dev
100 Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
 bvandeusen
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a3bc98a53c |
feat(translation): Post translation columns + settings + migration (#143 step 1)
Post gains post_title_translated / description_translated / translated_source_lang / translation_engine_version / translated_at — filled by the translate sweep so viewing is instant. ImportSettings gains translation_enabled (OFF by default), interpreter_base_url (EMPTY — no default host; the operator points it at their own Interpreter proxy behind a reverse proxy) and translation_target_lang (en), exposed + validated via /settings/import. Migration 0083. Settings defaults + patch + validation test. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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ab63d94249 |
feat(ml): presentation auto-hide settings + review table (#141 step 3)
MLSettings gains presentation_auto_apply_enabled / _threshold (default 0.90) + presentation_conflict_threshold (default 0.50): banner/editor auto-hide with a FLAT threshold (decoupled from content-head graduation), plus the "also looks like content" conflict cut. New presentation_review table (image, presentation tag, conflict tag + score, created/resolved_at) records auto-hides flagged for review. Migration 0082 (columns + table), ml_admin API (editable + get_settings + _validate bounds), settings roundtrip/bounds test. The sweep that reads these knobs + the Settings UI land in step 4. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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7d3a3b4a83 |
revert(ml): keep head auto-apply precision at 0.97 (operator: general tuning was fine)
Milestone 139 raised head_auto_apply_precision 0.97→0.98; operator confirmed the general-tag confidence was already well tuned, so revert that. The support floor (min_positives 30→50) and CCIP match confidence (0.92→0.95) stay. Migration 0081 (not yet deployed) edited to drop the precision bump. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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cbc3e11a53 |
feat(ml): stricter auto-apply defaults to cut misfires (milestone 139)
head_auto_apply_precision 0.97→0.98, head_auto_apply_min_positives 30→50, ccip_auto_apply_threshold 0.92→0.95 (operator-asked). Model defaults change for fresh installs; migration 0081 bumps the existing singleton row IFF still at the old default (won't clobber a deliberate operator change). ml_admin bounds already permit these. Fixed a stale comment in the auto-apply test. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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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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bvandeusen
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f24dc81764 |
feat(ccip): schema for precomputed incremental character prototypes (#1317, m138 step 1)
Foundation for making CCIP character references a precomputed, INCREMENTAL artifact instead of a request-path rebuild (kills the per-accept ~4s suggestions stall; cost will scale with change, not library size): - character_prototype: a character's reference CCIP vectors, capped to MLSettings.ccip_prototype_cap so match cost doesn't grow with popularity. - ccip_prototype_state: per-character fingerprint (ref count + max region id) + updated_at → drives per-character incremental rebuilds and the matcher cache's reload-only-what-advanced. - MLSettings.ccip_ref_signature (cheap global change gate) + ccip_prototype_cap. Migration 0079. Schema + models only — the builder service, refresh task/beat, and matcher rewrite land in the following steps. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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62ec70b9e4 |
feat(ml): detector config in MLSettings with working defaults (#134 step 1)
Move the crop-proposer config (per-proposer enable + weights + conf, caps, dedupe IoU) into the DB so it's UI-tunable and can be announced to the GPU agent in the lease (like the embedder model) — no restart, agent env becomes bootstrap-only. Migration 0078 adds the columns with working server_defaults so existing rows + fresh installs crop out-of-the-box with all three proposers ON (operator: default-on): person=yolo11n.pt, anatomy=booru_yolo yolov11m_aa22 (URL, license unstated/private-homelab-OK), panel=mosesb best.pt. Plain columns, no CHECK enum. Steps 2 (lease announce + agent apply) and 3 (Settings UI) follow. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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87d53db0cb |
feat(artist): editable display name + rename surface; drop name-uniqueness (#130 step 1)
First step of decoupling artist identity/storage/display. migration 0077 drops uq_artist_name so the display name is free text (two genuinely different creators can share a name); the slug stays the immutable, unique storage/identity key (the on-disk path component — untouched, so nothing moves). ArtistService.rename + PATCH /api/artists/<id> change the name ONLY. Frontend: inline pencil-edit on the artist header (mirrors TagCard), slug/route unaffected so no navigation. Fixes the operator's 'no surface to rename an artist' + the name-collision fragility. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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0563b2d750 |
feat(pixiv): ledger models + migration 0076 + PixivIngester adapter (#129 step 3)
pixiv_seen_media / pixiv_failed_media mirror the Patreon/SubscribeStar ledgers (keys are always synthesized <illust_id>:p<num> / <illust_id>:ugoira — pximg URLs carry no content hash). PixivIngester wires client/downloader/ ledgers into ingest_core with drift label 'Pixiv app API' and the new body_canary=False opt-out: caption-less pixiv artists are common, so the zero-bodies #862 alarm would false-positive here — the client's response-shape drift checks cover that failure class instead. auth_token joins the uniform adapter constructor (pixiv is the first token-auth native platform). verify_pixiv_credential = one OAuth refresh, no feed walk. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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e9891ee9f3 |
feat(tags): system tags — is_system column, seeded hygiene tags, protection guards
Training hygiene step 1 (milestone #128). Migration 0075 adds tag.is_system and seeds wip / banner / editor screenshot (kind=general), ADOPTING an existing same-(name,kind) tag case-insensitively instead of duplicating. These rows drive the upcoming training exclusions, so they are protected: rename and merge-away refuse system tags (merge-INTO stays allowed — folding an operator's old hygiene tag into the system row is the intended move; merge is the only tag-delete path, so that guard covers deletion). is_system rides every tag serialization. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM |
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bvandeusen
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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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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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c22f37d64d |
feat(gallery): sort by earliest post date across all posts (new default)
The gallery's newest/oldest sort keys off image_record.effective_date = COALESCE(primary post's post_date, created_at). The primary post is often the repost/download the file came from, so the grid led with download dates rather than when content was first posted (operator-flagged). Add a second materialized sort key, earliest_post_date = MIN(post_date) across ALL of an image's provenance posts (every post it appears in), else created_at — the original publish date. Mirrors the effective_date pattern so the sort stays a forward index scan. - alembic 0071: add earliest_post_date + index (DESC, id DESC); backfill created_at baseline then MIN over image_provenance ⋈ post. - importer: recompute earliest_post_date whenever a dated post is linked (MIN over the image's provenance, which now includes the just-added row). - gallery_service: new sorts posted_new / posted_old key off earliest_post_date; cursor + year/month grouping follow the active column transparently. - api: accept posted_new|posted_old; DEFAULT is now posted_new so the grid leads with original publish date. newest/oldest (effective_date) still available. - frontend: sort dropdown gains "Newest/Oldest post date" (default Newest post date); existing effective-date sorts relabelled "Newest/Oldest added". - tests: service test asserts posted_new/posted_old key off earliest_post_date; frontend default-sort omission test updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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181f1c6a27 |
perf(gpu-queue): partial indexes + two-phase lease so leasing stays O(batch)
The throughput bottleneck was curator-side, not the network. lease() claimed the lowest-id pending/expired jobs with `... ORDER BY id LIMIT n`, but with only a plain `status` index Postgres walked the primary key from id=1, skipping the entire prefix of already done/error rows before reaching pending ones. As `done` grew (69k+), every lease became an O(done) scan — leasing crawled, the DB saturated, and even /status (the queue GROUP BY count) stalled the agent. - Migration 0070 adds two partial indexes over just the live slice: pending rows indexed by id (hot path), and leased rows by lease_expires_at (crash-recovery + orphan sweep). They stay tiny no matter how large the done/error history. - lease() split into two phases so each uses a partial index: claim pending first (id-ordered, O(batch)); reclaim expired leases only when pending can't fill the batch. Same semantics (SKIP LOCKED, attempts++, expired reclaim). - Model __table_args__ declares the indexes so ORM and schema agree. - Test: a done-prefix at low ids must not stop the lease reaching pending. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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359bc5a283 |
feat(ml): default to SigLIP 2 (new installs) + model dropdown, no free-text (#1203)
- Migration 0069: new installs default to SigLIP 2 (so400m, 512px, 1152-d drop-in) — UPDATE applies ONLY where no image is embedded yet (fresh install), so an existing library is NOT silently invalidated; it switches deliberately via the dropdown → Re-embed → Retrain. Column server_defaults moved to SigLIP 2. - GET /api/ml/embedder-models: server-authoritative supported list (SigLIP 2 512 recommended / 384 faster / SigLIP 1 384 original) so the UI never free-types. - GpuAgentCard: the two name/version text fields → a single model dropdown; Save sets name+version from the picked option (the current model is always selectable even if off-list). - embedder.py DEFAULT_MODEL_NAME unchanged (stays the baked local-dir SigLIP 1) to avoid a local-dir/weights mismatch; SigLIP 2 loads by HF name, cached on the ml-worker's persistent HF_HOME. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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bc6d43d3f2 |
refactor(ml): drop dead tagger/suggestion settings + columns (#1199)
Hygiene follow-up to the Camie retirement (#1189) — these were left inert to bound that change; nothing reads them now. Migration 0068 drops: - ml_settings: tagger_store_floor, tagger_model_version, suggestion_threshold_ character/general (already dead pre-retirement — scoring uses per-head thresholds), video_min_tag_frames (only the deleted video-prediction aggregator used it). - image_record: tagger_model_version (no writer), centroid_scores (dead JSON cache, no reader). Also: ml_admin _EDITABLE/GET/_validate pruned (dropped the store-floor invariant + video_min_tag_frames check); MLThresholdSliders trimmed to a video-embedding card (interval + max frames only); importer no longer resets the dropped cols; download_models drops the Camie fetch; stale CASCADE comments in cleanup_service no longer name the removed tables. Tests updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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485387ff0b |
refactor(ml): retire the Camie tagger + allowlist bulk-apply (#1189)
Heads + CCIP are the tag source and head auto-apply is the earned propagation.
The Camie tagger ran only to feed the allowlist bulk-apply (its ImagePrediction
rows had no other consumer), and the allowlist was a SECOND, un-earned auto-apply
path firing in parallel with heads on every accept — exactly the un-earned spray
the v2 pivot replaced. Retire both.
Behavior change: accepting a suggestion now applies the tag to THAT image only
(source='ml_accepted', a head-training positive) — it no longer allowlists +
fans the tag across the library via Camie. Propagation is heads' earned
auto-apply. (Loses instant cold-start propagation for booru-vocab tags; that was
un-earned and bypassed the precision gate.)
- tag_and_embed is now EMBED-ONLY (no Camie load/infer, no ImagePrediction
writes); backfill enqueues it for images with no embedding.
- Removed: services/ml/tagger.py, apply_allowlist_tags + helpers + daily beat +
every enqueue caller (accept/alias/merge/per-image), api/allowlist.py +
blueprint, ImagePrediction + TagAllowlist models/tables (migration 0067),
AllowlistTable.vue + allowlist store, the accept coverage-projection payload.
- AllowlistService gutted to accept/dismiss/undismiss/reject (the rejection store
the rail still needs); accept returns nothing, API returns {accepted, tag_id}.
- tag merge no longer repoints/triggers the allowlist; _keep_as_alias now keys on
ML-applied image_tag sources (incl. head_auto) instead of the allowlist.
- UI: MLBackfillCard relabelled to embedding-only; accept toast simplified;
MaintenancePanel drops the allowlist tile.
Left for a follow-up hygiene pass (now-inert, harmless): the dead settings
columns (tagger_store_floor, tagger_model_version, suggestion_threshold_*,
video_min_tag_frames), image_record.tagger_model_version, MLThresholdSliders
trim, and the Camie model download in download_models.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
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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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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
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b735432d02 |
feat(gpu): video-ready regions + the HTTP GPU-job queue engine (#114 slice 3)
Answers "how are videos/all media handled by the GPU worker": a job is per ITEM, but the agent fans a VIDEO into per-frame instances (ffmpeg in the agent, the existing cadence), each stored with a timestamp — so a video becomes a BAG of frame embeddings (fixes the mean-embedding muddle) instead of one washed-out vector. Stills → frame_time NULL; animated GIF/WebP treated like short video. - image_region.frame_time (migration 0061, not yet deployed so folded in): the source frame's seconds for video/animated media; NULL for stills. RegionService passes it through. A whole frame is just kind='frame'. - gpu_job + GpuJobService (migration 0062): the durable work list that keeps the desktop agent HTTP-only — enqueue (dedupes (image,task)) / lease (FOR UPDATE SKIP LOCKED, re-claims expired leases so the queue self-heals) / heartbeat / complete / fail (re-queues until MAX_ATTEMPTS then 'error'). The server enqueues; the agent leases+submits over the web API; Redis/Postgres stay private. Tests: enqueue dedupe, lease-then-skip-when-held, expired-lease reclaim, scoped heartbeat, complete, fail-requeue-then-error. region test now covers frame_time. NEXT: the thin HTTP API (lease/submit/heartbeat) + bearer-token auth, then the agent container + control UI. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
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0ea7ecdea5 |
feat(regions): image_region storage + service for the crop pipeline (#114 slice 2)
The storage backbone both crop jobs write to and read from. image_region =
normalized bbox (rx/ry/rw/rh) + kind ('face'/'figure' → CCIP character id;
'concept' → SigLIP head bag) + the crop's embedding (nullable Vector(768) CCIP /
Vector(1152) SigLIP, one per kind) + version stamps for compute-once gating. The
bbox doubles as grounded-tag provenance. Migration 0061.
RegionService.replace_regions (scoped BY KIND so the figure + concept pipelines
don't clobber each other) + get_regions — the GPU agent's results endpoint will
call the writer; the character matcher + bag scorer read. Server-side, no GPU.
Tests: replace/get round-trip, kind-scoped replacement, CCIP vector round-trip.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusen
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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
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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
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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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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
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6e3c5f697f |
feat(ml): tag-eval backend — head-vs-centroid learning-curve eval (persisted)
Slice 1 of milestone #114 (tagging v2). Proves the frozen-embedding + trained- head spine on the operator's own data, reusing the SigLIP embeddings already stored on image_record — no re-embedding, no GPU. Per concept: train a logistic-regression HEAD (positives + negatives = explicit rejections + sampled unlabeled) vs the old single-CENTROID baseline; report cross-validated precision/recall/AP for both, a LEARNING CURVE (AP/F1 as tagged positives grow 10→30→100→300), and example image ids (head-would-suggest / head-doubts-positive) to eyeball. Persisted so the report SURVIVES navigation (operator-flagged): the run + full report live in a new tag_eval_run row (mirrors library_audit_run); the admin card will rehydrate from GET on mount, not transient state. - models.TagEvalRun + migration 0056; runs on the ml queue (only worker with numpy/sklearn) — numpy/sklearn lazy-imported so the API can still enqueue. - services/ml/tag_eval (compute + start helper, one-running guard), tasks.ml .tag_eval_run, api/tag-eval (POST create, GET history light / detail w/ report). - recover_stalled_tag_eval_runs sweep + retention (keep last 20) + 5-min beat (rule 89). scikit-learn added to requirements-ml. - tests: param normalization + the rehydrate read-path + create/conflict. Frontend admin card (trigger + render persisted report) follows next. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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5269cd0709 |
feat(provenance): capture which archive an extracted image came from (#87)
Images pulled out of a .zip/.rar previously kept no record of WHICH archive
they came from — the member->archive link was computed during extraction and
discarded, leaving only image->post. So the provenance modal could only scope
attachments to the whole post, showing every archive a 'High Resolution Files'
bundle carried instead of the one a given file lives in.
- ImageProvenance.from_attachment_id: nullable FK -> post_attachment.id
(SET NULL), migration 0055.
- importer: _import_archive stamps from_attachment_id on every member's
provenance row for the post (new + superseded + deduped members), resolving
the archive's own PostAttachment by (post, sha). Post-pass UPDATE, NULL-only
and idempotent, so it doesn't touch the dedup/supersede branches and the
backfill is safe to re-run. Nested members link to the outer stored archive.
- provenance_service.for_image: when the originating post's provenance row
records from_attachment_id, return ONLY that archive; else fall back to the
primary-post scoping from
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bvandeusen
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f678819093 |
feat(subscribestar): seen/failed ledger models + migration 0054 (#889)
Phase 1, step 1 of moving SubscribeStar off gallery-dl onto the native core ingester (milestone: SubscribeStar native). Mirror of the Patreon ledger: SubscribeStarSeenMedia (skip already-ingested media on routine walks; recovery bypasses) and SubscribeStarFailedMedia (dead-letter so persistently-failing media stops re-burning backfill chunks). Per operator decision, dedicated per-platform tables (not a generalized shared ledger). filehash is String(128): a CDN content hash when the URL carries one, else a synthesized <post_id>:<filename> key. UNIQUE (source_id, filehash) upsert key. Registered in models/__init__; migration 0054 creates both tables (down 0053). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusen
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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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bvandeusen
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f154603811 |
feat(import): Tier-1 video near-dup by duration+aspect (#871)
Videos deduped on sha256 only (pHash is images-only), so a different encode/remux of the same clip imported as a distinct record — the "same video from multiple sources" clutter surfaced by #859. Tier-1 metadata fingerprint: identity = container duration (±1.0s) + matching aspect ratio, scoped to the same artist; quality axis = pixel dimensions (mirrors image pHash: larger_exists→skip+link, smaller_exists→supersede). Codec/bitrate are deliberately NOT part of identity (the point is matching across re-encodes). Tight tolerances because a wrong video merge is destructive. - image_record.duration_seconds (Float, nullable; migration 0052). NULL for images. - safe_probe.probe_video also reads format=duration (one extra ffprobe field on the call that already runs); ProbeResult.duration. - _find_similar_video(duration,w,h,artist) shared by both import pipelines. - _import_media (filesystem/archive path): captures duration, video near-dup branch, persists duration. - attach_in_place (download path — handles #859's videos, previously didn't probe video at all): best-effort probe for dims+duration (LENIENT — never newly rejects a downloaded video on probe failure), video near-dup branch, persists duration. - _supersede carries duration onto the kept row. Reuses SkipReason.duplicate_phash so the existing download/external dup-cleanup (path-safe unlink, #859) applies unchanged. Tests: skip-smaller, supersede-larger (+ duration adopted), and distinct-durations-not-merged (false-merge guard). Follow-up (Phase 2, #871): a backfill to re-probe NULL-duration existing videos so the current library participates in dedup; retroactive merge of existing dups is a separate destructive maintenance action. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> |
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bvandeusen
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96c29c370b |
feat(ingest): localize inline post-body images to local copies (Phase 2)
Render a post body faithfully by serving our stored copies of inline images instead of hotlinking the public CDN. The join key is the CDN filehash (32-hex MD5) shared between a body <img src> and the media URL we downloaded (the same identity extract_media dedups by): - utils.paths.filehash_from_url — one source of truth for the extractor; patreon_client._filehash now delegates so capture- and render-time hashing cannot drift. - ImageRecord gains source_url (provenance) + source_filehash (indexed match key); migration 0051. - the per-media sidecar carries the file's source_url; the importer persists it (NULL-only) on the ImageRecord via _apply_sidecar. - post_feed_service.get_post remaps body <img src> -> /images/<path> for every inline image whose filehash maps to a stored image of THIS artist; unmatched / pre-Phase-2 images keep hotlinking. Pre-existing on-disk images have no filehash yet, so they fall back to hotlinking until re-downloaded; localization is forward-looking. 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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d96918d777 |
feat(posts): extract + record external file-host links (Phase 3)
Capture off-platform links (mega/gdrive/mediafire/dropbox/pixeldrain) embedded in post bodies so they're never silently dropped, and surface them in the post view. The download worker (Phase 4) walks these rows. - link_extract.py: pure extractor — <a href> + bare URLs, unwraps Patreon redirect shims, PRESERVES the full url incl. #fragment (mega's key), dedups. Reusable by every platform (runs off Post.description). - external_link model + migration 0049: post_id/artist_id/host/url/label/status /attempts/last_error/attachment_id/timing; CHECK whitelists (full enum incl. worker statuses up front) + (post_id,url) unique. - importer._sync_external_links: insert-missing on both import paths (_apply_sidecar + upsert_post_record) so a re-import never resets a link's status; runs for all platforms. - post_feed_service.get_post: returns external_links (detail-only). - PostCard: renders the links (host chip + label + status) once expanded. - tests: extractor (5 hosts, fragment, shim unwrap, dedup), importer (record + no-dup on reimport), serializer. Refs FC #830. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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7bb765b6ed |
feat(series): pending staging for add-from-post (#789 Phase 2)
Add-from-post no longer appends straight into the run — it STAGES the post's
pages as pending (per-page status; page_number NULL), grouped by source post,
so the operator drops junk (text-free alts, bumpers) and places the keepers
into the sequence with clean series-global numbering.
- migration 0048: series_page.status ('placed' default | 'pending') + nullable
page_number.
- series_service: placed/pending split everywhere (list_pages returns the
placed run + a `pending` section grouped by source post; reorder/cover/
list_series operate on placed only); add_post stages pending; new
place_pending(image_ids, before_image_id=None) flips pending→placed spliced
before a page (or appended) and renumbers; junk removal reuses remove_images.
- api/tags: /add-post now returns staged count; new POST /series/<id>/pending/
place.
- frontend: PostSeriesMenu navigates to the series after staging; seriesManage
store surfaces `pending` + placePending; SeriesManageView gains a pending
tray (per-post groups, place-all / place-one / drop-junk).
- tests: pending staging, place (append + insert-before), ignore-already-
placed, drop-junk, route guard; updated add_post + match-accept expectations.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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bvandeusen
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59746d213d |
feat(series): flat series sequence + cosmetic chapter dividers (#789 Phase 1)
Reframe a series from "ordered chapters that own pages" to ONE flat, series-global ordered run of pages with optional cosmetic chapter DIVIDERS over it. A chapter no longer wraps content — it's a labeled divider anchored to the page that begins it; a page's chapter is derived as the nearest preceding divider. This is what lets installments assembled from multiple sources sit in one continuous, correctly-numbered sequence (operator's Goblin Juice case). - migration 0047: flatten each series to a series-global page_number (preserving today's reading order); convert each existing chapter to a divider anchored at its first page (keeping title/stated_part); drop series_page.chapter_id; reshape series_chapter (anchor_page_id UNIQUE FK, drop chapter_number/is_placeholder/stated_page_start/end). Loss-safe for content; drops empty placeholder chapters + a redundant page-1 divider. - series_page: page_number is now the series-global order; no chapter_id. - series_chapter: anchored divider (anchor_page_id, title, stated_part). - series_service: flat list_pages (one run + derived dividers + per-page source_post + part_gaps), series-wide reorder/renumber, divider CRUD (create/update/move/delete); retired per-chapter reorder/merge/placement. - api/tags: drop chapter_id from add; /chapters endpoints are divider create/update/delete (removed chapter reorder/merge/page-reorder). - series_match_service: series "end" reads max(series_page.stated_page); accept appends via add_post. tag_service series-merge appends src's pages after tgt's max so the merged series stays one clean run. - frontend: seriesManage store + SeriesManageView → one continuous drag-reorder grid with inline divider bars + series-global page numbers; reader walks the flat run, headings from dividers; PostSeriesMenu copy. - tests reworked across the series suite for the divider model. Phase 2 (pending staging for add-from-post) is separate. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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3610ba495f |
feat(ml): drop image_record.tagger_predictions — image_prediction is sole store (#768 step 3)
Read cutover verified in prod (suggestions + allowlist read image_prediction; backfill complete at 908k rows / 51k images). Removes the old JSON column and everything that fed it: - ImageRecord.tagger_predictions column removed; migration 0046 DROPs it. tagger_model_version kept as the "tagged / current?" signal the backfill sweep reads (needs-tagging check switched to tagger_model_version IS NULL). - tag_and_embed no longer dual-writes the JSON — image_prediction is the only write path. - importer re-import reset drops the JSON line (image_prediction rows are already deleted on re-import). - Retired the one-time #768 backfill task + the #764 prune task, their admin endpoints, and their Maintenance cards (Backfill/PrunePredictionsCard). - Tests seed/assert via image_prediction; stale column refs removed. Disk reclaim is NOT automatic: DROP COLUMN is a catalog change. Run `VACUUM FULL image_record` off-hours afterward to return the ~100 GB to the OS so DB backups go small (#739). image_prediction (~90 MB) stays in pg_dump — it's the source of truth now. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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65211a3f2f |
fix(migration): make 0045 DDL-only; backfill image_prediction via batched task (#768)
The inline INSERT…SELECT backfill in migration 0045 wrapped the table creation and a ~100 GB pass over image_record.tagger_predictions in one transaction: nothing committed until the end, it was unmonitorable, and an earlier MATERIALIZED-CTE form spilled the full 100 GB to temp on NFS. A deploy got stuck on it for ~2h with image_prediction never appearing. Split the concerns: - 0045 now creates ONLY the table + indexes (instant DDL → web boots). - New backend.app.tasks.admin.backfill_image_predictions_task copies the >= store-floor predictions from the JSON into image_prediction, batched by id window and committed per chunk: live progress, resumable (re-enqueues from the last committed id), idempotent (ON CONFLICT DO NOTHING). json_each stays in the DB executor streaming each window — no Python-side 100 GB load, no materialization. - POST /api/admin/maintenance/backfill-predictions + a Maintenance-tab card to trigger the one-time run after upgrading. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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e6d5f67f11 |
perf(migration): 0045 streams json_each via inline CASE guard (no temp spill)
The MATERIALIZED-CTE scalar guard forced Postgres to materialize all object rows with their full JSON (~100 GB) to temp before json_each — on NFS that's a huge spill and pathologically slow (risks disk-full). Replace with an inline CASE that feeds json_each an empty object for non-object rows: same scalar guard, but a single streaming pass with no materialization. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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a712cef92d |
fix(migration): 0045 backfill guards json_each against non-object rows
Some image_record rows store tagger_predictions as a JSON scalar/null rather than an object; json_each throws 'cannot deconstruct a scalar' on those, rolling back the whole migration. Filter to json_typeof = 'object' in a MATERIALIZED CTE so the guard runs before json_each ever evaluates a scalar. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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75eab188c8 |
fix(migration): 0045 backfill filters to >= store floor (supersedes #764 prune)
The #764 in-place prune (rewrite tagger_predictions to >=0.70) is too slow on 100 GB of TOAST and fails at its soft limit (interrupts a query mid-flight -> 'another command is already in progress'). #768 supersedes it: extract only the >=floor predictions into image_prediction via this set-based backfill, then drop the column (step 3) — reading 100 GB once + writing ~840k small rows beats rewriting 100 GB in place. So this backfill no longer assumes the prune ran: it filters by ml_settings.tagger_store_floor (default 0.70) itself, handling the full or partially-pruned JSON identically. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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79089b50b0 |
feat(ml): image_prediction table + backfill + dual-write (#768 step 1)
Normalize tagger predictions out of the image_record.tagger_predictions JSON blob into a queryable per-prediction table. Step 1 of the cutover (expand): additive + low-risk — reads still use the JSON, this just adds the table and keeps it populated. - ImagePrediction(image_record_id, raw_name, category, score) — stores the RAW tagger vocab name (not tag_id) so read-time alias→canonical resolution is unchanged. Indexed for per-image reads + by (raw_name, score). - Migration 0045: create table + set-based backfill from the JSON via json_each (fast post-#764-prune). The old column stays (vestigial) and is dropped in a later follow-up — DROP needs an ACCESS EXCLUSIVE lock on the hot image_record table, so it waits for a quiesced-worker window. - tag_and_embed dual-writes the rows (delete-then-insert, idempotent); tagger_store_floor already applied in infer(). Next: switch suggestion + allowlist reads to the table, then drop the JSON write. Plan-task #768. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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3f92669f12 |
feat(ml): DB-backed tagger_store_floor (default 0.70), the ingest confidence floor
Promotes the prediction store-floor from the TAGGER_STORE_FLOOR env (default 0.05) to a DB-backed, Settings-UI-tunable ml_settings column (default 0.70). Storing every tag down to 0.05 from a ~10k-tag tagger is what grew image_record's TOAST to ~100 GB; the suggestion path already filters at 0.70 and the centroid/learned path covers lower-confidence preferred tags, so the sub-0.70 tail is redundant. Foundation for plan-task #764 (backfill + reclaim land next; this only changes the write gate for NEW imports). - ml_settings.tagger_store_floor (migration 0044, default 0.70) - tagger.Tagger.infer(store_floor=...); ml task passes settings.tagger_store_floor - ML admin GET/PATCH expose it; PATCH rejects a category suggestion threshold below the floor (nothing below the floor is stored, so the gap surfaces nothing) — server backstop for the UI slider clamp - Settings → ML: store-floor slider + caption; category sliders min-bound to it Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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a8f624a0f1 |
fix(posts): link duplicate items to every post + prune bare shells
The native Patreon backfill flooded the feed with bare 'Post <id>' shells (1589 for Anduo). Root cause: PostAttachment.sha256 was GLOBALLY unique, so a non-art file reused across posts only ever linked to the first one, and _capture_attachment created the Post before that dedup check — leaving later posts with no image and no attachment. Duplicate IMAGES had the mirror gap: attach_in_place returned duplicate_hash/duplicate_phash before _apply_sidecar, so the second post got no provenance row, and the feed only rendered via primary_post_id (one post per image). Operator requirement: a duplicate item must show on EVERY post it appears in. Unify the fix as link-not-suppress: - importer: on duplicate_hash / duplicate_phash(larger_exists), append an image_provenance row for the new post (keep primary on the first). Both the download path (attach_in_place) and the filesystem path (_import_media). - post_feed_service: render thumbnails by image_provenance UNION primary_post_id, so a cross-posted image shows on every post (and legacy primary-only images still show). - PostAttachment: per-post uniqueness — drop UNIQUE(sha256), add partial UNIQUE(post_id, sha256) + partial UNIQUE(sha256) WHERE post_id IS NULL (migration 0043); _capture_attachment dedups per-(post,sha) over the shared sha-addressed blob, so no post is left bare. - cleanup: new prune-bare-posts maintenance action (cleanup_service _bare_post_conditions shared by preview/count/delete per preview/apply parity; admin endpoint; PostMaintenanceCard). Deletes posts with zero image links (primary or provenance) AND zero attachments. Run after the feed fix so a hidden provenance link spares the post instead of deleting it. Tests: dup image shows on both posts; dup attachment shows on both posts; feed renders provenance-linked duplicates; prune-bare delete-path == preview. Operator redeploys (migration 0043) then runs the prune to clear the shells. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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8e98e79968 |
fix(alembic): lock_timeout on migrations, drop the advisory lock
Reverses the advisory-lock approach (
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bvandeusen
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978959bdc4 |
feat(series): manage-view redesign — big pages, editable Part #, slide-over picker (FC-6.4)
Operator feedback: thumbnails too small to judge order, no obvious way to mark
'this installment is Part 2', and the permanent two-pane picker was busy and
competed with the ordering work.
- Full-width parts, each a card with a big page grid (150px, contain so whole
pages are visible) and drag-to-reorder; positional page number as a badge.
- Editable Part # (hero field) backed by new series_chapter.stated_part —
separate from the auto-managed chapter_number, mirroring the page_number vs
stated_page split so reorder/delete renumbering can't wipe a hand-set part.
Missing-Part hints when consecutive parts' stated_part jump >1.
- Each part labels its source post (derived from pages' primary_post_id) and
shows the printed-page range with clear labels.
- Picker demoted to an on-demand right slide-over ('Add pages') with a target-
part selector; part actions (move/merge/delete) collapsed into an overflow ⋮.
alembic 0042 adds series_chapter.stated_part (nullable int).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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bvandeusen
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7309d1d6d4 |
fix(alembic): serialize concurrent migrators with an advisory lock
Every web replica runs 'alembic upgrade head' in its entrypoint, so under docker stack deploy two replicas can boot at once and race the same DDL — 0040 raced in prod (operator-flagged 2026-06-07): one backend wedged on the series_page lock while a second tried to re-CREATE series_chapter, and the loser died with AdminShutdown, crash-looping the web service. Wrap run_migrations() in a transaction-scoped pg_advisory_xact_lock acquired BEFORE the version table is read. The first replica to reach it migrates and holds the lock for the whole upgrade; siblings block, then find the version already at head and apply nothing. Works regardless of replica count and needs no Swarm depends_on ordering (which stack deploy ignores anyway). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
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c0fd80e694 |
feat(series): assisted-continuation matcher + suggestion queue — backend (FC-6.3)
Confirm-only "this post may continue this series" matcher. - series_suggestion table (post_id, series_tag_id, score, signals jsonb, status pending|added|dismissed, UNIQUE(post,series)); migration 0041 + two settings knobs (series_suggest_enabled, series_suggest_threshold). - series_match_service: weighted additive score (title-stem / same-artist / page-continuity / shared-distinctive-tags), no single signal gating. The title "pattern" is derived on the fly from the post titles already in a series, so it sharpens as more are confirmed (no persisted state to drift). Candidates are bounded to the post's artist. match_post upserts pending suggestions (UNIQUE + on-conflict, respecting prior added/dismissed decisions). - accept reuses add_post_as_chapter then marks 'added'; dismiss marks 'dismissed'. - rescan_series_suggestions_task: settings-gated, time-boxed + self-resuming from a post-id cursor (maintenance_long lane), like normalize_tags_task. - API: GET /series/suggestions, POST .../<id>/accept|dismiss, POST .../rescan. - Settings: enabled + threshold exposed via /settings/import. - Tests: pure scoring helpers + matcher/accept/dismiss/rescan lifecycle + UNIQUE dedup. Frontend (Suggestions tab + settings card) lands next. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |