Update chat_handler.py

chat_handler.py

@@ -53,48 +53,60 @@ def _safe_intent_parse(raw: str) -> tuple[str, str]:
 
 # ─────────────────────────  CONFIG ───────────────────────── #
 SPARK_URL = str(cfg.global_config.spark_endpoint).rstrip("/")
+_cfg = None
 
-# ✅ Her kullanımda fresh config al
-def get_fresh_config():
-    """Get fresh config on each call"""
+def get_config():
+    """Get or reload config"""
+    global _cfg
+    if _cfg is None:
+        from config_provider import ConfigProvider
+        _cfg = ConfigProvider.get()
+    return _cfg
+
+def reload_config():
+    """Force reload config"""
+    global _cfg
     from config_provider import ConfigProvider
-    # Force reload
     ConfigProvider._instance = None
-    return ConfigProvider.get()
+    _cfg = ConfigProvider.get()
+    return _cfg
     
 # ─────────────────────────  SPARK ───────────────────────── #
 def initialize_llm(force_reload=False):
     """Initialize LLM provider based on work_mode"""
     global llm_provider
     
-    # Force reload config
-    if force_reload or llm_provider is None:
-        cfg = get_fresh_config()
-        work_mode = cfg.global_config.work_mode
+    # Get fresh config if forced or first time
+    if force_reload:
+        cfg = reload_config()
+    else:
+        cfg = get_config()
+    
+    work_mode = cfg.global_config.work_mode
+    
+    if cfg.global_config.is_gpt_mode():
+        # GPT mode
+        api_key = cfg.global_config.get_plain_token()
+        if not api_key:
+            raise ValueError("OpenAI API key not configured")
         
-        if cfg.global_config.is_gpt_mode():
-            # GPT mode
-            api_key = cfg.global_config.get_plain_token()
-            if not api_key:
-                raise ValueError("OpenAI API key not configured")
+        model = cfg.global_config.get_gpt_model()
+        llm_provider = GPT4oLLM(api_key, model)
+        log(f"✅ Initialized {model} provider")
+    else:
+        # Spark mode
+        spark_token = _get_spark_token()
+        if not spark_token:
+            raise ValueError("Spark token not configured")
             
-            model = cfg.global_config.get_gpt_model()
-            llm_provider = GPT4oLLM(api_key, model)
-            log(f"✅ Initialized {model} provider")
-        else:
-            # Spark mode
-            spark_token = _get_spark_token()
-            if not spark_token:
-                raise ValueError("Spark token not configured")
-                
-            spark_endpoint = str(cfg.global_config.spark_endpoint)
-            llm_provider = SparkLLM(spark_endpoint, spark_token)
-            log("✅ Initialized Spark provider")
+        spark_endpoint = str(cfg.global_config.spark_endpoint)
+        llm_provider = SparkLLM(spark_endpoint, spark_token)
+        log("✅ Initialized Spark provider")
 
 # ─────────────────────────  SPARK ───────────────────────── #
 def _get_spark_token() -> Optional[str]:
     """Get Spark token based on work_mode"""
-    cfg = get_fresh_config()  # Fresh config al
+    cfg = get_config()
     work_mode = cfg.global_config.work_mode
     
     if work_mode in ("hfcloud", "cloud"):
@@ -108,7 +120,7 @@ def _get_spark_token() -> Optional[str]:
         from dotenv import load_dotenv
         load_dotenv()
         return os.getenv("SPARK_TOKEN")
-        
+
 async def spark_generate(s: Session, prompt: str, user_msg: str) -> str:
     """Call LLM provider with proper error handling"""
     try:
@@ -219,8 +231,6 @@ async def chat(body: ChatRequest, x_session_id: str = Header(...)):
 # ─────────────────────────  MESSAGE HANDLERS ───────────────────────── #
 async def _handle_new_message(session: Session, user_input: str, version) -> str:
     """Handle new message (not parameter followup)"""
-    cfg = get_fresh_config()  # Fresh config
-    
     # Build intent detection prompt
     prompt = build_intent_prompt(
         version.general_prompt,