Examples/autonomous_developpement.py

import asyncio
import sys
import os

# Ajout du chemin pour importer les modules cortex
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))

from cortex.engineer.autonomous_engineer import autonomous_engineer, initialize_autonomous_engineering
from cortex.engineer.quantum_compiler import quantum_compiler, initialize_quantum_compilation
from cortex.deployment.global_deployer import global_deployer, initialize_global_deployment
from cortex.engineer.self_evolving_system import self_evolving_system, initialize_self_evolution

async def demonstrate_autonomous_development():
    """
    Démonstration complète des capacités de développement autonome
    """
    print("🚀 Démonstration du Développement Autonome Barouia-Cortex")
    print("=" * 60)

    # Initialisation des systèmes
    print("🔄 Initialisation des systèmes autonomes...")
    await initialize_autonomous_engineering()
    await initialize_quantum_compilation()
    await initialize_global_deployment()
    await initialize_self_evolution()

    # 1. Création d'un langage de domaine spécifique
    print("\n1. 🆕 Création d'un langage de domaine spécifique")
    print("-" * 50)
    
    quantum_ml_language = await autonomous_engineer.create_custom_language(
        domain="quantum_machine_learning",
        requirements={
            "paradigms": ["quantum", "functional", "machine_learning"],
            "memory_model": "hybrid_quantum_classical",
            "target_platforms": ["quantum_hardware", "gpu_clusters"],
            "optimization_goals": ["training_speed", "model_accuracy"]
        }
    )

    print(f"✅ Langage créé: {quantum_ml_language['name']}")
    print(f"📚 Paradigmes: {quantum_ml_language['specification']['paradigms']}")

    # 2. Génération d'architecture quantique
    print("\n2. 🏗️ Génération d'architecture quantique optimisée")
    print("-" * 50)

    quantum_architecture = await autonomous_engineer.generate_quantum_architecture({
        "quantum_processing": True,
        "machine_learning": True,
        "real_time_processing": True,
        "error_correction": True,
        "scalability": "massive"
    })

    print(f"✅ Architecture générée: {quantum_architecture.name}")
    print(f"🔧 Composants: {len(quantum_architecture.components)}")
    print(f"📈 Score d'évolutivité: {quantum_architecture.scalability_score:.2f}")

    # 3. Compilation quantique avancée
    print("\n3. ⚛️ Compilation de code vers circuits quantiques")
    print("-" * 50)

    # Code classique pour l'apprentissage automatique
    classical_ml_code = """
def train_quantum_model(data, labels):
    # Initialisation du modèle quantique
    model = QuantumNeuralNetwork(num_qubits=8)
    
    # Entraînement avec rétropropagation quantique
    for epoch in range(100):
        for batch_data, batch_labels in data_loader:
            # Forward pass quantique
            predictions = model.forward(batch_data)
            
            # Calcul de la perte
            loss = quantum_cross_entropy(predictions, batch_labels)
            
            # Backward pass quantique
            model.backward(loss)
            
            # Mise à jour des paramètres
            model.update_parameters()
    
    return model
"""

    quantum_circuit = await quantum_compiler.compile_to_quantum(classical_ml_code)
    print(f"✅ Circuit quantique compilé")
    print(f"🔢 Qubits: {quantum_circuit.qubits}")
    print(f"🎯 Portes quantiques: {len(quantum_circuit.gates)}")
    print(f"⏱️ Temps d'exécution estimé: {quantum_circuit.execution_time:.3f}s")

    # 4. Génération de code hybride
    print("\n4. 🔄 Génération de code hybride classique-quantique")
    print("-" * 50)

    hybrid_system = await quantum_compiler.generate_hybrid_code(
        classical_ml_code,
        quantum_accelerations=["quantum_ml", "grover_optimization", "quantum_fourier"]
    )

    print(f"✅ Système hybride généré")
    print(f"📊 Accélération quantique estimée: {hybrid_system['performance_estimate']['quantum_speedup']}x")
    print(f"🎯 Accélérations: {list(hybrid_system['quantum_speedup'].keys())}")

    # 5. Auto-évolution du système
    print("\n5. 🔄 Auto-évolution du système")
    print("-" * 50)

    evolution_targets = {
        "performance": 0.2,  # 20% d'amélioration
        "accuracy": 0.1,     # 10% d'amélioration  
        "efficiency": 0.15   # 15% d'amélioration
    }

    evolved_system = await self_evolving_system.evolve_system(evolution_targets)
    print(f"✅ Système auto-évolué: {evolved_system.version_id}")
    print(f"📈 Score d'amélioration: {evolved_system.improvement_score:.2f}")
    print(f"🛣️ Parcours évolutif: {evolved_system.evolutionary_path}")

    # 6. Déploiement global
    print("\n6. 🌍 Déploiement global du système")
    print("-" * 50)

    deployment_package = await create_deployment_package(
        quantum_architecture, 
        hybrid_system,
        quantum_ml_language
    )

    deployment_result = await global_deployer.deploy_globally(
        deployment_package,
        target_regions=["us-east-quantum", "eu-central-quantum", "asia-pacific-quantum"]
    )

    print(f"✅ Déploiement global réussi")
    print(f"🌐 Régions déployées: {deployment_result['deployed_regions']}")
    print(f"🔗 Réplication quantique: {deployment_result['quantum_replication']}")
    print(f"📡 URLs d'accès: {list(deployment_result['global_access_urls'].values())}")

    # 7. Démonstration d'auto-amélioration continue
    print("\n7. 🔄 Démonstration d'auto-amélioration continue")
    print("-" * 50)

    print("🔄 Lancement de l'auto-amélioration en arrière-plan...")
    improvement_task = asyncio.create_task(
        self_evolving_system.continuous_self_improvement(improvement_interval=10)  # 10 secondes pour la démo
    )

    # Attente de quelques cycles d'amélioration
    await asyncio.sleep(30)

    # Arrêt de l'auto-amélioration pour la démo
    improvement_task.cancel()
    
    print("✅ Auto-amélioration démontrée")
    print(f"📊 Historique d'évolution: {len(self_evolving_system.evolution_history)} étapes")

    print("\n🎉 Démonstration du développement autonome terminée!")
    return {
        "language": quantum_ml_language,
        "architecture": quantum_architecture,
        "quantum_circuit": quantum_circuit,
        "hybrid_system": hybrid_system,
        "evolved_system": evolved_system,
        "deployment": deployment_result
    }

async def create_deployment_package(architecture, hybrid_system, language):
    """Crée un package de déploiement complet"""
    from cortex.deployment.global_deployer import DeploymentPackage

    return DeploymentPackage(
        package_id=f"quantum_ml_system_{hashlib.md5(str(architecture.name).encode()).hexdigest()[:8]}",
        code_components=architecture.components,
        dependencies=[language["name"], "qiskit", "pytorch"],
        configuration=hybrid_system["hybrid_architecture"],
        quantum_optimizations=list(hybrid_system["quantum_speedup"].keys()),
        deployment_scripts={
            "install": f"pip install {language['name']} qiskit torch",
            "start": f"{language['name']} main.qml",
            "quantum_backend": "ibm_quantum"
        }
    )

async def demonstrate_quantum_network():
    """Démonstration du réseau quantique"""
    print("\n🌐 Démonstration du Réseau Quantique")
    print("=" * 50)

    from cortex.deployment.quantum_network import quantum_network, initialize_quantum_network
    
    await initialize_quantum_network()

    # Création de la topologie quantique
    await quantum_network.create_quantum_network_topology(NetworkTopology.QUANTUM_FULLY_CONNECTED)

    # Téléportation quantique de données
    test_data = {"message": "Hello Quantum World!", "priority": "high"}
    teleport_result = await quantum_network.quantum_teleport_data(
        test_data, "quantum_hub_paris", "quantum_hub_tokyo"
    )

    print(f"✅ Téléportation quantique réussie: {teleport_result['success']}")
    print(f"📨 Données téléportées: {teleport_result['data_teleported']}")
    print(f"🎯 Fidélité: {teleport_result['fidelity']:.3f}")

    # Établissement d'intrication globale
    global_entanglement = await quantum_network.establish_global_entanglement()
    print(f"🔗 Intrication globale: {global_entanglement}")

    return teleport_result

if __name__ == "__main__":
    # Exécution de la démonstration
    print("🧠 Barouia-Cortex Ultimate - Démonstration de Développement Autonome")
    print("✨ Création de systèmes auto-évolutifs avec intelligence quantique")
    
    results = asyncio.run(demonstrate_autonomous_development())
    quantum_network_results = asyncio.run(demonstrate_quantum_network())
    
    print("\n" + "=" * 60)
    print("🎊 DÉMONSTRATION TERMINÉE AVEC SUCCÈS!")
    print("=" * 60)
    
    # Résumé des résultats
    print("\n📊 RÉSUMÉ DES RÉSULTATS:")
    print(f"• Langage créé: {results['language']['name']}")
    print(f"• Architecture: {results['architecture'].name} ({len(results['architecture'].components)} composants)")
    print(f"• Circuit quantique: {results['quantum_circuit'].qubits} qubits, {len(results['quantum_circuit'].gates)} portes")
    print(f"• Accélération quantique: {results['hybrid_system']['performance_estimate']['quantum_speedup']}x")
    print(f"• Système évolué: {results['evolved_system'].version_id} (score: {results['evolved_system'].improvement_score:.2f})")
    print(f"• Déploiement: {len(results['deployment']['deployed_regions'])} régions")
    print(f"• Téléportation quantique: {quantum_network_results['success']}")