723f023e6af63f735fa2b8f763b30bcbcde9bc27
172 Commits
| Author | SHA1 | Message | Date | |
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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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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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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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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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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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3d7f60a6e3 |
fix(lint): use dict() not a dict-comprehension in tag_stats (C416)
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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3d97667f5b |
fix(lint): drop unused select import in tags.py after allowlist removal
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
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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
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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
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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
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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
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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
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60f26247e9 |
style: alphabetize ccip_bp import (ruff I001)
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
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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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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
|
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
|
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
|
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
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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
|
b69c70ab2b |
feat(tag-eval): "keep" records a confirmation so doubts stop resurfacing
"Keep" on a doubted positive was a no-op, so the same confirmed-correct images came back in "head doubts" every run (operator-flagged: reinforcement keeps surfacing the same images). Add tag_positive_confirmation (mirror of tag_suggestion_rejection): keep → POST /images/<id>/tags/<tag_id>/confirm, and the eval excludes confirmed positives from the doubts list — exactly as rejected items already drop out of the suggest list. The tag stays a positive either way (confirmation is a "reviewed" marker, not a training change). - model TagPositiveConfirmation + migration 0057; confirm endpoint (idempotent). - tag_eval: _confirmed_ids + exclude from head_doubts_positive examples. - store.confirmTag + card "keep" calls it. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
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
|
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
|
0ecd1ce4f1 |
feat(explore): cluster-consensus tag-gaps service + route (#94a)
Cluster C, milestone #94. BulkTagService.tag_gaps(image_ids, threshold) finds tags applied to >= threshold fraction of a visual neighbour set but not all of it (the '7 of 10 share Miku; these 3 don't' signal). Each gap carries the laggard image ids minus any TagSuggestionRejection rows, so apply-to-cluster never re-proposes a tag a neighbour dismissed. 100%-common tags and <2-image sets are excluded. New POST /api/images/cluster/tag-gaps. Tests: consensus found / common excluded / missing ids; rejected laggard excluded from missing; tag dropped when all laggards rejected; <2 images empty; route shape + bad input. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XCUHUGQLrBrkgyk1t49kpX |
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bvandeusen
|
7127714316 |
feat(tags): non-mutating merge preview + admin dry_run (#8a)
Cluster B, milestone #99. TagService.merge_preview(source, target) computes the same counts the apply produces (rule 93 parity) without mutating: images_moving (source links the apply UPDATEs), images_already_on_target (links it drops), source_total, series_pages, will_alias (_keep_as_alias), a kind/fandom compatible flag (surfaced, not raised, so the UI can warn), and up to 6 thumbnails of the moving images. The admin /tags/<dest>/merge route gains a dry_run flag returning the preview JSON. Tests: preview moving-count == apply merged_count (parity), incompatible flagged without raising, self/missing raise, admin dry_run returns preview + no mutation. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XCUHUGQLrBrkgyk1t49kpX |
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bvandeusen
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e206778a5c |
feat(allowlist): coverage projection + applied-count + post-accept projection (#7a/#7b)
Cluster B, milestone #99. Backend for the allowlist tuning dashboard. #7a: AllowlistService.coverage(tag_id, threshold) counts distinct images with a prediction resolving to the tag (raw_name==tag.name OR (raw_name,category) in the tag's aliases) scoring >= threshold — the gross candidate pool, mirroring tasks.ml._confidence_for_tag resolution. list_all now carries applied_count (grouped image_tag count) + coverage_count (at the row's threshold). New GET /api/tags/<id>/allowlist/coverage?threshold= for the live what-if number. #7b: /suggestions/accept + /alias return {allowlisted, tag_id, tag_name, projected_count} (projection at the tag's threshold) instead of 204, so the UI can show a non-blocking 'auto-applying to ~N images' toast. Apply still runs async via apply_allowlist_tags — projected_count is an estimate. Tests: coverage by threshold (direct + alias-with-category), list applied vs coverage, coverage route (explicit/default/bad threshold), accept/alias payload (newly-allowlisted vs already-on-list). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XCUHUGQLrBrkgyk1t49kpX |
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bvandeusen
|
23fab983a0 |
feat(gallery): tag→gallery nav from modal chips (#5) + OR/exclude tag scope (#6a)
Cluster A, milestone #97. #5: clicking an image-modal tag chip's body now closes the modal and opens the gallery filtered for that one tag (fresh filter); ✕/kebab stay as the explicit remove/rename controls. #6a (backend of OR/exclude filtering): gallery_service._apply_scope gains a structured tag model — tag_or_groups (AND-of-OR: one EXISTS(tag_id IN group) per group) + tag_exclude (NOT EXISTS(tag_id IN exclude)) — layered additively on the existing tag_ids AND path so cursors/facets/deep-links are untouched. Threaded through scroll/timeline/jump_cursor/facets/similar + facets common dict; _require_single_filter rejects post_id combined with OR/exclude. API parses tag_or (repeatable → one OR-group each) + tag_not (csv exclude). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01XCUHUGQLrBrkgyk1t49kpX |
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bvandeusen
|
6599a07468 |
refactor(admin): consolidate maintenance-trigger 202 responses onto _queued() (#753 Finding B)
DRY pass follow-up (note #1026). Five handlers returned the identical jsonify({task_id, status:queued}), 202 shape; extract _queued(async_result). Consumers routed through it: tags_normalize (live branch), trigger_reextract_archives, trigger_prune_missing_files, trigger_dedup_videos, trigger_purge_gated_previews. trigger_vacuum stays bespoke (returns no task_id — the UI doesn't poll it). Added route-level tests for all five consumers (these trigger endpoints had no route coverage before): 202 + task_id via _queued, and the dry_run flag threading through to dedup/purge-gated. Behavior unchanged. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
6281cb1e66 |
refactor(admin): consolidate Tier-A dry-run/apply handlers onto one helper (#753)
DRY pass on the cleanup/admin destructive-ops surface (task #753, hardened process #594). Five Tier-A endpoints repeated the same get_json -> dry_run -> run_sync(service_fn) -> jsonify block verbatim. Extract _run_dry_run_op(service_fn, **kwargs); the five route handlers now delegate. reconcile keeps its source_id validation and passes it through **kwargs. The cleanup_service predicates were already shared between preview and apply (find_*_conditions / find_duplicate_post_groups) — the post-data-loss fix — so no backend-logic change; this is purely the HTTP-handler boilerplate. Consumers (all routed through the helper, verified no copy left behind): prune_unused_tags, prune_bare_posts, reconcile_duplicate_posts (+source_id), purge_legacy_tags, reset_content_tagging. Added route-level tests for prune-bare (apply) and reconcile (apply + source_id passthrough + invalid-source_id 400) — the two helper consumers that previously had only service-level coverage, so every consumer is exercised at the route. Findings B (queued-response helper) and C (store dry-run POST helper) identified but not applied this pass (operator scoped to A). The card preview->commit state machine is deferred to a frontend pattern-consistency sweep. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
eff64275fc |
feat(maintenance): reconcile duplicate posts (gallery-dl→native unify)
An artist first downloaded by gallery-dl gets Post rows keyed by the per-
attachment id; a later native walk keys the SAME real post by the post id. They
never dedup (uq_post_source_external_id is on external_post_id) → duplicate post
rows (cheunart: 943→1109). The real post id is recoverable in-DB from
raw_metadata['post_id'] (both eras store the sidecar there).
reconcile_duplicate_posts (cleanup_service): group posts by (source_id, canonical
post_id = raw_metadata.post_id else external_post_id); for each group >1, keep the
row already keyed by the post id (the format the CURRENT native downloader
produces, so future walks dedup and this can't recur), re-point
ImageRecord.primary_post_id / ImageProvenance / PostAttachment / ExternalLink onto
it conflict-safe (drop the loser's row where the keeper already has the equivalent,
per each table's uniqueness), backfill the keeper's empty date/title/body/raw_meta
from a loser, set external_post_id=post_id + derive post_url, delete losers.
IMAGES ARE NOT TOUCHED (content-addressed/deduped already; operator-confirmed).
Preview/apply share find_duplicate_post_groups (rule 93). API
/api/admin/posts/reconcile-duplicates (dry_run→{groups,posts_to_merge,sample};
apply→{groups,merged,sample}; optional source_id). UI: a second section on
PostMaintenanceCard (preview groups+sample → confirm merge). Tests: merge +
metadata backfill + image move, no-op when unique, provenance-collision dedup.
Design: milestone #73. Forensics: note #917. Out of scope (flagged): cheunart vs
Cheunart case-variant artist dirs/rows.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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bvandeusen
|
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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bvandeusen
|
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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bvandeusen
|
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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bvandeusen
|
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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bvandeusen
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65ec29ba9b |
feat(ingest): Recapture mode — re-grab post bodies/links + localize on-disk inline images (#830)
A plain backfill gates post-body capture on the seen-ledger, so a post whose media is already on disk AND whose post key is already seen never gets its body recaptured (operator-flagged: Industrial Lust description missing). Recovery recaptures unconditionally but re-downloads the whole source. New 'recapture' walk mode (4th beside tick/backfill/recovery): bypasses the post-record gate so EVERY post's body + external links are re-captured (detail-fetching empty bodies) WITHOUT re-downloading on-disk media; and surfaces already-present media via a separate non-deleting relink channel so the importer backfills ImageRecord.source_filehash for inline-image localization. - ingest_core: recapture mode + recapture_records gate bypass + relink collect - patreon_downloader: recapture surfaces seen-on-disk as skipped_disk(path), never refetches seen-missing media, still downloads genuinely-new - importer.relink_source_filehash: NULL-only sha256 backfill, never unlinks - download_service: mode derivation + phase-3 relink loop + lifecycle clear - source_service/api: start_recapture + backfill_recapture field + action - frontend: Recapture kebab action + 'Recapturing' badge across SourceActions/ Row/Card/SubscriptionsTab + sources store - tests across ingester/downloader/importer/source_service/api/download_service Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusen
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8dbf29f803 |
feat(external): per-host enable toggles in Settings (Phase 4d)
Operator lever: disable a single file host (e.g. mega.nz when it's banning) without touching the others. Five booleans on import_settings (extdl_<host>_enabled, default true — works out of the box, rule #26); the worker already reads them via getattr so no worker change. Migration 0050 + model fields + settings GET/PATCH (uniform boolean validation) + a 'External file-host downloads' card in the subscriptions Settings tab. Completes Phase 4. Refs FC #830. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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5c3f8ebd70 |
fix(aliases): store modal alias under raw model key + make aliases visible/manageable
The headline bug: aliases created from the modal NEVER resolved. Create
sent the normalized display name ('Sword', 'Uchiha Sasuke') while
resolution keys on the raw booru model key ('sword', 'uchiha_sasuke',
case-sensitive) — so the mapping was stored under a key nothing looks up,
and the prediction kept reappearing unaliased. The raw key wasn't even in
the /suggestions response, so the modal couldn't send it.
- Suggestion now carries raw_name (the model key an alias must use) and
via_alias (surfaced via an operator alias); both serialized by the API.
- Modal alias-create sends raw_name, not display_name (the fix). Aliased
suggestions show an 'alias' badge and a 'Remove alias' action; 'Treat as
alias for…' is hidden for centroid hits (no model key) and already-aliased
rows.
- Tag-side management: TagCard ⋮ → 'Aliases…' opens a dialog listing the
model keys that fold into a tag, with remove (GET /api/tags/<id>/aliases +
AliasService.list_for_tag). Creation stays in the modal suggestion flow.
Tests: full API round-trip locking the raw-key contract (raw_name exposed →
alias authored with it → resolves + via_alias on a later image);
list_for_tag (service + API); via_alias/raw_name on the existing service
suggestion tests. No migration.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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bvandeusen
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2c544ad5af |
feat(browse): sticky tabs + per-tab search bar (server-side, scope-aware)
The Browse tab nav scrolled away (operator didn't know it existed) and Posts had no search. Roll the tab strip + a shared search field into one sticky block pinned under the 64px TopNav. - Posts gains server-side text search: PostFeedService.scroll()/around() + /api/posts accept q (ILIKE over post_title OR description), applied INSIDE the artist/platform WHERE so search stays scoped to the active filter. Scope shown as clearable chips next to the search field. - Artists/Tags search consolidates into the sticky bar: their inner search boxes are removed; they react to route.query.q (q is deep- linkable, e.g. /browse?tab=posts&q=foo). Platform/kind filters stay. - Posts empty state now distinguishes 'no matches' from 'no posts yet'. Tests: posts q-search matches title|description and stays artist-scoped (service); q passthrough (api). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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013b9d7f06 |
feat(series): operator-set sparse page numbers + gap blocks (#789 tweak)
Replaces the auto-renumbered 1..N position key with operator-OWNED page numbers: sparse, gaps allowed, editable, never auto-renumbered. Order follows the numbers; unnumbered pages sort to the tail. This is the fix for the model that clobbered hand-set numbers on the flatten — numbers are now data, not a derived sequence. - series_service: drop the renumber-on-reorder/remove; order by page_number NULLS LAST; new set_page_number(image_id, n|None); list_pages returns `gaps` (one entry per missing-number run) + each pending group's parsed `start_page`; set_cover renumbers below the current min; place_pending(image_ids, start_page) numbers placed pages sequentially from the start (drop junk first → numbers line up); add_post stamps the parsed start on staged pages. - api/tags: POST /series/<id>/pages/number (set one page's number); /pending/ place takes start_page; removed /reorder. - frontend: per-card editable number input; one gap block per gap with drop-on-edge to assign the adjacent number (middle → type); append drop zone; pending tray gets a "from page N" field + "Place from page N". - tests reworked: sparse numbers + gaps, place-from-start, set-page-number route. No migration; nothing destructive. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |