File size: 3,923 Bytes
b707206 9b9c96c b707206 9b9c96c b707206 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 | """Bağımlılıksız HTTP sunucu — her yere tak-çalıştır entegrasyon.
POST /decide {"state": ..., "questions": {...}} → kalibre olasılıklar (+ bellek/güven bilgisi)
POST /learn {"state": ..., "questions": {...}, "answers": {qid: anahtar}} → anında öğrenme (ms)
POST /v1/chat/completions OpenAI uyumlu: son kullanıcı mesajı JSON {"state","questions"} → yanıt içeriği JSON karar
GET /health
Model AubinLearning ile sarılıysa /learn etkin; değilse /decide düz modelle çalışır.
"""
import json, time
from http.server import BaseHTTPRequestHandler, ThreadingHTTPServer
import threading
def serve(model, port=8009):
lock = threading.Lock()
class H(BaseHTTPRequestHandler):
def _send(self, code, obj):
data = json.dumps(obj, ensure_ascii=False).encode()
self.send_response(code); self.send_header("Content-Type", "application/json")
self.send_header("Content-Length", str(len(data))); self.end_headers(); self.wfile.write(data)
def do_GET(self):
if self.path.rstrip("/") in ("/health", ""):
self._send(200, {"ok": True, "model": type(model).__name__, "learning": hasattr(model, "learn")})
else:
self.send_error(404)
def do_POST(self):
path = self.path.rstrip("/")
try:
body = json.loads(self.rfile.read(int(self.headers.get("Content-Length", 0))))
with lock: # tek GPU: istekler sırayla
if path == "/decide":
out = model.decide(body["state"], body["questions"])
elif path in ("/v1/systemone", "/v1/systemone/batch", "/predict"):
# Laya/Jev uyumlu: {"state", "questions"} (ya da batch: {"items": [...]}) → {"answers", "routing"}
from .engine import AubinEngine
eng = model if isinstance(model, AubinEngine) else AubinEngine([("aubin", model, 0.0)])
if path.endswith("/batch"):
out = {"results": [eng.predict(it["state"], it["questions"], it.get("min_confidence")) for it in body["items"]]}
else:
out = eng.predict(body["state"], body["questions"], body.get("min_confidence"))
elif path == "/learn":
if not hasattr(model, "learn"):
return self._send(400, {"error": "model AubinLearning ile sarılı değil"})
out = {"learned_ms": model.learn(body["state"], body["questions"], body["answers"])}
elif path == "/v1/chat/completions":
msg = [m for m in body.get("messages", []) if m.get("role") == "user"][-1]["content"]
req = json.loads(msg) if isinstance(msg, str) else msg
dec = model.decide(req["state"], req["questions"])
out = {"id": f"aubin-{int(time.time() * 1000)}", "object": "chat.completion", "created": int(time.time()),
"model": body.get("model", "aubin"),
"choices": [{"index": 0, "finish_reason": "stop",
"message": {"role": "assistant", "content": json.dumps(dec, ensure_ascii=False)}}]}
else:
return self.send_error(404)
self._send(200, out)
except Exception as e:
self._send(400, {"error": str(e)})
def log_message(self, *a):
pass
print(f"AUBIN hazır: http://127.0.0.1:{port} (/decide, /learn, /v1/systemone[/batch] Laya/Jev-uyumlu, /v1/chat/completions, /health)", flush=True)
ThreadingHTTPServer(("0.0.0.0", port), H).serve_forever()
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