bvandeusen
22c3b54746
The eval (#1130) proved the frozen-embedding + trained-head spine; this lands its production form (the first of three slices that make heads the suggestion source, replacing Camie + centroid). - tag_head: one logistic-regression head per general/character concept with enough labelled positives. Weights (pgvector), honest CV-derived suggest threshold + earned-auto-apply point, and per-concept quality metrics. - head_training_run: persisted batch lifecycle (mirrors tag_eval_run) so the admin card shows live + historical status across navigation. - services/ml/heads.py: TRAIN (sync, ml worker, reuses tag_eval's proven data loaders + metric math so production heads match measured eval numbers) and SCORE (async, API worker — numpy via pgvector, no scikit-learn): score one image's embedding against all heads → the rail's suggestions, cached on (count, max trained_at) so a retrain invalidates without per-request loads. - tasks.ml.train_heads (ml queue, commits per head so a kill leaves progress) + recover_stalled_head_training_runs sweep + retention(20) + 5-min beat (rule 89). - api/heads.py: POST /api/heads/train (one run at a time, 409 guard) + GET /api/heads (count, graduated, last-trained, running, per-concept table, recent runs). - ml_settings: head_min_positives + head_auto_apply_precision, tunable via /api/ml/settings. Scoring isn't wired into the rail yet (slice C) and the admin UI is slice B — this slice makes training + scoring exist and CI-verifiable. 'precision' column stored as precision_cv (SQL reserved word). Migration 0058. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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.