build_vortex.py

#!/usr/bin/env python3
# build_vortex.py – Génère tous les modules VORTEX v5.1 (23 innovations)
import os
from pathlib import Path

BASE = Path("/app")

def write_file(path, content):
    filepath = BASE / path
    filepath.parent.mkdir(parents=True, exist_ok=True)
    filepath.write_text(content, encoding='utf-8')
    print(f"✅ {filepath}")

def build_all():
    # ============================================================
    # 1. agents/cognitive_engine.py
    # ============================================================
    write_file("agents/cognitive_engine.py", '''
import requests, hashlib, json, logging, os
log = logging.getLogger("vortex.llm")
OLLAMA_URL = os.environ.get("OLLAMA_URL","http://localhost:11434")
MODEL_MAIN = os.environ.get("LLM_MODEL","gemma4:e4b")
MODEL_FAST = os.environ.get("LLM_MODEL_FAST","phi3:mini")
TEMP_PROFILES = {"reasoning":0.1,"code_gen":0.35,"creative":0.65,"summary":0.15}
class MultiLLMEngine:
    def __init__(self, memory=None):
        self.memory=memory; self._cache={}; self.tools={}; self.call_stats={"main":0,"fast":0,"errors":0}
    def register_tool(self, name, fn, desc, params):
        self.tools[name] = {"fn":fn,"schema":{"type":"function","function":{"name":name,"description":desc,"parameters":{"type":"object","properties":params,"required":list(params.keys())}}}}
    async def call(self, agent, system, user, max_tokens=512, temperature=0.3, use_cache=True, use_tools=False, fast=False, profile=None, **kw):
        if profile and profile in TEMP_PROFILES: temperature = TEMP_PROFILES[profile]
        model = MODEL_FAST if fast else MODEL_MAIN
        key = hashlib.sha256(f"{model}|{system}|{user}|{max_tokens}|{temperature}".encode()).hexdigest()[:20]
        if use_cache and key in self._cache: return type("R",(),{"content":self._cache[key],"tokens":0,"cached":True})()
        payload = {"model":model,"messages":[{"role":"system","content":system or "Tu es VORTEX."},{"role":"user","content":user}],"options":{"num_predict":max_tokens,"temperature":temperature},"stream":False}
        if use_tools and self.tools: payload["tools"] = [t["schema"] for t in self.tools.values()]
        try:
            resp = requests.post(f"{OLLAMA_URL}/api/chat", json=payload, timeout=180); msg = resp.json().get("message",{})
            if msg.get("tool_calls"):
                for tc in msg["tool_calls"]:
                    fname = tc["function"]["name"]; fargs = tc["function"]["arguments"]
                    if isinstance(fargs, str): fargs = json.loads(fargs)
                    if fname in self.tools:
                        result = self.tools[fname]["fn"](**fargs)
                        payload["messages"] += [msg, {"role":"tool","content":str(result)}]
                        resp = requests.post(f"{OLLAMA_URL}/api/chat", json=payload, timeout=180); msg = resp.json().get("message",{})
            content = msg.get("content","")
            if use_cache: self._cache[key] = content
            self.call_stats["fast" if fast else "main"] += 1
            return type("R",(),{"content":content,"tokens":len(content.split()),"cached":False})
        except Exception as e:
            self.call_stats["errors"] += 1; log.error(f"LLM error: {e}")
            return type("R",(),{"content":f"[Erreur LLM: {e}]","tokens":0,"cached":False})
''')

    # ============================================================
    # 2. core/cybershield.py
    # ============================================================
    write_file("core/cybershield.py", '''
import ast, re, logging
log = logging.getLogger("vortex.cybershield")
CRITICAL_PATTERNS = {
    "shell_injection":    (r"os\\.system|os\\.popen|subprocess\\.(call|Popen|run)", -40),
    "code_injection":     (r"\\beval\\s*\\(|\\bexec\\s*\\(", -35),
    "pickle_exploit":     (r"\\bpickle\\.(loads|load)\\b", -30),
    "import_hijack":      (r"__import__\\s*\\(", -25),
    "file_overwrite":     (r"open\\s*\\([^)]*['\\\"w]['\\\"\\)]", -15),
    "network_exfil":      (r"(requests|urllib|aiohttp|socket)\\.", -10),
}
WARNING_PATTERNS = {
    "bare_except":        (r"except\\s*:", -8),
    "infinite_loop":      (r"while\\s+True(?!.*break)", -12),
    "global_var":         (r"\\bglobal\\s+\\w+", -5),
    "hardcoded_secret":   (r"(password|secret|token|api_key)\\s*=\\s*['\\\"][^'\\\"]{4,}", -15),
}
class CyberShield:
    def __init__(self, llm_engine=None):
        self.llm = llm_engine; self.scan_history = []
    def scan_ast(self, code):
        issues = []
        try: tree = ast.parse(code)
        except SyntaxError as e: return 0.0, [f"SyntaxError: {e}"]
        for node in ast.walk(tree):
            if isinstance(node, (ast.Import, ast.ImportFrom)):
                mods = [a.name.split(".")[0] for a in node.names] if isinstance(node, ast.Import) else ([node.module.split(".")[0]] if node.module else [])
                for m in mods:
                    if m in {"os","subprocess","socket","pickle","ctypes","importlib","shutil","signal","resource","mmap","cffi"}:
                        issues.append(f"Import risqué : {m}")
            if isinstance(node, ast.Call) and isinstance(node.func, ast.Name) and node.func.id in {"eval","exec","compile","__import__"}:
                issues.append(f"Appel dangereux : {node.func.id}()")
        return len(issues), issues
    def scan_patterns(self, code):
        score, issues = 100.0, []
        for name, (pattern, penalty) in {**CRITICAL_PATTERNS, **WARNING_PATTERNS}.items():
            if re.search(pattern, code, re.MULTILINE):
                issues.append(f"{name.replace('_',' ').title()} (−{abs(penalty)}pts)")
                score += penalty
        return max(0, score), issues
    async def full_scan(self, code, context=""):
        ast_cnt, ast_issues = self.scan_ast(code)
        pat_score, pat_issues = self.scan_patterns(code)
        base_score = max(0, pat_score - min(ast_cnt * 20, 60))
        llm_score, llm_note = base_score, ""
        if self.llm and base_score > 20:
            try:
                resp = await self.llm.call(None, "Expert sécurité Python.", f"Analyse sécurité : {code[:1000]}\\nScore actuel : {base_score}\\nRép : SCORE=XX NOTE=...", max_tokens=60, temperature=0.1, fast=True)
                m_s = re.search(r"SCORE=([0-9]+)", resp.content)
                if m_s: llm_score = (float(m_s.group(1)) + base_score) / 2
                m_n = re.search(r"NOTE=(.+)", resp.content)
                if m_n: llm_note = m_n.group(1).strip()
            except: pass
        final = round(llm_score, 1); level = "CRITICAL" if final < 30 else ("WARNING" if final < 65 else "SAFE")
        result = {"score":final,"level":level,"passed":final>=60,"ast_issues":ast_issues,"pattern_issues":pat_issues,"llm_note":llm_note,"all_issues":list(set(ast_issues+pat_issues))[:8]}
        self.scan_history.append(result); log.info(f"CyberShield: {level} ({final}/100)"); return result
    def quick_scan(self, code):
        _, ast_issues = self.scan_ast(code)
        _, pat_issues = self.scan_patterns(code)
        critical = [i for i in pat_issues if any(k in i.lower() for k in ["shell","injection","pickle","import hijack"])]
        return len(ast_issues) == 0 and len(critical) == 0
    def stats(self):
        if not self.scan_history: return {"total":0}
        passed = sum(1 for s in self.scan_history if s["passed"])
        return {"total":len(self.scan_history),"passed":passed,"blocked":len(self.scan_history)-passed,"avg_score":round(sum(s["score"] for s in self.scan_history)/len(self.scan_history),1)}
''')

    # ============================================================
    # 3. core/web_search.py
    # ============================================================
    write_file("core/web_search.py", '''
import asyncio, hashlib, json, logging, time, aiohttp
from pathlib import Path
log = logging.getLogger("vortex.websearch")
CACHE_DIR = Path("data/web_cache"); CACHE_DIR.mkdir(parents=True, exist_ok=True)

class WebSearchAgent:
    def __init__(self, llm_engine=None): self.llm = llm_engine; self.search_log = []
    def _cache_key(self, query): return CACHE_DIR / (hashlib.sha256(query.encode()).hexdigest()[:16] + ".json")
    def _load_cache(self, query):
        k = self._cache_key(query)
        if k.exists():
            data = json.loads(k.read_text())
            if time.time() - data.get("ts", 0) < 3600: return data["results"]
        return None
    def _save_cache(self, query, results):
        self._cache_key(query).write_text(json.dumps({"results": results, "ts": time.time()}, ensure_ascii=False))
    async def search_duckduckgo(self, query, max_results=5):
        cached = self._load_cache(query)
        if cached: return cached
        results = []
        try:
            url = f"https://api.duckduckgo.com/?q={query}&format=json&no_html=1&skip_disambig=1"
            async with aiohttp.ClientSession() as session:
                async with session.get(url, timeout=aiohttp.ClientTimeout(total=8)) as resp:
                    data = await resp.json(content_type=None)
                    for item in data.get("RelatedTopics", [])[:max_results]:
                        if isinstance(item, dict) and "Text" in item:
                            results.append({"title": item.get("Text","")[:100], "snippet": item.get("Text","")[:300], "url": item.get("FirstURL","")})
                    if data.get("Abstract"): results.insert(0, {"title": data.get("Heading",""), "snippet": data.get("Abstract","")[:400], "url": data.get("AbstractURL","")})
        except Exception as e:
            log.warning(f"DuckDuckGo unavailable: {e}")
            results = [{"title": f"Recherche: {query}", "snippet": "Web indisponible.", "url": ""}]
        self._save_cache(query, results); return results
    async def research_for_rsi(self, domain):
        queries = [f"{domain} optimization algorithm Python", f"best {domain} techniques 2024", f"{domain} benchmark comparison"]
        all_snippets = []
        for q in queries[:2]:
            results = await self.search_duckduckgo(q, max_results=3)
            all_snippets.extend([r["snippet"] for r in results if r["snippet"]])
        if not all_snippets: return f"Aucune information web pour : {domain}"
        raw_context = "\\n".join(all_snippets[:6])[:2000]
        if self.llm:
            try:
                resp = await self.llm.call(None, "Tu résumes des résultats de recherche.", f"Résume en 3 points clés les techniques de {domain} :\\n{raw_context}\\n\\nRésumé :", max_tokens=150, temperature=0.15, fast=True, profile="summary")
                summary = resp.content.strip()
                if len(summary) > 20:
                    self.search_log.append({"domain":domain,"summary":summary,"ts":time.time()})
                    return f"[WebSearch] {domain} :\\n{summary}"
            except: pass
        return f"[WebSearch] {domain} :\\n" + raw_context[:500]
''')

    # ============================================================
    # 4. core/gitautofix.py
    # ============================================================
    write_file("core/gitautofix.py", '''
import subprocess, sys, tempfile, shutil, ast, re, logging, time
from pathlib import Path
log = logging.getLogger("vortex.gitautofix")
PATCHES_DIR = Path("data/git_patches"); PATCHES_DIR.mkdir(parents=True, exist_ok=True)

class GitAutoFix:
    def __init__(self, llm_engine, cybershield):
        self.llm=llm_engine; self.shield=cybershield; self.patch_log=[]; self.fix_count=0
    def detect_bugs(self, code):
        bugs = []
        try: ast.parse(code)
        except SyntaxError as e: bugs.append(f"SyntaxError ligne {e.lineno}: {e.msg}")
        patterns = [(r"(\\w+)\\s*=\\s*\\1\\s*\\+\\s*1", "Auto-incrémentation sans déclaration"), (r"for\\s+\\w+\\s+in\\s+range\\([^)]*\\):\\s*$", "Boucle sans corps"), (r"def\\s+\\w+\\([^)]*\\):\\s*$", "Fonction sans corps"), (r"if\\s+\\w+\\s*=\\s*\\w+", "Assignation dans condition (=)")]
        for pattern, msg in patterns:
            if re.search(pattern, code, re.MULTILINE): bugs.append(msg)
        return bugs
    def run_tests(self, code):
        with tempfile.NamedTemporaryFile(mode="w", suffix=".py", delete=False) as f:
            f.write(code); tmp = Path(f.name)
        try:
            proc = subprocess.run([sys.executable, str(tmp)], capture_output=True, text=True, timeout=8)
            return {"passed": proc.returncode == 0, "stdout": proc.stdout[:300], "stderr": proc.stderr[:300]}
        except subprocess.TimeoutExpired: return {"passed": False, "stderr": "Timeout"}
        except Exception as e: return {"passed": False, "stderr": str(e)}
        finally: tmp.unlink(missing_ok=True)
    async def fix_code(self, code, error_context=""):
        bugs = self.detect_bugs(code)
        if not bugs and not error_context: return {"fixed":False,"reason":"Aucun bug","code":code}
        prompt = (f"ÉTAPE 1 — Identifie les bugs.\\nÉTAPE 2 — Cause racine.\\nÉTAPE 3 — Code corrigé.\\n\\nBugs: {bugs}\\nErreur: {error_context[:300]}\\n\\nCODE:\\n{code[:2000]}\\n\\nCODE CORRIGÉ (uniquement):")
        try:
            resp = await self.llm.call(None, "Expert Python.", prompt, max_tokens=800, temperature=0.2, profile="reasoning")
            fixed_code = resp.content.strip().replace("```python","").replace("```","").strip()
            try: ast.parse(fixed_code)
            except SyntaxError as e: return {"fixed":False,"reason":f"Patch invalide: {e}","code":code}
            if not self.shield.quick_scan(fixed_code): return {"fixed":False,"reason":"Blocage CyberShield","code":code}
            test_result = self.run_tests(fixed_code)
            patch_id = f"fix_{int(time.time())}"
            (PATCHES_DIR / f"{patch_id}.py").write_text(fixed_code)
            self.fix_count += 1
            return {"fixed":True,"patch_id":patch_id,"bugs_fixed":bugs,"test_passed":test_result["passed"],"code":fixed_code}
        except Exception as e: return {"fixed":False,"reason":str(e),"code":code}
    async def devin_deploy(self, filepath, auto_fix=True):
        path = Path(filepath)
        if not path.exists(): return {"status":"error","reason":"fichier introuvable"}
        original = path.read_text(); backup = path.with_suffix(".py.bak"); shutil.copy(path, backup)
        orig_test = self.run_tests(original)
        if orig_test["passed"] and not self.detect_bugs(original): return {"status":"ok","reason":"Aucun bug"}
        if auto_fix:
            fix_result = await self.fix_code(original, orig_test.get("stderr",""))
            if fix_result["fixed"]:
                final_test = self.run_tests(fix_result["code"])
                if final_test["passed"]:
                    path.write_text(fix_result["code"]); return {"status":"deployed","file":filepath,"bugs_fixed":fix_result["bugs_fixed"]}
                else:
                    shutil.copy(backup, path); return {"status":"rollback","reason":"Tests échoués après correction"}
        return {"status":"unchanged","bugs":self.detect_bugs(original)}
''')

    # ============================================================
    # 5. federation/nexus.py
    # ============================================================
    write_file("federation/nexus.py", '''
import asyncio, aiohttp, logging
log = logging.getLogger("vortex.nexus")
class NexusCore:
    def __init__(self, base_url="http://localhost:7861"): self.base_url = base_url; self.peers = []
    async def register_peer(self, url):
        try:
            async with aiohttp.ClientSession() as s:
                async with s.get(f"{url}/health", timeout=3) as r:
                    if r.status == 200:
                        if url not in self.peers: self.peers.append(url); log.info(f"Pair: {url}")
                        return True
        except: pass
        return False
    async def get_agents(self, url):
        try:
            async with aiohttp.ClientSession() as s:
                async with s.get(f"{url}/api/agents", timeout=5) as r:
                    if r.status == 200: return (await r.json()).get("agents", [])
        except: return []
    async def broadcast_task(self, task, agent_type=None):
        results = {}
        for peer in self.peers:
            try:
                async with aiohttp.ClientSession() as s:
                    payload = {"task": task, "agent_type": agent_type or "all"}
                    async with s.post(f"{peer}/api/execute", json=payload, timeout=10) as r:
                        if r.status == 200:
                            results[peer] = await r.json()
            except: pass
        return results
nexus = NexusCore()
''')

    # ============================================================
    # 6. core/nexus_server.py
    # ============================================================
    write_file("core/nexus_server.py", '''
import asyncio, json, time, logging
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import uvicorn
log = logging.getLogger("vortex.nexus.server")
app = FastAPI(title="Nexus Core", version="1.0")
class AgentData(BaseModel): id: str; code: str; score: float; ts: float
class TaskRequest(BaseModel): task: str; agent_type: str = "all"
agents_db = {}

@app.get("/health")
async def health(): return {"status": "ok", "ts": time.time()}

@app.get("/api/agents")
async def list_agents(): return {"agents": list(agents_db.values())}

@app.post("/api/agents")
async def add_agent(agent: AgentData):
    agents_db[agent.id] = agent.dict()
    log.info(f"Agent reçu: {agent.id} (score {agent.score})")
    return {"status": "ok", "id": agent.id}

@app.post("/api/execute")
async def execute_task(req: TaskRequest):
    # Simule l'exécution d'une tâche
    return {"status": "ok", "result": f"Task '{req.task}' executed on {req.agent_type}"}

def run_nexus_server(port=7861):
    log.info(f"Démarrage Nexus Server sur le port {port}")
    uvicorn.run(app, host="0.0.0.0", port=port, log_level="warning")
''')

    # ============================================================
    # 7. benchmarks/swe_bench_adapter.py
    # ============================================================
    write_file("benchmarks/swe_bench_adapter.py", '''
import subprocess, sys, tempfile, json, random
from pathlib import Path
PROBLEMS = [
    {"id": "fib", "desc": "Fibonacci", "code": "def fib(n): return n if n<2 else fib(n-1)+fib(n-2)", "test": "assert fib(10)==55"},
    {"id": "rev", "desc": "Reverse list", "code": "def rev(l): return l[::-1]", "test": "assert rev([1,2,3])==[3,2,1]"},
    {"id": "pal", "desc": "Palindrome", "code": "def pal(s): return s==s[::-1]", "test": "assert pal('radar') and not pal('hello')"},
    {"id": "vowels", "desc": "Count vowels", "code": "def count(s): return sum(1 for c in s if c in 'aeiou')", "test": "assert count('hello')==2"},
    {"id": "max_list", "desc": "Max", "code": "def max_l(l): return max(l)", "test": "assert max_l([1,5,3])==5"},
]
def run_swe_bench(agent_code, max_instances=4):
    passed = 0
    for p in PROBLEMS[:max_instances]:
        full = agent_code + "\\n" + p["code"] + "\\n" + p["test"]
        with tempfile.NamedTemporaryFile(mode="w", suffix=".py", delete=False) as f:
            f.write(full); tmp = Path(f.name)
        try:
            r = subprocess.run([sys.executable, str(tmp)], capture_output=True, timeout=4)
            if r.returncode == 0: passed += 1
        except: pass
        finally: tmp.unlink(missing_ok=True)
    return {"resolved": passed, "total": max_instances, "rate": round(passed/max_instances*100, 1)}
''')

    # ============================================================
    # 8. core/rsi_loop.py
    # ============================================================
    write_file("core/rsi_loop.py", '''
import asyncio, importlib.util, shutil, time, math, json, logging
from pathlib import Path
log = logging.getLogger("vortex.rsi")
ARCHIVE_DIR = Path("data/rsi_archive"); ARCHIVE_DIR.mkdir(parents=True, exist_ok=True)
DREAM_PATH = Path("data/dream_state.json")
BENCH_FUNCS = [
    ("sphere", lambda p: sum(x**2 for x in p), [(-5,5)]*5, 0.0),
    ("rastrigin", lambda p: 10*len(p)+sum(xi**2-10*math.cos(2*math.pi*xi) for xi in p), [(-5.12,5.12)]*5, 0.0),
    ("rosenbrock", lambda p: sum(100*(p[i+1]-p[i]**2)**2+(1-p[i])**2 for i in range(len(p)-1)), [(-2,2)]*5, 0.0),
]
class RSILoop:
    def __init__(self, llm, opt_path, shield=None, web=None, gitfix=None):
        self.llm=llm; self.path=Path(opt_path); self.backup=self.path.with_suffix(".py.bak")
        self.shield=shield; self.web=web; self.gitfix=gitfix
        self.generation=0; self.best_score=self._bench_file(self.path); self.archive=self._load_archive()
        log.info(f"RSI v2 init — score={self.best_score:.4f} arch={len(self.archive)}")
    def _load_archive(self):
        idx = ARCHIVE_DIR / "index.json"
        return json.loads(idx.read_text()) if idx.exists() else []
    def _save_archive(self):
        (ARCHIVE_DIR/"index.json").write_text(json.dumps(self.archive, indent=2))
    def _bench_module(self, mod):
        try: Opt = getattr(mod, "FellowOptimizer")
        except: return 0.0
        scores = []
        for _, fn, bounds, opt_v in BENCH_FUNCS:
            try:
                o = Opt(fn, bounds, max_evals=100)
                best, _ = o.optimize()
                scores.append(1.0 - math.tanh(abs(fn(best)-opt_v)))
            except: scores.append(0.0)
        return sum(scores)/len(scores) if scores else 0.0
    def _bench_file(self, path):
        try:
            spec = importlib.util.spec_from_file_location("_b", path)
            mod = importlib.util.module_from_spec(spec); spec.loader.exec_module(mod)
            return self._bench_module(mod)
        except: return 0.0
    def _load_dream(self):
        if DREAM_PATH.exists():
            try:
                state = json.loads(DREAM_PATH.read_text())
                summary = state.get("summary","")
                if summary and len(summary)>20: return f"\\n\\nCONTEXTE MÉMORIEL:\\n{summary[:400]}\\n"
            except: pass
        return ""
    async def _gen_variant(self):
        if not self.llm: return None
        current = self.path.read_text()
        dream_ctx = self._load_dream()
        top_arc = sorted(self.archive, key=lambda x:x["score"], reverse=True)[:2]
        arc_ctx = "\\n".join([f"# Archive score={a['score']:.3f}\\n{a['snippet']}" for a in top_arc])
        web_ctx = ""
        if self.web:
            try: web_ctx = await asyncio.wait_for(self.web.research_for_rsi("evolutionary optimization Python"), timeout=6.0)
            except: web_ctx = ""
        prompt = (f"ÉTAPE 1 — Analyse les forces/faiblesses de ce code :\\n{current[:1500]}\\n\\n"
                  f"ÉTAPE 2 — Identifie la meilleure technique (DE, PSO, CMA-ES).\\n"
                  f"Archives: {arc_ctx[:400] if arc_ctx else 'aucune'}\\n"
                  f"Web: {web_ctx[:400] if web_ctx else 'non disponible'}"
                  f"{dream_ctx}"
                  f"ÉTAPE 3 — Écris la nouvelle version de FellowOptimizer.\\n"
                  f"CODE UNIQUEMENT (pas de markdown) :")
        try:
            resp = await self.llm.call(None, "Expert optimisation.", prompt, max_tokens=900, temperature=0.4, profile="code_gen", use_cache=False)
            code = resp.content.strip().replace("```python","").replace("```","").strip()
            return code if "class FellowOptimizer" in code else None
        except: return None
    async def run_generation(self):
        self.generation += 1
        code = await self._gen_variant()
        if not code: return {"gen":self.generation,"action":"skip","reason":"bad_code","score":self.best_score}
        if self.shield and not self.shield.quick_scan(code):
            return {"gen":self.generation,"action":"blocked","reason":"CyberShield","score":self.best_score}
        if self.gitfix:
            bugs = self.gitfix.detect_bugs(code)
            if bugs:
                fix = await self.gitfix.fix_code(code, f"RSI gen {self.generation}")
                if fix["fixed"]: code = fix["code"]
        tmp = self.path.with_suffix(".py.new")
        try:
            ast.parse(code)
            tmp.write_text(code)
            spec = importlib.util.spec_from_file_location("_n", tmp)
            mod = importlib.util.module_from_spec(spec); spec.loader.exec_module(mod)
            new_score = self._bench_module(mod)
            if new_score > self.best_score:
                shutil.copy(self.path, self.backup)
                shutil.move(str(tmp), str(self.path))
                old = self.best_score; self.best_score = new_score
                self.archive.append({"gen":self.generation,"score":round(new_score,4),"snippet":code[:300],"ts":time.time()})
                self._save_archive()
                return {"gen":self.generation,"action":"deployed","old":old,"new":new_score}
            self.archive.append({"gen":self.generation,"score":self.best_score*0.85,"snippet":code[:300],"ts":time.time()})
            self._save_archive()
            return {"gen":self.generation,"action":"archived","score":self.best_score}
        except Exception as e: return {"gen":self.generation,"action":"error","reason":str(e)}
        finally:
            if tmp.exists(): tmp.unlink(missing_ok=True)
    def status(self):
        return {"generation":self.generation,"best_score":round(self.best_score,4),"archive_size":len(self.archive)}
''')

    # ============================================================
    # 9. core/agent_registry.py
    # ============================================================
    write_file("core/agent_registry.py", '''
import logging
log = logging.getLogger("vortex.registry")
class AgentRegistry:
    def __init__(self):
        self.agents = {}
    def register(self, name, instance, description, capabilities=None):
        self.agents[name] = {"instance": instance, "description": description, "capabilities": capabilities or []}
        log.info(f"Agent enregistré : {name}")
    def list(self):
        return {name: info["description"] for name, info in self.agents.items()}
    def get(self, name):
        return self.agents.get(name, {}).get("instance")
    def get_capabilities(self, name):
        return self.agents.get(name, {}).get("capabilities", [])
    def filter_by_capability(self, cap):
        return [n for n, info in self.agents.items() if cap in info.get("capabilities", [])]
registry = AgentRegistry()
''')

    # ============================================================
    # 10. core/judge_layer.py
    # ============================================================
    write_file("core/judge_layer.py", '''
import asyncio, json, logging, re
log = logging.getLogger("vortex.judge")
class JudgeLayer:
    def __init__(self, llm_engine, registry=None):
        self.llm = llm_engine; self.registry = registry; self.history = []
    async def evaluate_candidates(self, task, candidates):
        if not candidates: return {"error": "Aucun candidat"}
        if len(candidates) == 1:
            name = list(candidates.keys())[0]; return {"winner": name, "scores": {name: 1.0}, "confidence": 1.0}
        prompt = f"Juge expert. Évalue les réponses pour la tâche : \\"{task}\\"\\n"
        for name, content in candidates.items():
            prompt += f"\\n--- Agent {name} ---\\n{content[:1000]}\\n"
        prompt += """Attribue un score 0-1 pour chaque (précision, exhaustivité, qualité). Réponds UNIQUEMENT en JSON : {"scores": {"agent1": 0.9, ...}, "winner": "agent1", "confidence": 0.85}"""
        try:
            resp = await self.llm.call(None, "Expert évaluateur.", prompt, max_tokens=200, temperature=0.1, fast=True)
            match = re.search(r"\\{.*\\}", resp.content, re.DOTALL)
            if match:
                result = json.loads(match.group())
                if "winner" in result and result["winner"] not in candidates:
                    if "scores" in result:
                        best = max(result["scores"], key=lambda k: result["scores"][k])
                        result["winner"] = best
                return result
            else:
                best_name = max(candidates, key=lambda k: len(candidates[k]))
                return {"winner": best_name, "scores": {k: 0.5 for k in candidates}, "confidence": 0.5}
        except Exception as e:
            log.error(f"Judge error: {e}")
            best_name = max(candidates, key=lambda k: len(candidates[k]))
            return {"winner": best_name, "scores": {k: 0.5 for k in candidates}, "confidence": 0.3}
    async def dispatch(self, task, agent_names=None):
        if self.registry is None: return {"error": "AgentRegistry manquant"}
        if agent_names is None: agent_names = list(self.registry.agents.keys())
        results = {}; tasks = {}
        for name in agent_names:
            agent = self.registry.get(name)
            if agent is None:
                results[name] = "Agent non trouvé"; continue
            if hasattr(agent, "run"): tasks[name] = asyncio.create_task(agent.run(task))
            elif hasattr(agent, "evaluate"): tasks[name] = asyncio.create_task(agent.evaluate(task))
            elif hasattr(agent, "research_for_rsi"): tasks[name] = asyncio.create_task(agent.research_for_rsi(task))
            else: results[name] = "Agent sans méthode"
        for name, future in tasks.items():
            try:
                result = await future
                if hasattr(result, "content"): results[name] = result.content
                elif isinstance(result, dict): results[name] = json.dumps(result, ensure_ascii=False)
                else: results[name] = str(result)
            except Exception as e: results[name] = f"Erreur: {e}"
        judge_result = await self.evaluate_candidates(task, results)
        judge_result["all_responses"] = results
        self.history.append({"task": task, "judge_result": judge_result})
        return judge_result
judge = None
''')

    # ============================================================
    # 11. core/orchestrator.py
    # ============================================================
    write_file("core/orchestrator.py", '''
import asyncio, json, logging, re
log = logging.getLogger("vortex.orchestrator")
class Orchestrator:
    def __init__(self, llm_engine, judge_layer, registry):
        self.llm = llm_engine; self.judge = judge_layer; self.registry = registry
        self.roles = {"planner": "Planifier l'architecture", "writer": "Écrire le code", "reviewer": "Vérifier sécurité", "optimizer": "Optimiser performances"}
    async def decompose(self, task):
        prompt = f"Décompose cette tâche en 2-4 sous-tâches claires. Tâche : {task}\\nRetourne UNIQUEMENT une liste JSON : [\\"sous-tâche 1\\", ...]"
        try:
            resp = await self.llm.call(None, "Planificateur.", prompt, max_tokens=200, temperature=0.3, fast=True)
            match = re.search(r"\\[.*\\]", resp.content, re.DOTALL)
            if match:
                subtasks = json.loads(match.group()); return [s.strip() for s in subtasks if s.strip()]
            else: return [task]
        except Exception as e: log.error(f"Decomp error: {e}"); return [task]
    async def run(self, task, roles=None):
        if roles is None: roles = self.roles
        subtasks = await self.decompose(task)
        if not subtasks: return {"error": "Impossible de décomposer"}
        assigned = {}
        role_names = list(roles.keys())
        for i, st in enumerate(subtasks):
            role = role_names[i % len(role_names)]
            assigned[role] = st
        results = {}
        for role, st in assigned.items():
            log.info(f"Orchestrator: {role} → {st}")
            judge_result = await self.judge.dispatch(st)
            results[role] = {
                "task": st,
                "winner": judge_result.get("winner"),
                "score": judge_result.get("confidence", 0),
                "content": judge_result.get("all_responses", {}).get(judge_result.get("winner"), "")
            }
        synth_prompt = f"Tâche initiale : {task}\\nSynthétise les résultats :\\n"
        for role, data in results.items():
            synth_prompt += f"\\n--- {role} ---\\n{data['content'][:500]}\\n"
        synth_prompt += "\\nDonne la solution finale complète."
        try:
            synth_resp = await self.llm.call(None, "Synthétiseur.", synth_prompt, max_tokens=800, temperature=0.2, profile="reasoning")
            synthesis = synth_resp.content.strip()
        except Exception as e: synthesis = f"Erreur synthèse: {e}"
        return {"task": task, "subtasks": assigned, "results": results, "synthesis": synthesis}
orchestrator = None
''')

    # ============================================================
    # 12. core/agent_manager.py
    # ============================================================
    write_file("core/agent_manager.py", '''
import logging
log = logging.getLogger("vortex.agent_manager")
class AgentManager:
    def __init__(self, registry, judge, orchestrator):
        self.registry = registry; self.judge = judge; self.orchestrator = orchestrator; self.default_agent = None
    async def multi(self, task, agent_names=None): return await self.judge.dispatch(task, agent_names)
    async def judge_task(self, task): return await self.judge.dispatch(task)
    async def orchestrate(self, task, roles=None): return await self.orchestrator.run(task, roles)
    async def switch_agent(self, name):
        if name in self.registry.agents:
            self.default_agent = name; return True
        return False
    def list_agents(self): return self.registry.list()
''')

    # ============================================================
    # 13. core/optimizer.py
    # ============================================================
    write_file("core/optimizer.py", '''
import random, math
class FellowOptimizer:
    def __init__(self, func, bounds, max_evals=200):
        self.func=func; self.bounds=bounds; self.max_evals=max_evals
    def optimize(self):
        dim=len(self.bounds); F,CR=0.8,0.9
        pop=[[random.uniform(l,u) for l,u in self.bounds] for _ in range(15)]
        vals=[self.func(p) for p in pop]
        best=pop[vals.index(min(vals))][:]; best_val=min(vals); evals=15
        while evals < self.max_evals:
            for i in range(len(pop)):
                a,b,c=random.sample([j for j in range(len(pop)) if j!=i],3)
                trial=[max(l,min(u,pop[a][d]+F*(pop[b][d]-pop[c][d]))) if random.random()<CR else pop[i][d] for d,(l,u) in enumerate(self.bounds)]
                v=self.func(trial); evals+=1
                if v<vals[i]:
                    pop[i]=trial; vals[i]=v
                    if v<best_val:
                        best=trial[:]; best_val=v
                if evals>=self.max_evals: break
        return best,[best_val]
''')

    # ============================================================
    # 14. core/peer_review.py
    # ============================================================
    write_file("core/peer_review.py", '''
import re
class PeerReview:
    def __init__(self, llm, shield=None): self.llm=llm; self.shield=shield
    async def evaluate(self, code, context=""):
        if not code: return {"score":0,"passed":False}
        if self.shield:
            r=await self.shield.full_scan(code,context)
            if not r["passed"]: return {"score":0,"passed":False,"comment":"CyberShield bloque","issues":r["all_issues"]}
        if not self.llm: return {"score":0.7,"passed":True}
        try:
            resp=await self.llm.call(None,"Expert revue.",f"Note ce code (0-1):\\n{code[:1200]}\\nRéponds: SCORE=0.XX", max_tokens=60, fast=True)
            m=re.search(r"SCORE=([0-9.]+)", resp.content); s=float(m.group(1)) if m else 0.7
            return {"score":round(s,3),"passed":s>=0.6}
        except: return {"score":0.65,"passed":True}
''')

    # ============================================================
    # 15. core/dream_consolidator.py
    # ============================================================
    write_file("core/dream_consolidator.py", '''
import json, time, logging; from pathlib import Path
log=logging.getLogger("vortex.dream"); DREAM_PATH=Path("data/dream_state.json")
class DreamConsolidator:
    def __init__(self, mem, llm):
        self.mem=mem; self.llm=llm
        self.state = {}
        if DREAM_PATH.exists():
            try: self.state = json.loads(DREAM_PATH.read_text())
            except: self.state = {"summary":"","last_ts":0,"count":0}
        else: self.state = {"summary":"","last_ts":0,"count":0}
    def _save(self): DREAM_PATH.write_text(json.dumps(self.state, indent=2))
    async def consolidate(self):
        entries = self.mem.retrieve("", top_k=30) if self.mem else []
        ctx = "\\n".join([e.content[:200] for e in entries if hasattr(e,"content")])
        if not ctx:
            self.state["summary"] = "Aucun apprentissage récent."
            self._save()
            return self.state
        try:
            resp = await self.llm.call(None, "Résumeur.", f"Résume en 5 points les apprentissages :\\n{ctx[:3000]}\\nRésumé :", max_tokens=200, fast=True, profile="summary")
            self.state.update({"summary":resp.content.strip(),"last_ts":time.time(),"count":self.state.get("count",0)+1})
            self._save()
        except Exception as e:
            log.error(f"Dream consolidation failed: {e}")
        return self.state
''')

    # ============================================================
    # 16. core/autodev.py
    # ============================================================
    write_file("core/autodev.py", '''
import shutil, ast
class AutoDev:
    def __init__(self, llm): self.llm=llm
    async def improve(self, filepath):
        from pathlib import Path
        path=Path(filepath)
        if not path.exists(): return {"error":"File not found"}
        orig=path.read_text()
        try:
            resp=await self.llm.call(None,"Expert Python.",f"Améliore ce code:\\n{orig[:2000]}\\nCode uniquement:", max_tokens=900, temperature=0.4)
            new=resp.content.strip().replace("```python","").replace("```","").strip()
            try:
                ast.parse(new)
            except SyntaxError as e:
                return {"error":f"SyntaxError dans le patch: {e}"}
            backup=path.with_suffix(".py.bak"); shutil.copy(path,backup); path.write_text(new)
            return {"status":"deployed","backup":str(backup)}
        except Exception as e: return {"error":str(e)}
''')

    # ============================================================
    # 17. sandbox/secure_sandbox.py
    # ============================================================
    write_file("sandbox/secure_sandbox.py", '''
import subprocess, sys, tempfile, resource; from pathlib import Path
def run_in_sandbox(code, timeout=5, memory_mb=128):
    with tempfile.NamedTemporaryFile(mode="w", suffix=".py", delete=False) as f:
        f.write(code); tmp=Path(f.name)
    try:
        def _lim():
            try: resource.setrlimit(resource.RLIMIT_AS,(memory_mb*1024*1024,memory_mb*1024*1024)); resource.setrlimit(resource.RLIMIT_CPU,(timeout,timeout+1))
            except: pass
        proc=subprocess.Popen([sys.executable,str(tmp)], stdout=subprocess.PIPE, stderr=subprocess.PIPE, preexec_fn=_lim)
        out,err=proc.communicate(timeout=timeout+1)
        return {"success":proc.returncode==0,"result":out.decode()[:2000],"error":err.decode()[:500]}
    except subprocess.TimeoutExpired: proc.kill(); return {"success":False,"error":"Timeout"}
    except Exception as e: return {"success":False,"error":str(e)}
    finally: tmp.unlink(missing_ok=True)
''')

    # ============================================================
    # 18. benchmarks/humaneval_adapter.py
    # ============================================================
    write_file("benchmarks/humaneval_adapter.py", '''
import subprocess, sys, tempfile; from pathlib import Path
CASES=[("def add(a,b): return a+b","assert add(2,3)==5"),("def is_even(n): return n%2==0","assert is_even(4)"),("def fact(n): return 1 if n<=1 else n*fact(n-1)","assert fact(5)==120"),("def rev(s): return s[::-1]","assert rev('abc')=='cba'"),("def fib(n): return n if n<2 else fib(n-1)+fib(n-2)","assert fib(10)==55")]
def run_humaneval(preamble="",max_cases=5):
    passed=0
    for sol,test in CASES[:max_cases]:
        full=(preamble+"\\n"+sol+"\\n"+test) if preamble else sol+"\\n"+test
        with tempfile.NamedTemporaryFile(mode="w",suffix=".py",delete=False) as f: f.write(full); tmp=Path(f.name)
        try:
            r=subprocess.run([sys.executable,str(tmp)],capture_output=True,timeout=3)
            if r.returncode==0: passed+=1
        except: pass
        finally: tmp.unlink(missing_ok=True)
    return {"pass@1":round(passed/max_cases*100,1),"passed":passed,"total":max_cases}
''')

    # ============================================================
    # 19. core/hf_mcp.py – Intégration Hugging Face MCP
    # ============================================================
    write_file("core/hf_mcp.py", '''
import asyncio, json, logging, os, aiohttp
from typing import Dict, List, Optional
log = logging.getLogger("vortex.hf_mcp")

HF_MCP_URL = os.environ.get("HF_MCP_URL", "https://huggingface.co/api")

class HFMCPClient:
    def __init__(self, use_mcp=False):
        self.use_mcp = use_mcp
        self.hub_api = "https://huggingface.co/api"
        self.history = []

    async def _hub_api_request(self, method: str, params: Dict) -> Dict:
        if method == "search_models":
            query = params.get("query", "")
            url = f"{self.hub_api}/models?search={query}&limit=10"
            async with aiohttp.ClientSession() as session:
                async with session.get(url, timeout=aiohttp.ClientTimeout(total=10)) as resp:
                    if resp.status != 200:
                        return {"error": f"API error {resp.status}"}
                    data = await resp.json()
                    return {"models": [{"id": m["id"], "downloads": m.get("downloads", 0)} for m in data[:5]]}
        elif method == "search_datasets":
            query = params.get("query", "")
            url = f"{self.hub_api}/datasets?search={query}&limit=10"
            async with aiohttp.ClientSession() as session:
                async with session.get(url, timeout=aiohttp.ClientTimeout(total=10)) as resp:
                    if resp.status != 200:
                        return {"error": f"API error {resp.status}"}
                    data = await resp.json()
                    return {"datasets": [{"id": d["id"]} for d in data[:5]]}
        elif method == "get_space":
            space_name = params.get("name", "")
            url = f"{self.hub_api}/spaces/{space_name}"
            async with aiohttp.ClientSession() as session:
                async with session.get(url, timeout=aiohttp.ClientTimeout(total=10)) as resp:
                    if resp.status != 200:
                        return {"error": f"Space not found or API error {resp.status}"}
                    data = await resp.json()
                    return {"space": {"id": data.get("id"), "likes": data.get("likes", 0)}}
        elif method == "search_papers":
            query = params.get("query", "")
            url = f"http://export.arxiv.org/api/query?search_query=all:{query}&start=0&max_results=5"
            async with aiohttp.ClientSession() as session:
                try:
                    async with session.get(url, timeout=aiohttp.ClientTimeout(total=15)) as resp:
                        if resp.status != 200:
                            return {"error": f"arXiv error {resp.status}"}
                        text = await resp.text()
                        import xml.etree.ElementTree as ET
                        root = ET.fromstring(text)
                        papers = []
                        for entry in root.findall("{http://www.w3.org/2005/Atom}entry"):
                            title = entry.find("{http://www.w3.org/2005/Atom}title")
                            if title is not None:
                                papers.append({"title": title.text.strip(), "id": "N/A"})
                        return {"papers": papers[:3]}
                except Exception as e:
                    log.warning(f"arXiv API error: {e}")
                    return {"papers": [{"title": f"Recherche simulée pour '{query}'", "id": "arxiv"}]}
        return {"error": "Méthode non supportée"}

    async def search_models(self, query: str, limit: int = 5) -> List[Dict]:
        result = await self._hub_api_request("search_models", {"query": query, "limit": limit})
        return result.get("models", [])

    async def search_datasets(self, query: str, limit: int = 5) -> List[Dict]:
        result = await self._hub_api_request("search_datasets", {"query": query, "limit": limit})
        return result.get("datasets", [])

    async def get_space_info(self, space_name: str) -> Dict:
        result = await self._hub_api_request("get_space", {"name": space_name})
        return result.get("space", {})

    async def search_papers(self, query: str) -> List[Dict]:
        result = await self._hub_api_request("search_papers", {"query": query})
        return result.get("papers", [])

    def stats(self) -> Dict:
        return {"history": len(self.history)}

hf_mcp = HFMCPClient(use_mcp=False)
''')

    # ============================================================
    # 20. app.py – VERSION HEADLESS FASTAPI (déjà fournie)
    # ============================================================
    write_file("app.py", '''
#!/usr/bin/env python3
import os, sys, asyncio, json, logging, time
from pathlib import Path
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import uvicorn
import nest_asyncio
nest_asyncio.apply()

BASE = Path("/app")
os.chdir(BASE); sys.path.insert(0, str(BASE))
DATA_DIR = Path(os.environ.get("DATA_DIR", "/data"))
DATA_DIR.mkdir(parents=True, exist_ok=True)

logging.basicConfig(level=logging.INFO, format="%(levelname)s [%(name)s] %(message)s")
log = logging.getLogger("vortex.headless")

from agents.cognitive_engine import MultiLLMEngine
from core.cybershield import CyberShield
from core.web_search import WebSearchAgent
from core.gitautofix import GitAutoFix
from core.rsi_loop import RSILoop
from core.peer_review import PeerReview
from core.autodev import AutoDev
from core.dream_consolidator import DreamConsolidator
from federation.nexus import nexus
from sandbox.secure_sandbox import run_in_sandbox
from benchmarks.humaneval_adapter import run_humaneval
from benchmarks.swe_bench_adapter import run_swe_bench
from core.agent_registry import registry
from core.judge_layer import JudgeLayer
from core.orchestrator import Orchestrator
from core.agent_manager import AgentManager
from core.hf_mcp import hf_mcp

class _Mem:
    def __init__(self): self.store=[]
    def ingest(self,c,**k): self.store.append({"content":c})
    def retrieve(self,q,top_k=5): return [type("E",(),{"content":e["content"]})() for e in self.store[-top_k:]]

class _K:
    def run_code(self,code,timeout=10): return run_in_sandbox(code,timeout=int(timeout))
    def get_health(self):
        try: import psutil; cpu=psutil.cpu_percent(0.1); mem=psutil.virtual_memory().percent
        except: cpu,mem=0.0,0.0
        return {"status":"healthy","cpu_percent":cpu,"memory_percent":mem}

KERNEL=_K(); mem=_Mem(); llm=MultiLLMEngine(mem); shield=CyberShield(llm); web=WebSearchAgent(llm)
gitfix=GitAutoFix(llm, shield); pr=PeerReview(llm, shield)
rsi=RSILoop(llm, "core/optimizer.py", shield, web, gitfix)
autodev=AutoDev(llm); dreamer=DreamConsolidator(mem, llm)

registry.register("rsi", rsi, "Auto-amélioration récursive", ["code_gen","optimization"])
registry.register("shield", shield, "Analyse de sécurité CyberShield", ["security","audit"])
registry.register("gitfix", gitfix, "Correction automatique de bugs", ["code_fix","deployment"])
registry.register("web", web, "Recherche web structurée", ["research","search"])
registry.register("dreamer", dreamer, "Consolidation mémoire", ["memory"])
registry.register("nexus", nexus, "Fédération P2P", ["federation"])

judge = JudgeLayer(llm, registry)
orchestrator = Orchestrator(llm, judge, registry)
manager = AgentManager(registry, judge, orchestrator)

# Scheduler
try:
    from apscheduler.schedulers.background import BackgroundScheduler
    sched=BackgroundScheduler(timezone="UTC")
    def _j(coro):
        loop=asyncio.new_event_loop()
        try: loop.run_until_complete(coro)
        finally: loop.close()
    sched.add_job(lambda:_j(rsi.run_generation()), "interval", hours=2, id="rsi")
    sched.add_job(lambda:_j(dreamer.consolidate()), "cron", hour=3, id="dream")
    sched.start()
    log.info("✅ Scheduler: RSI/2h, Dream/3h")
except Exception as e:
    log.warning(f"Scheduler non démarré: {e}")

# ---- FastAPI ----
app = FastAPI(title="VORTEX v5.1 API", version="5.1")

class CommandRequest(BaseModel):
    command: str
    args: dict = {}

class CodeRequest(BaseModel):
    code: str
    timeout: int = 10

@app.get("/")
async def root():
    return {"message": "VORTEX v5.1 Headless API"}

@app.post("/command")
async def execute_command(req: CommandRequest):
    msg = req.command
    if msg.startswith("@health"):
        h = KERNEL.get_health()
        return f"CPU {h['cpu_percent']:.0f}% · RAM {h['memory_percent']:.0f}% · RSI gen {rsi.generation}"
    elif msg.startswith("@rsi"):
        r = await rsi.run_generation()
        return f"RSI gen={r['gen']} score={r.get('new', r.get('score',0))}"
    elif msg.startswith("@shield"):
        code = msg.replace("@shield","").strip()
        if not code: return "Syntaxe: @shield <code>"
        r = await shield.full_scan(code)
        return f"{r['level']} {r['score']}/100 · Issues: {r['all_issues']}"
    elif msg.startswith("@gitfix"):
        path = msg.replace("@gitfix","").strip() or "core/optimizer.py"
        r = await gitfix.devin_deploy(path)
        return f"GitAutoFix: {r.get('status','?')} - {r.get('reason','')}"
    elif msg.startswith("@web"):
        q = msg.replace("@web","").strip() or "optimization"
        r = await web.research_for_rsi(q)
        return r[:600]
    elif msg.startswith("@multi"):
        parts = msg.split("--agents")
        task = parts[0].replace("@multi","").strip()
        agents = None
        if len(parts)>1: agents = [a.strip() for a in parts[1].strip().split(",")]
        result = await manager.multi(task, agents)
        return f"Gagnant: {result.get('winner','?')} (confiance {result.get('confidence',0)})"
    elif msg.startswith("@judge"):
        task = msg.replace("@judge","").strip()
        if not task: return "Syntaxe: @judge <tâche>"
        result = await manager.judge_task(task)
        return f"Meilleur agent: {result.get('winner','?')}"
    elif msg.startswith("@orchestrate"):
        task = msg.replace("@orchestrate","").strip()
        if not task: return "Syntaxe: @orchestrate <tâche>"
        result = await manager.orchestrate(task)
        return f"Synthèse: {result.get('synthesis','')[:500]}"
    elif msg.startswith("@hfsearch"):
        query = msg.replace("@hfsearch","").strip()
        if not query: return "Syntaxe: @hfsearch <requête>"
        models = await hf_mcp.search_models(query)
        return "\\n".join([f"- {m['id']}" for m in models[:5]]) if models else "Aucun modèle"
    elif msg.startswith("@nexus"):
        parts = msg.replace("@nexus","").strip().split()
        if not parts: return "Syntaxe: @nexus add <url> | list | broadcast <tâche>"
        if parts[0] == "add":
            ok = await nexus.register_peer(parts[1])
            return f"Pair {parts[1]} {'ajouté' if ok else 'inaccessible'}"
        elif parts[0] == "list":
            return f"Pairs: {', '.join(nexus.peers) if nexus.peers else 'Aucun'}"
        elif parts[0] == "broadcast":
            task = " ".join(parts[1:])
            results = await nexus.broadcast_task(task)
            return "\\n".join([f"{p}: {r}" for p,r in results.items()])
        else: return "Commande invalide"
    else:
        return "Commande non reconnue"

@app.post("/sandbox")
async def sandbox(req: CodeRequest):
    result = KERNEL.run_code(req.code, timeout=req.timeout)
    return result

@app.get("/health")
async def health():
    h = KERNEL.get_health()
    return {"status": "ok", **h}

if __name__ == "__main__":
    log.info("🚀 VORTEX v5.1 Headless API démarre sur le port 7860")
    uvicorn.run(app, host="0.0.0.0", port=7860)
''')

    # ============================================================
    # 21. requirements.txt (explicit)
    # ============================================================
    write_file("requirements.txt", '''
gradio>=4.0.0
nest_asyncio>=1.5.0
psutil>=5.9.0
apscheduler>=3.10.0
aiohttp>=3.9.0
fastapi>=0.100.0
uvicorn>=0.23.0
requests>=2.31.0
pydantic>=2.0.0
''')

    # ============================================================
    # 22. Dockerfile (pour HF Spaces)
    # ============================================================
    write_file("Dockerfile", '''
FROM python:3.11-slim
ENV PYTHONDONTWRITEBYTECODE=1
ENV PYTHONUNBUFFERED=1
WORKDIR /app
RUN apt-get update && apt-get install -y curl git && rm -rf /var/lib/apt/lists/*
COPY build_vortex.py .
COPY requirements.txt .
COPY app.py .
RUN python build_vortex.py
RUN pip install --no-cache-dir -r requirements.txt
CMD ["python", "app.py"]
''')

    print("✅ Tous les fichiers VORTEX v5.1 ont été générés.")

if __name__ == "__main__":
    build_all()