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 gen_paragraph(
    data: dict = Body(..., description="包含文件全文内容的JSON对象", example={"context": "文件的全文内容"})
):
    """
    接收文件的全文内容生成章节速览。
    """
    context: Optional[str] = data.get("context")
    if not context:
        return {"error": "缺少必要参数 'context'"}

    logger.info(f"开始生成章节速览...")

    # 定义生成摘要的函数
    def generate_summary(text: str) -> str:
        """使用 TextRank 生成文本摘要"""
        summary = TextRank(text, num_sentences=1)  # 生成80句话的摘要
        return summary

    # 根据正文长度决定是否生成摘要
    # if len(context) >= 30000:
    #     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30000字符，使用原文")
    if len(context) >= 40000:
        context_summary = context[:40000]
    else:
        context_summary = context
    # 调用模型生成生成摘要内容
    try:
        article_paragraph = get_llm_model_response(
            strategy_name="gen_paragraph",
            llm_model_name=LLM_MODELS[0],
            template_prompt_name="gen_paragraph",
            prompt_param_dict={
                "context": context_summary,  # 使用摘要或原文
            },
            temperature=TEMPERATURE,
            max_tokens=MAX_TOKENS
        )
    except Exception as e:
        logger.error("生成章节速览时出错: %s", e)
        return {"error": "生成章节速览时发生错误，请重试"}

    logger.info("生成章节速览内容: %s", article_paragraph)
    return article_paragraph