20 Commits

Include renames

Author	SHA1	Message	Date
bvandeusenandClaude Opus 5	1e45e2c56c	feat: an open grouping — a later drop joins its post (milestone 388 step E3) ... A synthetic post is no longer sealed at creation. A creator who adds two more variants the next day extends the existing post, its body grows with the new messages, and no rival post appears. That is what makes chat capture read as content trickling in rather than as a stream of separate arrivals. The sweep now runs two passes per source and the ORDER is load-bearing: offer new messages to still-open groups BEFORE founding new ones, because whichever runs first claims a message. E3's three named problems, each answered rather than discovered later: **Bridging.** A candidate near two groups joins NEITHER. Nearest-wins would silently make an arbitrary choice between two posts the operator may already have seen; merging them is worse still, because a merge rewrites history and anything pointing at the absorbed post dangles. Leaving it to found its own group is the recoverable failure. AMBIGUITY_MARGIN is a module constant and deliberately not a setting — it is not a quality dial anyone would tune toward a better feed, and exposing it would invite turning it to zero, which is exactly the silent arbitrary choice it prevents. **Re-surfacing without thrashing.** A grouping has two dates, and which one orders the feed is a real decision, so the feed orders by neither directly. Ordering by when the drop STARTED buries a group that grows a week later under a week of other posts — defeating the point of keeping it open. Ordering by every growth lets a group gaining one image a day live permanently at the top, so chat out-competes authored posts for the front page — the opposite of "post pacing stays front and centre". Instead `resurfaced_at` moves only when growth clears BOTH a minimum-images bar and a cooldown, so a drip-feed updates in place and a genuine second wave resurfaces exactly once. It is NULL on every ordinary post, so the sort key COALESCEs through it without moving anything that is not a grouping. **Reopening forever.** Groups close after a quiet period — artists reuse characters for years, and a group left open indefinitely will eventually absorb something it shouldn't. Openness is DERIVED, not stored: a group is open if it grew (or started) within the window. Lowering the setting closes old groups and raising it reopens them, with nothing to repair either way; a stored closed_at would have needed a sweep to set it and a repair path to ever change the policy. Rule 89 is satisfied structurally rather than by a parallel mechanism: celery_signals writes a TaskRun for every task, which already supplies duration, the 5-minute stalled-run recovery, and retention pruning. What this step owed on top of that was a wall-clock limit (present) and idempotence — re-running the joiner adds nothing, asserted directly rather than left to the unique (image, post) constraint to catch. Two bugs fixed in the writing, one of which my own test would have hit: * `assign_to_group` sorted bare (distance, Post) tuples, which falls through to comparing Posts when two distances tie — and a perfectly symmetric bridge, the exact case the function exists for, would have raised TypeError instead of declining to choose. Now keyed on the distance alone. * The cursor was still built from `post_date or downloaded_at` while the ORDER BY had gained `resurfaced_at`. Two expressions that disagree at a page boundary don't error, they silently skip or repeat rows; both sites now go through one `_post_sort_value`, and a test pages through one row at a time to prove the walk matches the whole list. Image linking is now one shared helper rather than written twice, because creation and joining would otherwise be free to drift on exactly the detail (which post owns the image) that makes a grouping reversible. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LNXXULQDjVZmbuNa2G9mD9	2026-09-10 11:30:17 -04:00
bvandeusenandClaude Opus 5	73eeb7a377	feat: FC authors the post that Discord never wrote (milestone 388 step E2) ... Discord is a delivery channel, not a publisher. One message is not one post, and today every message lands as its own `post` row, so chat lines compete with authored work for the same surface. Rather than demote them into a second-class feed, FC now writes the post itself: one row per DROP, its images the drop's images, its body the messages' text in arrival order. Synthesising a `Post` (rather than inventing a parallel entity) is the whole point — the result is post-shaped by construction, so feed, provenance, translation, attachments and series keep working on it unchanged. The predicate is three axes ANDed, and the time one does the real work: same source AND cosine distance <= threshold AND no gap > window Similarity alone over-groups, and that is the failure that would make this useless: any two pieces of the same character by the same artist sit close in SigLIP space, so a cosine-only rule collapses a month of one character into a single "post". Two details inside the predicate are load-bearing — * distance is measured to the group's SEED, never to the previous member, because chaining lets a group DRIFT: twenty small steps walk from one piece to a completely different one, each hop individually within threshold; * the window is measured between CONSECUTIVE messages, not from the first, so an artist trickling variants out over an evening stays one drop. Why a post-import sweep and not part of ingest. The obvious alternative was to migrate Discord to the native post-first ingester (#1266) and group at capture time. That cannot work: the grouping signal is `siglip_embedding`, which is produced asynchronously AFTER import (tasks/ml.py, the GPU backfill), so at capture time there is nothing to group on. Grouping is necessarily something that happens once the vectors catch up — hence a re-runnable sweep that skips what it cannot yet place, and an hourly (not daily) cadence. The honesty rule, enforced in the schema. `post.synthesized_by` names the grouper; `synthesis_details` records the members, the count, and the thresholds AS THEY WERE (they are operator-tunable, so without that "why did it group these" is unanswerable a month later). Member posts are absorbed, not destroyed — they remain the images' true origin and the audit trail — and `absorbed_by_post_id` is ON DELETE SET NULL, so deleting a synthetic post releases its members back into the feed in one DELETE with no repair step. `post_title` stays NULL deliberately: a synthesised title is the one place this could put words in a creator's mouth. Two guards the first draft would have failed: * the per-run cap took the lowest post IDs, not the oldest posts — DISTINCT ON forces its own ORDER BY, so the sort now happens outside the subquery; * a cap landing mid-drop would have published a truncated group claiming to be a whole drop, so the last group is left for the next run. And one vacuous test caught before it shipped: the support vector perturbed a single component of an all-ones vector, moving it ~1e-6, so every distance assertion passed regardless of what the predicate did. `_vec` now builds a unit vector at a stated angle, where distance is exactly 1 - cos(delta) — rule 167, a guard has to be able to fail. UI (rule 27) follows in the next commit. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01LNXXULQDjVZmbuNa2G9mD9	2026-09-10 11:15:14 -04:00
bvandeusenandClaude Opus 4.8	666b3a2ec8	refactor(ml): DRY pass — shared sweep helpers + table-driven settings (#161) ... Consolidate duplication accrued across the ML tagging + settings backend, behavior-preserving (over-DRY guard applied — the three auto-apply sweep BODIES stay separate; only their shared inner helpers are extracted). - _sigmoid / _conflict_scores / _insert_presentation_review (heads.py): the score→prob transform (6 inlined sites), the presentation conflict signal (2 sites), and the ring-loud PresentationReview insert (2 sites, single- sourced so the mode column can't drift on the shared composite PK). - _applied_or_rejected (training_data.py): the per-tag "applied ∪ rejected" skip-set, byte-identical at 3 sweep sites (heads.py x2, tasks/ml.py ccip). - ccip sweep divergence fixes: import ccip._FIGURE_KINDS + training_data._l2norm instead of local copies that silently drift when the canonical changes. - MLSettings.load / .load_sync classmethods (mirror ImportSettings); route all 8 scalar_one singleton reads through them (the session.get None-path stays). - GET serializers for MLSettings + ImportSettings are now table-driven off the same _EDITABLE tuples PATCH writes, so a new field can't be silently absent from GET (the split that historically dropped fields). - AUTO_APPLY_THRESHOLD_MIN/MAX constant single-sources the [0.5,0.999] operating range across the service clamp + the 5 API validators. - test_ml_dry_helpers.py pins _applied_or_rejected + _sigmoid. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01NsmJSQxnNxGgtM5Yz4GAqi	2026-07-13 21:41:24 -04:00
bvandeusenandClaude Opus 4.8	ad2a5fc5fe	feat(system-tags): process vs chrome groups + WIP provisional auto-apply (#1464) ... Backend for the system-tag behavior refactor (milestone #157). editor screenshot moves from chrome (hidden) to the PROCESS group (shown, like wip); wip+editor gain provisional auto-apply so they stop needing endless manual identification — without a runaway loop. - tag.py: split PRESENTATION_SYSTEM_TAGS → CHROME_SYSTEM_TAGS (banner) + PROCESS_SYSTEM_TAGS (wip, editor screenshot). - heads.py: generalize presentation_auto_apply_sweep → system_tag_auto_apply_sweep (mode chrome|process). Same Guard 1 (skip human/confirmed) + Guard 2 (ring-loud conflict → PresentationReview). process mode uses source 'process_auto' and does NOT hide (hide is a gallery-query effect of group membership). - training_data._AUTO_SOURCES += 'process_auto' → the head never trains on its own auto-applied output; only wip_title/manual train it (the runaway break). - ml_settings: process_auto_apply_enabled (OFF, opt-in) + threshold + conflict threshold. presentation_review.mode ('chrome'|'process'). Migration 0086. - gallery_service: default-hide reads CHROME only (editor now shows); Explore neighbors exclude the whole PROCESS group. - tasks/ml + celery beat: scheduled_process_auto_apply (daily, opt-in); prune covers both modes. - api: ml_admin process_* CRUD+validation; hidden-review returns mode. - tests: rename chrome sweep calls; new test_process_auto_apply (apply, guards, mode flag, no-self-train); gallery test asserts editor now visible. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>	2026-07-12 23:15:59 -04:00
bvandeusenandClaude Opus 4.8	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	2026-07-06 22:59:00 -04:00
bvandeusenandClaude Opus 4.8	ab362bc79c	feat(ml): Settings → Tagging 'Crop proposers' card (#134 step 3) ... Exposes the detector config (per-proposer enable + weights + confidence, caps, dedupe IoU) in Settings → Tagging, backed by MLSettings via /api/ml/settings. ml_admin adds the detector fields to _EDITABLE + GET payload + validation (conf 0..1, caps >=1, IoU 0..1). New CropProposersCard.vue (mirrors HeadsCard) with working defaults pre-filled, per-field live-save (no restart — the agent picks changes up on its next lease), weights-format help, switch-revert on error. Closes milestone #134: all three proposers are on out-of-the-box and tunable in the UI. Test: detector defaults GET + patch round-trip + range validation. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01CDgx8bQS5YrGRK76v8HUnM	2026-07-05 19:51:55 -04:00
bvandeusenandClaude Fable 5	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	2026-07-02 16:53:08 -04:00
bvandeusenandClaude Opus 4.8	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	2026-06-30 16:29:27 -04:00
bvandeusenandClaude Opus 4.8	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	2026-06-30 13:41:25 -04:00
bvandeusenandClaude Opus 4.8	3d77a38a25	refactor(ml): remove the dead per-tag centroid subsystem (#1189) ... The v2 pivot replaced per-tag SigLIP centroids with learned heads + CCIP. Centroids were still recomputed (on every tag merge + a daily beat) but NOTHING read them — suggestions come from heads+CCIP and apply_allowlist_tags applies via Camie predictions, not centroids. Pure dead wiring; remove it. Removed: CentroidService, recompute_centroid/recompute_centroids tasks, the daily beat, POST /api/ml/recompute-centroids, the recompute-on-merge trigger, the tag_reference_embedding table + model, the centroid_similarity_threshold + min_reference_images settings (migration 0066), the CentroidRecomputeCard + its store action + MaintenancePanel tile, and the centroid slider in MLThresholdSliders. _keep_as_alias drops its vestigial has-centroid branch (the allowlist branch already covers "could re-emit"); tag merge no longer clears a table that no longer exists. NOT touched (still live, parallel to heads): the Camie tagger, ImagePrediction, and the allowlist bulk-apply — accepting a suggestion still allowlists + applies it across the library. The tag-eval "centroid" baseline metric is unrelated (in-memory) and stays. (image_record.centroid_scores JSON column also remains — separate legacy field, its own micro-cleanup.) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa	2026-06-30 11:48:09 -04:00
bvandeusenandClaude Opus 4.8	4daa3f2790	feat(ml): operator model swap — GPU re-embed + embedder as a setting (#1190) ... Make the SigLIP embedder an operator choice (drop-in to SigLIP 2: google/siglip2-so400m-patch16-512 is a verified 1152-d model at 512px → no schema change, better small-cue fidelity). A swap = set model + re-embed + retrain, all operator-driven; the GPU agent does the re-embed so it's fast. - settings: embedder_model_name is now a setting (migration 0065) alongside the existing embedder_model_version; both editable + validated (non-empty) in the ml admin API. The server embedder loads by HF name (AutoImageProcessor/Model, model-agnostic), preferring the pre-downloaded local dir for the default so existing deploys don't re-download; rebuilds on a name change. - agent: new 'embed' job = whole-image SigLIP embedding (mean-pool video frames) under the lease-announced model → POST /jobs/submit_embedding writes image_record.siglip_embedding + siglip_model_version. The lease now announces the model FROM THE SETTING (not a constant). - re-embed routing: enqueue_gpu_backfill('embed') selects unembedded + stale- version images; 'siglip' now re-embeds concept crops whose version != current (so a swap re-triggers crops, not just the never-embedded back-catalogue). The CPU ml-worker backfill no longer re-embeds on a version mismatch (it can't churn the library at 512px) — the GPU agent owns version re-embeds. Daily 'embed' + 'siglip' beats self-heal. - scoring: score_image only bags embeddings in the CURRENT model's space (whole- image gated by siglip_model_version, concept regions by embedding_version) so a mid-swap stale vector isn't scored by new-space heads; legacy NULL = current. - UI: GpuAgentCard "Embedding model (advanced)" — edit name/version, Save, and "Re-embed library (GPU)" (queues embed + siglip); points at SigLIP 2. Tests: lease announces model + submit_embedding round-trip; enqueue 'embed' selects stale/unembedded; stale-version excluded from scoring; embedder model settable + empty rejected; siglip gate updated to current-version concept. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa	2026-06-30 10:24:30 -04:00
bvandeusenandClaude Opus 4.8	b91a230f12	feat(ccip): automation + reference quality — keep identity flowing hands-free (#114) ... Works through the optional CCIP ideas + the "keep moving even if I forget" ask: AUTOMATION (no button needed): - Hourly beat auto-enqueues CCIP backfill — new images get embedded (and errored ones retried) on their own; the queue never goes idle waiting for a click. - CCIP auto-apply: a daily sweep tags confident matches (source='ccip_auto') so identity tags keep flowing. ON by default (opt-out, like head auto-apply); ml_settings.ccip_auto_apply_enabled + _threshold (0.92, above the suggest cut), migration 0064. Vectorized (one matmul + reduceat per image), reversible, skips already-applied/rejected. Switch + threshold in the GPU agent card; GET/PATCH /api/ml/settings; auto_applied count in /api/ccip/overview. REFERENCE QUALITY (the over-fire root cause): - character_references now draws ONLY from single-character images — on a multi-character image the tag is image-level, so every figure would otherwise pollute each character's prototypes (a 2-char image tagged 'Velma' made Daphne's figure a Velma reference). This is the contamination behind residual over-firing. - Cached on a cheap signature (char-tag count + ccip-region count/max-id) so the reference load isn't redone on every modal open. Tests: multi-character image not used as a reference; auto-apply tags a confident match as ccip_auto. NEXT (not done, confirmed): comic-panel cropping + SigLIP concept crops ("spot interesting content"). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa	2026-06-29 22:25:40 -04:00
bvandeusenandClaude Opus 4.8	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	2026-06-29 20:41:09 -04:00
bvandeusenandClaude Opus 4.8	74fef908d2	feat(heads): earned auto-apply — sweep mechanism, off by default (#114 auto-apply A) ... Graduated heads can now apply their tag without a human — gated so it's safe: - FIRING GATE: a head fires only when the master switch (head_auto_apply_enabled, default OFF) is on AND it has >= head_auto_apply_min_positives (default 30) clean labels. A precise-looking but under-supported low-N head can't spray tags. - auto_apply_sweep (heads.py): streams every embedded image in chunks, scores against the eligible heads (numpy, no sklearn), applies each head's tag where score >= its auto_apply_threshold and the tag isn't already applied/rejected, with source='head_auto' (distinguishable + reversible). dry_run counts only. - HeadAutoApplyRun (migration 0059) tracks each sweep / preview; apply_head_tags task (ml queue) + scheduled_apply_head_tags daily beat (no-op unless enabled) + recovery sweep + retention(20). - API: POST /api/heads/auto-apply {dry_run} (202 / 409 running / 400 disabled), GET /api/heads/auto-apply (recent runs + per-concept report). Settings head_auto_apply_enabled + min_positives via /api/ml/settings. Tests: sweep applies above threshold, dry-run writes nothing, skips under- supported + ungraduated heads; API disabled/dry-run/conflict guards. NEXT (slice 2): the observability the operator asked for — per-concept misfire (auto-applied-then-removed) + under-fire tracking, time-series snapshots, and a reporting API to tune. Slice 3: the UI (enable, preview, trends). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa	2026-06-29 00:22:54 -04:00
bvandeusenandClaude Opus 4.8	22c3b54746	feat(heads): production per-concept heads — train + score backend (#114 A) ... The eval (#1130) proved the frozen-embedding + trained-head spine; this lands its production form (the first of three slices that make heads the suggestion source, replacing Camie + centroid). - tag_head: one logistic-regression head per general/character concept with enough labelled positives. Weights (pgvector), honest CV-derived suggest threshold + earned-auto-apply point, and per-concept quality metrics. - head_training_run: persisted batch lifecycle (mirrors tag_eval_run) so the admin card shows live + historical status across navigation. - services/ml/heads.py: TRAIN (sync, ml worker, reuses tag_eval's proven data loaders + metric math so production heads match measured eval numbers) and SCORE (async, API worker — numpy via pgvector, no scikit-learn): score one image's embedding against all heads → the rail's suggestions, cached on (count, max trained_at) so a retrain invalidates without per-request loads. - tasks.ml.train_heads (ml queue, commits per head so a kill leaves progress) + recover_stalled_head_training_runs sweep + retention(20) + 5-min beat (rule 89). - api/heads.py: POST /api/heads/train (one run at a time, 409 guard) + GET /api/heads (count, graduated, last-trained, running, per-concept table, recent runs). - ml_settings: head_min_positives + head_auto_apply_precision, tunable via /api/ml/settings. Scoring isn't wired into the rail yet (slice C) and the admin UI is slice B — this slice makes training + scoring exist and CI-verifiable. 'precision' column stored as precision_cv (SQL reserved word). Migration 0058. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa	2026-06-28 10:36:25 -04:00
bvandeusenandClaude Opus 4.8	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>	2026-06-16 11:07:00 -04:00
bvandeusenandClaude Opus 4.8	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>	2026-06-10 13:50:30 -04:00
bvandeusen	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.	2026-06-01 02:08:10 -04:00
bvandeusen	f7f75fcac6	refactor(fc2c-i): sweep blueprints + ml tasks onto shared get_session (covers tasks/ml.py, a survey gap)	2026-05-15 15:48:02 -04:00
bvandeusenandClaude Opus 4.7	aa4cc7c629	feat(fc2b): add tag rename (PATCH /api/tags/<id>) + /api/ml admin ... Rename returns 409 on collision (frontend shows FC-2c merge hint), 400 on empty. ml_admin: GET/PATCH settings (thresholds + min_reference; model versions read-only here), POST backfill / recompute-centroids returning 202 + celery task id. Tests integration-marked, eager Celery. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>	2026-05-15 07:54:12 -04:00