Update controllers/admin_controller.py

controllers/admin_controller.py

@@ -3,78 +3,111 @@ 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
+import json, os, shutil, threading
 
 router = APIRouter()
 
 @router.post("/reload_config")
 async def reload_config(request: Request):
-    try:
-        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."}
-
-        current_project = service_config.projects.get(project_name)
-        incoming_project = new_config_data.get("projects", {}).get(project_name)
-
-        if not incoming_project:
-            return {"error": f"'{project_name}' proje tanımı yeni config içinde bulunamadı."}
-
-        # Eğer farklılık yoksa erken çık
-        if current_project == incoming_project:
-            return {"status": "ok", "message": "Hiçbir değişiklik bulunamadı."}
-
-        log(f"🔄 '{project_name}' güncellemesi tespit edildi, güncelleme başlatılıyor...")
-
-        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"]
-
-        # Geçici model yüklemesi
-        temp_instance = LLMModel()
-
-        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
-
-        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
-
-        # Ana klasörü sıfırla ve temp içeriğini taşı
-        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.")
-        return {"status": "ok", "message": f"'{project_name}' projesi güncellendi ve yeniden yüklendi."}
-
-    except Exception as e:
-        log(f"❌ reload_config hatası: {e}")
-        return {"error": str(e)}
-
+    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."
+    }