from typing import Any, Coroutine

from fastapi import Request, Response, status
from fastapi.responses import JSONResponse

from .blackbox import Blackbox
from lagent.llms.lmdepoly_wrapper import LMDeployClient
from lagent.llms.meta_template import INTERNLM2_META as META
from injector import singleton

@singleton
class Emotion(Blackbox):

    def __init__(self, model_name, model_url) -> None:
        self.model = LMDeployClient(
            model_name=model_name,
            url=model_url,
            meta_template=META,
            top_p=0.8,
            top_k=100,
            temperature=0,
            repetition_penalty=1.0,
            stop_words=['<|im_end|>'])

    def __call__(self, *args, **kwargs):
        return self.processing(*args, **kwargs)

    def valid(self,  *args, **kwargs) -> bool:
        data = args[0]
        return isinstance(data, str)
    
    def processing(self, *args, **kwargs) -> int:
        text = args[0]
        text = "Please use one word to infer the emotion of the following passage:\n" + text + "\nJust print out that signle word pls."
        text = [{'role': 'user', 'content': text}]
        return self.model.stream_chat(text)
    
    async def fast_api_handler(self, request) -> Response:
        try:
            data = await request.json()
        except:
            return JSONResponse(content={"error": "json parse error"}, status_code=status.HTTP_400_BAD_REQUEST)
        text = data.get("text")
        if text is None:
            return JSONResponse(content={"error": "text is required"}, status_code=status.HTTP_400_BAD_REQUEST)
        text = "Please use one word to infer the emotion of the following passage:\n" + text + "\nJust print out that signle word pls."
        text = [{'role': 'user', 'content': text}]
        sentiment = self.processing(text)
        return JSONResponse(content={"sentiment": sentiment }, status_code=status.HTTP_200_OK)