import datetime
import json
import time
from typing import Union, Annotated

from fastapi import FastAPI
from langchain_core.messages import HumanMessage, AIMessage, ToolMessage
from langchain_core.tools import tool
from langgraph.graph.state import CompiledStateGraph
from langgraph.prebuilt import create_react_agent
from langgraph.types import interrupt, Command
from pydantic import BaseModel, Field
from starlette.responses import StreamingResponse

from app.model.ConversationModel import ConversationService
from app.utils.db_utils import AsyncSessionDep
from app.utils.llm_utils import create_llm
from app.utils.postgres_checkpointer import PostgresCheckpointerManager, AsyncPostgresSaverDep


# /*---------------------------------------获取时间工具-------------------------------------------*/
@tool(
  name_or_callable="tool_get_datetime",
  description="一个用于获取当前时间的工具，没有参数"
)
def tool_get_datetime():
  return {
    "result": f"当前时间为：{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}",
    "render": {
      "type": "DataTable"
    }
  }


@tool(
  name_or_callable="tool_multiply",
  description="一个乘法工具，用于计算两个数字相乘"
)
def tool_multiply(
  number1: Annotated[float, "第一个要相乘的数字"],
  number2: Annotated[float, "第二个要相乘的数字"],
) -> float:
  print("\n invoke tool_multiply", number1, number2, '\n')

  return float(number1) * float(number2)


@tool(
  name_or_callable="tool_add",
  description="一个加法工具，用于计算两个数字相加"
)
def tool_add(
  number1: Annotated[float, "第一个要相加的数字"],
  number2: Annotated[float, "第二个要相加的数字"],
) -> float:
  print("\n invoke tool_add", number1, number2, '\n')

  return float(number1) + float(number2)


@tool(name_or_callable="tool_book_hotel", description="一个用于预定酒店的工具")
def tool_book_hotel() -> str:
  resume_data = interrupt({
    "title": "请确认酒店预定信息",
    "form": [
      {
        "field": "hotel_name",
        "type": "input",
        "label": "酒店名称",
        "required": True,
      },
      {
        "field": "room_type",
        "type": "select",
        "label": "客房类型",
        "options": [
          {"label": "标间", "value": "标间"},
          {"label": "单间", "value": "单间"},
          {"label": "双人间", "value": "双人间"},
        ],
        "required": True,
      },
      {
        "field": "check_in_date",
        "type": "date",
        "label": "入住时间",
      }
    ],
    "formData": {
      "hotel_name": '',
      "room_type": '',
      "check_in_date": '',
    }
  })
  if resume_data == "N":
    return f"用户选择取消预定酒店"

  return ToolMessage(content="预定成功", additional_kwargs={"render": {"type": "DataTable"}})


class ChatMessage(BaseModel):
  id: str = Field(..., description="消息id")
  type: str = Field(..., description="消息类型")
  content: str = Field(..., description="消息内容")


# 对话接口参数类型
class ChatParam(BaseModel):
  thread_id: str = Field(..., description="线程id")
  human_message: ChatMessage = Field(..., description="用户消息")


class ChatAgent:
  agent: Union[CompiledStateGraph, None] = None

  @staticmethod
  async def get_agent() -> CompiledStateGraph:
    if not ChatAgent.agent:
      ChatAgent.agent = create_react_agent(
        model=create_llm(),
        tools=[
          tool_book_hotel,
          tool_get_datetime,
          tool_add,
          tool_multiply,
        ],
        checkpointer=await PostgresCheckpointerManager.get_instance(),
        prompt="""
        你是一名擅长使用工具的智能助手，你需要根据用户问题来进行回答，请使用中文进行回答。
        当用户问题需要调用工具时再调用工具，否则按照你的知识来回答问题。
        某些工具会触发中断让用户来编辑工具执行参数，这些工具会将新的执行参数作为信息返回，你需要回复用户最新的信息
        """
      )
    return ChatAgent.agent

  @staticmethod
  async def get_chat_state(thread_id: str):
    graph = await ChatAgent.get_agent()
    graph_state = await graph.aget_state(config={"configurable": {"thread_id": thread_id}})
    if graph_state.values.get('messages', None) is None:
      graph_state.values['messages'] = []
    return {
      **graph_state.values,
      "__interrupt__": graph_state.interrupts,
    }


def add_langgraph_chat_route(app: FastAPI):
  # 流式对话接口
  @app.post("/langgraph/stream")
  async def langgraph_stream(body: dict, checkpointer: AsyncPostgresSaverDep):
    print(":::::::::::::::::::::::::langgraph_stream:::::::::::::::::::::")
    print(body)

    stream_input = body.get('input')
    stream_config = body.get('config')

    print("stream_input", stream_input)
    print("stream_config", stream_config)

    # 暂时每次对话的时候清理掉对话历史
    await checkpointer.adelete_thread(stream_config.get('configurable').get('thread_id'))

    async def generator_function():

      graph = await ChatAgent.get_agent()
      # chat_state = await ChatAgent.get_chat_state(thread_id)
      # chat_history_list = chat_state.get('messages')
      async for chunk in graph.astream(
        stream_input,
        config=stream_config,
        stream_mode=['messages', 'updates']
      ):
        print("chunk-------->>>>>>>>>>", chunk)

        result_template = {
          "choices": [{"delta": {}, "index": 0}],
          "created": time.time(),
          "id": "",
          "usage": None
        }

        emit_chunk = {"stream_type": chunk[0], }

        if emit_chunk['stream_type'] == "messages":
          # messages模式流式输出，此时 chunk[1][0] 为AIMessageChunk
          chunk_message = chunk[1][0]
        else:
          # updates模式流式输出
          for k, v in chunk[1].items():
            if k == 'agent' or k == 'tool':
              chunk_message = v.get('messages')[0]
            elif k == '__interrupt__':
              chunk_message = AIMessage(content='')
              emit_chunk['stream_type'] = 'interrupt'
              emit_chunk['interrupt'] = v[0].value

        emit_chunk = {
          **emit_chunk,
          "msg_id": chunk_message.id,
          "msg_type": chunk_message.type,
          "msg_content": chunk_message.content,
        }

        if isinstance(chunk_message, AIMessage):
          if chunk_message.tool_calls:
            emit_chunk["tool_calls"] = chunk_message.tool_calls
        elif isinstance(chunk_message, ToolMessage):
          emit_chunk["tool_name"] = chunk_message.name
          emit_chunk["additional_kwargs"] = chunk_message.additional_kwargs

        result_template["choices"][0]["delta"]['content'] = emit_chunk
        result_template["choices"][0]["delta"]['role'] = 'assistant'

        result_template['id'] = chunk_message.id
        result_template['created'] = int(time.time())

        yield f"data: {json.dumps(result_template, ensure_ascii=False)}\n\n"
      # yield "data: [DONE]\n\n"

    return StreamingResponse(generator_function(), media_type="text/event-stream")

  # 恢复中断接口
  @app.post("/langgraph/chat_resume/{thread_id}")
  async def langgraph_chat(body: dict, thread_id: str):
    graph = await ChatAgent.get_agent()
    chat_state = await ChatAgent.get_chat_state(thread_id)
    chat_history_list = chat_state.get('messages')
    graph_state = await graph.ainvoke(
      Command(resume=body.get('resume_data')),
      config={"configurable": {"thread_id": thread_id}}
    )
    return {
      **graph_state,
      # 这里不需要加1，因为我们并没有往messages中增加消息
      "messages": graph_state.get('messages')[len(chat_history_list):],
    }

  # 查询聊天记录
  @app.get("/langgraph/chat_state/{thread_id}")
  async def langgraph_chat(thread_id: str):
    return await ChatAgent.get_chat_state(thread_id)  # 查询聊天记录

  # 删除聊天记录
  @app.post("/langgraph/chat_remove/{thread_id}")
  async def langgraph_chat(thread_id: str, checkpointer: AsyncPostgresSaverDep, session: AsyncSessionDep):
    await ConversationService.item_delete(session=session, row_dict={"id": thread_id})
    await checkpointer.adelete_thread(thread_id)
    return {"result": "success"}