from huggingface_hub import InferenceClient
from auditqa.process_chunks import getconfig
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain_community.llms import HuggingFaceEndpoint
from langchain_community.chat_models.huggingface import ChatHuggingFace
import os
from dotenv import load_dotenv
load_dotenv()

# TESTING DEBUG LOG
from auditqa.logging_config import setup_logging
setup_logging()
import logging
logger = logging.getLogger(__name__)

model_config = getconfig("model_params.cfg")
# NVIDIA_SERVER = os.environ["NVIDIA_SERVERLESS"] #TESTING
HF_token = os.environ["LLAMA_3_1"]

def nvidia_client():
    logger.info("NVIDIA client activated")
    """ returns the nvidia server client """
    try:
        NVIDIA_SERVER = os.environ["NVIDIA_SERVERLESS"]
        client = InferenceClient(
            base_url=model_config.get('reader','NVIDIA_ENDPOINT'),
            api_key=NVIDIA_SERVER)
        print("getting nvidia client")
        return client
    except KeyError:
        raise KeyError("NVIDIA_SERVERLESS environment variable not set. Required for NVIDIA endpoint.")

# TESTING VERSION
def dedicated_endpoint():
    logger.info("Serverless endpoint activated")
    try:
        HF_token = os.environ["LLAMA_3_1"]
        if not HF_token:
            raise ValueError("LLAMA_3_1 environment variable is empty")
            
        model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
        logger.info(f"Initializing InferenceClient with model: {model_id}")

        client = InferenceClient(
            model=model_id,
            api_key=HF_token,
        )
        
        logger.info("Serverless InferenceClient initialization successful")
        return client

    except Exception as e:
        logger.error(f"Error initializing dedicated endpoint: {str(e)}")
        raise