Update chat_handler.py

chat_handler.py

@@ -52,39 +52,52 @@ def _safe_intent_parse(raw: str) -> tuple[str, str]:
     return name, tail
 
 # ─────────────────────────  CONFIG ───────────────────────── #
-cfg = ConfigProvider.get()
 SPARK_URL = str(cfg.global_config.spark_endpoint).rstrip("/")
 
+# ✅ Her kullanımda fresh config al
+def get_fresh_config():
+    """Get fresh config on each call"""
+    from config_provider import ConfigProvider
+    # Force reload
+    ConfigProvider._instance = None
+    return ConfigProvider.get()
+    
 # ─────────────────────────  SPARK ───────────────────────── #
-def initialize_llm():
+def initialize_llm(force_reload=False):
     """Initialize LLM provider based on work_mode"""
     global llm_provider
     
-    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")
+    # Force reload config
+    if force_reload or llm_provider is None:
+        cfg = get_fresh_config()
+        work_mode = cfg.global_config.work_mode
         
-        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")
+        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")
             
-        spark_endpoint = str(cfg.global_config.spark_endpoint)
-        llm_provider = SparkLLM(spark_endpoint, spark_token)
-        log("✅ Initialized Spark provider")
+            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 ───────────────────────── #
 def _get_spark_token() -> Optional[str]:
     """Get Spark token based on work_mode"""
-    if cfg.global_config.is_cloud_mode():
+    cfg = get_fresh_config()  # Fresh config al
+    work_mode = cfg.global_config.work_mode
+    
+    if work_mode in ("hfcloud", "cloud"):
         # Cloud mode - use HuggingFace Secrets
         token = os.getenv("SPARK_TOKEN")
         if not token:
@@ -99,8 +112,11 @@ def _get_spark_token() -> Optional[str]:
 async def spark_generate(s: Session, prompt: str, user_msg: str) -> str:
     """Call LLM provider with proper error handling"""
     try:
+        # Always reinitialize to get fresh config
+        initialize_llm(force_reload=True)
+        
         if not llm_provider:
-            initialize_llm()
+            raise ValueError("Failed to initialize LLM provider")
         
         # Use the abstract interface
         raw = await llm_provider.generate(prompt, user_msg, s.chat_history)
@@ -203,6 +219,8 @@ 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,