Update chat_handler.py

chat_handler.py

@@ -69,18 +69,32 @@ def setup_llm_provider():
     """Initialize LLM provider based on internal_prompt config"""
     global llm_provider
     
-    cfg = ConfigProvider.get()  # Her zaman güncel config'i al
+    cfg = ConfigProvider.get()
     internal_prompt = cfg.global_config.internal_prompt
+    
     if not internal_prompt:
         log("⚠️ No internal_prompt configured, using default Spark")
-        llm_provider = SparkLLM(cfg)
+        # Get Spark token
+        spark_token = _get_spark_token()
+        if not spark_token:
+            log("❌ SPARK_TOKEN not found")
+            raise ValueError("SPARK_TOKEN not configured")
+        
+        spark_endpoint = str(cfg.global_config.spark_endpoint)
+        llm_provider = SparkLLM(spark_endpoint, spark_token)
         return
     
     # Parse internal prompt format: "provider:model"
     parts = internal_prompt.split(":", 1)
     if len(parts) != 2:
         log(f"⚠️ Invalid internal_prompt format: {internal_prompt}, using Spark")
-        llm_provider = SparkLLM(cfg)
+        spark_token = _get_spark_token()
+        if not spark_token:
+            log("❌ SPARK_TOKEN not found")
+            raise ValueError("SPARK_TOKEN not configured")
+        
+        spark_endpoint = str(cfg.global_config.spark_endpoint)
+        llm_provider = SparkLLM(spark_endpoint, spark_token)
         return
     
     provider, model = parts[0].lower(), parts[1]
@@ -90,14 +104,41 @@ def setup_llm_provider():
         api_key = os.getenv("OPENAI_API_KEY")
         if not api_key:
             log("❌ OPENAI_API_KEY not found in environment")
-            llm_provider = SparkLLM(cfg)
+            # Fallback to Spark
+            spark_token = _get_spark_token()
+            if not spark_token:
+                raise ValueError("Neither OPENAI_API_KEY nor SPARK_TOKEN configured")
+            
+            spark_endpoint = str(cfg.global_config.spark_endpoint)
+            llm_provider = SparkLLM(spark_endpoint, spark_token)
             return
         
         log(f"🤖 Using OpenAI with model: {model}")
         llm_provider = GPT4oLLM(api_key, model)
     else:
         log(f"⚠️ Unknown provider: {provider}, using Spark")
-        llm_provider = SparkLLM(cfg)
+        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)
+
+def _get_spark_token() -> Optional[str]:
+    """Get Spark token based on work_mode"""
+    cfg = ConfigProvider.get()
+    
+    if cfg.global_config.is_cloud_mode():
+        # Cloud mode - use HuggingFace Secrets
+        token = os.getenv("SPARK_TOKEN")
+        if not token:
+            log("❌ SPARK_TOKEN not found in HuggingFace Secrets!")
+        return token
+    else:
+        # On-premise mode - use .env file
+        from dotenv import load_dotenv
+        load_dotenv()
+        return os.getenv("SPARK_TOKEN")
 
 # ─────────────────────────  SPARK/LLM CALL ───────────────────────── #
 async def spark_generate(s: Session, prompt: str, user_msg: str) -> str: