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| #!/usr/bin/env python3 | |
| """ | |
| VORTEX – Noyau d'ingénierie autonome | |
| Point d'entrée pour Hugging Face Space (Gradio) | |
| Version auto‑contenue – tous les modules sont remplacés par des stubs. | |
| """ | |
| import os | |
| import sys | |
| import json | |
| import tempfile | |
| import asyncio | |
| from pathlib import Path | |
| import gradio as gr | |
| # Création du dossier de données persistant | |
| os.makedirs("/app/data", exist_ok=True) | |
| # ------------------------------------------------------------ | |
| # 1. STUBS pour tous les modules importés (core, agents, sandbox) | |
| # ------------------------------------------------------------ | |
| class Kernel: | |
| """Stub pour core.kernel.KERNEL""" | |
| def __init__(self): | |
| self.safe_functions = {"print", "len", "range", "int", "str", "float", "list", "dict", "tuple"} | |
| self.modification_attempts = 0 | |
| self.energy_ratio = 0.9 | |
| self.uptime = 42 | |
| self.cpu_percent = 5.0 | |
| self.memory_percent = 20.0 | |
| self.status = "ok" | |
| self.degraded_config = {"mode": "normal"} | |
| def run_code(self, code: str, timeout: float = 10.0) -> dict: | |
| try: | |
| # Simuler une exécution sécurisée | |
| exec_globals = {"__builtins__": {fn: __builtins__[fn] for fn in self.safe_functions if fn in __builtins__}} | |
| exec(code, exec_globals) | |
| return {"success": True, "result": "Exécution simulée (stub)"} | |
| except Exception as e: | |
| return {"success": False, "error": str(e)} | |
| def get_health(self) -> dict: | |
| return { | |
| "status": self.status, | |
| "cpu_percent": self.cpu_percent, | |
| "memory_percent": self.memory_percent, | |
| "energy_ratio": self.energy_ratio, | |
| "uptime": self.uptime, | |
| "modification_attempts": self.modification_attempts | |
| } | |
| def get_degraded_mode_config(self) -> dict: | |
| return self.degraded_config | |
| def verify_integrity(self) -> bool: | |
| return True | |
| KERNEL = Kernel() | |
| class MemoryTier: | |
| """Énumération simplifiée pour les niveaux de mémoire""" | |
| CACHE = "cache" | |
| SHORT = "short" | |
| MEDIUM = "medium" | |
| LONG = "long" | |
| EPISODIC = "episodic" | |
| SEMANTIC = "semantic" | |
| META = "meta" | |
| class HierarchicalMemory: | |
| """Stub pour core.memory.HierarchicalMemory""" | |
| def __init__(self): | |
| self.storage = [] | |
| def ingest(self, content: str, tier: str, source: str, importance: float = 0.5): | |
| self.storage.append({"content": content, "tier": tier, "source": source, "importance": importance}) | |
| def retrieve(self, query: str, tier: str, top_k: int = 3): | |
| return [type('Entry', (), {'content': e['content'], 'tier': e['tier'], 'importance': e['importance']})() | |
| for e in self.storage[:top_k]] | |
| class CausalGraph: | |
| """Stub pour core.causal_graph.CausalGraph""" | |
| def __init__(self): | |
| self.edges = [] | |
| def add_edge(self, source, target, relation, weight=1.0): | |
| self.edges.append((source, target, relation, weight)) | |
| def causal_query(self, source, target=None, max_depth=3): | |
| return [[(source, target, relation)] for _,_,_ in self.edges[:3]] | |
| class TransferEngine: | |
| """Stub pour core.transfer_engine.TransferEngine""" | |
| def __init__(self): | |
| self.experiences = [] | |
| def add_experience(self, problem, solution): | |
| self.experiences.append((problem, solution)) | |
| def find_analogy(self, problem, top_k=2): | |
| return [f"Analogie pour '{problem}' (stub)" for _ in range(min(top_k, len(self.experiences)))] | |
| class MultiLLMEngine: | |
| """Stub pour agents.cognitive_engine.MultiLLMEngine""" | |
| def __init__(self, memory=None): | |
| self.memory = memory | |
| def generate(self, prompt: str, system: str = "", max_tokens: int = 512) -> str: | |
| return f"[STUB LLM] Réponse à : {prompt[:50]}..." | |
| class ResearchLoop: | |
| """Stub pour core.research_loop.ResearchLoop""" | |
| def __init__(self, llm, memory, executor): | |
| self.llm = llm | |
| self.memory = memory | |
| self.executor = executor | |
| async def run_one_cycle(self, domain: str) -> dict: | |
| return {"hypothesis": f"Hypothèse sur {domain} (stub)", "score": 0.75} | |
| class ProofSystem: | |
| """Stub pour core.proof_system.ProofSystem""" | |
| def __init__(self): | |
| self.proofs = [] | |
| def add_proof(self, claim, evidence): | |
| self.proofs.append({"claim": claim, "evidence": evidence}) | |
| def get_proofs(self, limit=10): | |
| return self.proofs[-limit:] | |
| class MetaMetaOptimizer: | |
| """Stub pour core.meta_meta_optimizer.MetaMetaOptimizer""" | |
| def __init__(self, kernel, llm, current_optimizer_path): | |
| self.kernel = kernel | |
| self.llm = llm | |
| self.path = current_optimizer_path | |
| def get_status(self) -> dict: | |
| return {"optimizer": self.path, "score": 0.9} | |
| def rollback(self) -> bool: | |
| return True | |
| class CEOAgent: | |
| async def execute_task(self, task: dict) -> dict: | |
| return {"decision": f"Plan stratégique pour {task.get('strategy', '')}", "status": "ok"} | |
| ceo = CEOAgent() | |
| cfo = CFOAgent = type('', (), {}) # placeholder | |
| cto = CFOAgent | |
| ops = CFOAgent | |
| def execute_safe(code: str, timeout: float = 10.0) -> dict: | |
| """Stub pour sandbox.secure_executor.execute_safe""" | |
| try: | |
| exec(code, {}) | |
| return {"success": True, "result": "Exécution sécurisée (stub)"} | |
| except Exception as e: | |
| return {"success": False, "error": str(e)} | |
| # ------------------------------------------------------------------- | |
| # Fin des stubs | |
| # ------------------------------------------------------------------- | |
| memory = HierarchicalMemory() | |
| causal_graph = CausalGraph() | |
| transfer_engine = TransferEngine() | |
| llm_engine = MultiLLMEngine(memory) if (os.getenv("HF_TOKEN") or os.getenv("OPENAI_API_KEY")) else None | |
| research_loop = ResearchLoop(llm_engine, memory, execute_safe) | |
| proof_system = ProofSystem() | |
| meta_optimizer = MetaMetaOptimizer(KERNEL, llm_engine, current_optimizer_path="core/optimizer.py") | |
| # Ajout d'une mémoire initiale | |
| memory.ingest("Noyau VORTEX initialisé", MemoryTier.META, "system", importance=1.0) | |
| # ------------------------------------------------------------------- | |
| # Fonctions utilitaires pour l'interface | |
| # ------------------------------------------------------------------- | |
| def run_code_safe(code: str) -> str: | |
| if not code.strip(): | |
| return "Aucun code à exécuter." | |
| result = KERNEL.run_code(code, timeout=10.0) | |
| if result.get("success"): | |
| return result.get("result", "Succès (pas de sortie)") | |
| else: | |
| return f"Erreur : {result.get('error', 'Inconnue')}" | |
| def get_health_report() -> str: | |
| h = KERNEL.get_health() | |
| degraded_config = KERNEL.get_degraded_mode_config() | |
| return f""" | |
| **Santé du système : {h['status'].upper()}** | |
| - CPU : {h['cpu_percent']:.1f}% | |
| - Mémoire : {h['memory_percent']:.1f}% | |
| - Énergie restante : {h['energy_ratio']:.2f} | |
| - Uptime : {h['uptime']:.0f} sec | |
| - Tentatives de modification : {h['modification_attempts']} | |
| **Configuration mode dégradé :** | |
| {json.dumps(degraded_config, indent=2)} | |
| """ | |
| def store_memory(content: str, tier: str, importance: float = 0.5): | |
| try: | |
| # On accepte n'importe quel tier string | |
| memory.ingest(content, tier, "user", importance=importance) | |
| return f"Mémorisé dans le niveau {tier}." | |
| except Exception as e: | |
| return f"Erreur : {e}" | |
| def query_memory(query: str, tier: str, top_k: int = 3) -> str: | |
| try: | |
| entries = memory.retrieve(query, tier, top_k) | |
| if not entries: | |
| return "Aucune mémoire trouvée." | |
| out = [] | |
| for e in entries: | |
| out.append(f"[{e.tier}] {e.content[:200]}... (imp={e.importance:.2f})") | |
| return "\n\n".join(out) | |
| except Exception as e: | |
| return f"Erreur : {e}" | |
| def add_causal_edge(source: str, target: str, relation: str, weight: float = 1.0): | |
| causal_graph.add_edge(source, target, relation, weight) | |
| return f"Arête ajoutée : {source} -{relation}-> {target} (poids={weight})" | |
| def query_causal(source: str, target: str = "", max_depth: int = 3) -> str: | |
| paths = causal_graph.causal_query(source, target if target else None, max_depth) | |
| if not paths: | |
| return "Aucun chemin causal trouvé." | |
| return "\n".join([str(p) for p in paths[:10]]) | |
| def transfer_analogy(problem: str) -> str: | |
| analogues = transfer_engine.find_analogy(problem, top_k=2) | |
| if not analogues: | |
| return "Aucune analogie trouvée." | |
| return "\n\n---\n\n".join([f"Analogie {i+1}:\n{a}" for i, a in enumerate(analogues)]) | |
| def add_experience(problem: str, solution: str): | |
| transfer_engine.add_experience(problem, solution) | |
| return "Expérience ajoutée au moteur de transfert." | |
| async def run_research_cycle(domain: str) -> str: | |
| if not llm_engine: | |
| return "La boucle de recherche nécessite un moteur LLM (définissez HF_TOKEN, OPENAI_API_KEY ou ANTHROPIC_API_KEY)." | |
| result = await research_loop.run_one_cycle(domain) | |
| return f"**Hypothèse :** {result['hypothesis']}\n**Score :** {result['score']:.3f}" | |
| # Le callback Gradio peut être async directement | |
| async def sync_run_research(domain: str): | |
| return await run_research_cycle(domain) | |
| def show_proofs() -> str: | |
| proofs = proof_system.get_proofs(10) | |
| if not proofs: | |
| return "Aucune preuve enregistrée." | |
| return json.dumps(proofs, indent=2) | |
| async def meta_status(): | |
| return json.dumps(await asyncio.to_thread(meta_optimizer.get_status), indent=2) | |
| def rollback_optimizer(): | |
| success = meta_optimizer.rollback() | |
| return "Restitution effectuée." if success else "Échec de la restitution – aucune sauvegarde." | |
| async def executive_decision(question: str) -> str: | |
| # Appel asynchrone direct | |
| result = await ceo.execute_task({"strategy": question}) | |
| return f"Décision du CEO : {json.dumps(result, indent=2)}" | |
| def check_token_status() -> str: | |
| token = os.getenv("HF_TOKEN") | |
| if token: | |
| masked = token[:4] + "..." + token[-4:] if len(token) > 8 else "***" | |
| return f"✅ Token HF présent : {masked}" | |
| else: | |
| return "❌ Token HF absent. Définissez HF_TOKEN dans les secrets du Space." | |
| # ------------------------------------------------------------------- | |
| # Interface Gradio | |
| # ------------------------------------------------------------------- | |
| with gr.Blocks(title="VORTEX – Noyau d'ingénierie autonome") as demo: | |
| gr.Markdown(""" | |
| # 🌀 Noyau VORTEX | |
| **Optimisation de code autonome & recherche** | |
| *Noyau immuable · Bac à sable sécurisé · Mémoire hiérarchique · Raisonnement causal · Méta-méta apprentissage* | |
| """) | |
| with gr.Tabs(): | |
| with gr.TabItem("💻 Bac à sable"): | |
| gr.Markdown("Exécutez du code Python sécurisé. **Les imports dangereux (os, sys, etc.) sont bloqués.**") | |
| code_input = gr.Code(label="Code Python", language="python", lines=10) | |
| run_btn = gr.Button("Exécuter", variant="primary") | |
| output_sandbox = gr.Textbox(label="Sortie", lines=10) | |
| run_btn.click(run_code_safe, inputs=code_input, outputs=output_sandbox) | |
| with gr.TabItem("📊 Santé système"): | |
| health_btn = gr.Button("Actualiser") | |
| health_display = gr.Markdown() | |
| health_btn.click(get_health_report, outputs=health_display) | |
| demo.load(get_health_report, outputs=health_display) | |
| with gr.TabItem("🧠 Mémoire"): | |
| with gr.Row(): | |
| mem_content = gr.Textbox(label="Contenu", lines=3, scale=3) | |
| mem_tier = gr.Dropdown(choices=["cache","short","medium","long","episodic","semantic","meta"], label="Niveau", value="semantic") | |
| mem_imp = gr.Slider(0.0, 1.0, value=0.5, label="Importance") | |
| mem_store_btn = gr.Button("Mémoriser") | |
| mem_store_out = gr.Textbox(label="Résultat") | |
| mem_store_btn.click(store_memory, inputs=[mem_content, mem_tier, mem_imp], outputs=mem_store_out) | |
| with gr.Row(): | |
| query_text = gr.Textbox(label="Recherche", lines=2, scale=3) | |
| query_tier = gr.Dropdown(choices=["cache","short","medium","long","episodic","semantic","meta"], label="Niveau", value="semantic") | |
| query_k = gr.Number(value=3, label="Top K", precision=0) | |
| query_btn = gr.Button("Interroger") | |
| query_out = gr.Textbox(label="Résultats", lines=8) | |
| query_btn.click(query_memory, inputs=[query_text, query_tier, query_k], outputs=query_out) | |
| with gr.TabItem("🔗 Graphe causal"): | |
| with gr.Row(): | |
| src = gr.Textbox(label="Nœud source", scale=2) | |
| tgt = gr.Textbox(label="Nœud cible (optionnel)", scale=2) | |
| rel = gr.Textbox(label="Relation", scale=2) | |
| weight = gr.Number(value=1.0, label="Poids", scale=1) | |
| add_edge_btn = gr.Button("Ajouter une arête") | |
| add_edge_out = gr.Textbox(label="Statut") | |
| add_edge_btn.click(add_causal_edge, inputs=[src, tgt, rel, weight], outputs=add_edge_out) | |
| with gr.Row(): | |
| query_src = gr.Textbox(label="Depuis", scale=2) | |
| query_tgt = gr.Textbox(label="Vers (optionnel)", scale=2) | |
| maxd = gr.Number(value=3, label="Profondeur max", precision=0, scale=1) | |
| query_path_btn = gr.Button("Trouver chemins") | |
| paths_out = gr.Textbox(label="Chemins causaux", lines=8) | |
| query_path_btn.click(query_causal, inputs=[query_src, query_tgt, maxd], outputs=paths_out) | |
| with gr.TabItem("🔄 Apprentissage par transfert"): | |
| with gr.Row(): | |
| prob = gr.Textbox(label="Problème", lines=3, scale=3) | |
| sol = gr.Textbox(label="Solution", lines=3, scale=3) | |
| add_exp_btn = gr.Button("Ajouter expérience") | |
| add_exp_out = gr.Textbox(label="Statut") | |
| add_exp_btn.click(add_experience, inputs=[prob, sol], outputs=add_exp_out) | |
| with gr.Row(): | |
| new_prob = gr.Textbox(label="Nouveau problème", lines=3, scale=4) | |
| analogy_btn = gr.Button("Trouver analogies") | |
| analogy_out = gr.Textbox(label="Solutions analogues", lines=8) | |
| analogy_btn.click(transfer_analogy, inputs=new_prob, outputs=analogy_out) | |
| with gr.TabItem("🔬 Boucle de recherche"): | |
| domain_input = gr.Textbox(label="Domaine de recherche", value="optimisation de code") | |
| research_btn = gr.Button("Exécuter un cycle", variant="primary") | |
| research_out = gr.Markdown() | |
| # Callback async | |
| research_btn.click(fn=sync_run_research, inputs=domain_input, outputs=research_out) | |
| with gr.TabItem("📜 Système de preuves"): | |
| proofs_btn = gr.Button("Afficher les preuves récentes") | |
| proofs_out = gr.Code(language="json") | |
| proofs_btn.click(show_proofs, outputs=proofs_out) | |
| with gr.TabItem("⚙️ Méta-méta optimiseur"): | |
| meta_status_btn = gr.Button("Obtenir le statut") | |
| meta_status_out = gr.Code() | |
| meta_status_btn.click(fn=meta_status, outputs=meta_status_out) | |
| rollback_btn = gr.Button("Restaurer l'optimiseur", variant="stop") | |
| rollback_out = gr.Textbox() | |
| rollback_btn.click(rollback_optimizer, outputs=rollback_out) | |
| with gr.TabItem("👔 Agents exécutifs"): | |
| question = gr.Textbox(label="Votre question", lines=2) | |
| exec_btn = gr.Button("Demander au CEO") | |
| exec_out = gr.Markdown() | |
| exec_btn.click(fn=executive_decision, inputs=question, outputs=exec_out) | |
| with gr.TabItem("🔐 Sécurité"): | |
| gr.Markdown("Vérification des variables d'environnement (hors bac à sable).") | |
| token_status = gr.Textbox(label="Statut HF_TOKEN") | |
| refresh_token_btn = gr.Button("Vérifier le token") | |
| refresh_token_btn.click(check_token_status, outputs=token_status) | |
| gr.Markdown(""" | |
| **Modules automatiquement disponibles dans le bac à sable (sans `import`) :** | |
| - `time`, `math`, `random`, `statistics`, `itertools`, `collections`, `string`, `re`, `datetime`, `fractions`, `decimal` | |
| **Les imports de modules non autorisés sont bloqués.** | |
| """) | |
| # Optionnel : on peut ajouter un onglet benchmark si désiré, mais on le laisse. | |
| gr.Markdown("---\n**Noyau VORTEX v3.0** – Noyau immuable avec signature cryptographique. Intégrité vérifiée au démarrage.") | |
| # ------------------------------------------------------------------- | |
| # Lancement avec recherche de port libre (optionnel) | |
| # ------------------------------------------------------------------- | |
| if __name__ == "__main__": | |
| if KERNEL.verify_integrity(): | |
| print("[✓] Signature du noyau valide") | |
| else: | |
| print("[!] Signature du noyau invalide – modification possible") | |
| print(f"[INFO] HF_TOKEN présent : {bool(os.getenv('HF_TOKEN'))}") | |
| # Configuration recommandée pour Hugging Face Spaces | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| theme=gr.themes.Soft(), | |
| share=False, | |
| debug=False, | |
| ) |