controllers/admin_controller.py

from fastapi import APIRouter, Request
from core import service_config, llm_models
from llm_model import LLMModel
from intent_utils import background_training
from log import log
import json, os, shutil, threading

router = APIRouter()

@router.post("/reload_config")
async def reload_config(request: Request):
    body = await request.json()
    project_name = body.get("project_name")
    new_config_data = body.get("service_config")

    if not project_name or not new_config_data:
        return {"error": "project_name ve service_config gereklidir."}

    def background_reload():
        try:
            current_project = service_config.projects.get(project_name)
            incoming_project = new_config_data.get("projects", {}).get(project_name)

            if not incoming_project:
                log(f"❌ '{project_name}' yeni config içinde bulunamadı, işlem durduruldu.")
                return

            project_path = f"/data/projects/{project_name}"
            temp_path = f"/data/projects/{project_name}_temp"

            if os.path.exists(temp_path):
                shutil.rmtree(temp_path)
            os.makedirs(temp_path, exist_ok=True)

            llm_config = incoming_project["llm"]
            intents = incoming_project["intents"]

            temp_instance = LLMModel()

            # 🆕 Yeni proje ekleniyor
            if current_project is None:
                log(f"🆕 Yeni proje '{project_name}' tespit edildi, yükleme başlatılıyor...")

                temp_instance.setup(service_config, llm_config, temp_path)
                intent_model_path = os.path.join(temp_path, "intent", "trained_model")
                background_training(
                    project_name,
                    intents,
                    llm_config["intent_model_id"],
                    intent_model_path,
                    llm_config["train_confidence_treshold"]
                )
                temp_instance.load_intent_model(intent_model_path)

                if os.path.exists(project_path):
                    shutil.rmtree(project_path)
                shutil.copytree(temp_path, project_path)

                llm_models[project_name] = temp_instance
                service_config.projects[project_name] = incoming_project

                log(f"✅ Yeni proje '{project_name}' başarıyla yüklendi ve belleğe alındı.")
                return

            # 🔄 Var olan projede değişiklik varsa güncelle
            if current_project == incoming_project:
                log(f"ℹ️ '{project_name}' için değişiklik bulunamadı, işlem atlandı.")
                return

            log(f"🔄 '{project_name}' güncellemesi tespit edildi, güncelleme başlatılıyor...")

            # Ana model değiştiyse yükle
            if current_project["llm"]["model_base"] != llm_config["model_base"]:
                temp_instance.setup(service_config, llm_config, temp_path)
            else:
                temp_instance.model = llm_models[project_name].model
                temp_instance.tokenizer = llm_models[project_name].tokenizer

            # Intent değiştiyse yeniden eğit
            if current_project["intents"] != intents:
                intent_model_path = os.path.join(temp_path, "intent", "trained_model")
                background_training(
                    project_name,
                    intents,
                    llm_config["intent_model_id"],
                    intent_model_path,
                    llm_config["train_confidence_treshold"]
                )
                temp_instance.load_intent_model(intent_model_path)
            else:
                temp_instance.intent_model = llm_models[project_name].intent_model
                temp_instance.intent_tokenizer = llm_models[project_name].intent_tokenizer
                temp_instance.intent_label2id = llm_models[project_name].intent_label2id

            if os.path.exists(project_path):
                shutil.rmtree(project_path)
            shutil.copytree(temp_path, project_path)

            llm_models[project_name] = temp_instance
            service_config.projects[project_name] = incoming_project

            log(f"✅ '{project_name}' güncellemesi tamamlandı ve belleğe alındı.")

        except Exception as e:
            log(f"❌ reload_config background hatası: {e}")

    # Arka planda başlat
    threading.Thread(target=background_reload, daemon=True).start()

    return {
        "status": "accepted",
        "message": f"'{project_name}' için güncelleme arka planda başlatıldı. İşlem loglardan takip edilebilir."
    }