import asyncio
import json
from typing import AsyncIterable, List, Optional
from urllib.parse import urlencode

from fastapi import Body, Request
from fastapi.concurrency import run_in_threadpool
from langchain.callbacks import AsyncIteratorCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.prompts.chat import ChatPromptTemplate
from sse_starlette.sse import EventSourceResponse

from configs import (TEMPERATURE,
                     USE_RERANKER,
                     RERANKER_MODEL,
                     RERANKER_MAX_LENGTH,
                     MODEL_PATH,
                     MAX_TOKENS,
                     MAX_CUT_TOKENS, LLM_MODELS)
from server.chat.utils import History
from server.knowledge_base.kb_service.base import KBServiceFactory
from server.reranker.reranker import LangchainReranker
from server.utils import BaseResponse, get_prompt_template
from server.utils import embedding_device
from server.utils import wrap_done, get_ChatOpenAI
from collections import defaultdict
from server.custom.custom_fun import chi_translation,eng_translation

async def paper_translation(query: str = Body("为我总结这些内容", description="用户输入", examples=["你好"]),
                           knowledge_base_name: str = Body(..., description="知识库名称", examples=["samples"]),
                           stream: bool = Body(False, description="流式输出"),
                           model_name: str = Body(LLM_MODELS[0], description="LLM 模型名称。"),
                           temperature: float = Body(TEMPERATURE, description="LLM 采样温度", ge=0.0, le=1.0),
                           max_tokens: Optional[int] = Body(
                               MAX_TOKENS,
                               description="限制LLM生成Token数量，默认None代表模型最大值"
                           ),
                           prompt_name: str = Body(
                               "eng_chi",
                               description="使用的prompt模板名称(在configs/prompt_config.py中配置)"
                           ),
                           source_name_list: List[str] = Body([], description="资源列表"),
                           request: Request = None,
                           ):
    kb = KBServiceFactory.get_service_by_name(knowledge_base_name)
    if kb is None:
        return BaseResponse(code=404, msg=f"未找到知识库 {knowledge_base_name}")
    async def translation_iterator(
            query: str,
            model_name: str = model_name,
            prompt_name: str = prompt_name,
    ) -> AsyncIterable[str]:
        nonlocal max_tokens
        callback = AsyncIteratorCallbackHandler()
        if isinstance(max_tokens, int) and max_tokens <= 0:
            max_tokens = None

        docs = []
        docs = await run_in_threadpool(kb.get_doc_by_sources_name,
                                    source_name_list=source_name_list)
        
        if len(docs) == 0:  # 如果没有找到相关文档，使用empty模板
            prompt_template = get_prompt_template("knowledge_base_chat", "empty")
        else:
            prompt_template = get_prompt_template("knowledge_base_chat", prompt_name)
        # input_msg = History(role="user", content=prompt_template).to_msg_template(False)
        # chat_prompt = ChatPromptTemplate.from_messages([input_msg])
        # chain = LLMChain(prompt=chat_prompt, llm=model)
        if prompt_name == "chi_eng":
            async for chunk in chi_translation(docs, prompt_template, model_name, temperature, max_tokens):
                yield chunk
        else:
            async for chunk in eng_translation(docs, prompt_template, model_name, temperature, max_tokens):
                yield chunk
       

    return EventSourceResponse(translation_iterator(query, model_name=model_name, prompt_name=prompt_name))