)
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@ -3651,7 +3681,17 @@ class CleanTradingDashboard:
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available_models = self._get_available_models()
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toggle_states = {}
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for model_name in available_models.keys():
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toggle_states[model_name] = self.orchestrator.get_model_toggle_state(model_name)
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# Map component model name to orchestrator model name for getting toggle state
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reverse_mapping = {
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'dqn': 'dqn_agent',
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'cnn': 'enhanced_cnn',
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'decision_fusion': 'decision',
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'extrema_trainer': 'extrema_trainer',
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'cob_rl': 'cob_rl',
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'transformer': 'transformer'
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}
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orchestrator_model_name = reverse_mapping.get(model_name, model_name)
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toggle_states[model_name] = self.orchestrator.get_model_toggle_state(orchestrator_model_name)
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else:
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# Fallback to default states for known models
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toggle_states = {
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@ -3711,8 +3751,19 @@ class CleanTradingDashboard:
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try:
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if self.orchestrator:
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# Map component model name to orchestrator model name for getting statistics
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reverse_mapping = {
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'dqn': 'dqn_agent',
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'cnn': 'enhanced_cnn',
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'decision_fusion': 'decision',
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'extrema_trainer': 'extrema_trainer',
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'cob_rl': 'cob_rl',
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'transformer': 'transformer'
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}
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orchestrator_model_name = reverse_mapping.get(model_name, model_name)
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# Use the new model statistics system
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model_stats = self.orchestrator.get_model_statistics(model_name.lower())
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model_stats = self.orchestrator.get_model_statistics(orchestrator_model_name)
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if model_stats:
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# Last inference time
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timing['last_inference'] = model_stats.last_inference_time
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@ -3755,7 +3806,7 @@ class CleanTradingDashboard:
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dqn_prediction_count = len(self.recent_decisions) if signal_generation_active else 0
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# Get latest DQN prediction from orchestrator statistics
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dqn_stats = orchestrator_stats.get('dqn_agent')
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dqn_stats = orchestrator_stats.get('dqn_agent') # Use orchestrator's internal name
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if dqn_stats and dqn_stats.predictions_history:
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# Get the most recent prediction
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latest_pred = list(dqn_stats.predictions_history)[-1]
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@ -3786,8 +3837,8 @@ class CleanTradingDashboard:
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last_confidence = 0.68
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last_timestamp = datetime.now().strftime('%H:%M:%S')
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# Get real DQN statistics from orchestrator (try both old and new names)
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dqn_stats = orchestrator_stats.get('dqn_agent') or orchestrator_stats.get('dqn')
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# Get real DQN statistics from orchestrator (use orchestrator's internal name)
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dqn_stats = orchestrator_stats.get('dqn_agent')
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dqn_current_loss = dqn_stats.current_loss if dqn_stats else None
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dqn_best_loss = dqn_stats.best_loss if dqn_stats else None
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dqn_accuracy = dqn_stats.accuracy if dqn_stats else None
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@ -3867,8 +3918,8 @@ class CleanTradingDashboard:
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cnn_state = model_states.get('cnn', {})
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cnn_timing = get_model_timing_info('CNN')
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# Get real CNN statistics from orchestrator (try both old and new names)
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cnn_stats = orchestrator_stats.get('enhanced_cnn') or orchestrator_stats.get('cnn')
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# Get real CNN statistics from orchestrator (use orchestrator's internal name)
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cnn_stats = orchestrator_stats.get('enhanced_cnn')
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cnn_active = cnn_stats is not None
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# Get latest CNN prediction from orchestrator statistics
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@ -4095,7 +4146,10 @@ class CleanTradingDashboard:
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# 4. COB RL Model Status - using orchestrator SSOT
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cob_state = model_states.get('cob_rl', {})
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cob_timing = get_model_timing_info('COB_RL')
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cob_active = True
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# Get real COB RL statistics from orchestrator (use orchestrator's internal name)
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cob_stats = orchestrator_stats.get('cob_rl')
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cob_active = cob_stats is not None
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cob_predictions_count = len(self.recent_decisions) * 2
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# Get COB RL toggle states
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@ -4154,10 +4208,8 @@ class CleanTradingDashboard:
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decision_inference_enabled = decision_toggle_state.get("inference_enabled", True)
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decision_training_enabled = decision_toggle_state.get("training_enabled", True)
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# Get real decision fusion statistics from orchestrator
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decision_stats = None
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if self.orchestrator and hasattr(self.orchestrator, 'model_statistics'):
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decision_stats = self.orchestrator.model_statistics.get('decision_fusion')
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# Get real decision fusion statistics from orchestrator (use orchestrator's internal name)
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decision_stats = orchestrator_stats.get('decision')
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# Get real last prediction
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last_prediction = 'HOLD'
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