from fastapi import Body
from fastapi.responses import StreamingResponse
from configs.model_config import LLM_MODEL, TEMPERATURE
from server.chat.utils import wrap_done, get_ChatOpenAI
from langchain import LLMChain
from langchain.callbacks import AsyncIteratorCallbackHandler
from typing import AsyncIterable
import asyncio
from langchain.prompts.chat import ChatPromptTemplate
from typing import List
from server.chat.utils import History


async def chat(query: str = Body(..., description="用户输入", examples=["恼羞成怒"]),
                history: List[History] = Body([],
                                       description="历史对话",
                                       examples=[[
                                           {"role": "user", "content": "我们来玩成语接龙，我先来，生龙活虎"},
                                           {"role": "assistant", "content": "虎头虎脑"}]]
                                       ),
                stream: bool = Body(False, description="流式输出"),
                model_name: str = Body(LLM_MODEL, description="LLM 模型名称。"),
                temperature: float = Body(TEMPERATURE, description="LLM 采样温度", gt=0.0, le=1.0),
                # top_p: float = Body(TOP_P, description="LLM 核采样。勿与temperature同时设置", gt=0.0, lt=1.0),
         ):
    history = [History.from_data(h) for h in history]

    async def chat_iterator(query: str,
                            history: List[History] = [],
                            model_name: str = LLM_MODEL,
                            ) -> AsyncIterable[str]:
        callback = AsyncIteratorCallbackHandler()
        model = get_ChatOpenAI(
            model_name=model_name,
            temperature=temperature,
            callbacks=[callback],
        )
        input_msg = History(role="user", content="{{ input }}").to_msg_template(False)
        chat_prompt = ChatPromptTemplate.from_messages(
            [i.to_msg_template() for i in history] + [input_msg])
        chain = LLMChain(prompt=chat_prompt, llm=model)

        # Begin a task that runs in the background.
        task = asyncio.create_task(wrap_done(
            chain.acall({"input": query}),
            callback.done),
        )

        if stream:
            async for token in callback.aiter():
                # Use server-sent-events to stream the response
                yield token
        else:
            answer = ""
            async for token in callback.aiter():
                answer += token
            yield answer

        await task

    return StreamingResponse(chat_iterator(query, history, model_name),
                             media_type="text/event-stream")