359bc5a28385cc7b70469421a515b046d71c8608
85 Commits
| Author | SHA1 | Message | Date | |
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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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
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
|
8e98e79968 |
fix(alembic): lock_timeout on migrations, drop the advisory lock
Reverses the advisory-lock approach (
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bvandeusen
|
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
|
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
|
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> |
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bvandeusen
|
1804a2c622 |
feat(series): chapter layer over series_page — backend (FC-6.1)
Adds an ordered chapter layer to series. Reading order becomes (series_chapter.chapter_number, series_page.page_number); a chapter may be a placeholder reserving a slot, and carries an optional parsed stated-page range used to flag missing-page gaps. An image still lives in at most one series ⇒ one chapter (image_id stays UNIQUE). - models: series_chapter; series_page gains chapter_id (NOT NULL, cascade) + stated_page. Migration 0040 backfills every existing series into one auto-chapter holding its current flat pages — no data loss. - SeriesService: chapter CRUD (create/update/reorder/delete/merge), page→chapter assignment, reorder_pages, chapter-aware set_cover; list_pages now returns chapters[] + gaps[] alongside a back-compat flat pages[]. Legacy series-wide reorder operates on the single default chapter and rejects multi-chapter series. - API: chapter endpoints under /api/series/<tag>/chapters; POST pages accepts an optional chapter_id. - TagService.merge now repoints series_chapter too, so a merged series' chapters (and their pages) survive the source tag's deletion instead of cascading away. - Tests: new chapter suite; updated the 4 direct SeriesPage(...) constructions to supply chapter_id. Frontend (chapter-aware manage view + reader) lands next; until then the existing UI keeps working via the flat pages[] + single default chapter. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
f2e9ae07dc |
fix(audit): chunk + self-resume library scans (stop the 2h queue-hog timeouts)
scan_library_for_rule ran one 2-hour pass that timed out on large libraries and held the concurrency-1 maintenance queue the whole time, starving vacuum/backup/ normalize (operator-flagged — it was the dominant entry in the 24h failures). It now runs ~10-min chunks and re-enqueues itself until the library is exhausted, matching the operator's preferred pattern (reasonable timeout → retry queued → other things process between). New columns (alembic 0039): resume_after_id persists the keyset cursor so a chunk continues where the last left off; last_progress_at lets the recovery sweep tell a progressing multi- chunk audit from a dead one (it now measures staleness from last_progress_at, not started_at). Matches accumulate across chunks. soft/hard limits dropped 2h→15/16.7 min so the in-chunk budget fires first; a soft-limit backstop re-enqueues to resume instead of erroring the whole run. Tests: time-box → re-enqueue (status stays running); resume carries prior matches and appends new ones. Existing full-scan tests unchanged (small sets finish in one chunk). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
7a872a3619 |
feat(patreon): dead-letter ledger for permanently-failing media — #705 step 2 (#7)
A media that fails every walk (404'd CDN, deleted post, geo-blocked Mux, persistently-corrupt bytes) used to re-error forever and re-burn chunks. New `patreon_failed_media` table (alembic 0038, chains 0037) records per-media attempts; once attempts reach DEAD_LETTER_THRESHOLD (3) the ingester skips it on routine tick/backfill walks (tier-1.5, folded into the seen/skip predicate). Recovery BYPASSES it (the operator's "try everything again" re-attempts dead media). A clean download clears the row (recovered); errors/quarantines upsert-increment it. Surfaced as run_stats.dead_lettered_count. - New PatreonFailedMedia model + migration; ingester _dead_keys / _record_failures (on_conflict increment) / _clear_failures. - skip = seen | dead (empty in recovery); failures recorded post-fetch on short sessions (same pattern as the seen-ledger). Tests: a media erroring 3× is dead-lettered + skipped (no download attempt); recovery re-attempts a dead media and clears it on success; a clean download clears a sub-threshold failure. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
6222928746 |
feat(patreon): seen-ledger table + model — ingester build step 2a (plan #697)
patreon_seen_media(source_id, filehash, post_id, seen_at), UNIQUE(source_id, filehash) — our own queryable replacement for gallery-dl's archive.sqlite3. Routine walks skip seen media; recovery mode bypasses the ledger. filehash is a 32-hex CDN MD5 or a video:<post>:<media> sentinel (String(128)). alembic 0037 (← 0036). Integration test covers dedup + savepoint recovery. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
79cd1234e2 |
feat(gallery): visual 'more like this' search (Phase 3 backend)
GalleryService.similar() ranks images by pgvector cosine distance to a source image's precomputed SigLIP embedding — no query-time ML inference. Composes with the Phase-1/2 scope filters (AND) but replaces the date sort (always nearest-first, bounded top-N, no cursor). Returns None for a missing source (→404), [] for a source with no embedding (video / pending ML); excludes self and NULL-embedding rows. New GET /api/gallery/similar?similar_to=<id>&limit=N. Image-detail payload gains has_embedding so the UI can hide the surface. Alembic 0036 adds an HNSW vector_cosine_ops index on siglip_embedding (1152<2000 dims) so the search is sub-50ms ANN instead of a full scan; one-time ~30-60s build over existing embeddings on deploy. Shared _gallery_images/_image_json helpers de-dup the scroll/similar builders. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
e05e0b9f37 |
perf(gallery): materialize indexed effective_date sort key
The gallery cursored on COALESCE(post.post_date, image_record.created_at) across the Post outer join — an expression spanning two tables that no index can serve, so every /scroll sorted a large slice of the library (and the old frontend fired ten serially). Materialize it: - image_record.effective_date column + ix_image_record_effective_date (effective_date DESC, id DESC); alembic 0035 backfills COALESCE(primary post's post_date, created_at) for existing rows. - gallery_service._effective_date_col() now returns the column, so scroll / timeline / jump / neighbors all order off the index instead of re-deriving the COALESCE. _neighbors reads record.effective_date directly (drops an extra Post lookup). - importer._apply_sidecar maintains it: when a primary post with a date is linked, effective_date = post.post_date; plain inserts keep the created_at-equivalent server default. Tests: sidecar import asserts effective_date == post.post_date; gallery ordering/timeline/jump test seeds set effective_date alongside created_at. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusen
|
b65e956ad2 |
feat(artist): "new since last visit" badge + banner
Per-artist "+N" accent pill on the artists directory and a "N new since last visit" banner inside ArtistView. Counts new IMAGES (not posts) so multi-image posts increment correctly. - alembic 0034: artist_visit (artist_id PK, last_viewed_at NOT NULL). Seeds every existing artist with last_viewed_at=NOW() so the badge starts at 0 across the board — no noisy "5000 unseen images" on first deploy. - ArtistService.find_or_create autoseeds a visit row alongside new artists, so freshly imported content doesn't read as unseen. - ArtistService.overview reads pre-visit last_viewed_at, counts images created since, then atomically UPSERTs last_viewed_at=NOW() via postgres ON CONFLICT DO UPDATE (no SELECT-then-INSERT race per reference_scalar_one_or_none_duplicates). Returns the pre-update count as `unseen_count_at_visit` so the banner has data. - ArtistDirectoryService.list_artists adds an `unseen_count` aggregate to each card via LEFT JOIN artist_visit + conditional COUNT. NULL last_viewed_at (artist created before this code shipped) defensively counts as "never visited" → all images unseen. - Frontend: ArtistCard renders an accent pill in the preview-strip corner when unseen_count > 0 (capped at 99+); ArtistView shows a closable v-alert banner on initial load when unseen_count_at_visit > 0, re-arms on slug change. Single-row-per-artist (no user_id) — rule #47 multi-user ACL is aspirational; widens to (user_id, artist_id) PK when User lands, per rule #22. Scribe plan #597. |
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bvandeusen
|
1fd594baaf |
chore(ml): suggestion_threshold default 0.50 → 0.70
Operator-flagged 2026-06-02 — the 0.50 default (set on 2026-06-01) surfaces too many low-confidence picks in the modal's Suggestions rail. 0.70 keeps the rail signal-rich while still showing more than the original 0.95 (which hid almost everything). Alembic 0033 updates the singleton row conditionally — only rows still at the old 0.50 default flip to 0.70. Operators who tuned to some other value via Settings → ML keep their pick. Settings UI already exposes both sliders (MLThresholdSliders.vue), so further tuning continues to work without a deploy. |
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bvandeusen
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f05aaa707b |
fix(audit-g5d): surface ErrorType taxonomy on FailingSourcesCard
Alembic 0032 adds Source.error_type (varchar(32), indexed).
_update_source_health stamps it alongside last_error on status='error'
and clears it on 'ok'. SourceRecord/to_dict exposes it.
FailingSourcesCard renders a colored chip next to the consecutive-
failures count, with a tooltip explaining the suggested operator
action. Color reflects intent:
- warning (yellow) — operator action needed (auth_error)
- info (blue) — backend-paced (rate_limited / timeout /
network_error / partial / tier_limited)
- error (red) — likely terminal without intervention
(not_found / access_denied / validation_failed /
unsupported_url / http_error / unknown_error)
Audit 2026-06-02: the backend computed 13 ErrorType categories but
only the free-text last_error reached the operator. Bulk-triage by
class ("all auth_error → rotate cookies", "12 rate_limited → just
wait") required opening Logs per row.
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bvandeusen
|
19aece1fc4 |
feat(download): tick/backfill modes + partial-success classifier (plan #544)
Routine subscription polls walked the entire post history every tick
even when nothing had changed, because gallery-dl's default `skip: True`
continues iterating archived posts. A creator with ~550 archived posts
(Knuxy patreon) saturates the 870s wall-clock cap before completing,
even with zero downloads needed. Plus, a tier-limited run that
downloaded hundreds of files but ran out the clock should be a
warning, not an error.
Two coupled changes, both operator-flagged 2026-06-01:
* **Tick mode (default, cron polls).** New `TICK_SKIP_VALUE = "exit:20"`
asks gallery-dl to exit after 20 contiguous archived items. Fresh
subscriptions + new-content cases still walk normally; established
subscription with zero new content exits in ~30s of HEAD requests
instead of pegging the timeout. 20 (not 5) gives headroom against
paywall warnings interleaving with archived items.
* **Backfill mode (explicit, operator-triggered).** Sticky for N runs
via new `Source.backfill_runs_remaining` (alembic 0031). While > 0,
downloads use `skip: True` + 1800s timeout. Auto-decrements per run
with early-reset to 0 when a clean run finds zero files (queue
drained). N defaults to 3 — multiple runs give the system enough
budget to finish a deep walk across timeout boundaries. New
`POST /api/sources/{id}/backfill` arms the source; "Deep scan"
button on each SourceRow (chip shows remaining count) wires it.
Plus partial-success classifier: non-zero gallery-dl exit + ≥1 file
downloaded + no source-level error fires `ErrorType.PARTIAL`, which
download_service maps to `status=\"ok\"`. The run did real work; the
next tick continues via gallery-dl's archive. No more red events for
"timed out mid-walk after downloading 300 files."
Retires `SourceConfig.skip_existing` — skip value is now derived from
the source state and passed as a separate `skip_value` parameter
through download() / _build_config_for_source(). `GD_DEFAULTS` drops
the now-dead key (was inert data after this refactor).
Tests cover:
* tick + backfill skip-value emission in _build_config_for_source
* PARTIAL classifier branch + TIER_LIMITED-wins-over-PARTIAL ordering
* SourceService.set_backfill_runs validation + persistence
* /api/sources/{id}/backfill 200/400/404 paths
* download_service auto-decrement / auto-reset / tick-mode-no-touch
* PARTIAL → status=ok in the orchestrator (no consecutive_failures bump)
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bvandeusen
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644d538bab | fix(migration): use canonical fk_<table>_<col>_<ref> names per Base naming_convention | ||
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bvandeusen
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2f66de2928 |
feat(model): nullable Post.source_id + denormalized Post.artist_id; retire sidecar synthetics
Operator-asked 2026-06-01 after the Dymkens orphan investigation (Scribe plan #540). The pre-2030 sidecar-synthetic Source pattern (`sidecar:<platform>:<slug>` enabled=false rows) existed solely to satisfy `Post.source_id NOT NULL`, and leaked into the Subscriptions UI as phantom subscriptions. Now the data model says what's true: filesystem-imported content with no live subscription has NULL source_id, full stop. ## Schema (alembic 0030) - `post.artist_id` — NEW NOT NULL FK to artist (CASCADE). Backfilled from source.artist_id in the migration. Indexed for the artist-filter queries. - `post.source_id` — NOT NULL → nullable; FK ondelete CASCADE → SET NULL. Deleting a Source detaches its Posts instead of destroying archived content (subscription ends, archive stays). - `image_provenance.source_id` — same nullable + SET NULL. - Partial unique index `uq_post_artist_external_id_null_source` on (artist_id, external_post_id) WHERE source_id IS NULL — guards filesystem-import dedup since the existing source-bound unique ignores NULLs (Postgres treats NULL != NULL). - Sidecar synthetic Sources deleted: NULL out FKs in post, image_provenance first, then DELETE FROM source WHERE url LIKE 'sidecar:%'. The Dymkens cleanup. ## Model + service changes - `Post.source_id` → `Mapped[int | None]`; new `Post.artist_id` denormalized. - `ImageProvenance.source_id` → `Mapped[int | None]`. - Importer: `_source_for_sidecar` (synthetic-creating) → `_lookup_source_for_sidecar` (returns None when no subscription). `_find_or_create_post` takes required `artist_id`; matches on (source_id, external_post_id) for source-bound posts or (artist_id, external_post_id) for NULL-source posts. - Service queries switched off the Source detour to use Post.artist_id directly: post_feed_service.scroll/around/get_post (LEFT JOIN to Source so NULL-source posts surface); artist_service date_row/ activity/post_count; provenance_service.for_image/for_post (LEFT JOIN); gallery_service._provenance_exists_where_artist via Post.artist_id instead of ImageProvenance.source_id → Source. - `_to_dict` and provenance dict-builders emit `"source": null` for NULL-source rows. ## Frontend - `ProvenancePanel.vue` + `PostCard.vue`: render `e.source?.platform ?? 'filesystem import'` so NULL-source posts get a clear "filesystem import" affordance instead of a NaN crash. ## Tests - `test_importer_upsert_helpers`: removed the four synthetic-anchor tests; added `_find_or_create_post_idempotent_with_null_source` (dedup via the partial unique index) and `_lookup_source_for_sidecar_returns_*` (existing-subscription + none cases). The existing `_find_or_create_post_idempotent` now also passes `artist_id` and asserts it. - 8 other test files updated: every direct `Post(...)` construction gains `artist_id=<artist>.id`. The `_seed_post` helper in `test_post_feed_service` looks up artist_id from the source row so callsites stay one-arg. ## Verification on deploy After alembic 0030 runs: - `SELECT COUNT(*) FROM source WHERE url LIKE 'sidecar:%'` → 0. - `SELECT COUNT(*) FROM post WHERE source_id IS NULL` → count of filesystem-imported posts (Dymkens + any other historical). - Every `post.artist_id` non-null; consistent with source.artist_id for source-bound rows. - Subscriptions tab: no Dymkens phantom row. - Artist detail → Posts/Gallery: Dymkens's content still reachable via Post.artist_id. - Provenance panel renders "filesystem import" chip for NULL-source posts; PostCard same. ## Out of scope - UI to manage/delete orphan NULL-source Posts. Data model is right; UI follows if operator wants it. |
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bvandeusen
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af7b5c95e9 |
feat(modal): autofocus tag input, expand general suggestions, retire copyright/artist categories
Four coupled operator-asked changes to the view modal (Scribe plan #509): 1. **Autofocus tag entry on modal open** — TagAutocomplete grabs focus in onMounted/nextTick so the caret is in the input the moment the modal renders. No click needed to start typing. 2. **General suggestions expanded by default** — SuggestionsPanel's general-category group now mounts with `:default-open="true"`. Operator can collapse if too noisy, but the v1 frame shows them. 3. **Lower general threshold default 0.95 → 0.50** — MLSettings. suggestion_threshold_general default matches character. Alembic 0029 also bumps the existing singleton row's value if it's still at the old 0.95. Operator can re-tune from Settings → ML. 4. **Retire `copyright` + `artist` as ML suggestion categories** — neither feeds a Tag.kind (`artist` retired in FC-2d-vii-c, never really existed as a copyright tag-kind). They were surfaced in the suggestions pipeline + threshold settings UI but had no follow- through. Drop from SURFACED_CATEGORIES, suggestions._threshold_for, ml_admin GET/PATCH allowlist, MLSettings columns (alembic 0029 drops the two columns), frontend CATEGORY_ORDER + CATEGORY_LABELS, SuggestionsPanel.peopleCats, AliasPickerDialog kind-check, and MLThresholdSliders rows. Out of scope (intentional): `tag_kind` Postgres enum still includes `artist` for historic Tag row queryability (per the model comment); no operator pain reported, no enum-shrink needed. Tests: - test_surfaced_categories asserts {character, general}, excludes artist + copyright. - test_threshold_for_artist_is_unsurfaced extended to cover copyright. - test_get_and_patch_settings asserts new 0.50 default and the absent artist + copyright keys in the GET payload. |
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bvandeusen
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6fc8ae3106 |
fix(subscriptions): hide sidecar synthetic Sources + prefer real on lookup
Two coupled bugs surfaced 2026-05-31 by the Subscriptions UI showing "phantom" subscriptions like `sidecar:patreon:dpmaker`: 1. `SourceService.list()` returned every Source, no filter on URL. alembic 0022 (2026-05-26) consolidated old per-post-URL Sources into one canonical row per (artist, platform); when no real campaign URL was salvageable it rewrote the canonical to `sidecar:<plat>:<slug>` enabled=false as a disabled anchor. The UI then listed those anchors as if they were polls — disabled, but visible. Fix: `list()` excludes `url LIKE 'sidecar:%'` by default; `include_synthetic=True` opts back in for admin tooling. 2. `importer._source_for_sidecar` picked the lowest-id Source for (artist, platform). When alembic 0022 had rewritten a per-post row into a synthetic anchor (lower id) AND the operator later added the real subscription (higher id), every gallery-dl download silently attached its Post to the SYNTHETIC instead of the real Source. Fix: prefer a non-`sidecar:%` URL when one exists; fall back to the synthetic; only create a new synthetic when nothing exists for (artist, platform). alembic 0028 is the data half: for every (artist, platform) with both a synthetic AND a real Source, pre-merge Post+ImageProvenance collisions on the canonical, bulk-repoint Posts/ImageProvenance/ DownloadEvent.source_id onto the real Source, and delete the synthetic. Lone synthetics (no real twin) are left intact — they anchor real imported content the operator may still want; the list-filter hides them so they no longer surface as phantoms. |
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bvandeusen
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8649a13118 |
refactor(I5): remove one-and-done GS/IR migration tooling
The GS/IR migration cutover is complete, so the runbook tooling is dead weight. Removed: - services/migrators/ (gs_ingest, ir_ingest, tag_apply, ml_queue, verify, cleanup), tasks/migration.py, api/migrate.py (+ blueprint registration) - MigrationRun model; alembic 0027 drops the migration_run table - frontend LegacyMigrationCard + migration store (+ MaintenancePanel ref) - celery include + task route + celery_signals queue mapping for migration.* - the 1 GB MAX_CONTENT_LENGTH / MAX_FORM_MEMORY override (added solely for the ir_ingest upload) - migration-surface tests (test_api_migrate, test_migration_verify, test_ir_ingest, test_gs_ingest, test_tag_apply) Kept: the alembic schema-migration tests (test_migration_00XX — unrelated) and cleanup_service.py (the permanent artist-cascade/unlink home). Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com> |