dev
503 Commits
| Author | SHA1 | Message | Date | |
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 bvandeusen
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86ae396914 |
feat(pixiv): native app-API client — gallery-dl-parity profile, post-first seams (#129 step 1)
PixivClient mirrors gallery-dl 1.32.5's PixivAppAPI request profile exactly (iOS app headers, OAuth refresh with X-Client-Time/X-Client-Hash, /v1/user/illusts pagination via next_url — whose query string doubles as the resumable page cursor). Post-first seams (post_record_key / post_is_gated / post_meta) + extract_media covering multi-page, single-page, ugoira zip (600x600→1920x1080 swap, frame delays memoized for the post record), and limit_* placeholder gating. No PHPSESSID web fallback: FC holds only the refresh token, same effective coverage as the gallery-dl path. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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65bd1c22c3 |
test: whole-table tag counts become non-system counts
The four remaining run-1895 failures were stale expectations, not predicate bugs — prune/reset returned the right counts, but these tests verified no-deletion by counting the ENTIRE tag table (or asserting the full kind set), which now includes the three seeded hygiene tags that survive prunes and resets by design. Filter is_system=false with a pointer to #128 so future system tags cannot re-break them. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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f77e75147d |
feat(tags): system-tag UI markers + full protection sweep (step 4 of #128)
UI: shield marker + tooltip on TagChip and TagCard; system tags hide rename/merge/delete affordances (chip kebab entirely — set-fandom never applies to their general kind; remove stays, un-tagging is normal use). Aliases stay available: mapping model outputs ONTO a system tag is useful. Directory cards carry is_system. Every destructive path that could take out a system row is now guarded, found by sweeping run 1891s off-by-three failures — each one was a surface that would have eaten the seeded tags: - prune-unused: predicate exempts is_system (they ship with zero applications and matched every unused condition) - reset-content: predicate exempts is_system AND keeps their applications — hygiene flags describe the file, not content tagging - admin tag DELETE: refused with system_tag error - normalize_existing_tags: scan excludes is_system — canonicalization would recase wip -> Wip behind TagService.rename's guard, breaking the name-keyed presentation lookup Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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723f023e6a |
feat(gallery): similar() hides presentation images (banner / editor screenshot)
Step 3 of milestone #128. Presentation-tagged images cluster on UI chrome rather than content, so near any one of them they fill the whole more-like-this grid. Excluded from candidates in the ONE whole-image similarity surface (gallery similar mode, explore walk, and RelatedStrip all ride GalleryService.similar) — the anchor itself may be a banner, and wip stays surfaced: only the training pipelines exclude it. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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19744fa41d |
fix(tests): resync serial sequences after baseline restore
TRUNCATE ... RESTART IDENTITY resets every sequence to 1, and the baseline restore re-inserts seeded rows WITH their explicit ids — leaving each sequence pointing below MAX(id). Harmless while the only baseline rows lived in tables tests never sequence-insert into (ml_settings id=1); migration 0075 seeded tag rows and every Tag insert after the first truncate collided on pk_tag id=1 (205 failures, run 1888 — find_or_create then surfaced it as NoResultFound via its conflict-recovery re-select). setval every restored table with a serial id column past its restored rows. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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e6f128c894 |
feat(ml): training hygiene — system-tagged images are absent from other concepts training
Step 2 of milestone #128. _hygiene_excluded_ids (training_data.py) is the one shared predicate: images carrying any system tag are dropped from every OTHER concepts head training — not positives (a rough wip tagged as a character drags the head toward generic-sketch) and not rejection or sampled negatives (a wip OF character X is not evidence against X). A system tags own head trains on them unfiltered; that is what makes auto-flagging banners work. Selection is split out of train_head as the sklearn-free head_training_ids so CI (no sklearn) can pin the behavior. CCIP: reference prototypes skip hygiene-tagged images — a faceless wip figure region must never become an identity reference — and the ref cache signature now counts hygiene applications, since tagging an image wip changes the reference set without touching character/region counts. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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e9891ee9f3 |
feat(tags): system tags — is_system column, seeded hygiene tags, protection guards
Training hygiene step 1 (milestone #128). Migration 0075 adds tag.is_system and seeds wip / banner / editor screenshot (kind=general), ADOPTING an existing same-(name,kind) tag case-insensitively instead of duplicating. These rows drive the upcoming training exclusions, so they are protected: rename and merge-away refuse system tags (merge-INTO stays allowed — folding an operator's old hygiene tag into the system row is the intended move; merge is the only tag-delete path, so that guard covers deletion). is_system rides every tag serialization. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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b54243a1ff |
fix(subscribestar): inject the 18+ age cookie on every SubscribeStar domain
The cookie was pinned to .subscribestar.adult only; cookies are domain-scoped, so sources on subscribestar.art (Elasid, event #54116) never sent it and every poll 302d to /age_confirmation_warning. Emit one line per domain (.com/.adult/.art) with a per-domain presence check, and admit .art in the platform url_pattern. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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aa12a57f97 |
feat(recovery): surgical re-fetch for deep posts via ExternalLink reset
Operator-flagged: the recovered defective files live DEEP in their artists'
back-catalogues — the normal download cadence (by design, via the seen-gates)
will never re-walk them, so recovery's source re-check alone can't bring them
back. The durable per-post handle is the ExternalLink row, which survives the
image delete:
- services/external_links.refetch_links_for_post: reset settled links to
pending (fresh attempt budget, in-flight left alone) + dispatch their
fetches; sha-dedupe at import discards payload files that still exist, so
only the missing file lands.
- recover_defective_image now captures the image's post ids BEFORE the delete
cascades provenance away and resets those posts' links — future recoveries
are surgical automatically (response gains links_reset; source re-check
stays for gallery-dl-native files within walk reach).
- POST /api/admin/posts/refetch-external {external_post_id, source_id?} — the
manual tool for the three files recovered before this fix existed.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
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bvandeusen
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5b34c9221c |
feat(ia): wave 1 — Import tab dissolves, Maintenance regroups by system, one extension home
Settings IA per the approved A3 design (the old layout was the two-app merge fossilized): - Import tab retired: ImportTriggerPanel + ImportTaskList deleted (manual /import scans stay API-level; imports arrive via downloads/extension, heal via the Layer-2 auto-refetch sweep, and show in Activity). ImportFiltersForm moves to Maintenance → 'Ingestion & filters' and loads its own settings; the import store shrinks to settings-only (no remaining consumers of the scan/task-list machinery). Overview's pending banner now points at Activity. - Maintenance regrouped: Ingestion & filters / GPU agent & embeddings (GpuAgent, Failed processing, CPU embedding backfill) / Tagging (sliders, Heads, Aliases) / Library health (MissingFiles, Thumbnails, DB, Archive re-extract demoted last) / Storage. - One extension home: BrowserExtensionCard moves from Settings → Overview to Subscriptions → Settings, above the API key bar it authenticates. - Single-color import filter WIRED: skip_single_color/threshold existed since FC-2 but nothing read them (the audit module's docstring said as much) — now enforced on both import paths via the audit's canonical predicate (tolerance 30, matching the Cleanup card default; animated images exempt like the transparency check). Default stays off; test added. - Dead weight: PlaceholderView (zero refs) and the permanently-disabled 'Export failed logs (CSV — v2)' menu stub deleted; stale docs fixed (celery queue docstring, threshold comment citing retired tasks, ml package docstring, HeadsCard 'replaces Camie' blurb). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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19b962f1a7 |
feat(b3): ml-worker becomes optional — embed-only role, decoupled GPU coordination, cpu-embed switch
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 |
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bvandeusen
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7c19ad91ed |
feat: cap-aware autoscaler + token-gated whole-instance tag reset (operator feedback)
Autoscaler (agent 2026-07-02.5): the buffer-occupancy signal alone would peg downloaders at DL_MAX while the bandwidth CAP — not concurrency — is the real constraint (8 streams sharing 8 MB/s move no more data than 4). Growth is now gated on the pipe having headroom (net < 85% of cap) and a pipe pinned at the cap (>= 95%) sheds streams down to 3; dead band prevents flapping. The UI hint says 'holding at the bandwidth cap' and /status reports bw_capped, so the behavior is legible without tests that need the ML stack. Reset content tagging: stays a FULL-instance reset (operator's call), but now lives in a fenced 'Danger zone' section on Cleanup and the apply is gated by a preview-derived confirm token (mirrors the Tier-C bulk-delete pattern — stale counts are rejected server-side). Copy no longer claims suggestions repopulate: it says plainly the heads' training examples are deleted and re-tagging starts fresh. Moved out of TagMaintenanceCard into DangerZoneCard. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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eaea4308fc |
chore: retire the tag-eval harness — it proved the heads system, job done (operator-approved)
The head-vs-centroid eval (#1130) existed to prove the 'frozen embedding + trained head' spine; the operator accepted the tagging system and dropped the harness. Removed per rule 22: TagEvalCard + store, /api/tag_eval blueprint, tag_eval_run ml task, recover-stalled-tag-eval-runs sweep + beat entry, TagEvalRun model + table (migration 0073), and its tests. The eval's data loaders + metric helpers were NOT eval-specific — the nightly heads trainer runs on them — so they moved verbatim to services/ml/training_data.py (heads.py import updated; behavior unchanged). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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a7abcc41ca |
feat(triage): failed-processing triage — probe errored files, flag defects, recover (#125 C1-C3)
An errored GPU job's stored reason is a suspicion; the file probe is the
verdict. A 15-min beat sweep (triage_gpu_errors) runs verify_integrity's own
probe (sha256 + decode) on each errored image ONCE and writes both verdicts:
ImageRecord.integrity_status and the new GpuJob.triage_status ('defect' |
'file_ok', migration 0072). Every classification logs at WARNING so it
surfaces in Logs/System Activity.
- 'defect' rows are excluded from /retry_errors (re-running a known-bad file
burns agent time re-minting the tombstone); response now reports
defects_kept and the GpuAgentCard toast says so.
- GET /api/gpu/errors: triage view — reason buckets (classify_reason),
probe verdicts, per-job detail. POST /errors/triage runs the sweep now.
- POST /api/gpu/errors/<id>/recover: reuses the Layer-2 refetch pattern —
delete the defective copy + record (full cascade takes the tombstones too)
and re-poll its subscription Source so a fresh copy re-imports and re-enters
the pipeline; 'no_source' when nothing pollable resolves.
- New 'Failed processing' card (GpuTriageCard) in Maintenance: verdict counts,
reason summary, probe-now, defect list with thumbnails + per-image Recover.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
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bvandeusen
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1f27189b8f |
chore: retire ml-backfill-daily beat + the spent purge-legacy action (operator-approved)
- ml-backfill-daily: the CPU tag_and_embed backfill raced the GPU agent's daily embed backfill for the same NULL-embedding images at ~100x the cost (B1 audit verdict, milestone #124). The backfill TASK stays — the manual /api/ml/backfill button remains the deliberate CPU fallback pending B3. - purge-legacy: one-time IR-migration cleanup, dry-run verified 0 targets on the live library before removal (A2 audit, milestone #123). Fully retired per rule 22: tile, store action, route, service fn, tests. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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95d2ae1d58 |
feat(agent): global bandwidth cap — the agent can't saturate the desktop's network
One shared TokenBucket (default 8 MB/s; BANDWIDTH_LIMIT_MB_S, 0 = unlimited; live MB/s dial + net readout in the control UI) is charged by every still download (streamed chunk reads) and every ffmpeg video stream (metered from outside via /proc/<pid>/io and SIGSTOP/SIGCONTed into budget). Why: D1 re-measurement 2026-07-02 — the idle link moves ~38 MB/s, but 8 unthrottled downloaders bufferbloated it to ~1-1.5 MB/s PER STREAM (operator's browser included). Capping the aggregate keeps the desktop usable and still beats the collapsed sweep throughput it replaces. Agent build 2026-07-02.4. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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09e2772628 |
fix(gpu-jobs): end the error-tombstone loop — deliberate retry semantics + poison-job guards
The hourly ccip backfill's skip-list lacked 'error' (and the daily
siglip/embed variants re-gated failures on their missing results), so every
permanently-bad file got a fresh doomed job each run — ~24 duplicate error
rows/day per file, the perpetual 'unprocessable' flood. An errored job is now
a TOMBSTONE: no backfill re-enqueues it; retry is deliberate-only via
/retry_errors (an errored back-catalogue needs one button press after a
model swap).
One shared set of dedupe DELETEs (services/ml/gpu_jobs.error_dedupe_statements)
runs before every backfill and inside /retry_errors: error rows made moot by a
later pending/leased/done row go first, then older duplicates (newest reason
survives) — so the error count reads as distinct failing files and a retry
can't fan one file out into duplicate pending jobs. /retry_errors now returns
{requeued, pruned} and the toast shows both.
Poison-loop guards (release and lease-expiry burn no attempts, so a job that
stalls its transfer or crashes the agent every time cycled forever —
operator-observed jobs 99044/125288/131594/143131):
- agent: 3 in-session transient bounces (fetch or submit) → fail with the real
reason instead of another release; strikes never count while stopping, and
clear on submit success. Agent build 2026-07-02.3.
- server: the 60s orphan sweep (statements shared between the beat task and
GpuJobService so they can't drift) converts expired leases with >=5 lease
grants and pending jobs with >=10 to 'error', preserving the last stored
failure reason. Backstops old agent builds.
Tests: tombstone rule across all three backfill variants, moot-row pruning,
poison conversions, and the extended /retry_errors dedupe contract.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM
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bvandeusen
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686808d3f3 |
feat(gpu): "Retry errored jobs" — scoped requeue of errors only
After an agent-side fix (e.g. the short-video sampler), the errored jobs
(~2.8k) have exhausted their 3 attempts and stay parked: backfill skips
images that already have a job, and /reprocess is the nuclear option (it
resets the 179k DONE jobs too). There was no way to re-run just the errors.
POST /api/gpu/retry_errors resets every status='error' job (all task types)
to pending with attempts=0 and the stored error cleared — a small inline
UPDATE that returns {requeued: n} so the UI toast can show the count.
UI: a "Retry errored jobs" button on the GPU-agent card, right under the
queue tiles; disabled when errored==0. With the agent now logging ffmpeg's
stderr on failure, retrying also reveals which errors were real vs victims
of the fps-filter bug.
Test: retry_errors requeues the errored job (fresh attempts, error cleared)
and leaves done work untouched; asserts via column selects (Core-DML
gotcha), not ORM refresh.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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bvandeusen
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c22f37d64d |
feat(gallery): sort by earliest post date across all posts (new default)
The gallery's newest/oldest sort keys off image_record.effective_date = COALESCE(primary post's post_date, created_at). The primary post is often the repost/download the file came from, so the grid led with download dates rather than when content was first posted (operator-flagged). Add a second materialized sort key, earliest_post_date = MIN(post_date) across ALL of an image's provenance posts (every post it appears in), else created_at — the original publish date. Mirrors the effective_date pattern so the sort stays a forward index scan. - alembic 0071: add earliest_post_date + index (DESC, id DESC); backfill created_at baseline then MIN over image_provenance ⋈ post. - importer: recompute earliest_post_date whenever a dated post is linked (MIN over the image's provenance, which now includes the just-added row). - gallery_service: new sorts posted_new / posted_old key off earliest_post_date; cursor + year/month grouping follow the active column transparently. - api: accept posted_new|posted_old; DEFAULT is now posted_new so the grid leads with original publish date. newest/oldest (effective_date) still available. - frontend: sort dropdown gains "Newest/Oldest post date" (default Newest post date); existing effective-date sorts relabelled "Newest/Oldest added". - tests: service test asserts posted_new/posted_old key off earliest_post_date; frontend default-sort omission test updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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181f1c6a27 |
perf(gpu-queue): partial indexes + two-phase lease so leasing stays O(batch)
The throughput bottleneck was curator-side, not the network. lease() claimed the lowest-id pending/expired jobs with `... ORDER BY id LIMIT n`, but with only a plain `status` index Postgres walked the primary key from id=1, skipping the entire prefix of already done/error rows before reaching pending ones. As `done` grew (69k+), every lease became an O(done) scan — leasing crawled, the DB saturated, and even /status (the queue GROUP BY count) stalled the agent. - Migration 0070 adds two partial indexes over just the live slice: pending rows indexed by id (hot path), and leased rows by lease_expires_at (crash-recovery + orphan sweep). They stay tiny no matter how large the done/error history. - lease() split into two phases so each uses a partial index: claim pending first (id-ordered, O(batch)); reclaim expired leases only when pending can't fill the batch. Same semantics (SKIP LOCKED, attempts++, expired reclaim). - Model __table_args__ declares the indexes so ORM and schema agree. - Test: a done-prefix at low ids must not stop the lease reaching pending. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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359bc5a283 |
feat(ml): default to SigLIP 2 (new installs) + model dropdown, no free-text (#1203)
- Migration 0069: new installs default to SigLIP 2 (so400m, 512px, 1152-d drop-in) — UPDATE applies ONLY where no image is embedded yet (fresh install), so an existing library is NOT silently invalidated; it switches deliberately via the dropdown → Re-embed → Retrain. Column server_defaults moved to SigLIP 2. - GET /api/ml/embedder-models: server-authoritative supported list (SigLIP 2 512 recommended / 384 faster / SigLIP 1 384 original) so the UI never free-types. - GpuAgentCard: the two name/version text fields → a single model dropdown; Save sets name+version from the picked option (the current model is always selectable even if off-list). - embedder.py DEFAULT_MODEL_NAME unchanged (stays the baked local-dir SigLIP 1) to avoid a local-dir/weights mismatch; SigLIP 2 loads by HF name, cached on the ml-worker's persistent HF_HOME. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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80f8eb4756 |
feat(gpu): re-process trigger to apply new crop detectors to the existing library (#1202)
The siglip/ccip backfills skip images that already have current-version regions, so adding crop detectors only affected NEW images — the back-catalogue would never be re-cropped. Add a reprocess trigger that resets every done/error job of a task back to pending, so the agent re-runs the FULL pipeline (figure detection + CCIP + concept/panel crops) over the whole library under the current detectors. - reprocess_gpu_jobs(task='ccip') task + POST /api/gpu/reprocess. - gpu store reprocess() + GpuAgentCard "Re-process library (re-detect + re-crop)" button with a confirm (it's heavy). - Test: a done job resets to pending (attempts cleared). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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9a3cda007a |
feat(api): agent-friendly tag analysis endpoints — /tags/top + /tags/<id>/stats (#1136)
Fast, read-only, indexed aggregates shaped for ANALYSIS (not the paged UI directory, which is alphabetical + builds previews and timed out at 10 min on a full count sweep). - GET /api/tags/top — top tags by image count, desc. ?kind, ?limit (cap 500), ?min_count, ?source=all|human|manual|accepted|auto (human=manual+ml_accepted, auto=head_auto+ccip_auto+ml_auto). One GROUP BY over image_tag (indexed on tag_id). - GET /api/tags/<id>/stats — per-tag dataset health: total + per-source counts (manual/accepted/head_auto/ccip_auto), human vs auto rollups, rejection count, and whether a trained head exists. Backs concept-readiness + source-split analysis. Plain-HTTP homelab posture, no auth change. Tests cover ranking, source filter, min_count, the source breakdown, and 404. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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bc6d43d3f2 |
refactor(ml): drop dead tagger/suggestion settings + columns (#1199)
Hygiene follow-up to the Camie retirement (#1189) — these were left inert to bound that change; nothing reads them now. Migration 0068 drops: - ml_settings: tagger_store_floor, tagger_model_version, suggestion_threshold_ character/general (already dead pre-retirement — scoring uses per-head thresholds), video_min_tag_frames (only the deleted video-prediction aggregator used it). - image_record: tagger_model_version (no writer), centroid_scores (dead JSON cache, no reader). Also: ml_admin _EDITABLE/GET/_validate pruned (dropped the store-floor invariant + video_min_tag_frames check); MLThresholdSliders trimmed to a video-embedding card (interval + max frames only); importer no longer resets the dropped cols; download_models drops the Camie fetch; stale CASCADE comments in cleanup_service no longer name the removed tables. Tests updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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485387ff0b |
refactor(ml): retire the Camie tagger + allowlist bulk-apply (#1189)
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
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bvandeusen
|
3d77a38a25 |
refactor(ml): remove the dead per-tag centroid subsystem (#1189)
The v2 pivot replaced per-tag SigLIP centroids with learned heads + CCIP. Centroids were still recomputed (on every tag merge + a daily beat) but NOTHING read them — suggestions come from heads+CCIP and apply_allowlist_tags applies via Camie predictions, not centroids. Pure dead wiring; remove it. Removed: CentroidService, recompute_centroid/recompute_centroids tasks, the daily beat, POST /api/ml/recompute-centroids, the recompute-on-merge trigger, the tag_reference_embedding table + model, the centroid_similarity_threshold + min_reference_images settings (migration 0066), the CentroidRecomputeCard + its store action + MaintenancePanel tile, and the centroid slider in MLThresholdSliders. _keep_as_alias drops its vestigial has-centroid branch (the allowlist branch already covers "could re-emit"); tag merge no longer clears a table that no longer exists. NOT touched (still live, parallel to heads): the Camie tagger, ImagePrediction, and the allowlist bulk-apply — accepting a suggestion still allowlists + applies it across the library. The tag-eval "centroid" baseline metric is unrelated (in-memory) and stays. (image_record.centroid_scores JSON column also remains — separate legacy field, its own micro-cleanup.) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
4daa3f2790 |
feat(ml): operator model swap — GPU re-embed + embedder as a setting (#1190)
Make the SigLIP embedder an operator choice (drop-in to SigLIP 2:
google/siglip2-so400m-patch16-512 is a verified 1152-d model at 512px → no
schema change, better small-cue fidelity). A swap = set model + re-embed +
retrain, all operator-driven; the GPU agent does the re-embed so it's fast.
- settings: embedder_model_name is now a setting (migration 0065) alongside the
existing embedder_model_version; both editable + validated (non-empty) in the
ml admin API. The server embedder loads by HF name (AutoImageProcessor/Model,
model-agnostic), preferring the pre-downloaded local dir for the default so
existing deploys don't re-download; rebuilds on a name change.
- agent: new 'embed' job = whole-image SigLIP embedding (mean-pool video frames)
under the lease-announced model → POST /jobs/submit_embedding writes
image_record.siglip_embedding + siglip_model_version. The lease now announces
the model FROM THE SETTING (not a constant).
- re-embed routing: enqueue_gpu_backfill('embed') selects unembedded + stale-
version images; 'siglip' now re-embeds concept crops whose version != current
(so a swap re-triggers crops, not just the never-embedded back-catalogue). The
CPU ml-worker backfill no longer re-embeds on a version mismatch (it can't
churn the library at 512px) — the GPU agent owns version re-embeds. Daily
'embed' + 'siglip' beats self-heal.
- scoring: score_image only bags embeddings in the CURRENT model's space (whole-
image gated by siglip_model_version, concept regions by embedding_version) so a
mid-swap stale vector isn't scored by new-space heads; legacy NULL = current.
- UI: GpuAgentCard "Embedding model (advanced)" — edit name/version, Save, and
"Re-embed library (GPU)" (queues embed + siglip); points at SigLIP 2.
Tests: lease announces model + submit_embedding round-trip; enqueue 'embed'
selects stale/unembedded; stale-version excluded from scoring; embedder model
settable + empty rejected; siglip gate updated to current-version concept.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
|
0f472b2f9e |
fix(explore): diversify "more like this" so it stops getting stuck (#1188)
Pure nearest-cosine piled near-identical images into the neighbour grid — a reposted banner filled all 24 slots, and once you wandered into a B&W / comic-panel cluster every neighbour was more of the same with no way back to colour without the Random button (operator-reported, with screenshot). similar() now over-fetches a wide candidate pool (5x the requested limit, cap 200), then diversifies down to `limit`: - pHash near-duplicate collapse: drop candidates within 6 Hamming bits of the anchor or an already-kept candidate, so a repost (and the anchor's own clones) appears at most once. - MMR re-rank: greedily pick for closeness-to-anchor minus similarity-to-already -picked (lambda 0.55), so the result SPANS clusters instead of returning 40 variations of one image. Falls back to nearest-order on any failure / small pool, so existing nearest-first behaviour is unchanged when there's nothing to diversify. Frontend forwardTarget drops the now-redundant skip-nearest-third hack (the list is already diversified server-side) — plain random-over-unvisited gives the variance now. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
c6f38b0dac |
feat(tagging): SigLIP concept crops + max-over-bag scoring (#114)
Lift recall on small/local concepts (glasses, cum, stomach-bulge, xray, lactation) that the whole-image SigLIP vector washes out: the GPU agent now embeds figure crops with SigLIP too, stored as kind='concept' regions, and the suggestion rail scores each image as a BAG (whole-image + every concept crop), taking each head's MAX over the bag. The whole-image vector is always in the bag, so this can never score lower than before. Model-agnostic by construction: the server ANNOUNCES the embedding model (HF name + version) in the lease, so the agent loads whatever the heads were trained in and stays in lock-step — a model swap is a server setting + a re-embed migration, never an agent change. - agent: model-agnostic CropEmbedder (torch/transformers get_image_features, fp16 on CUDA, inference-locked); worker branches on job.task — 'ccip' emits figure(CCIP)+concept(SigLIP) in one pass, 'siglip' emits concept-only so the back-catalogue backfill never churns figure/CCIP regions; torch cu124 + transformers in the image. - server: lease announces embed_model_name/embed_version; score_image is max-over-bag (version-filtered region embeddings); enqueue_gpu_backfill 'siglip' gates on a missing concept region (drains the back-catalogue, retries failures, no double-enqueue); daily siglip-backfill beat; UI button; /api/ccip/overview reports images_with_concept_siglip. - v1 scope: suggestion rail only — auto-apply stays whole-image (conservative; heads' thresholds were calibrated on whole-image). Bulk-apply bag = follow-up. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
b91a230f12 |
feat(ccip): automation + reference quality — keep identity flowing hands-free (#114)
Works through the optional CCIP ideas + the "keep moving even if I forget" ask:
AUTOMATION (no button needed):
- Hourly beat auto-enqueues CCIP backfill — new images get embedded (and errored
ones retried) on their own; the queue never goes idle waiting for a click.
- CCIP auto-apply: a daily sweep tags confident matches (source='ccip_auto') so
identity tags keep flowing. ON by default (opt-out, like head auto-apply);
ml_settings.ccip_auto_apply_enabled + _threshold (0.92, above the suggest cut),
migration 0064. Vectorized (one matmul + reduceat per image), reversible, skips
already-applied/rejected. Switch + threshold in the GPU agent card; GET/PATCH
/api/ml/settings; auto_applied count in /api/ccip/overview.
REFERENCE QUALITY (the over-fire root cause):
- character_references now draws ONLY from single-character images — on a
multi-character image the tag is image-level, so every figure would otherwise
pollute each character's prototypes (a 2-char image tagged 'Velma' made
Daphne's figure a Velma reference). This is the contamination behind residual
over-firing.
- Cached on a cheap signature (char-tag count + ccip-region count/max-id) so the
reference load isn't redone on every modal open.
Tests: multi-character image not used as a reference; auto-apply tags a confident
match as ccip_auto.
NEXT (not done, confirmed): comic-panel cropping + SigLIP concept crops ("spot
interesting content").
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
|
625336b6b4 |
feat(ccip): tunable match threshold, default 0.85 (#114)
Live data showed the v1 flat 0.75 cosine over-fired — ~64% of matched images got
3-10 character guesses dominated by the most-referenced characters (a 27-ref
character clears a low bar on many images). A sweep showed 0.85 collapses the
noise (noisy multi-matches 47→3) while keeping the confident single-character
matches.
- ml_settings.ccip_match_threshold (migration 0063, default 0.85); match_image
reads it (override still accepted). DEFAULT_SIM_THRESHOLD fallback 0.75→0.85.
- Exposed in GET/PATCH /api/ml/settings (validated 0.5–0.999).
- Slider in the GPU agent card ("Character-match strictness") — tune live, no
redeploy, same observe-and-tune loop as auto-apply.
Test: a ~0.9-cosine figure matches at 0.85, dropped at 0.95.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
|
2cb0427868 |
feat(gpu): fast orphan recovery — graceful release + 60s sweep (#114)
So work an agent orphaned gets picked back up quickly, three layers: - GpuJobService.release(): a graceful agent stop hands its still-leased jobs back to pending instantly (POST /api/gpu/jobs/release), no waiting out the lease. - GpuJobService.recover_orphaned() + recover_orphaned_gpu_jobs Celery task on a 60s beat: resets expired leases (a hard-crashed agent) to pending and keeps the queue counts honest even when nothing is leasing. - Lease TTL 300→180s: still well above any single job (a capped-frame video embed is tens of seconds, and a live worker heartbeats), but a hard crash recovers faster once the sweep fires. Tests: release returns-to-pending (token-scoped), recover_orphaned resets only expired leases, release API round-trip. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
de33bab41c |
feat(ccip): read-only observability API for the crop/CCIP work (#114)
So the work can be checked through an API as the agent fills in vectors (same pattern as /api/heads/metrics): - GET /api/ccip/overview: regions by kind, images with figure CCIP vectors, the per-character reference counts (which characters have enough examples to match on), and the embedding versions present. - GET /api/ccip/images/<id>: that image's stored regions (bbox, frame_time, has_ccip/has_siglip, versions) + the CCIP character matches it would get — for spot-checking detector + matcher output. Read-only, no GPU. (Queue depth is already at /api/gpu/status.) Tests: overview coverage counts + per-character refs; per-image regions + matches. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
5faf34a3b5 |
feat(suggestions): overlay CCIP character matches onto the rail (#114)
SuggestionService.for_image now merges CCIP character matches with the SigLIP head suggestions — they're complementary, not exclusive: CCIP is the identity- specialized signal but needs a detected figure; the heads work whole-image but conflate identity with style. Merged by tag: 'both' when they corroborate (higher score wins), 'ccip' / 'head' otherwise. Cheap when no CCIP vectors exist yet (match_image returns early without a figure vector), so it's a no-op until the agent runs. Suggestion.source is now 'head' | 'ccip' | 'both'. Test: a character with a CCIP reference figure surfaces (source='ccip') on a new image whose figure matches. NEXT: the agent container (real CCIP/detector models, hands-on) that produces the vectors this consumes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
d57ca847e7 |
feat(ccip): few-shot character matcher (#114 slice 5)
The server-side brain that turns stored CCIP vectors into character suggestions
— no GPU. character_references() gathers each character tag's prototype vectors
(figure/face-region CCIP embeddings on images carrying that tag); match_image()
cosine-matches an image's figure vectors against every character (multi-
prototype: best over a character's examples), surfacing those above a tunable
threshold as {tag_id, name, category:'character', score, source:'ccip'},
excluding already-applied characters. v1 = cosine on raw CCIP vectors; the exact
CCIP metric/threshold gets validated against the model in the hands-on eval.
Tests (synthetic vectors): same-character match across images, no-match for an
orthogonal figure, already-applied exclusion, no-figure-vectors empty.
NEXT: merge CCIP character suggestions into the rail; the agent container that
actually produces the vectors (hands-on, GPU — not CI-verifiable).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
|
6cabef07a4 |
feat(gpu): HTTP job API + token auth + backfill — the agent's server side (#114 slice 3b)
The thin HTTP surface over the queue so the desktop agent stays HTTP-only: - Agent endpoints (Authorization: Bearer <token>): POST /api/gpu/jobs/lease (returns jobs + image_url + mime + video frame cadence), /submit (stores regions via RegionService + closes the job; 409 on a stale lease), /heartbeat, /fail. Token validated against AppSetting (mirrors the extension-key pattern, constant-time compare). - Admin (browser): GET/POST /api/gpu/token[/rotate] (generate + show the agent token), GET /api/gpu/status (queue counts), POST /api/gpu/backfill → dispatches enqueue_gpu_backfill. - enqueue_gpu_backfill(task): one INSERT…SELECT enqueues a job per image lacking one for the task (scales to the full library; idempotent). Agent flow: lease over HTTP → fetch pixels via the normal FC image URL → compute on the GPU → submit. Redis/Postgres never exposed. Tests: bearer required (+ wrong-token 401), lease→submit round-trip (region+CCIP vector stored, job done via /status), stale-lease 409, backfill enqueue + idempotency. NEXT: the agent container + control UI, then the CCIP detector/embedder + matcher. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
b735432d02 |
feat(gpu): video-ready regions + the HTTP GPU-job queue engine (#114 slice 3)
Answers "how are videos/all media handled by the GPU worker": a job is per ITEM, but the agent fans a VIDEO into per-frame instances (ffmpeg in the agent, the existing cadence), each stored with a timestamp — so a video becomes a BAG of frame embeddings (fixes the mean-embedding muddle) instead of one washed-out vector. Stills → frame_time NULL; animated GIF/WebP treated like short video. - image_region.frame_time (migration 0061, not yet deployed so folded in): the source frame's seconds for video/animated media; NULL for stills. RegionService passes it through. A whole frame is just kind='frame'. - gpu_job + GpuJobService (migration 0062): the durable work list that keeps the desktop agent HTTP-only — enqueue (dedupes (image,task)) / lease (FOR UPDATE SKIP LOCKED, re-claims expired leases so the queue self-heals) / heartbeat / complete / fail (re-queues until MAX_ATTEMPTS then 'error'). The server enqueues; the agent leases+submits over the web API; Redis/Postgres stay private. Tests: enqueue dedupe, lease-then-skip-when-held, expired-lease reclaim, scoped heartbeat, complete, fail-requeue-then-error. region test now covers frame_time. NEXT: the thin HTTP API (lease/submit/heartbeat) + bearer-token auth, then the agent container + control UI. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
0ea7ecdea5 |
feat(regions): image_region storage + service for the crop pipeline (#114 slice 2)
The storage backbone both crop jobs write to and read from. image_region =
normalized bbox (rx/ry/rw/rh) + kind ('face'/'figure' → CCIP character id;
'concept' → SigLIP head bag) + the crop's embedding (nullable Vector(768) CCIP /
Vector(1152) SigLIP, one per kind) + version stamps for compute-once gating. The
bbox doubles as grounded-tag provenance. Migration 0061.
RegionService.replace_regions (scoped BY KIND so the figure + concept pipelines
don't clobber each other) + get_regions — the GPU agent's results endpoint will
call the writer; the character matcher + bag scorer read. Server-side, no GPU.
Tests: replace/get round-trip, kind-scoped replacement, CCIP vector round-trip.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
|
e8d3400d22 |
feat(crops): shared crop primitive for the region/crop pipeline (#114)
The trunk of both crop jobs — CCIP figure-crops and SigLIP concept-crops call the SAME crop_region(): normalized-bbox crop with optional context padding, edge-clamping, and the lower-bound size floor (max of a fraction-of-short-side and an absolute pixel floor) below which a region is too small to embed and returns None. Only the proposer (where) and embedder (what) differ; the crop is shared. Pure Pillow — importable + testable anywhere (the GPU agent imports it for the crop step). Unit-lane tests (no DB): region pixels, floor rejection, edge clamp, pad expansion, out-size resize. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
a5a95320df |
fix(test): disable switch explicitly now that auto-apply defaults ON
test_auto_apply_disabled_blocks_real_run assumed head_auto_apply_enabled defaulted False; it now defaults True (opt-out), so a real sweep is accepted (202). Set the switch off in the test to exercise the disabled→400 path. (run 1629) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
48c8811d69 |
feat(heads): auto-apply observability + on by default (#114 auto-apply B)
Auto-apply is now ON by default (operator-asked: opt-OUT, not opt-in) — migration 0059 + model default flipped. The support (>=30) + measured-precision gates keep it safe and every auto-tag is reversible. Observability so the operator can tune from real data: - MISFIRE = an auto-applied (source='head_auto') tag the operator later removes. UNDER-FIRE = a tag with a head the operator adds by hand (the head missed it). Both captured at correction time in TagService.add_to_image/remove_from_image (source is lost on delete) into durable per-tag counters (head_metric), keyed by tag so they survive head retrain/prune. - Daily snapshot_head_metrics writes a per-concept time-series point (head_metrics_snapshot): auto-applied volume + cumulative misfires/under-fires + head quality; 180-day retention; daily beat. - GET /api/heads/metrics: per-concept current counts + realized misfire rate + head quality, plus the snapshot time-series — the report to tune the precision target + support floor. Migration 0060. Tests: misfire/under-fire counting (and the negatives — manual removal isn't a misfire, headless manual add isn't an under-fire), snapshot time-series, metrics API. What's the autofire threshold? There's no single number — each graduated head derives its OWN probability cutoff from its PR curve: the operating point that holds precision >= head_auto_apply_precision (0.97) at max recall. The global knobs are that target + the >=30 support floor. NEXT (slice 3): UI — enable toggle, dry-run preview, per-concept trends. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
01933c5b26 |
style(test): drop unused img in ungraduated-head sweep test (ruff F841)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
74fef908d2 |
feat(heads): earned auto-apply — sweep mechanism, off by default (#114 auto-apply A)
Graduated heads can now apply their tag without a human — gated so it's safe:
- FIRING GATE: a head fires only when the master switch (head_auto_apply_enabled,
default OFF) is on AND it has >= head_auto_apply_min_positives (default 30)
clean labels. A precise-looking but under-supported low-N head can't spray tags.
- auto_apply_sweep (heads.py): streams every embedded image in chunks, scores
against the eligible heads (numpy, no sklearn), applies each head's tag where
score >= its auto_apply_threshold and the tag isn't already applied/rejected,
with source='head_auto' (distinguishable + reversible). dry_run counts only.
- HeadAutoApplyRun (migration 0059) tracks each sweep / preview; apply_head_tags
task (ml queue) + scheduled_apply_head_tags daily beat (no-op unless enabled)
+ recovery sweep + retention(20).
- API: POST /api/heads/auto-apply {dry_run} (202 / 409 running / 400 disabled),
GET /api/heads/auto-apply (recent runs + per-concept report). Settings
head_auto_apply_enabled + min_positives via /api/ml/settings.
Tests: sweep applies above threshold, dry-run writes nothing, skips under-
supported + ungraduated heads; API disabled/dry-run/conflict guards.
NEXT (slice 2): the observability the operator asked for — per-concept misfire
(auto-applied-then-removed) + under-fire tracking, time-series snapshots, and a
reporting API to tune. Slice 3: the UI (enable, preview, trends).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
|
ca1c17446c |
feat(suggestions): heads are the suggestion source — Camie + centroid removed (#114 C)
The rail's Suggestions now come from the trained per-concept heads. SuggestionService.for_image scores the image's frozen SigLIP embedding against every head (heads.score_image) and surfaces concepts above each head's own suggest threshold; the typed-dropdown's min=0 "show everything" mode maps to a flat floor so any head-scored concept can still be picked. Already-applied tags drop; rejected tags stay flagged + reversible (unchanged). REMOVED from the suggestion path (rule 22, no fallback): the Camie ImagePrediction candidate/alias/merge pipeline and the per-tag centroid augmentation, plus the now-dead SuggestionService internals (_load_predictions, _threshold_for, _settings, self.aliases, self.centroids). Head suggestions are always canonical tags, so raw_name/via_alias are null/false and the rail's alias kebab is inert by data (its removal + the Camie ingest-tagger rip are the flagged follow-up). for_selection (bulk consensus) now aggregates head suggestions unchanged. Tests rewritten to the head path: test_ml_suggestions (surfaces/applied/ rejected-reversible/override/no-embedding/no-heads), test_suggestions_bulk (consensus), test_api_suggestions (get + dropped the Camie-alias roundtrip), and test_ml_artist_retired (artist not head-eligible via _HEAD_KINDS). DEPLOY NOTE: after this lands, the rail is empty until you run Train heads (Settings → Tagging → Concept heads) — deploy, train, then the rail populates. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
291b90803d |
fix(test): match rejected suggestion by id, not display casing
test_rejected_tag_surfaced_flagged_then_reversible asserted "Rejectme" but an
existing tag keeps its stored name ("rejectme"), so the suggestion's
display_name is lowercase. Match by canonical_tag_id instead (casing-robust).
The feature was correct — only the assertion was wrong (run 1595 integration).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
|
22c3b54746 |
feat(heads): production per-concept heads — train + score backend (#114 A)
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 |
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bvandeusen
|
179c1a9dcc |
feat(suggestions): visible, reversible rejection in the modal rail
A red-✗ dismissal no longer makes the suggestion vanish. The rejected tag stays in the rail — dimmed, struck-through, with a "rejected" pill and a one-click undo (↶) in place of the ✗ — so a misclick is recoverable and the operator can see what they've said no to (operator-asked 2026-06-27). Backend: SuggestionService.for_image now KEEPS rejected tags, flagged rejected=True, sorted to the bottom of their category, instead of dropping them. New AllowlistService.undismiss + POST /suggestions/undismiss clears the TagSuggestionRejection. Rejected items are still excluded from bulk consensus (for_selection) and the type-to-add dropdown, whose jobs are unchanged. Frontend: store.dismiss flags in place (canonical tags) rather than dropping; new store.undismiss reverts. SuggestionItem renders the rejected state and swaps ✗→↶; ✓ still accepts (which clears the rejection server-side). Tests: rejected-surfaced-flagged-then-reversible (service) + undismiss endpoint idempotency (API). Completes #1134's reversible-rejection half. Heads-as-suggestion-source is the remaining piece. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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6e3c5f697f |
feat(ml): tag-eval backend — head-vs-centroid learning-curve eval (persisted)
Slice 1 of milestone #114 (tagging v2). Proves the frozen-embedding + trained- head spine on the operator's own data, reusing the SigLIP embeddings already stored on image_record — no re-embedding, no GPU. Per concept: train a logistic-regression HEAD (positives + negatives = explicit rejections + sampled unlabeled) vs the old single-CENTROID baseline; report cross-validated precision/recall/AP for both, a LEARNING CURVE (AP/F1 as tagged positives grow 10→30→100→300), and example image ids (head-would-suggest / head-doubts-positive) to eyeball. Persisted so the report SURVIVES navigation (operator-flagged): the run + full report live in a new tag_eval_run row (mirrors library_audit_run); the admin card will rehydrate from GET on mount, not transient state. - models.TagEvalRun + migration 0056; runs on the ml queue (only worker with numpy/sklearn) — numpy/sklearn lazy-imported so the API can still enqueue. - services/ml/tag_eval (compute + start helper, one-running guard), tasks.ml .tag_eval_run, api/tag-eval (POST create, GET history light / detail w/ report). - recover_stalled_tag_eval_runs sweep + retention (keep last 20) + 5-min beat (rule 89). scikit-learn added to requirements-ml. - tests: param normalization + the rehydrate read-path + create/conflict. Frontend admin card (trigger + render persisted report) follows next. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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e3855a5ae0 |
chore(tags): remove orphaned cluster tag-gaps route + service method
The cluster tag-gap feature's only UI (Explore's TagGapPanel) was removed in the 3-pane rework, leaving the backend that fed it with no caller. Surgical removal: - drop the POST /api/images/cluster/tag-gaps route (cluster_tag_gaps) - drop BulkTagService.tag_gaps (+ the now-unused `import math`) - drop the tag_gaps tests (test_bulk_tag_service, test_api_bulk_tags) BulkTagService's common_tags / bulk_add / bulk_remove stay — they still back the gallery bulk editor. Pure deletion, no behaviour change. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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1aadf3267b |
fix(tags): correct directory image_count — fandom leg must correlate the outer tag
The directory card count regressed to a globally-inflated number (~every card showed the same ~469): the fandom leg used a doubly-nested correlated subquery — image_tag.tag_id IN (SELECT member.id WHERE member.fandom_id == Tag.id) — whose inner predicate did not correlate the outer Tag, so it matched EVERY character that has any fandom and counted all their images for every tag. The gallery scope and cleanup count were unaffected (they pass a literal tag id, a single-level subquery), which is why only the card diverged from the gallery. Rewrite the count as a single-level correlated scalar subquery: join `member` (the tag applied to the image) and match image_tag.tag_id == Tag.id (direct) OR member.fandom_id == Tag.id (a character of this fandom). Strengthen the directory test with a second unrelated fandom/character so a non-correlating fandom leg fails (count would read 4 instead of 3). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |