Spaces:
Running
Running
stouchinglhh
Claude
sync processor from GitLab c697b6e: 版本 1.19.4 / 前端 1.12.4 —— 服务端 i18n(首屏不再闪英文)、推理模块并入模型页、前端抽出 shared/、缓存键改用前端号
bb59211 | """Application configuration via environment variables.""" | |
| from pathlib import Path | |
| from pydantic_settings import BaseSettings | |
| class Settings(BaseSettings): | |
| # Database. In local mode this normally points to the server through an | |
| # SSH tunnel (for example 127.0.0.1:15432). | |
| DATABASE_URL: str = "postgresql+asyncpg://odoo:myodoo@172.19.0.2:5432/data_acq" | |
| DB_CONNECT_TIMEOUT: float = 5.0 | |
| DB_COMMAND_TIMEOUT: float = 10.0 | |
| DB_POOL_TIMEOUT: float = 5.0 | |
| # Local cache/staging directory. In SFTP mode the authoritative copy is | |
| # SFTP_ROOT on the storage server. | |
| STORAGE_DIR: str = "data" | |
| STORAGE_BACKEND: str = "local" # local | sftp | |
| SFTP_HOST: str = "192.168.110.41" | |
| SFTP_PORT: int = 22 | |
| SFTP_USERNAME: str = "Stouch" | |
| SFTP_PASSWORD: str = "" | |
| SFTP_KEY_FILE: str = "" | |
| SFTP_ROOT: str = "/vol1/docker/odoo-dev/Data Acquisition/data" | |
| SFTP_KNOWN_HOSTS: str = "" | |
| SFTP_STRICT_HOST_KEY: bool = False | |
| SFTP_CONNECT_TIMEOUT: int = 15 | |
| # Auth | |
| API_KEY: str = "change-me" | |
| WORKER_API_KEY: str = "" | |
| DEFAULT_WORKFLOW_ID: str = "" | |
| # A cold NAS input-package build can take longer than the normal polling | |
| # request. Active jobs still send heartbeats during processing. | |
| WORKER_LEASE_SECONDS: int = 300 | |
| # Upload limits | |
| MAX_UPLOAD_SIZE: int = 2 * 1024 * 1024 * 1024 # 2GB | |
| # Uploads are first spooled on the server's local disk. The authoritative | |
| # dataset may live on the SSHFS/NAS mount, but receiving a large archive | |
| # must not make the API wait on remote filesystem writes. | |
| UPLOAD_STAGING_DIR: str = "" | |
| ALLOWED_VIDEO_EXTENSIONS: set[str] = {"mp4", "mkv", "mov", "avi", "m4v"} | |
| ALLOWED_IMAGE_EXTENSIONS: set[str] = {"jpg", "jpeg", "png", "bmp"} | |
| # Local server | |
| HOST: str = "0.0.0.0" | |
| PORT: int = 8000 | |
| PUBLIC_BASE_URL: str = "http://192.168.110.41:2586" | |
| LOG_LEVEL: str = "info" | |
| # Cleaning | |
| CLEANING_ENABLED: bool = False | |
| # Export | |
| EXPORT_SHARD_SIZE: int = 100000 | |
| VIDEO_SHARD_FRAMES: int = 5000 | |
| # Training. Checkpoints are produced *outside* the platform by the official | |
| # ``lerobot-train`` CLI (conda env ``lerobot052_base_piper``), so this points | |
| # at an existing directory we only ever read. Empty = use the default below. | |
| TRAIN_OUTPUT_ROOT: str = "" | |
| def train_root(self) -> Path: | |
| """Trained-model directory — **read-only**, never created here. | |
| Defaults to ``<repo>/文件测试/umi_act_model``, i.e. beside ``processor/``. | |
| The directory is git-ignored and may legitimately be absent (fresh | |
| clone, or training never run on this host): callers must treat a | |
| missing path as "no runs" rather than creating it. Creating it would | |
| make an empty training dir appear out of nowhere on every boot. | |
| """ | |
| raw = str(self.TRAIN_OUTPUT_ROOT or "").strip() | |
| if raw: | |
| return Path(raw).expanduser() | |
| return (Path(__file__).resolve().parent.parent.parent | |
| / "文件测试" / "umi_act_model") | |
| model_config = { | |
| "env_prefix": "", | |
| "case_sensitive": False, | |
| "env_file": ".env", | |
| "extra": "ignore", | |
| } | |
| def storage_root(self) -> Path: | |
| """Absolute path to storage directory.""" | |
| p = Path(self.STORAGE_DIR) | |
| if not p.is_absolute(): | |
| p = Path(__file__).parent.parent / p | |
| p.mkdir(parents=True, exist_ok=True) | |
| return p | |
| def temp_root(self) -> Path: | |
| """Project-local temporary workspace; never falls back to the OS temp dir.""" | |
| p = self.storage_root / "tmp" | |
| p.mkdir(parents=True, exist_ok=True) | |
| return p | |
| def upload_staging_root(self) -> Path: | |
| """Local durable spool for incoming archives. | |
| ``storage_root`` can be an SSHFS mount. Keep the receive spool on the | |
| local machine so the upload response is independent of NAS latency. | |
| The queue persists its small job records in the authoritative state | |
| directory and resumes jobs whose spool file still exists. | |
| """ | |
| raw = str(self.UPLOAD_STAGING_DIR or "").strip() | |
| p = Path(raw).expanduser() if raw else Path.home() / ".cache" / "egodata-ingest" | |
| p.mkdir(parents=True, exist_ok=True) | |
| return p | |
| settings = Settings() | |
| # Init subdirectories | |
| # exports/cache 已弃用(导出改到 state/exports,缓存为内存级),不再自动创建 | |
| for sub in ["videos", "sessions", "tmp"]: | |
| (settings.storage_root / sub).mkdir(parents=True, exist_ok=True) | |