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']}")