Tier-3 frontend DRY for the ML settings cards, plus the F-D2 clamp bug and the
F-D3 card misgrouping.
New primitives (components/common + composables):
- <SettingToggleRow> — the accent-icon + .fc-section-h label + right-aligned
switch row (HeadsCard x3, CropProposersCard). iconColor prop absorbs the
on/off dim.
- <SettingNumberField> — compact numeric field that CLAMPS to [min,max] on
commit. This fixes F-D2: HeadsCard/CropProposersCard previously sent
Number(raw) straight to the API, so an out-of-range threshold bounced off the
400 validator (only TranslationCard clamped). density prop for the grid cards.
- useSettingSave(patchFn) — the busy + patch + toast + revert-on-failure flow
each card hand-rolled (HeadsCard x6 handlers, CropProposersCard, MLBackfillCard,
VideoEmbeddingCard). Returns ok/false for the optimistic-switch revert.
Adopted in HeadsCard, CropProposersCard, MLBackfillCard (handler only — its
plain labelled switch is a different affordance), VideoEmbeddingCard.
F-D3: MLThresholdSliders.vue actually rendered a "Video embedding" (frame-
sampling) card but sat under "Tagging → Suggestion thresholds". Renamed it
VideoEmbeddingCard.vue and moved it to the "GPU agent & embeddings" section.
Left deliberately (over-DRY guard): TranslationCard uses an inline error ALERT
(not a toast), already clamps its confidence with a NaN fallback, and lives on
the ImportStore — a genuinely different save pattern, so forcing it onto
useSettingSave would change its UX.
Behaviour-preserving refactor; CI has no Vue type-check so this needs a live
UI pass (toggles persist + revert on failure, thresholds clamp on blur, video
card now under Embeddings).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi
- Promote .fc-section-h to a global token (app.css). It was copied identically
into 4 cards, and TranslationCard used the class with NO local def — so its
section headers rendered unstyled. Now fixed everywhere.
- Promote .fc-good / .fc-weak status colours to globals; delete the local copies
in the GPU/heads cards. (.fc-ok stays local — divergent: on-surface in
HeadsCard vs success in QueuesTable. .fc-bad stays — different name.)
- Delete 10 identical local .fc-muted redefinitions that crept back after the
2026-06-09 sweep; the global utility already covers them.
- DbMaintenanceCard: opacity:0.6 muted text → the .fc-muted token (the exact
anti-pattern that token's comment forbids).
- HeadsCard: collapse byte-identical ratePct() into pct().
CSS-only + one template class swap; no logic change.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi
The ml-worker's ONLY processing role is now the CPU whole-image embed fallback
(tag_and_embed renamed embed_image — Camie tagging was retired #1189 and the
name kept implying otherwise; videos were already handled agent-style: frame
sampling + mean-pool). Detection/cropping/CCIP stay GPU-agent-only, and their
completion is judged per-pipeline: ccip by gpu_job rows, siglip by concept
regions at the current model version — never by image_record.siglip_embedding.
A CPU embed therefore can NEVER close crop work for the agent (regression test
pins this; only the whole-image 'embed' job, the same artifact, is satisfied).
Making removal actually safe (operator will drop the container):
- GPU-queue coordination (enqueue_gpu_backfill, recover_orphaned_gpu_jobs,
reprocess_gpu_jobs) moved verbatim to tasks/gpu_queue.py on the maintenance
quick lane — it lived on the 'ml' queue only by module colocation, which made
the ml-worker a hard dependency of the whole agent pipeline.
- New ml_settings.cpu_embed_enabled (migration 0074, default ON so agent-less
installs keep working): OFF stops the four import hooks queueing embed work
nothing will consume and no-ops the manual backfill; switch lives on the
renamed 'CPU embedding backfill' card.
- NB heads training / auto-apply still run on the ml image (sklearn) — a stack
that removes the container gives those up too.
Deploy note: in-flight messages under the old task names are dropped by the
new workers; the 60s orphan sweep + hourly backfill re-fire under the new
names immediately.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
Heads + CCIP are the tag source and head auto-apply is the earned propagation.
The Camie tagger ran only to feed the allowlist bulk-apply (its ImagePrediction
rows had no other consumer), and the allowlist was a SECOND, un-earned auto-apply
path firing in parallel with heads on every accept — exactly the un-earned spray
the v2 pivot replaced. Retire both.
Behavior change: accepting a suggestion now applies the tag to THAT image only
(source='ml_accepted', a head-training positive) — it no longer allowlists +
fans the tag across the library via Camie. Propagation is heads' earned
auto-apply. (Loses instant cold-start propagation for booru-vocab tags; that was
un-earned and bypassed the precision gate.)
- tag_and_embed is now EMBED-ONLY (no Camie load/infer, no ImagePrediction
writes); backfill enqueues it for images with no embedding.
- Removed: services/ml/tagger.py, apply_allowlist_tags + helpers + daily beat +
every enqueue caller (accept/alias/merge/per-image), api/allowlist.py +
blueprint, ImagePrediction + TagAllowlist models/tables (migration 0067),
AllowlistTable.vue + allowlist store, the accept coverage-projection payload.
- AllowlistService gutted to accept/dismiss/undismiss/reject (the rejection store
the rail still needs); accept returns nothing, API returns {accepted, tag_id}.
- tag merge no longer repoints/triggers the allowlist; _keep_as_alias now keys on
ML-applied image_tag sources (incl. head_auto) instead of the allowlist.
- UI: MLBackfillCard relabelled to embedding-only; accept toast simplified;
MaintenancePanel drops the allowlist tile.
Left for a follow-up hygiene pass (now-inert, harmless): the dead settings
columns (tagger_store_floor, tagger_model_version, suggestion_threshold_*,
video_min_tag_frames), image_record.tagger_model_version, MLThresholdSliders
trim, and the Camie model download in download_models.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
Goal (operator 2026-06-18): the overview of a Settings tab fits one unscrolled
viewport; expanding a tile to read into it is the only reason to scroll.
- Every Maintenance card converted to the collapsible MaintenanceTile (collapsed
by default = icon + short title + one-line blurb). Task cards (ML backfill,
centroids, thumbnails, archive re-extract, missing-file repair, DB maintenance)
sit in a responsive grid; running tasks auto-expand. Tagging config (suggestion
thresholds, allowlist, aliases) grouped in one Tagging section as collapsible
tiles; Backup is its own collapsible tile.
- Three labeled sections mirror the Cleanup tab: Backfills and reprocessing /
Tagging / Storage.
- Center the whole Settings surface: SettingsView is now a centered, width-capped
(1140px) column so the tab strip and every panel sit in a tidy centered measure
(was full-width). CleanupView drops its own left-aligned max-width to fill it.
All card logic unchanged - only the chrome.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Each maintenance button (ML backfill, centroid recompute → ml queue;
thumbnail backfill → thumbnail queue) now shows a status bar with the live
pending count for its Celery queue, so the operator can see work is already
queued/running before re-triggering and piling on. MaintenancePanel polls
/api/system/activity/queues every 4s; QueueStatusBar reads the depth and
turns warning-colored when the queue is busy.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
MaintenancePanel hosts: backfill + centroid recompute trigger cards,
the five suggestion-threshold sliders (autosave on slider release),
the allowlist table (inline editable min_confidence, delete), and the
alias table (mapping display, delete). Wired as a third Settings tab,
ML settings loaded lazily when the tab opens.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusenandClaude Opus 4.8