from langchain.memory import ConversationBufferWindowMemory
from server.agent.tools import tools, tool_names
from langchain.agents import AgentExecutor, LLMSingleActionAgent
from server.agent.custom_template import CustomOutputParser, prompt
from fastapi import Body
from fastapi.responses import StreamingResponse
from configs.model_config import LLM_MODEL, TEMPERATURE, HISTORY_LEN
from server.utils import wrap_done, get_ChatOpenAI
from langchain import LLMChain
from langchain.callbacks import AsyncIteratorCallbackHandler
from langchain.callbacks.streaming_aiter_final_only import AsyncFinalIteratorCallbackHandler
from typing import AsyncIterable
import asyncio
from langchain.prompts.chat import ChatPromptTemplate
from typing import List
from server.chat.utils import History

memory = ConversationBufferWindowMemory(k=HISTORY_LEN)
async def agent_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 = AsyncFinalIteratorCallbackHandler()
        model = get_ChatOpenAI(
            model_name=model_name,
            temperature=temperature,
            callbacks=[callback],
        )
        output_parser = CustomOutputParser()
        llm_chain = LLMChain(llm=model, prompt=prompt)
        agent = LLMSingleActionAgent(
            llm_chain=llm_chain,
            output_parser=output_parser,
            stop=["\nObservation:"],
            allowed_tools=tool_names
        )
        agent_executor = AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True, memory=memory,
                                                            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])
        task = asyncio.create_task(wrap_done(
            agent_executor.acall(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")