app.py

#!/usr/bin/python3
# -*- coding: utf-8 -*-
from __future__ import annotations

import asyncio
from collections import deque
import contextlib
from functools import partial
import urllib.parse
from datetime import datetime
import uuid
from enum import Enum
from metagpt.logs import set_llm_stream_logfunc
import pathlib

from fastapi import FastAPI, Request, HTTPException
from fastapi.responses import StreamingResponse, RedirectResponse
from fastapi.staticfiles import StaticFiles
import fire
from pydantic import BaseModel, Field
import uvicorn

from typing import Any, Optional

from metagpt.schema import Message
from metagpt.actions.action import Action
from metagpt.actions.action_output import ActionOutput
from metagpt.config import CONFIG

from software_company import RoleRun, SoftwareCompany


class QueryAnswerType(Enum):
    Query = "Q"
    Answer = "A"


class SentenceType(Enum):
    TEXT = "text"
    HIHT = "hint"
    ACTION = "action"


class MessageStatus(Enum):
    COMPLETE = "complete"


class SentenceValue(BaseModel):
    answer: str


class Sentence(BaseModel):
    type: str
    id: Optional[str] = None
    value: SentenceValue
    is_finished: Optional[bool] = None


class Sentences(BaseModel):
    id: Optional[str] = None
    action: Optional[str] = None
    role: Optional[str] = None
    skill: Optional[str] = None
    description: Optional[str] = None
    timestamp: str = datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z")
    status: str
    contents: list[dict]


class NewMsg(BaseModel):
    """Chat with MetaGPT"""

    query: str = Field(description="Problem description")
    config: dict[str, Any] = Field(description="Configuration information")


class ErrorInfo(BaseModel):
    error: str = None
    traceback: str = None


class ThinkActStep(BaseModel):
    id: str
    status: str
    title: str
    timestamp: str
    description: str
    content: Sentence = None


class ThinkActPrompt(BaseModel):
    message_id: int = None
    timestamp: str = datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z")
    step: ThinkActStep = None
    skill: Optional[str] = None
    role: Optional[str] = None

    def update_think(self, tc_id, action: Action):
        self.step = ThinkActStep(
            id=str(tc_id),
            status="running",
            title=action.desc,
            timestamp=datetime.now().strftime("%Y-%m-%dT%H:%M:%S.%f%z"),
            description=action.desc,
        )

    def update_act(self, message: ActionOutput | str, is_finished: bool = True):
        if is_finished:
            self.step.status = "finish"
        self.step.content = Sentence(
            type="text",
            id=str(1),
            value=SentenceValue(answer=message.content if is_finished else message),
            is_finished=is_finished,
        )

    @staticmethod
    def guid32():
        return str(uuid.uuid4()).replace("-", "")[0:32]

    @property
    def prompt(self):
        v = self.json(exclude_unset=True)
        return urllib.parse.quote(v)


class MessageJsonModel(BaseModel):
    steps: list[Sentences]
    qa_type: str
    created_at: datetime = datetime.now()
    query_time: datetime = datetime.now()
    answer_time: datetime = datetime.now()
    score: Optional[int] = None
    feedback: Optional[str] = None

    def add_think_act(self, think_act_prompt: ThinkActPrompt):
        s = Sentences(
            action=think_act_prompt.step.title,
            skill=think_act_prompt.skill,
            description=think_act_prompt.step.description,
            timestamp=think_act_prompt.timestamp,
            status=think_act_prompt.step.status,
            contents=[think_act_prompt.step.content.dict()],
        )
        self.steps.append(s)

    @property
    def prompt(self):
        v = self.json(exclude_unset=True)
        return urllib.parse.quote(v)


async def create_message(req_model: NewMsg, request: Request):
    """
    Session message stream
    """
    config = {k.upper(): v for k, v in req_model.config.items()}
    set_context(config, uuid.uuid4().hex)

    msg_queue = deque()
    CONFIG.LLM_STREAM_LOG = lambda x: msg_queue.appendleft(x) if x else None

    role = SoftwareCompany()
    role.recv(message=Message(content=req_model.query))
    answer = MessageJsonModel(
        steps=[
            Sentences(
                contents=[
                    Sentence(type=SentenceType.TEXT.value, value=SentenceValue(answer=req_model.query), is_finished=True)
                ],
                status=MessageStatus.COMPLETE.value,
            )
        ],
        qa_type=QueryAnswerType.Answer.value,
    )

    tc_id = 0

    while True:
        tc_id += 1
        if request and await request.is_disconnected():
            return
        think_result: RoleRun = await role.think()
        if not think_result:  # End of conversion
            break

        think_act_prompt = ThinkActPrompt(role=think_result.role.profile)
        think_act_prompt.update_think(tc_id, think_result)
        yield think_act_prompt.prompt + "\n\n"
        task = asyncio.create_task(role.act())

        while not await request.is_disconnected():
            if msg_queue:
                think_act_prompt.update_act(msg_queue.pop(), False)
                yield think_act_prompt.prompt + "\n\n"
                continue

            if task.done():
                break

            await asyncio.sleep(0.5)

        act_result = await task
        think_act_prompt.update_act(act_result)
        yield think_act_prompt.prompt + "\n\n"
        answer.add_think_act(think_act_prompt)
    yield answer.prompt + "\n\n"  # Notify the front-end that the message is complete.


default_llm_stream_log = partial(print, end="")


def llm_stream_log(msg):
    with contextlib.suppress():
        CONFIG._get("LLM_STREAM_LOG", default_llm_stream_log)(msg)


def set_context(context, uid):
    context["WORKSPACE_PATH"] = pathlib.Path("workspace", uid)
    for old, new in (("DEPLOYMENT_ID", "DEPLOYMENT_NAME"), ("OPENAI_API_BASE", "OPENAI_BASE_URL")):
        if old in context and new not in context:
            context[new] = context[old]
    CONFIG.set_context(context)
    return context


class ChatHandler:
    @staticmethod
    async def create_message(req_model: NewMsg, request: Request):
        """Message stream, using SSE."""
        event = create_message(req_model, request)
        headers = {"Cache-Control": "no-cache", "Connection": "keep-alive"}
        return StreamingResponse(event, headers=headers, media_type="text/event-stream")


app = FastAPI()

app.mount(
    "/static",
    StaticFiles(directory="./static/", check_dir=True),
    name="static",
)
app.add_api_route(
    "/api/messages",
    endpoint=ChatHandler.create_message,
    methods=["post"],
    summary="Session message sending (streaming response)",
)


@app.get("/{catch_all:path}")
async def catch_all(request: Request):
    if request.url.path == "/":
        return RedirectResponse(url="/static/index.html")
    if request.url.path.startswith("/api"):
        raise HTTPException(status_code=404)

    new_path = f"/static{request.url.path}"
    return RedirectResponse(url=new_path)


set_llm_stream_logfunc(llm_stream_log)


def main():
    uvicorn.run(app="__main__:app", host="0.0.0.0", port=7860)


if __name__ == "__main__":
    fire.Fire(main)