bvandeusen
e3cdd0f92b
Layer 3 — prevent the hard worker crash rather than just recovering from it. The realistic process-crash vectors (operator's observed slow/heavy tasks) are video decode and archive extraction; images decode in-process and Pillow raises-and-skips cleanly, and a subprocess per image would wreck deep-scan throughput, so images are intentionally not probed. New backend/app/utils/safe_probe.py (leaf module, lazy heavy imports so the spawned child stays light): - probe_video(path): validates the container + first video stream via ffprobe (a separate binary — a decoder crash kills only ffprobe, not the worker). Returns width/height, which the importer didn't capture for videos before. crashed=True only on ffprobe timeout. - probe_archive(path): an uncompressed-size bomb guard (MAX_ARCHIVE_UNCOMPRESSED_BYTES = 4 GiB) plus the format integrity test (zipfile.testzip / rarfile.testrar / py7zr.test) run in a spawned child process. A decompression-bomb OOM or native-lib segfault on a malformed archive shows up as a non-zero child exit code → crashed=True, never a dead worker. ProbeResult.crashed distinguishes a HARD failure (subprocess killed / timed out — the poison-pill signature → caller returns terminal 'failed') from a CLEAN rejection (corrupt-but-handled, bomb cap, integrity mismatch → caller's choice of skipped/attached). Wired: - importer._import_media video branch: probe_video before the pipeline; crash → failed, clean reject → invalid_image skip, ok → capture dims. - importer._import_archive: probe_archive before extract_archive; crash → failed, clean reject → still preserve the archive as a PostAttachment (matches extract_archive's fail-soft contract). - ml.tag_and_embed video branch: probe_video before sampling 10 frames, so a corrupt video is rejected (status='bad_video') instead of crashing the ml-worker on frame decode. Tests (test_safe_probe.py): valid/corrupt zip via probe_archive, direct _inspect_archive size+integrity, in-process _archive_probe_target bomb guard (monkeypatch can't reach a spawned child, so the target is called directly), and a non-video → ok=False that's robust to ffprobe presence in CI.
FabledCurator
Self-hosted media curation — gallery, ML tagging, and subscription-driven downloading in one app. Part of the FabledSword family.
Combines what was ImageRepo (gallery, ML, importer) and GallerySubscriber (gallery-dl wrapper, subscriptions, credential capture) into a single product.
Status
Pre-v1. Not yet functional.
Quick start
For local development and testing, just:
docker compose up -d
# UI: http://localhost:8080
That uses sane dev defaults baked into docker-compose.yml and the dev
override (docker-compose.override.yml, auto-merged) — local builds, DEBUG
logging, exposed Postgres + Redis ports on the host. No .env required.
For a production-like deployment, override the dev defaults via shell env
or a .env file (see .env.example for the variable names) and use:
docker compose -f docker-compose.yml up -d
# (skips the override so containers pull registry images)
Deployment posture
FabledCurator is designed to run inside a self-hosted homelab environment over plain HTTP. If you want TLS, terminate it at your reverse proxy. The app does not generate certificates, redirect to HTTPS, or set HSTS.
CI / Forgejo setup
The repo's workflows expect:
-
Runner label
python-ci— a Forgejo runner with Python 3.14, ruff, and Node 22 pre-installed. Bothci.ymlandbuild.ymluse this label. The runner image (runner-base:python-ci) is built fromCI-Runner/CI-python/in the operator's workspace;make pushfrom that directory builds and pushes a new image when toolchain pins change. -
Repo secret
RELEASE_TOKEN— a Forgejo PAT with the following scopes:write:package+read:package— fordocker pushtogit.fabledsword.comwrite:release— for future release-cutting workflowswrite:issue— for future issue-management automation
Generate at https://git.fabledsword.com/user/settings/applications. The injected
GITHUB_TOKENcannot be used because it lackswrite:package.
License
Personal project; use at your own discretion.