0db38cc11192a112fcba2f04d52012bf99e91a94
135
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
  bvandeusenandClaude Opus 5
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2e0f8f8c61 |
feat(cleanup): reclaim orphaned attachments — rows and store blobs (#3068)
PostAttachment's two FKs are both ON DELETE SET NULL, so a deleted post or artist left the row behind rather than taking it. Nothing ever pruned those rows, and nothing in the repo had ever unlinked a file under the attachment store — so both rows and bytes accumulated permanently, invisible to every existing diagnostic. Why a disk->DB reconciliation rather than a row sweep: the store is sha-addressed and idempotent, so ONE blob backs MANY rows. Deleting a row does not free its blob, and since the artist cascade (#3066) now deletes its attachment rows outright, a freed blob has no DB pointer left to find it by. Walking the store and asking "does any row still reference this sha?" catches orphans from every cause, including ones no future delete path will think to report. Preview and apply share `_orphan_attachment_conditions` (rule 93). The dry-run derives its surviving-sha set by NEGATING that same predicate, so it is honest about blobs the delete would free rather than counting them as still-referenced — the one place this was easy to get backwards, so it has its own parity test. Guards, each with a reason: - A blob is written before its row commits, so a just-stored file legitimately has no referencing row. Files under 6h are never judged — same guard and reasoning as ORPHAN_TEMP_MIN_AGE_HOURS. - `.partial` staging files belong to cleanup_orphaned_temp_files; skipped rather than raced. - The sha is parsed as the first 64 chars, not via Path.stem: store() takes the extension from the source filename, and a URL-encoded basename yields a multi-dot suffix that would make stem eat part of the sha. - A 900s walk budget reports partial=True instead of running to the task's hard limit (rule 89). - TASK_STUCK_THRESHOLD_MINUTES override at 30 (= time_limit 25 + 5). Without it a healthy 20-minute walk is phantom-flagged 'RecoverySweep' at the bare 5-min default — the #883 failure class; its invariant test is mirrored here. Defaults to the safe preview at both the task and the route, unlike the other maintenance triggers: this apply unlinks files. Operator-triggered only, never on a beat. Ships with its UI (rule 27): AttachmentReclaimCard in Cleanup → Duplicates & leftovers, built on the existing useMaintenanceTask/MaintenanceTile shapes, so a run survives navigating away. Surfaces files_failed and partial explicitly, since both change what the numbers mean. Also promotes humanBytes to utils/bytes.js — it was byte-identical in VideoDedupCard and GatedPurgeCard and this card would have been the third copy. The three divergent `formatBytes` helpers are deliberately left alone. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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c87f8a1bb3 |
refactor(maintenance): DRY the recover_stalled head-run twins (#161)
recover_stalled_head_training_runs and recover_stalled_head_auto_apply_runs were near-exact copies (coalesce-flip + keep-last-N prune, differing only in model + two constants). Extract _recover_stalled_runs(model, stall_minutes, keep_runs, label); the two tasks become thin wrappers. The other two recover tasks are deliberately NOT folded in (library-audit has no prune tail; backup uses a single started_at cutoff). test_recover_stalled_head_runs.py covers both wrappers (stalled→error, fresh survives) — previously untested. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi |
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bvandeusenandClaude Opus 4.8
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666b3a2ec8 |
refactor(ml): DRY pass — shared sweep helpers + table-driven settings (#161)
Consolidate duplication accrued across the ML tagging + settings backend, behavior-preserving (over-DRY guard applied — the three auto-apply sweep BODIES stay separate; only their shared inner helpers are extracted). - _sigmoid / _conflict_scores / _insert_presentation_review (heads.py): the score→prob transform (6 inlined sites), the presentation conflict signal (2 sites), and the ring-loud PresentationReview insert (2 sites, single- sourced so the mode column can't drift on the shared composite PK). - _applied_or_rejected (training_data.py): the per-tag "applied ∪ rejected" skip-set, byte-identical at 3 sweep sites (heads.py x2, tasks/ml.py ccip). - ccip sweep divergence fixes: import ccip._FIGURE_KINDS + training_data._l2norm instead of local copies that silently drift when the canonical changes. - MLSettings.load / .load_sync classmethods (mirror ImportSettings); route all 8 scalar_one singleton reads through them (the session.get None-path stays). - GET serializers for MLSettings + ImportSettings are now table-driven off the same _EDITABLE tuples PATCH writes, so a new field can't be silently absent from GET (the split that historically dropped fields). - AUTO_APPLY_THRESHOLD_MIN/MAX constant single-sources the [0.5,0.999] operating range across the service clamp + the 5 API validators. - test_ml_dry_helpers.py pins _applied_or_rejected + _sigmoid. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi |
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bvandeusenandClaude Opus 4.8
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af0d39ed52 |
feat(wip): soft title tier — sketch/doodle vocab + ring-loud audit (#1474)
Extends WIP title-tagging to lower-precision cues (sketch/doodle/scribble) safely. - wip_title.py: soft matcher (word-anchored; sketchbook/kadoodle don't trip it); WIP_TITLE_SOFT_SOURCE + soft SQL prefilter; apply_wip_image_tags takes a source arg. - training_data._AUTO_SOURCES += 'wip_title_soft' → the soft tier is PROVISIONAL and never trains the wip head (a finished "sketch" can't pollute it). Only the hard tier (wip_title) + manual train. - ImportSettings.wip_soft_title_tagging_enabled (OFF by default, opt-in). Migration 0087. - importer: hard tier wins, soft is the fallback (source wip_title_soft). - backfill: refactored into a shared _backfill_wip_tier; hard always, soft when enabled. - heads.soft_wip_conflict_audit + daily beat: score soft-tagged images against content heads, flag ring-loud ones (PresentationReview mode=process) for the review strip — the operator's "measure if they got falsely tagged" safety. - api settings toggle; ImportFiltersForm soft toggle. - tests: soft matcher pos/neg; soft source not a training positive; audit flags ring-loud + spares quiet. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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ad2a5fc5fe |
feat(system-tags): process vs chrome groups + WIP provisional auto-apply (#1464)
Backend for the system-tag behavior refactor (milestone #157). editor screenshot moves from chrome (hidden) to the PROCESS group (shown, like wip); wip+editor gain provisional auto-apply so they stop needing endless manual identification — without a runaway loop. - tag.py: split PRESENTATION_SYSTEM_TAGS → CHROME_SYSTEM_TAGS (banner) + PROCESS_SYSTEM_TAGS (wip, editor screenshot). - heads.py: generalize presentation_auto_apply_sweep → system_tag_auto_apply_sweep (mode chrome|process). Same Guard 1 (skip human/confirmed) + Guard 2 (ring-loud conflict → PresentationReview). process mode uses source 'process_auto' and does NOT hide (hide is a gallery-query effect of group membership). - training_data._AUTO_SOURCES += 'process_auto' → the head never trains on its own auto-applied output; only wip_title/manual train it (the runaway break). - ml_settings: process_auto_apply_enabled (OFF, opt-in) + threshold + conflict threshold. presentation_review.mode ('chrome'|'process'). Migration 0086. - gallery_service: default-hide reads CHROME only (editor now shows); Explore neighbors exclude the whole PROCESS group. - tasks/ml + celery beat: scheduled_process_auto_apply (daily, opt-in); prune covers both modes. - api: ml_admin process_* CRUD+validation; hidden-review returns mode. - tests: rename chrome sweep calls; new test_process_auto_apply (apply, guards, mode flag, no-self-train); gallery test asserts editor now visible. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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571938781a |
feat(tagging): title-based WIP auto-tagging (#1458)
Auto-apply the `wip` system tag to posts whose TITLE explicitly declares
work-in-progress ("WIP" / "work in progress") — a deterministic, high-precision
complement to the image-based ML `wip` head. WIP images are excluded from the
Explore/gallery browse, so honouring the artist's own label keeps unfinished
pieces out of the main browse.
- services/wip_title.py: precision-first token-anchored matcher (swipe/wiped
never trip it) + sync apply helpers (source='wip_title', ON CONFLICT DO
NOTHING, chunked under the psycopg param ceiling).
- importer: live hook on FRESH import only (never on deep-scan/supersede), so a
manually-removed WIP tag is never re-applied by a routine re-scan.
- maintenance.backfill_wip_title_tags: operator-triggered back-catalogue sweep
(coarse SQL prefilter + regex confirm, keyset-paginated). Deliberately NOT a
beat — a periodic re-run would silently undo manual removals.
- ImportSettings.wip_title_tagging_enabled (default ON, migration 0085) gating
the live hook; GET/PATCH + POST /settings/wip-title/scan.
- Settings UI: toggle + "Scan existing posts" button.
- Tests: pure matcher unit tests + integration (apply idempotency, backfill
precision).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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bvandeusenandClaude Opus 4.8
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aea2701c28 |
feat(translation): tunable acceptance floor (0.90) + per-post sticky override (#155)
The gate at a fixed 0.80 couldn't catch the real pain: Interpreter (fresh == cached, verified by probe) confidently mis-detects short ASCII English like "... WIP Part 1" as German at 0.86 — above the floor — so it was accepted and a re-translate reproduced it. Confidence alone can't separate the 0.86 collision (genuine German lands there too), and single-word mis-flags sit at a confident 1.0 no floor catches. Two operator-approved levers: - Acceptance floor is now a live Settings value (ImportSettings. translation_min_confidence, default 0.90; surfaced in the Translation card), so it's tunable without a redeploy. _accept takes the threshold as a parameter. - Per-post sticky override (Post.translation_override: auto/force/original). 'force' stores a translation even below the floor (rescue a skipped legit-foreign title); 'original' keeps the original and clears any stored translation (kill a confident mis-flag no floor catches). The sweep honors it on every run and _reset_translations skips 'original', so the choice survives a Re-translate-all. POST /api/posts/<id>/translation-override applies it immediately (translate now when the service is up, else queue for the sweep). UI: PostTranslationControl on the posts-feed card. Migration 0084 (both columns + a CHECK on the override). The feed + provenance serializers expose translation_override. With a stricter floor the rollback finally works: raise it -> Re-translate all -> the 0.86 mis-flags are rejected and restored to the original; force / keep-original handle the residual either way. Tests: gate thresholds against the param (0.86 rejected at 0.90, explicit-floor cases); sweep force/original + re-translate-skips-original; override endpoint (validation, original clears, force queues when disabled, feed exposes it); settings min_confidence default/save/validate. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CgZP9v2otxVJymiYsnVuMy |
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bvandeusenandClaude Opus 4.8
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6ab7fd5c7f |
tune(translation): set latin acceptance floor to 0.80 (#1376)
Calibrated against fresh probes once Interpreter returned real langdetect confidence: genuine German detected at 1.0, a correctly-detected but ambiguous latin string at 0.86. Set _MIN_LATIN_CONFIDENCE to 0.80 (below that band) so legitimate ambiguous non-English still translates while genuinely-unsure guesses are rejected. Real langdetect also fixed the original mis-flag at the source, so this floor is a safety net, not the primary fix. Pin 0.86-accepted in the gate test to guard against bumping the floor back up. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CgZP9v2otxVJymiYsnVuMy |
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bvandeusenandClaude Opus 4.8
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7ddf94220d |
feat(translation): accept/reject gate on Interpreter's detection confidence (#1376)
Interpreter now returns a real per-detection confidence (source stays "auto"), so curator can reject the mis-detections it was blindly storing — e.g. a short English title mis-labelled as German and rewritten into the archive. The gate consumes ONLY Interpreter's own reported detection — curator does no language detection of its own (Scribe rule 133): a field is stored when the engine actually translated it AND either the detected language is CJK (script-detected, reliably high — ja/ko/zh trusted outright, incl. pure-kanji Japanese that lands as zh ~0.75) or the reported confidence clears a latin-script floor (_MIN_LATIN_CONFIDENCE = 0.90). A latin detection below the floor keeps the original and marks the post handled; a missing confidence fails open. The client already sent source="auto" and parsed confidence, so this is purely the gate + tests. Tests: pinned interpreter-client test now asserts source stays "auto"; new pure-unit gate tests (CJK trusted / latin floor / case-insensitive / fail-open) in the fast lane; end-to-end reject-low-latin, accept-high-latin, accept-low-cjk sweeps. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CgZP9v2otxVJymiYsnVuMy |
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bvandeusenandClaude Opus 4.8
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ffdbdbaf07 |
feat(translation): 8h sweep + drain button + detection probe (#1376)
Throughput: translate_posts now runs every 8h (was daily) as the steady-state cadence for newly-imported posts, and the Settings "Translate now" button runs it in drain mode (run-until-done, no reset) so one press clears the whole untranslated backlog instead of a single 300-post chunk. The interrupt/backoff re-enqueue now preserves the drain flag so a bulk drain resumes cleanly after an Interpreter restart. Misdetection groundwork: surface the detector's confidence from the Interpreter client (it was in the detectedLanguage payload but discarded) and add a read-only "Test translation" box — POST /settings/translation/ probe + TranslationCard UI — that shows detected language + confidence + engine + result for pasted text, without saving. Lets the operator see why a short/abbreviation-heavy English title gets mis-detected so the detection guard (min-length + confidence floor) can be tuned from real numbers. The guard itself follows once the mis-detected cases are probed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CgZP9v2otxVJymiYsnVuMy |
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bvandeusenandClaude Opus 4.8
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3c7ab44e74 |
feat(translation): per-field language detection for mixed-language posts
_translate_one translated [title, description] in ONE Interpreter call and keyed the whole-post passthrough on the aggregate detected_lang (the FIRST item). So an English title + non-English description detected "en" and marked the post handled, leaving the description untranslated. Now each field is translated independently (its own detected_lang / passthrough) and the non-target field is stored on its own; translated_source_lang reflects the translated field's language. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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0b78264d62 |
feat(maintenance): daily janitor for orphaned .part/.partial staging files
Downloads/imports stage into <name>.part / <name>.partial then os.replace() into place, so a kill mid-write leaves a discardable temp — never a corrupt final. cleanup_orphaned_temp_files sweeps ones left behind under the images root, only older than 6h so an in-flight download's staging file is never removed. Daily beat. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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9eae636047 |
feat(translation): pooled Interpreter session + manual sweep resumes after drain
- interpreter_client: shared requests.Session with a connect-only retry (connect=2, no status retries — we map 429/5xx ourselves) so a proxy reload is smoothed and the keep-alive connection is pooled across the sweep. - translate_posts: on an interrupt (drain), re-enqueue after the Retry-After hint / default backoff instead of waiting for the daily beat; self-terminates via the health gate. Steady-state one-chunk-per-run on success is unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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c64261593d |
feat(ops): graceful shutdown — worker stop-grace + Interpreter drain resilience
Deploys (docker SIGTERM→SIGKILL, default 10s) were killing Celery jobs mid-flight. Give in-flight work room to drain and make interrupted work resume cleanly instead of stalling. - docker-compose.yml: stop_grace_period per lane (web 30s / worker 90s / scheduler 60s / maintenance-long 180s / ml-worker 120s) so warm shutdown can actually drain before SIGKILL. - celery_app.py: task_reject_on_worker_lost=True — a task killed past the grace window is re-queued (safe: idempotent + chunked, recovery sweeps re-drive stragglers). - interpreter_client.py: map 429/5xx (502/503/504) → InterpreterUnavailable and parse Retry-After (delta-seconds or HTTP-date); a draining Interpreter behind a reverse proxy no longer raises an opaque HTTPError. - translation.py: thread retry_after out of _translate_batch; retranslate_posts resumes after the Retry-After hint (or 60s default, capped 900s) on an interrupt with _reset_done=True, self-terminating via the health gate. - tests: 429/5xx mapping + Retry-After parse; interrupt-resume + default backoff. No migration. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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1f6d94f51d |
feat(translation): re-translate on model change — artist-scoped + global re-run (#146)
retranslate_posts resets the 5 translation columns to NULL for a scoped set of posts (all, or WHERE artist_id IN ids) then reuses the untranslated sweep to re-run them, chasing the tail until drained (run-until-done). Interpreter cache keys on engine_version so a changed model re-translates, an unchanged one is cache-fast. Reset only happens when the service is configured+healthy so translations are never wiped when they can't be rebuilt. New POST /settings/translation/retranslate (artist_id | all=true). UI: per-artist 'Re-translate posts' on the Artist Management tab + 'Re-translate all' in the Settings Translation card, both with confirm dialogs. No migration (reuses m143 columns). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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7a4de7278d |
feat(translation): backfill sweep + beat + manual trigger (#143 step 3)
tasks/translation.py — translate_posts: picks untranslated posts (title OR description non-empty), per-post [title, description] batch via the Interpreter client, stores translations + detected lang + engine_version; passthrough / already-target posts are marked handled with no stored translation. 503 or a connection error interrupts (retry next cycle), 400 stops (fix config), per-post commit keeps progress; wall-clock bounded. Wired into celery (maintenance_long lane) + a daily beat. No-op unless enabled + base URL set + healthy. GET /settings/translation/status + POST .../run for the Settings card. Task tests (stubbed client, monkeypatched session). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusenandClaude Opus 4.8
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2bcaa20b22 |
feat(ml): schedule presentation auto-hide sweep + retention (#141 step 6)
scheduled_presentation_auto_apply (daily beat) runs presentation_auto_apply_sweep — idempotent, so an interrupted run just re-runs next cycle (that's the recovery), wall-clock bounded by soft/hard task time limits. prune_presentation_reviews (daily beat) drops RESOLVED review flags older than 30 days (rule 89 retention). Tests run both tasks via a monkeypatched session factory. Milestone 141 complete: the presentation-chrome auto-hide + conflict-flagged review is now live end-to-end. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusenandClaude Opus 4.8
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3006e84cc0 |
feat(ml): soft auto-apply — retract auto-tags now below threshold (milestone 139)
Daily scheduled_retract_auto_tags re-scores standing auto-applied tags and drops the ones the model no longer supports: - retract_auto_applied_heads: per graduated head, re-score its source='head_auto' images (bounded — only the images already carrying the auto-tag, not the whole library) and remove ones now < auto_apply_threshold. - retract_auto_applied_ccip: per source='ccip_auto' character tag, max-cosine the image's figure vectors vs that character's prototypes; remove ones now below the ccip auto-apply threshold. Both SKIP operator-confirmed tags (TagPositiveConfirmation) and are SILENT — a low score isn't proof the tag was wrong, so no hard negative is recorded (that's reserved for an operator removal). No-op unless the relevant auto-apply switch is on. New daily beat. sklearn-free tests for both paths + the disabled no-op. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusenandClaude Opus 4.8
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2cfbb284d5 |
feat(heads): incremental retraining — refit only changed tags (#1317 phase 2, m138)
train_all_heads is now incremental by default: a per-tag training-data fingerprint (positive + rejection count/latest-timestamp, stored on tag_head.train_fingerprint) means a manual Retrain refits ONLY the tags whose data changed — O(what you touched), not O(all heads). The nightly scheduled_train_heads passes full=True to reconcile sampled-negative + hygiene drift across every head. First incremental run after deploy still refits everyone (NULL fingerprints), stamping them, then it's incremental. The refit decision + fingerprint are split into sklearn-free helpers (_head_fingerprints, _heads_needing_retrain) so the incremental logic is unit-tested directly (train_head itself needs scikit-learn). Migration 0080. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusenandClaude Opus 4.8
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a1ed53136e |
feat(ccip): refresh task + beat + retrain hook for character prototypes (#1317, m138 step 3)
- refresh_character_prototypes celery task wraps the incremental builder (sync ml worker); returns skipped / rebuilt=N removed=N. - Beat: every ~15 min (cheap global-gate no-op when idle) + a nightly full=True reconcile as belt-and-suspenders. - train_heads enqueues it on success, so the Retrain button AND the nightly head retrain refresh CCIP on the SAME trigger — unified lifecycle, as asked. The initial (cold) full build loads the whole reference set once in the background, never on a /suggestions request. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusenandClaude Fable 5
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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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bvandeusenandClaude Fable 5
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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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bvandeusenandClaude Fable 5
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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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bvandeusenandClaude Fable 5
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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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bvandeusenandClaude Fable 5
|
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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|
bvandeusenandClaude Opus 4.8
|
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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|
bvandeusenandClaude Opus 4.8
|
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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||
|
bvandeusenandClaude Opus 4.8
|
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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|
bvandeusenandClaude Opus 4.8
|
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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|
bvandeusenandClaude Opus 4.8
|
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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|
bvandeusenandClaude Opus 4.8
|
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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bvandeusenandClaude Opus 4.8
|
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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|
bvandeusenandClaude Opus 4.8
|
558d965a1c |
fix(gpu): count backfill enqueues via RETURNING, not rowcount
result.rowcount is unreliable for INSERT…SELECT (returned -1), failing the idempotency assert. Use .returning(GpuJob.id) and count the rows. (run 1652) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
f247f9247c |
style(gpu): ruff — split as-import, dict(rows) over comprehension
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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|
bvandeusenandClaude Opus 4.8
|
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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|
bvandeusenandClaude Opus 4.8
|
9326a82b29 |
fix(heads): .all() before dict() in snapshot_head_metrics
dict(session.execute(...)) on a bare Result invokes the mapping protocol (a Result has .keys() = column names) and subscripts it → "CursorResult is not subscriptable". Materialize with .all() so dict() consumes rows as key-value pairs. The API path already did this; the snapshot task missed it. Caught by test_snapshot_records_timeseries_point (run 1628). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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|
bvandeusenandClaude Opus 4.8
|
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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|
bvandeusenandClaude Opus 4.8
|
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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|
bvandeusenandClaude Opus 4.8
|
77baee49fd |
feat(heads): nightly auto-retrain + inline Retrain button in Explore
Two cadences for keeping heads in sync with your tagging: - PASSIVE: a nightly `scheduled_train_heads` beat (skips if a run is already in flight; creates+commits the run row before dispatching train_heads so the ml worker always finds it). Folds the day's accepts/rejects + newly-eligible concepts into the heads without anyone clicking. - ACTIVE: a "Retrain heads" button in the Explore trail bar — bank the +/- feedback you just gave while walking content, without a trip to Settings. Shared logic in a new useHeadTraining composable (trigger + poll + start/finish toasts), used by the Explore button; reflects an already-running run (incl. the nightly one) on mount. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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|
bvandeusenandClaude Opus 4.8
|
1ed0895e8d |
style(heads): fix import ordering (ruff I001)
Alphabetize HeadTrainingRun in models/__init__ + maintenance imports (H before I), and drop the inline comment that split heads.py's import block. Pure import ordering — no behavior change. (run 1601 lint) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
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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bvandeusenandClaude Opus 4.8
|
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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bvandeusenandClaude Opus 4.8
|
4272a19d40 |
fix(external): split fetch timeout into read (60s) + total (30m) budgets (#883)
The single _FETCH_TIMEOUT=3000s meant different things per host: a TOTAL wall-clock for mega (subprocess), but only a per-read socket timeout for HTTP hosts (requests' timeout is the idle gap between bytes, never a total). So a stalled HTTP connection tied up a download-worker slot AND the per-host serialize lock for ~50 min before failing (operator-flagged 2026-06-17). Split into two limits in external_fetch: - read timeout (_READ_TIMEOUT=60s, with _CONNECT_TIMEOUT=30s) → requests gets (connect, read); a stalled socket now fails in ~60s. - total budget (_TOTAL_TIMEOUT=30min) → enforced as a wall-clock deadline across chunks in _stream_to_file (HTTP has no total-download timeout), and passed as the subprocess total for mega. fetch_external() signature: timeout= → read_timeout=/total_timeout=. gdrive (gdown) self-manages; the celery hard limit is the outer backstop. Also lowered the per-host lock TTL 3600→2400 so a worker that dies holding it can't wedge a host's links much past one fetch's budget. Each external link is already one Celery task (sweep enqueues one fetch_external_link.delay per link), so these budgets are per-link. Tests: total-budget-exceeded cleans the .part; HTTP gets (connect, read); mega gets the total. Worker fakes updated to **kwargs. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
|
258c77dfcd |
fix(maint): raise recovery-sweep threshold for fetch_external_link (#883)
External file-host fetches run to a 60-min hard limit (time_limit=3600, per-fetch _FETCH_TIMEOUT=3000s), far longer than the recovery sweep's 5-min default. recover_stalled_task_runs was phantom-flagging healthy in-flight fetches as "RecoverySweep: no completion signal received within 5 min" before the task's own timeout/error handling could surface the real error (operator-flagged: target 414 swept at 6.6min). The sweep already has per-queue/per-task overrides for long tasks, but fetch_external_link was never added and its TaskRun records queue='default' (no queue override) despite external.* routing to download. Add a task-name override of 65 min (time_limit 60 + 5 buffer); task-name precedence makes it robust regardless of the recorded queue. No new internal timeout needed — the existing _FETCH_TIMEOUT + soft_time_limit + except-block log.exception already capture the real failure once the sweep stops preempting. Pinned tests: external-fetch override survives a 10-min row / flags a 70-min row on queue='default'; invariant guard asserts override >= hard time_limit. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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51201b459e |
fix(ml): per-task async engine for recompute_centroid (#881)
recompute_centroid + recompute_centroids were the only tasks still using the process-wide singleton extensions.get_session() under asyncio.run(). The async engine's asyncpg pool is bound to the loop it was created on; each Celery task runs a fresh asyncio.run() loop, so after the first invocation the cached engine handed loop-A connections to loop B and raised "Future attached to a different loop" — every recompute after the first in a worker process failed (~35ms, fails on first DB await). Convert both to the established per-task async_session_factory() pattern (NullPool engine created + disposed inside the task's own loop), matching scan/download/admin tasks. No get_session usages remain in tasks/. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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540151290b |
feat(cleanup): purge misgrabbed gated-post blurred previews (#874 follow-up)
A one-shot Maintenance action to remove the blurred locked-preview images the ingester downloaded from tier-gated Patreon posts before #874. current_user_can_view was never persisted, so the cleanup re-walks each enabled Patreon source (read-only) to re-derive which posts are gated now and the blurred filehashes Patreon serves for them, then matches by CONTENT HASH against stored source_filehash. Because the hash is content-addressed, a real file downloaded when access existed has a different hash and can never match — regained-then-lost-access content is provably spared (operator's hard requirement). NULL source_filehash => unverifiable, kept + reported. On apply: delete matched ImageRecords + files (provenance cascades), clear seen/dead-letter ledger rows for those hashes so the real media re-ingests if access returns, and delete gated posts left bare. Shares one match predicate between preview and apply (rule 93). - cleanup_service: collect_gated_previews + purge_gated_previews - tasks.admin: purge_gated_previews_task (async re-walk bridge, timeboxed) - api.admin: POST /maintenance/purge-gated-previews - GatedPurgeCard.vue in Settings > Maintenance (preview -> confirm -> apply) - tests: collect predicate, hash-match delete/spare/unverifiable, ledger clear, bare-post removal, no-op Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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369e3de684 |
feat(ml): cadence-based video frame sampling + min-frame tag aggregation (#747)
Video tag noise root cause: frames were a FIXED count (6) max-pooled — a tag firing on one frame survived at peak confidence, and a fixed count under-samples long multi-scene videos so real scene-local tags looked like noise. Redesign (operator-steered): - Sample at a fixed CADENCE — one frame every `video_frame_interval_seconds` (default 4) across the 5–95% window — so a tag's frame-presence reflects real screen time independent of video length. Capped at `video_max_frames` (default 64): a long video stretches the spacing instead of exploding into hundreds of inferences, bounding per-video cost on the single ml-worker (per-frame ffmpeg timeout also cut 60s→30s). - Aggregate with `_aggregate_video_predictions`: keep a tag only if it appears in >= `video_min_tag_frames` sampled frames (≈ that many × interval seconds on screen — duration-independent noise rejection), with confidence = MEAN over the frames it appears in (not max). Clamps the threshold to the sample count so a 1–2-frame short video still tags. - All three knobs are DB-backed ml_settings (migration 0053), patchable via /api/ml/settings + sliders in the ML settings card — replaces the VIDEO_ML_FRAMES env var (product-not-project). Tests: aggregation drops one-frame noise + means corroborated tags + clamps on short videos; settings round-trip + min>max validation. Replaced the _maxpool_predictions unit test. NOTE: this is the QUALITY half of #747. The perf half — the ml-worker runs CPU-only — is GPU enablement, tracked separately in #872. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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41652db20f |
feat(maintenance): retroactive video-dedup action — preview + apply (#871)
Phase 2 of #871: clean up the duplicate videos already in the library (the #859 "same video from multiple sources" clutter). Import-time dedup (Phase 1) only prevents NEW dups; this is the operator-triggered cleanup of existing ones. cleanup_service.dedup_videos(dry_run): - backfill_video_durations: re-probe NULL-duration videos (pre-#871 rows) so the existing library participates; idempotent (only NULL rows), writes a negative sentinel for un-probeable files so they're neither re-probed forever nor matched. - find_video_dup_groups: cluster same-artist videos by duration (±tol) + aspect, anchored per cluster to bound the span (no chain drift); keeper = highest pixel area then bytes. Reuses the importer's _VIDEO_DUP_* tolerances. - apply: re-point each loser's post links to the keeper (so no post loses the video) THEN delete the redundant records + files via delete_images (cascade). dry_run shares the same discovery predicate and returns the projection only (rule 93). Tags on a loser are NOT merged (noted; videos rarely hand-curated). - dedup_videos_task (maintenance queue; summary → task_run.metadata). - POST /maintenance/dedup-videos {dry_run} + GET /maintenance/task-result/<id> so the card shows the dry-run projection before the destructive apply. - VideoDedupCard: Preview → shows groups/redundant/reclaimable, then Apply behind a confirm dialog. Mounted in the Maintenance panel. Tests: dedup collapses + re-links the loser's post to the keeper + removes the file; dry-run deletes nothing; distinct durations aren't grouped; task registered. (Migration 0052 for duration_seconds already shipped with Phase 1.) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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949c9abcc6 |
fix(external): path-safe unlink + per-link staging + orphan repair (#859)
External downloads import IN PLACE, so the post-attach dedup-skip unlink could delete a file that IS an ImageRecord's backing file — orphaning the record and 404-ing on playback. Two sources of that: - Two links on the same post (same film from mega + gdrive) emitted the same filename into one external/<post_id>/ dir; the second overwrote the first. Stage per-LINK now (external/<post_id>/<link_id>/) so each file keeps its path. - The duplicate_hash/duplicate_phash branch unlinked `f` unconditionally. Make it path-safe: only unlink when `f` is NOT the existing record's canonical file. Plus an operator-triggered orphan-repair maintenance task (prune_missing_file_records_task) to clean up records already orphaned by the bug: scans ImageRecords, deletes those whose file is gone (cascade), with an NFS-stall guard that aborts without deleting if a large sample is mostly missing. Wired through POST /api/admin/maintenance/prune-missing-files and a MissingFileRepairCard in the Maintenance panel. Tests: refetch-same-link keeps the canonical file; orphan repair deletes only real orphans and aborts on the mostly-missing guard. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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05f226a8f6 |
feat(external): zip-parity provenance/tagging + thorough worker logging
Operator-requested: a worker download must be tagged + provenance-associated exactly like an extracted zip, and the path must log well (we won't get it right first try). - _route_files now mirrors download_service._phase3_persist branch-for-branch: imported/superseded → collect member_image_ids+image_id (provenance-linked via the synthesized sidecar, same as extracted-zip members) → caller enqueues tag_and_embed + generate_thumbnail; attached → drop on-disk original, and warn on an UNEXTRACTED archive (#718 symptom); skipped duplicate → unlink; failed → unlink + warn. - Logging at every stage: start (link/host/post/artist/attempt/url), requeue, fetch result (files/bytes) or fetch failure, per-file import decision, dead- letter transitions, and done (files/images/duration). - Parity test: an archive downloaded by the worker is extracted, provenance- linked to the SAME post, and tag_and_embed+generate_thumbnail are queued for exactly the member images. Refs FC #830. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |