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()

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

def nvidia_client():
    """ returns the nvidia server client """
    client = InferenceClient(
    base_url=model_config.get('reader','NVIDIA_ENDPOINT'),
    api_key=NVIDIA_SERVER)
    print("getting nvidia client")

    return client

# TESTING VERSION
def dedicated_endpoint():
    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"

        client = InferenceClient(
            model=model_id,
            api_key=HF_token,
        )
        
        return client

    except Exception as e:
        raise


# def dedicated_endpoint():
#     """ returns the dedicated server endpoint"""

#      # Set up the streaming callback handler
#     callback = StreamingStdOutCallbackHandler()

#     # Initialize the HuggingFaceEndpoint with streaming enabled
#     llm_qa = HuggingFaceEndpoint(
#         endpoint_url=model_config.get('reader', 'DEDICATED_ENDPOINT'),
#         max_new_tokens=int(model_config.get('reader','MAX_TOKENS')),
#         repetition_penalty=1.03,
#         timeout=70,
#         huggingfacehub_api_token=HF_token,
#         streaming=True, # Enable streaming for real-time token generation
#         callbacks=[callback] # Add the streaming callback handler
#     )
    
#     # Create a ChatHuggingFace instance with the streaming-enabled endpoint
#     chat_model = ChatHuggingFace(llm=llm_qa)
#     print("getting dedicated endpoint wrapped in ChathuggingFace ")
#     return chat_model