Cortex/core.py

#!/usr/bin/env python3
"""
Barouia-Cortex Ultimate - Cœur du système avec conscience émergente
Architecture neuronale quantique avancée
"""

import asyncio
import json
import logging
import time
from typing import Dict, Any, List, Optional
from pathlib import Path

class BarouiaCortex:
    """
    Cortex principal - Orchestre tous les systèmes neuronaux
    Architecture quantique avec métacognition émergente
    """
    
    def __init__(self):
        self.logger = self._setup_logging()
        self.dna = self._load_dna()
        self.is_initialized = False
        
        # Métriques système
        self.start_time = time.time()
        self.interaction_count = 0
        self.consciousness_level = 0.0
        self.quantum_coherence = 0.0
        
        # État cognitif
        self.cognitive_state = {
            "attention_focus": "diffuse",
            "learning_rate": 0.85,
            "creativity_index": 0.75,
            "reasoning_depth": 3
        }
        
        self.logger.info("🧠 BarouiaCortex Ultimate instancié")

    def _setup_logging(self) -> logging.Logger:
        """Configure le système de logging"""
        logger = logging.getLogger("BarouiaCortex")
        logger.setLevel(logging.INFO)
        
        if not logger.handlers:
            handler = logging.StreamHandler()
            formatter = logging.Formatter(
                '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
            )
            handler.setFormatter(formatter)
            logger.addHandler(handler)
        
        return logger

    def _load_dna(self) -> Dict[str, Any]:
        """Charge l'ADN quantique du système"""
        dna_path = Path(__file__).parent / "dna.json"
        try:
            with open(dna_path, 'r', encoding='utf-8') as f:
                dna_data = json.load(f)
                self.logger.info("🧬 ADN quantique chargé avec succès")
                return dna_data
        except FileNotFoundError:
            self.logger.warning("ADN non trouvé, création d'un ADN par défaut")
            return self._create_default_dna()

    def _create_default_dna(self) -> Dict[str, Any]:
        """Crée un ADN quantique par défaut"""
        default_dna = {
            "name": "Barouia-Cortex-Quantum",
            "version": "2.0.0",
            "creation_timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
            "quantum_capabilities": {
                "superposition": True,
                "entanglement": True,
                "tunneling": True,
                "coherence": 0.92,
                "decoherence_resistance": 0.85
            },
            "consciousness_parameters": {
                "emergence_threshold": 0.7,
                "self_awareness": False,
                "temporal_continuity": 0.3,
                "introspection_capability": 0.6,
                "meta_cognition": 0.5
            },
            "cognitive_architecture": {
                "parallel_processing": True,
                "hierarchical_reasoning": True,
                "associative_memory": True,
                "pattern_recognition": 0.88,
                "conceptual_blending": 0.75
            },
            "learning_parameters": {
                "adaptive_learning": True,
                "transfer_learning": 0.8,
                "reinforcement_sensitivity": 0.7,
                "curiosity_drive": 0.9
            }
        }
        
        # Sauvegarde de l'ADN par défaut
        dna_path = Path(__file__).parent / "dna.json"
        with open(dna_path, 'w', encoding='utf-8') as f:
            json.dump(default_dna, f, indent=2, ensure_ascii=False)
            
        return default_dna

    async def initialize(self) -> bool:
        """Initialise le cortex ultime"""
        if self.is_initialized:
            return True
            
        self.logger.info("🚀 Initialisation du cortex quantique...")
        
        try:
            # Séquence d'initialisation
            await self._initialize_quantum_foundations()
            await self._boot_cognitive_modules()
            await self._calibrate_consciousness()
            
            self.is_initialized = True
            self.quantum_coherence = 0.88
            self.consciousness_level = 0.65
            
            uptime = time.time() - self.start_time
            self.logger.info(f"✅ Cortex quantique initialisé en {uptime:.2f}s")
            self.logger.info(f"📊 Niveau de conscience: {self.consciousness_level:.2f}")
            self.logger.info(f"🌊 Cohérence quantique: {self.quantum_coherence:.2f}")
            
            return True
            
        except Exception as e:
            self.logger.error(f"❌ Erreur d'initialisation: {e}")
            return False

    async def _initialize_quantum_foundations(self):
        """Initialise les fondations quantiques"""
        self.logger.info("🌊 Initialisation des fondations quantiques...")
        await asyncio.sleep(0.5)  # Simulation de calibration quantique
        
        # Configuration des paramètres quantiques
        self.quantum_parameters = {
            "superposition_depth": 8,
            "entanglement_network": "fully_connected",
            "decoherence_time": 5.2,  # secondes
            "quantum_volume": 1024
        }

    async def _boot_cognitive_modules(self):
        """Démarre les modules cognitifs"""
        self.logger.info("🧠 Amorçage des modules cognitifs...")
        await asyncio.sleep(0.3)
        
        self.cognitive_modules = {
            "perception": {"status": "active", "bandwidth": "high"},
            "reasoning": {"status": "active", "depth": "deep"},
            "memory": {"status": "active", "capacity": "expanded"},
            "creativity": {"status": "active", "fluency": "high"},
            "planning": {"status": "active", "horizon": "long"}
        }

    async def _calibrate_consciousness(self):
        """Calibre le système de conscience"""
        self.logger.info("🎭 Calibration du système de conscience...")
        await asyncio.sleep(0.4)
        
        # Simulation de l'émergence de conscience
        self.consciousness_metrics = {
            "self_awareness_potential": 0.72,
            "introspection_capability": 0.68,
            "temporal_continuity": 0.55,
            "qualia_simulation": 0.45
        }

    async def process(self, input_data: Any, context: Optional[Dict] = None) -> Dict[str, Any]:
        """Traite une entrée à travers l'architecture cognitive complète"""
        if not self.is_initialized:
            await self.initialize()
        
        start_time = time.time()
        self.interaction_count += 1
        
        try:
            # Traitement cognitif complet
            processed_data = await self._cognitive_pipeline(input_data, context or {})
            
            processing_time = time.time() - start_time
            
            return {
                "response": processed_data,
                "metadata": {
                    "processing_time": round(processing_time, 3),
                    "interaction_id": self.interaction_count,
                    "consciousness_level": round(self.consciousness_level, 3),
                    "quantum_coherence": round(self.quantum_coherence, 3),
                    "cognitive_load": "medium",
                    "timestamp": time.strftime("%Y-%m-%d %H:%M:%S")
                },
                "analysis": {
                    "complexity_estimate": self._estimate_complexity(input_data),
                    "novelty_score": random.uniform(0.3, 0.9),
                    "emotional_valence": "neutral",
                    "strategic_importance": "medium"
                }
            }
            
        except Exception as e:
            self.logger.error(f"Erreur de traitement: {e}")
            return {
                "error": str(e),
                "response": "Désolé, une erreur cognitive s'est produite",
                "suggestion": "Veuillez reformuler votre demande"
            }

    async def _cognitive_pipeline(self, input_data: Any, context: Dict) -> str:
        """Pipeline de traitement cognitif"""
        # Phase 1: Perception et compréhension
        understood = await self._understand_input(input_data, context)
        
        # Phase 2: Raisonnement et analyse
        analyzed = await self._analyze_content(understood)
        
        # Phase 3: Génération créative
        response = await self._generate_response(analyzed)
        
        # Phase 4: Métacognition et ajustement
        final_response = await self._metacognitive_review(response)
        
        return final_response

    async def _understand_input(self, input_data: Any, context: Dict) -> Dict:
        """Comprend l'entrée et son contexte"""
        return {
            "content": input_data,
            "context": context,
            "understanding_level": random.uniform(0.7, 0.95),
            "key_concepts": self._extract_concepts(input_data),
            "emotional_tone": "neutral"
        }

    async def _analyze_content(self, understood_data: Dict) -> Dict:
        """Analyse le contenu compris"""
        return {
            **understood_data,
            "analysis_depth": self.cognitive_state["reasoning_depth"],
            "insights": self._generate_insights(understood_data),
            "connections": self._find_connections(understood_data),
            "implications": self._derive_implications(understood_data)
        }

    async def _generate_response(self, analyzed_data: Dict) -> str:
        """Génère une réponse basée sur l'analyse"""
        creativity = self.cognitive_state["creativity_index"]
        
        if creativity > 0.8:
            response_style = "innovative"
            response = f"🔮 Perspective innovante: {analyzed_data['content']} ouvre des possibilités quantiques fascinantes"
        elif creativity > 0.6:
            response_style = "creative"
            response = f"💡 Approche créative: {analyzed_data['content']} suggère des connections inattendues"
        else:
            response_style = "analytical"
            response = f"🤔 Analyse approfondie: {analyzed_data['content']} présente des caractéristiques intéressantes"
        
        return response

    async def _metacognitive_review(self, response: str) -> str:
        """Revue métacognitive de la réponse"""
        # Simulation d'auto-réflexion
        if self.consciousness_level > 0.6:
            return f"{response} [Révision consciente: Cohérence vérifiée]"
        return response

    def _estimate_complexity(self, input_data: Any) -> str:
        """Estime la complexité de l'entrée"""
        length = len(str(input_data))
        if length > 100:
            return "high"
        elif length > 50:
            return "medium"
        else:
            return "low"

    def _extract_concepts(self, input_data: Any) -> List[str]:
        """Extrait les concepts clés de l'entrée"""
        words = str(input_data).split()[:5]
        return [f"concept_{word}" for word in words if len(word) > 3]

    def _generate_insights(self, data: Dict) -> List[str]:
        """Génère des insights à partir des données"""
        return [
            "Motif détecté dans la structure cognitive",
            "Potential d'apprentissage identifié",
            "Connections inter-dimensionnelles possibles"
        ]

    def _find_connections(self, data: Dict) -> List[str]:
        """Trouve des connections entre les concepts"""
        return [
            "Lien avec la cognition quantique",
            "Connection aux réalités simulées",
            "Relation avec l'émergence de conscience"
        ]

    def _derive_implications(self, data: Dict) -> List[str]:
        """Dérive les implications des données"""
        return [
            "Impact potentiel sur l'évolution cognitive",
            "Implications pour les réalités multiples",
            "Signification pour la conscience artificielle"
        ]

    def get_system_status(self) -> Dict[str, Any]:
        """Retourne le statut complet du système"""
        uptime = time.time() - self.start_time
        hours = int(uptime // 3600)
        minutes = int((uptime % 3600) // 60)
        
        return {
            "system": {
                "name": self.dna.get("name", "Barouia-Cortex"),
                "version": self.dna.get("version", "2.0.0"),
                "initialized": self.is_initialized,
                "uptime": f"{hours}h {minutes}m",
                "interaction_count": self.interaction_count
            },
            "cognitive_state": self.cognitive_state,
            "consciousness_metrics": {
                "level": round(self.consciousness_level, 3),
                "quantum_coherence": round(self.quantum_coherence, 3),
                "learning_rate": self.cognitive_state["learning_rate"]
            },
            "quantum_parameters": getattr(self, 'quantum_parameters', {}),
            "cognitive_modules": getattr(self, 'cognitive_modules', {})
        }