Update llm_handler.py

llm_handler.py

@@ -4,7 +4,7 @@ from llama_cpp_agent import MessagesFormatterType
 from llama_cpp_agent.providers import LlamaCppPythonProvider
 
 # Initialize the Llama model
-llama_model = Llama("Arcee-Spark-GGUF/Arcee-Spark-Q4_K_M.gguf", n_batch=1024, n_threads=10, n_gpu_layers=33, n_ctx=2048, verbose=False)
+llama_model = Llama("Arcee-Spark-GGUF/Arcee-Spark-Q4_K_M.gguf", n_batch=1024, n_threads=24, n_gpu_layers=33, n_ctx=2048, verbose=False)
 
 # Create the provider
 provider = LlamaCppPythonProvider(llama_model)
@@ -12,7 +12,7 @@ provider = LlamaCppPythonProvider(llama_model)
 # Create the agent
 agent = LlamaCppAgent(
     provider,
-    system_prompt="You are a helpful assistant.",
+    system_prompt="You are a helpful assistant who's purpose is it to help users craft and edit datasets.",
     predefined_messages_formatter_type=MessagesFormatterType.CHATML,
     debug_output=True
 )
@@ -24,19 +24,19 @@ settings.stream = True
 
 def send_to_llm(provider, msg_list):
     try:
-        # Concatenate all messages into a single string
+
         full_message = "\n".join([f"{msg['role']}: {msg['content']}" for msg in msg_list])
         
-        # Call get_chat_response with the full message string
+
         response = agent.get_chat_response(full_message, llm_sampling_settings=settings)
         
-        # Check if response is a string or an object with content attribute
+   
         if isinstance(response, str):
             return response, None
         elif hasattr(response, 'content'):
             return response.content, None
         else:
-            return str(response), None  # Convert to string if it's neither
+            return str(response), None  
     except Exception as e:
         print(f"Error in send_to_llm: {str(e)}")
         return f"Error: {str(e)}", None