from fastapi import Body
from configs import LLM_MODELS, TEMPERATURE, MAX_TOKENS
from server.chat.policy_fun_iast import get_llm_model_response
from typing import Optional
from langchain.chains import LLMChain
from langchain.prompts import ChatPromptTemplate
from server.chat.utils import History
from server.utils import wrap_done, get_ChatOpenAI, get_prompt_template
from langchain.callbacks import AsyncIteratorCallbackHandler
import asyncio
from server.knowledge_base.kb_service.base import TextRank
from configs.basic_config import *

async def sentence_reference(
    # context: str = Body(..., description="上文全文", examples=[""]),
    paragraph_content: str = Body(..., description="用户框选的内容，<=2句", examples=[""]),
    temperature: float = Body(0.9, description="LLM 采样温度", ge=0.0, le=2.0),
    max_tokens: Optional[int] = Body(1024, description="限制LLM生成Token数量，默认None代表模型最大值"),
):
    logger.info(f"开始提示句子...")

    # 定义生成摘要的函数
    # def generate_summary(text: str) -> str:
    #     """使用 TextRank 生成文本摘要"""
    #     if len(text) <= 20000:
    #         summary = TextRank(text, num_sentences=60)  # 生成60句话的摘要
    #     else:
    #         summary = TextRank(text, num_sentences=80)  # 生成80句话的摘要
    #     return summary

    # # 根据上下文长度决定是否生成摘要
    # if len(context) >= 15000:
    #     context_summary = generate_summary(context)
    #     logger.info(f"生成撰写文稿的摘要: %s", context_summary)
    # else:
    #     context_summary = context  # 直接使用原文
    #     logger.info(f"撰写文稿小于15000字符，使用原文")

# 定义一个函数来调用 get_llm_model_response，并异步封装它
    async def get_sentence_reference():
        try:
            # 使用 asyncio.to_thread 封装同步函数
            result = await asyncio.to_thread(
                get_llm_model_response,
                strategy_name="sentence_reference",
                llm_model_name=LLM_MODELS[0],
                template_prompt_name="sentence_reference",
                prompt_param_dict={
                    # "context": context_summary,  # 使用摘要或原文
                    "paragraph_content": paragraph_content
                },
                temperature=temperature,
                max_tokens=max_tokens
            )
            return result
        except Exception as e:
            logger.error("生成提示句子内容时出错: %s", e)
            return "出错了。。请重试。。"

    # 并行调用三次 get_llm_model_response
    try:
        responses = await asyncio.gather(
            get_sentence_reference(),
            get_sentence_reference(),
            get_sentence_reference()
        )
    except Exception as e:
        logger.error("并行调用 LLM 模型时出错: %s", e)
        return "出错了。。请重试。。"

    # 拼接结果
    final_output = "\n\n".join([f"句子{i + 1}：{response}" for i, response in enumerate(responses)])
    logger.info("生成的最终拼接内容: %s", final_output)

    return final_output