import gradio as gr
from pydantic import BaseModel, Field
from typing import Any, Optional, Dict, List
from huggingface_hub import InferenceClient
from langchain.llms.base import LLM

hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")


class KwArgsModel(BaseModel):
    kwargs: Dict[str, Any] = Field(default_factory=dict)

class CustomInferenceClient(LLM, KwArgsModel):
    model_name: str
    inference_client: InferenceClient

    def __init__(self, model_name: str, hf_token: str, kwargs: Optional[Dict[str, Any]] = None):
        inference_client = InferenceClient(model=model_name, token=hf_token)
        super().__init__(
            model_name=model_name,
            hf_token=hf_token,
            kwargs=kwargs,
            inference_client=inference_client
        )

    def _call(
        self,
        prompt: str,
        stop: Optional[List[str]] = None
    ) -> str:
        if stop is not None:
            raise ValueError("stop kwargs are not permitted.")
        response_gen = self.inference_client.text_generation(prompt, **self.kwargs, stream=True)
        response = ''.join(response_gen)  
        return response

    @property
    def _llm_type(self) -> str:
        return "custom"

    @property
    def _identifying_params(self) -> dict:
        return {"model_name": self.model_name}

kwargs = {"max_new_tokens":256, "temperature":0.9, "top_p":0.6, "repetition_penalty":1.3, "do_sample":True}