dashboard-nanobot/backend/bootstrap/app_runtime.py

import asyncio

from fastapi import FastAPI
from sqlmodel import Session, select

from core.cache import cache
from core.database import engine, init_database
from core.docker_instance import docker_manager
from core.speech_service import inspect_speech_model_status
from core.settings import DATABASE_URL_DISPLAY, REDIS_ENABLED
from models.bot import BotInstance
from services.default_assets_service import validate_runtime_data_assets
from services.platform_service import get_speech_runtime_settings, prune_expired_activity_events
from services.runtime_service import docker_callback, set_main_loop


def reload_platform_runtime(app: FastAPI) -> None:
    cache.delete_prefix("")
    speech_service = getattr(app.state, "speech_service", None)
    if speech_service is not None and hasattr(speech_service, "reset_runtime"):
        speech_service.reset_runtime()


def register_app_runtime(app: FastAPI) -> None:
    @app.on_event("startup")
    async def _on_startup() -> None:
        redis_state = "Disabled"
        if REDIS_ENABLED:
            redis_state = "Connected" if cache.enabled else f"Unavailable ({cache.status})"
        print(
            f"🚀 Dashboard Backend 启动中... (DB: {DATABASE_URL_DISPLAY}, REDIS: {redis_state})"
        )
        current_loop = asyncio.get_running_loop()
        app.state.main_loop = current_loop
        set_main_loop(current_loop)
        validate_runtime_data_assets()
        print("[init] data 目录校验通过")
        init_database()
        with Session(engine) as session:
            prune_expired_activity_events(session, force=True)
            bots = session.exec(select(BotInstance)).all()
            for bot in bots:
                docker_manager.ensure_monitor(bot.id, docker_callback)
        speech_settings = get_speech_runtime_settings()
        model_status = inspect_speech_model_status()
        if speech_settings["enabled"]:
            if model_status["ready"]:
                print(f"🎙️ 语音识别模型就绪: {model_status['resolved_path']}")
            else:
                hint = f"，请将模型文件放到 {model_status['expected_path']}" if model_status["expected_path"] else ""
                print(f"⚠️ 语音识别模型未就绪: {model_status['message']}{hint}")
        print("✅ 启动自检完成")