src/core/reflexive_layer.py

from dataclasses import dataclass
from typing import Dict, List, Any

@dataclass
class ReflectionOutput:
    insights: Dict[str, Any]
    adjustments: List[str]
    consciousness_state: float
    self_awareness_metrics: Dict[str, float]

class ReflexiveLayer:
    def __init__(self):
        self.meta_cognitive_monitor = MetaCognitiveMonitor()
        self.self_evaluation_system = SelfEvaluationSystem()
        self.consciousness_threshold = 0.7
        self.reflection_history = []
        
    def process_reflection(self, current_state):
        monitoring_results = self.meta_cognitive_monitor.analyze(current_state)
        evaluation_results = self.self_evaluation_system.evaluate(monitoring_results)
        return self._generate_reflection_output(monitoring_results, evaluation_results)
    
    def _generate_reflection_output(self, monitoring_results, evaluation_results):
        output = ReflectionOutput(
            insights=self._extract_insights(monitoring_results),
            adjustments=evaluation_results.recommendations,
            consciousness_state=self._calculate_consciousness_state(),
            self_awareness_metrics=self._compute_awareness_metrics()
        )
        self.reflection_history.append(output)
        return output
    
    def _extract_insights(self, monitoring_results):
        return {
            'cognitive_patterns': self._analyze_cognitive_patterns(),
            'learning_trends': self._analyze_learning_trends(),
            'attention_distribution': monitoring_results.attention_focus,
            'processing_efficiency': monitoring_results.processing_efficiency
        }
    
    def _calculate_consciousness_state(self):
        # Implementation of consciousness state calculation
        pass
    
    def _compute_awareness_metrics(self):
        # Implementation of self-awareness metrics computation
        pass