8a0237eeeaf9f9363f45415e8a7ce3d73122beee
100
Commits
| Author | SHA1 | Message | Date | |
|---|---|---|---|---|
  bvandeusenandClaude Opus 4.8
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8a0237eeea |
fix(agent): stream videos via ffmpeg-from-URL instead of downloading the whole file
The failing "poison" jobs were 800MB+ 4K VR videos: the agent pulled the ENTIRE file into memory (r.content) just to sample a few frames, which buffered ~1GB in RAM and — on any slow/contended media store — got cut off mid-download (ChunkedEncodingError), failed, and re-leased forever. Measured the media read at ~4–6 MB/s (raw off the share, curator out of the path), so no serving-layer tweak helps; the file simply shouldn't be fully downloaded. Environment-agnostic fix (works for any deployment, completes even when slow): - media.sample_frames_from_url(): point ffmpeg straight at curator's /images URL. It Range-reads only the video index + up to max_frames of content — never the whole file — and reconnect flags resume a dropped transfer instead of failing. Generous, env-tunable timeout (FFMPEG_TIMEOUT, default 1200s) = completion over speed. Removes the bytes-based sample_frames (dead once videos stream). - worker._download_decode: videos now stream (no fetch_image, no RAM blowup); stills still download+decode. On an ffmpeg miss, probe curator liveness (client.is_reachable) → fail the job if curator is up (unprocessable file, stops the infinite re-lease) vs release if curator is down (transient, survives a redeploy). Auth header passed so it works whether or not /images is gated. Build marker 2026-07-01.6. Refs issue #1225. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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aa0605585b |
fix(agent): log the REAL fetch/submit failure reason, not "curator unreachable"
"curator unreachable" was printed for every transient error, hiding whether a single file's transfer stalled (ReadTimeout — curator is up, that stream is slow) or curator itself is down (ConnectTimeout/ConnectionError) or errored (HTTP 5xx). Those need completely different fixes, and we've been diagnosing the download slowness blind. Add _transient_reason(exc) → a specific label (HTTP <code>, else the exception class: ReadTimeout / ConnectTimeout / ConnectionError / …) and use it in both transient paths: - downloader: "fetch failed job <id> (image <id>, ReadTimeout) — released, backing off" - consumer: "submit failed job <id> (<reason>) — released, re-lease later" Now the logs say which failure it actually is (and which image), so we can tell a slow/stalled transfer apart from an unreachable curator. Build marker 2026-07-01.5. Refs issue #1225. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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0fe1674753 |
perf(web): stream files in 4 MiB chunks + 4 hypercorn workers (fix 40s downloads)
The image library is on a CIFS/SMB share (mounted rsize=4 MiB, actimeo=1), and Quart's FileBody streams in 8 KiB chunks — so serving one large original was ~19k network round-trips to the storage server, i.e. 30–58s per download (operator-flagged). That's what starved the GPU agent (constant "curator unreachable" backoff) AND slowed the browser: every byte is read off CIFS and streamed through the Python app (no reverse-proxy sendfile), and only 2 hypercorn workers meant the agent + the browser's thumbnail grid queued behind each other. In-container fix, no new service: - Raise FileBody.buffer_size 8 KiB → 4 MiB in create_app, matching the mount's read size: one round-trip per read, ~500× fewer. buffer_size is the MAX read so small thumbnails still read in one gulp, and Range/mime/ETag/conditional handling lives on Response — all preserved. Guarded so a Quart-internal change can't break boot. - HYPERCORN_WORKERS default 2 → 4 so concurrent /images requests stop queuing. Expected: large-file transfers drop from ~40s toward link speed (a few seconds) for the agent and the browser. See issue #1223. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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c22f37d64d |
feat(gallery): sort by earliest post date across all posts (new default)
The gallery's newest/oldest sort keys off image_record.effective_date = COALESCE(primary post's post_date, created_at). The primary post is often the repost/download the file came from, so the grid led with download dates rather than when content was first posted (operator-flagged). Add a second materialized sort key, earliest_post_date = MIN(post_date) across ALL of an image's provenance posts (every post it appears in), else created_at — the original publish date. Mirrors the effective_date pattern so the sort stays a forward index scan. - alembic 0071: add earliest_post_date + index (DESC, id DESC); backfill created_at baseline then MIN over image_provenance ⋈ post. - importer: recompute earliest_post_date whenever a dated post is linked (MIN over the image's provenance, which now includes the just-added row). - gallery_service: new sorts posted_new / posted_old key off earliest_post_date; cursor + year/month grouping follow the active column transparently. - api: accept posted_new|posted_old; DEFAULT is now posted_new so the grid leads with original publish date. newest/oldest (effective_date) still available. - frontend: sort dropdown gains "Newest/Oldest post date" (default Newest post date); existing effective-date sorts relabelled "Newest/Oldest added". - tests: service test asserts posted_new/posted_old key off earliest_post_date; frontend default-sort omission test updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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ccbb5cbc9e |
fix(agent): stop the downloader pool stampeding a slow curator (congestion collapse)
Operator hit an outage after the machine slept overnight: the agent showed "curator unreachable" in a loop while curator's API (lease) was actually fine and the browser could still load images — just slowly. Root cause is a feedback loop in the new pipeline: every download streams a full original through curator's single Python file-serving path, and the autoscaler grows DOWNLOADERS whenever the buffer is empty. When downloads are merely SLOW/failing, the buffer is empty for that reason — so the agent piled on more concurrent large-file GETs, saturating curator's web workers + NFS, which slowed curator (and its browser) further and produced more failures → more downloaders. Classic congestion collapse. - Failure-aware autoscaling: if transient download failures rose since the last decision, SHRINK the downloader pool toward the floor instead of growing — the empty buffer is caused by failures, not the GPU starving. It ramps back up only once downloads succeed again. - DL_MAX 24 → 8: 24 concurrent large-file downloads through one Python serving path is too many; 8 keeps a fast GPU fed without stampeding curator. - fetch_image timeout 180 → (10, 60): the read timeout is between-bytes, so a large-but-flowing download still completes, but a stuck/dead connection fails in 60s instead of hanging a downloader for 3 min and piling up stuck requests. Build marker 2026-07-01.4. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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ef3318aac1 |
feat(explore): more variance in the related rail (stronger MMR diversification)
Operator wants the Explore "related" rail to span more — the #1188 diversifier was tuned conservatively. Push all three knobs so it reaches further across clusters instead of clumping near the anchor: - MMR lam 0.55 → 0.40 — weight the diversity penalty harder (the main dial). - candidate pool min(200, max(limit*5, 60)) → min(400, max(limit*8, 100)) — a wider nearest-cosine pool so MMR has genuinely distinct neighbourhoods to pick from, not just the near-dupes. - pHash dup_threshold 6 → 8 — collapse more near-duplicate reposts/clones, freeing rail slots for distinct picks. Still deterministic (same set per image, just more spread) and relevance-anchored via the lam*sim-to-anchor term. Backend-only; no migration. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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7cdce0c474 |
feat(agent): temporal video dedup — drop near-duplicate frames before the GPU
Near-static videos are the dominant GPU load: sampled into up to 64 frames, each re-runs the whole detect→CCIP→SigLIP chain on ~identical content. Add a CPU perceptual-hash frame dedup upstream of the GPU so the redundant frames are never processed at all (not just their embeds). - media.dedupe_frames() + _dhash(): 8×8 difference-hash (64-bit) per frame; greedy keep — a frame survives only if its hash differs from every kept frame by >= min_distance bits (Hamming). A static run collapses to one frame; genuinely distinct scenes all survive. Order + frame_time preserved. - Called in worker._download_decode right after sample_frames, so it runs in the decode stage on the downloader thread (CPU) — the GPU consumers only ever see deduped frames, and buffered video items shrink (less RAM too). - Env-tunable FRAME_DEDUPE_DISTANCE (default 8; higher keeps more frames for brief localized changes an 8×8 hash can miss; 0 disables). Logs `video frames N→M` when it drops any, so video load reduction is visible. Complements the spatial per-frame crop dedup (2026-07-01.2); this is the temporal axis. Build marker 2026-07-01.3. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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eaae896858 |
feat(agent): dedupe near-duplicate crops before the SigLIP embed
Figure boxes are already NMS-merged (iou 0.6) and each YOLO detector self-NMSes, but the combined per-frame crop pile (figure→concept ∪ anatomy component→concept ∪ panel) was embedded with no cross-proposer dedup — so genuine near-duplicates slipped through (a figure box ≈ an anatomy component on a solo bust; overlapping booru head classes on one head), embedding the same region twice and burning a slot against max_regions. Add detectors.dedupe_crops(): a greedy, high-IoU (default 0.85), kind-aware pass over the pending (crop, template) list right before embed_batch — drop boxes that overlap ≥ iou within the same kind, keep the highest score. The high threshold is deliberate: it collapses only true near-identical boxes while preserving intentional nested crops across scopes (a whole figure vs a small head component sit well below it) and distinct kinds (concept vs panel). Env-tunable DEDUPE_IOU (≥1.0 disables). Runs on CPU before the GPU work, so it cuts both embed cost and region count. Temporal (cross-frame) dedup deferred. Build marker 2026-07-01.2. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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afef95a87d |
feat(agent): download/GPU producer-consumer pipeline + fix detector fuse crash
The agent workload is download-bound (download 400–5462ms vs GPU ~300–600ms), so the old N-slot serial chain (each slot: lease→download→decode→GPU→submit) left the fast GPU idle during every download. Rearchitect worker.py into a producer/consumer pipeline: downloader pool (autoscaled by BUFFER OCCUPANCY) → bounded queue → 1–2 GPU consumers (detect+embed→submit) - Downloaders are I/O-bound → many overlap; the autoscaler now tunes DOWNLOADER count by buffer fill (empty = GPU starving → add; full = outpacing GPU → add a 2nd consumer if it has util/VRAM headroom and lifts throughput, else trim). - Bounded buffer (12) = backpressure: a full buffer blocks downloaders, capping RAM + lease look-ahead. VRAM pressure sheds a consumer immediately. - Heartbeat thread keeps every held lease alive (buffered jobs wait on the GPU; curator's 180s TTL would otherwise reclaim them mid-buffer). - Preserves all resilience: lease exp-backoff, submit-path retry (#169), release-on-stop, region caps + video early-exit (#171). Stop drains BOTH pools and releases every held lease at once (single held-set as source of truth). - Consumers SHARE one embedder + proposers instance (a 2nd consumer adds concurrent inference, not N× VRAM — bounds the VRAM creep seen with N slots). - UI reworked for the pipeline: tiles show downloaders · buffer · on-GPU · processed · errors, a buffer-occupancy meter, and a consumers/waited-out line; the dial now tunes downloaders. Build marker 2026-07-01.1. Also fix the operator-flagged detector warning: yolo11n + the comic-panel model threw "'Conv' object has no attribute 'bn'" on every image (ultralytics' load- time Conv+BN fusion on a version-mismatched graph), silently disabling 2 of 3 crop proposers and spamming the log per image. Disable that fusion (unfused inference is correct, marginally slower) and permanently self-disable a proposer on the first inference failure instead of re-throwing forever. Refs milestone 122. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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83f1070a11 |
fix(agent): bound video GPU work — early-exit the frame loop at max_regions
Image 81602 turned out to be a 156 MB mp4, not a huge still: the agent samples up to 64 frames × ~32 regions/frame → ~2000 regions (the 413) and 64 frames of detect+CCIP+embed (the 38s). The MAX_REGIONS backstop (#171) only truncated the SUBMIT — the GPU work was already spent. Break out of the frame loop once accumulated regions reach max_regions, so a long video costs ~a few frames of GPU (~2-3s), not all 64 (~38s). The whole-image 'embed' task is unaffected (it mean-pools all frames and returns before this loop). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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c587ac667c |
fix(agent): cap figures + global region cap + reset active on stop
Three safety/robustness fixes from the operator's run logs: - Cap figures per frame (MAX_FIGURES, default 8) like components/panels already are. Uncapped, a huge/busy image yielded hundreds of figure boxes → hundreds of per-figure CCIP calls + crops → a 38s job AND a submit too big to accept (image 81602 looped on 413). This is the acute fix. - Global per-JOB backstop (MAX_REGIONS, default 128): if total regions still exceed the cap (long video), keep the highest-scoring and log the drop, so a submit body can never blow past curator's limit. - Stale "active" meter: stop() now resets _active to 0 (no slots remain, so the meter must read 0 at once), and _bump clamps at 0 so a slot finishing after the reset can't drive it negative. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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7e74fa767c |
fix(agent): load huge images — disable PIL decompression-bomb guard
Trusted local library, not an upload surface, so a legitimately large image (90–95M px, operator-flagged) must load. PIL only WARNS at the 89M-px default but RAISES DecompressionBombError at ~179M px, which would fail those jobs. Set Image.MAX_IMAGE_PIXELS = None. (The agent works off individual extracted files — curator's archive_extractor unpacks zip/cbz/rar/7z at import — so this is about big single images, not archives.) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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9f1148b110 |
chore(agent): modernise base → Ubuntu 24.04 / Python 3.12 / CUDA 12.9
Bump the GPU-agent base image from 12.4.1-cudnn-runtime-ubuntu22.04 (Python 3.10, CUDA 12.4, early-2024) to 12.9.2-cudnn-runtime-ubuntu24.04: - Ubuntu 24.04 LTS → Python 3.12 — one modern runtime, no more 3.10. - CUDA 12.9 + cuDNN 9 — current within the CUDA-12 / cuDNN-9 line that the default onnxruntime-gpu wheel AND torch cu124 are built against. NOT CUDA 13: ONNX Runtime's CUDA-13 support is still nascent (separate wheels + open "Unsupported CUDA version: 13" reports), and torch bundles cu124 anyway. The GPU (Ampere/Ada, 12 GB) is fine on either — this is a library-alignment call, not a hardware limit. - PIP_BREAK_SYSTEM_PACKAGES=1: 24.04 marks system Python externally-managed (PEP 668); a single-purpose container owns its environment, so global installs are fine and simplest. - agent/ruff.toml pinned to py312 (was py310) so CI lints against the real runtime; from __future__ import annotations stays (PEP 649 lazy annotations are 3.14, so self-refs still evaluate on 3.12). CI builds the image but has no GPU — validate on the desktop after pull that it starts and loads CUDAExecutionProvider (not CPU fallback). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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f01b59f390 |
fix(agent): py3.10 startup crash + submit-path retry; pin agent ruff to py310
The agent container (CUDA base, Python 3.10) crashed on startup with `NameError: name 'Config' is not defined` — an earlier `ruff --fix` unquoted the `from_env(cls) -> Config` self-reference, which is safe on CI's Python 3.14 (PEP 649 lazy annotations) but is evaluated at class-definition time on 3.10. CI lint/compile run on 3.14, so it slipped through. - config.py: `from __future__ import annotations` so the self-referential annotation is a string, never evaluated — works on 3.10 and every version. - agent/ruff.toml: pin the agent to `target-version = "py310"` (its real runtime) and inherit the root rules. Ruff now flags exactly this class as F821, so CI's lint lane catches it instead of shipping a broken image. (CI otherwise lints on 3.14, masking 3.10 issues.) - client.py: submit path now retries in-place. A dedicated session with a urllib3 Retry (connect/read/status, 0.5s backoff, 500/502/503/504, POST) so a momentary blip after the GPU work is done doesn't discard it and force a full re-download + recompute elsewhere. A duplicate submit after a lost response is a harmless 409 no-op. Lease/fetch keep the plain session + loop-level backoff. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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79269da802 |
fix(agent): prompt stop + lazy curator polling + build marker; add agent to CI
Addresses operator reports: Stop never finishes, the agent polls curator constantly, and stale-cached pages get mistaken for a failed deploy. - Stop is prompt: flip _running BEFORE any lock so /status + worker loops see "stopped" immediately, and add a stop/shrink checkpoint in _process (after decode, before the expensive detect+embed) that releases the job and bails — so a Stop doesn't wait out heavy GPU work. - Lazy curator polling: the queue snapshot is fetched only while a browser is actually watching (a /status hit within UI_IDLE_GRACE) and on a 5s cadence, not a constant background loop. The work loop's own lease/submit is curator's only visitor otherwise — nothing polls just to poll. - Build marker: VERSION is embedded in the page and reported on /status; the UI shows a "reload" banner when they differ, so a browser-cached page can't be mistaken for "the new image didn't deploy" (complements the no-store header). CI: the lint lane now also `ruff check`s agent/ and compileall-parses it, so the GPU agent is linted + syntax-checked before its image builds (build.yml only `docker build`s it). Fixed the agent's pre-existing UP037/B905 so it passes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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e6a7fe7d03 |
feat(agent): per-stage timing breakdown (lease/download/decode/gpu/submit)
Instrument the job pipeline so we can see where wall-clock actually goes and decide — on data, not theory — whether a download/compute split is worth building. Each stage is timed per job and a rolling breakdown is logged every 30s to the agent console, e.g.: timing/30s — lease 8ms · download 310ms · decode 40ms · gpu 165ms · submit 70ms | wall/job 585ms (214 jobs) - lease timed around client.lease() in the slot loop (per batch). - download = fetch_image; decode = image/frame decode; gpu = detect + CCIP + batched embed; submit = the results POST. One-time model load is excluded from the gpu figure. - Thread-safe accumulator (stage -> [sum, count]) summarised + reset by a small daemon reporter thread; logs only when there was work. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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181f1c6a27 |
perf(gpu-queue): partial indexes + two-phase lease so leasing stays O(batch)
The throughput bottleneck was curator-side, not the network. lease() claimed the lowest-id pending/expired jobs with `... ORDER BY id LIMIT n`, but with only a plain `status` index Postgres walked the primary key from id=1, skipping the entire prefix of already done/error rows before reaching pending ones. As `done` grew (69k+), every lease became an O(done) scan — leasing crawled, the DB saturated, and even /status (the queue GROUP BY count) stalled the agent. - Migration 0070 adds two partial indexes over just the live slice: pending rows indexed by id (hot path), and leased rows by lease_expires_at (crash-recovery + orphan sweep). They stay tiny no matter how large the done/error history. - lease() split into two phases so each uses a partial index: claim pending first (id-ordered, O(batch)); reclaim expired leases only when pending can't fill the batch. Same semantics (SKIP LOCKED, attempts++, expired reclaim). - Model __table_args__ declares the indexes so ORM and schema agree. - Test: a done-prefix at low ids must not stop the lease reaching pending. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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f0f031782d |
fix(agent): unfreeze status view + smoothed throughput-aware autoscaler + log pane
Operator: the status tiles (state/active/processed) and the Start/Stop buttons freeze while the GPU meters stay live. Root cause: /status made an INLINE blocking curator call (queue_status) on every poll, and with curator buried under a 112k-job backlog that call stalled — freezing the whole status refresh (the GPU bars survived because /gpu is a lock-free local read). Made worse by the old util-band autoscaler, which grew workers toward the 32 cap forever because util plateaus ~50% on this IO-bound load and never hit the 70 grow threshold — piling load onto curator and the agent process. - /status is now a pure in-memory read: worker.status() is lock-free, and the curator queue snapshot is refreshed by a background poller (never inline). - Autoscaler replaced with a smoothed, throughput-aware climb that SETTLES: samples util every 2s and EWMA-smooths it (raw util swings 0↔99), then every ~24s grows by one only while each grow keeps lifting smoothed jobs/s; when a grow stops helping it backs off one and holds, re-probing occasionally. No runaway, no flopping. - GPU util bar now shows a smoothed value: the agent's own EWMA (util_smooth, exposed on /gpu) when running, else smoothed client-side — so it glides instead of bouncing 0↔99. - act() aborts a slow Start/Stop POST after 8s so the buttons can't stick; the now-always-fast /status refresh recovers state regardless. - Log pane: bound the page to the viewport (height:100vh) so the Logs card scrolls INTERNALLY instead of overflowing off-screen; cap the ring buffer at 400 lines. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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82e1a4e127 |
fix(agent): send Cache-Control: no-store so a new image isn't masked by cache
The control page is a static string served with no cache headers, so after pulling a fresh agent image the browser kept showing the OLD UI until a hard refresh (operator-flagged). Add a no-store middleware covering the page and the status/gpu/logs polls. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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c2e9157822 |
feat(agent): graceful Start/Stop with starting/stopping states + instant status
Operator: the buttons fire but the status view doesn't reflect the change. Cause: act() ignored the POST's own status response and waited on the separate /status poll (which lags behind the curator queue call). Now: - act() applies the POST's returned status immediately for instant feedback, and shows an optimistic "starting"/"stopping" state (pulsing, buttons disabled) the moment it's clicked. - A stop that still has in-flight jobs draining shows "stopping" until active hits 0, then resolves to "stopped" on its own. - applyStatus() guards the /status-only fields (connection pill + queue) so the lean action response can't blank them — the Start/Stop path deliberately skips the slow curator call to stay snappy. Also de-duplicate GPU reads: read_gpu() now caches (1s TTL) with one probe at a time, and /status no longer spawns its own nvidia-smi — so the fast /gpu poll + autoscaler + /status share a single subprocess instead of piling up in the server thread pool (which was what made clicks feel dead under load). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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3b34230fbd |
fix(agent): stable util-band autoscaler + live GPU meters
Two operator-reported issues with the GPU agent: 1. Worker count flopped almost every cycle, spiking the GPU. The hill-climb probed +1, judged it over a too-short noisy throughput window, saw no clear gain and reverted -1 — every tick. Replace it with a GPU-utilization-band controller: HOLD while smoothed util sits in a healthy band, grow only on clear spare capacity (util below the low mark + VRAM headroom), shrink under saturation or memory pressure. Util is EWMA-smoothed and decisions are spaced (DECIDE_EVERY samples), so a noisy nvidia-smi reading can't move the pool. Load stays consistent instead of probe/reverting. 2. GPU util/VRAM bars only updated on manual refresh. They rode the /status poll, which blocks on the curator queue call (slow when curator is busy), so the meters froze between refreshes. Give them a dedicated /gpu endpoint (local nvidia-smi only, no curator round-trip) polled every 1.5s, and drop the curator queue-status timeout 15s -> 5s so /status itself stays snappy. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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c259d03618 |
fix(agent): revert full-width page, grow the Logs section to the bottom
Operator meant the LOG section should fill down the viewport (vertical), not the whole page going full-width horizontally. Restore the centered column (820px), make .wrap a full-height flex column, and let the Logs card flex to fill the remaining height to the bottom (drop the fixed 230px log-pane cap). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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2713c3f773 |
perf(agent): batch SigLIP crop embeds per image + load truncated images
Two issues surfaced by the live logs (GPU pegged at ~0% util, 0.5 jobs/s,
truncated-image failures):
- BATCH the SigLIP embeds: collect all of an image's crops (figure + booru_yolo
components + panels) and embed them in ONE forward pass instead of one
forward+lock per crop. The per-crop path serialised every crop through the
inference lock and starved the GPU (≈0% util, autoscaler stuck oscillating);
batching gives a real GPU-bound workload + far higher throughput. CCIP still
runs per figure inline.
- LOAD_TRUNCATED_IMAGES in the agent (matches the server embedder): slightly-
truncated scraped images now load instead of failing the job 3× then erroring
("image file is truncated (N bytes not processed)").
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
|
9eaefac385 |
feat(agent): full-width control page, Copy-logs button, quiet HTTP log noise
- Page fills the viewport horizontally (drop the 780px cap). - Copy button on the Logs card → copies the console (clipboard API on localhost, textarea-execCommand fallback), with a brief "Copied" confirmation. - Silence httpx/httpcore/huggingface_hub/urllib3/filelock/uvicorn.access/ ultralytics to WARNING so the console shows agent activity (detector loads, job errors, autoscale moves) instead of per-request HF-download spam. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
c1b099e5a3 |
feat(agent): in-UI log console + a real styling pass on the control page
- logbuf.py: bounded in-memory log ring buffer + a logging.Handler on the root logger; GET /logs serves it; the control page polls it into a console pane — so runs are monitorable without `docker logs`. worker now logs autoscale moves (one line per change, with jobs/s + util + VRAM) and job failures (job + image + reason); detectors already log load/disable. - Restyled the whole control page: a proper dark layout with a header + live connection pill, cards (Control / Status / Logs), a styled Auto switch + worker stepper, status tiles, separate GPU-util and VRAM meters, and the log console. No longer feels like an afterthought; all the existing control hooks are preserved. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
6d7b17b0b5 |
feat(agent): autoscale the worker count (throughput hill-climb), Auto default-on
The new per-job workload (3 detectors + several SigLIP embeds) is far more GPU-bound than the old I/O-bound CCIP pass, so the right worker count shifted and is hard to guess. Add an Auto mode (default ON) that finds it: - _control_loop samples jobs/sec + GPU util/VRAM every ~6s and hill-climbs the target: grow while throughput keeps improving and VRAM stays under budget, revert a step that doesn't help, back off under memory pressure (VRAM >= 90%), then settle and periodically re-probe (the GPU/IO balance shifts over a run). - A manual concurrency set is an override → leaves Auto; an "Auto" toggle in the control UI re-enables it. status() reports `auto`; the dial reflects the auto-chosen count (read-only) while Auto is on. - AUTO_SCALE env (default on) + compose doc. Agent py-compiled (outside CI). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
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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bvandeusenandClaude Opus 4.8
|
80f8eb4756 |
feat(gpu): re-process trigger to apply new crop detectors to the existing library (#1202)
The siglip/ccip backfills skip images that already have current-version regions, so adding crop detectors only affected NEW images — the back-catalogue would never be re-cropped. Add a reprocess trigger that resets every done/error job of a task back to pending, so the agent re-runs the FULL pipeline (figure detection + CCIP + concept/panel crops) over the whole library under the current detectors. - reprocess_gpu_jobs(task='ccip') task + POST /api/gpu/reprocess. - gpu store reprocess() + GpuAgentCard "Re-process library (re-detect + re-crop)" button with a confirm (it's heavy). - Test: a done job resets to pending (attempts cleared). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
ce5db5caaf |
chore(agent): default the anatomy + panel proposer weights to working values
booru_yolo yolov11m_aa22.pt (40MB) + mosesb/best-comic-panel-detection::best.pt. Each self-disables if its download fails, so defaulting them on is safe. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
d5f29f7056 |
feat(agent): crop proposers — booru_yolo anatomy + COCO person + comic panels (#1202)
Better region PROPOSERS feeding the existing crop→SigLIP→max-over-bag heads (no change to the learned-tagging approach; no per-tag cost — propose once, embed each region, all heads in one matmul). - detectors.py: lazy ultralytics YOLO wrapper, each proposer independently optional + guarded (a bad weight spec / inference error self-disables that one, logged, never breaks the worker). Weights resolve from an ultralytics name | http(s) URL | "hf_repo::file", cached under HF_HOME. NMS merge so a figure two detectors both find collapses to one crop. - worker: figure boxes = imgutils detect_person ∪ general COCO person (merged) → CCIP + concept (anime + Western/realistic coverage); booru_yolo anatomy components (head/cat-head/anatomy/…) → concept crops; comic panels → kind= 'panel' concept crops. Capped per frame (MAX_COMPONENTS/MAX_PANELS). - config + compose: PERSON_WEIGHTS (default yolo11n.pt, works OOB), ANATOMY_WEIGHTS + PANEL_WEIGHTS (operator sets booru_yolo URL + mosesb panel hf::file; empty = off). ultralytics added to requirements. - backend: image_region 'kind' doc notes 'panel'; no migration (free String, and the bag scorer keys on a non-null siglip_embedding, not the kind, so any SigLIP region joins the bag automatically). Agent is outside CI — py-compiled here; operator tests on the GPU and checks Western-vs-anime crop quality via /api/ccip observability. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
3d7f60a6e3 |
fix(lint): use dict() not a dict-comprehension in tag_stats (C416)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
9a3cda007a |
feat(api): agent-friendly tag analysis endpoints — /tags/top + /tags/<id>/stats (#1136)
Fast, read-only, indexed aggregates shaped for ANALYSIS (not the paged UI directory, which is alphabetical + builds previews and timed out at 10 min on a full count sweep). - GET /api/tags/top — top tags by image count, desc. ?kind, ?limit (cap 500), ?min_count, ?source=all|human|manual|accepted|auto (human=manual+ml_accepted, auto=head_auto+ccip_auto+ml_auto). One GROUP BY over image_tag (indexed on tag_id). - GET /api/tags/<id>/stats — per-tag dataset health: total + per-source counts (manual/accepted/head_auto/ccip_auto), human vs auto rollups, rejection count, and whether a trained head exists. Backs concept-readiness + source-split analysis. Plain-HTTP homelab posture, no auth change. Tests cover ranking, source filter, min_count, the source breakdown, and 404. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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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 |
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bvandeusenandClaude Opus 4.8
|
3d97667f5b |
fix(lint): drop unused select import in tags.py after allowlist removal
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
485387ff0b |
refactor(ml): retire the Camie tagger + allowlist bulk-apply (#1189)
Heads + CCIP are the tag source and head auto-apply is the earned propagation.
The Camie tagger ran only to feed the allowlist bulk-apply (its ImagePrediction
rows had no other consumer), and the allowlist was a SECOND, un-earned auto-apply
path firing in parallel with heads on every accept — exactly the un-earned spray
the v2 pivot replaced. Retire both.
Behavior change: accepting a suggestion now applies the tag to THAT image only
(source='ml_accepted', a head-training positive) — it no longer allowlists +
fans the tag across the library via Camie. Propagation is heads' earned
auto-apply. (Loses instant cold-start propagation for booru-vocab tags; that was
un-earned and bypassed the precision gate.)
- tag_and_embed is now EMBED-ONLY (no Camie load/infer, no ImagePrediction
writes); backfill enqueues it for images with no embedding.
- Removed: services/ml/tagger.py, apply_allowlist_tags + helpers + daily beat +
every enqueue caller (accept/alias/merge/per-image), api/allowlist.py +
blueprint, ImagePrediction + TagAllowlist models/tables (migration 0067),
AllowlistTable.vue + allowlist store, the accept coverage-projection payload.
- AllowlistService gutted to accept/dismiss/undismiss/reject (the rejection store
the rail still needs); accept returns nothing, API returns {accepted, tag_id}.
- tag merge no longer repoints/triggers the allowlist; _keep_as_alias now keys on
ML-applied image_tag sources (incl. head_auto) instead of the allowlist.
- UI: MLBackfillCard relabelled to embedding-only; accept toast simplified;
MaintenancePanel drops the allowlist tile.
Left for a follow-up hygiene pass (now-inert, harmless): the dead settings
columns (tagger_store_floor, tagger_model_version, suggestion_threshold_*,
video_min_tag_frames), image_record.tagger_model_version, MLThresholdSliders
trim, and the Camie model download in download_models.py.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
|
3d77a38a25 |
refactor(ml): remove the dead per-tag centroid subsystem (#1189)
The v2 pivot replaced per-tag SigLIP centroids with learned heads + CCIP. Centroids were still recomputed (on every tag merge + a daily beat) but NOTHING read them — suggestions come from heads+CCIP and apply_allowlist_tags applies via Camie predictions, not centroids. Pure dead wiring; remove it. Removed: CentroidService, recompute_centroid/recompute_centroids tasks, the daily beat, POST /api/ml/recompute-centroids, the recompute-on-merge trigger, the tag_reference_embedding table + model, the centroid_similarity_threshold + min_reference_images settings (migration 0066), the CentroidRecomputeCard + its store action + MaintenancePanel tile, and the centroid slider in MLThresholdSliders. _keep_as_alias drops its vestigial has-centroid branch (the allowlist branch already covers "could re-emit"); tag merge no longer clears a table that no longer exists. NOT touched (still live, parallel to heads): the Camie tagger, ImagePrediction, and the allowlist bulk-apply — accepting a suggestion still allowlists + applies it across the library. The tag-eval "centroid" baseline metric is unrelated (in-memory) and stays. (image_record.centroid_scores JSON column also remains — separate legacy field, its own micro-cleanup.) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
4daa3f2790 |
feat(ml): operator model swap — GPU re-embed + embedder as a setting (#1190)
Make the SigLIP embedder an operator choice (drop-in to SigLIP 2:
google/siglip2-so400m-patch16-512 is a verified 1152-d model at 512px → no
schema change, better small-cue fidelity). A swap = set model + re-embed +
retrain, all operator-driven; the GPU agent does the re-embed so it's fast.
- settings: embedder_model_name is now a setting (migration 0065) alongside the
existing embedder_model_version; both editable + validated (non-empty) in the
ml admin API. The server embedder loads by HF name (AutoImageProcessor/Model,
model-agnostic), preferring the pre-downloaded local dir for the default so
existing deploys don't re-download; rebuilds on a name change.
- agent: new 'embed' job = whole-image SigLIP embedding (mean-pool video frames)
under the lease-announced model → POST /jobs/submit_embedding writes
image_record.siglip_embedding + siglip_model_version. The lease now announces
the model FROM THE SETTING (not a constant).
- re-embed routing: enqueue_gpu_backfill('embed') selects unembedded + stale-
version images; 'siglip' now re-embeds concept crops whose version != current
(so a swap re-triggers crops, not just the never-embedded back-catalogue). The
CPU ml-worker backfill no longer re-embeds on a version mismatch (it can't
churn the library at 512px) — the GPU agent owns version re-embeds. Daily
'embed' + 'siglip' beats self-heal.
- scoring: score_image only bags embeddings in the CURRENT model's space (whole-
image gated by siglip_model_version, concept regions by embedding_version) so a
mid-swap stale vector isn't scored by new-space heads; legacy NULL = current.
- UI: GpuAgentCard "Embedding model (advanced)" — edit name/version, Save, and
"Re-embed library (GPU)" (queues embed + siglip); points at SigLIP 2.
Tests: lease announces model + submit_embedding round-trip; enqueue 'embed'
selects stale/unembedded; stale-version excluded from scoring; embedder model
settable + empty rejected; siglip gate updated to current-version concept.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
|
0f472b2f9e |
fix(explore): diversify "more like this" so it stops getting stuck (#1188)
Pure nearest-cosine piled near-identical images into the neighbour grid — a reposted banner filled all 24 slots, and once you wandered into a B&W / comic-panel cluster every neighbour was more of the same with no way back to colour without the Random button (operator-reported, with screenshot). similar() now over-fetches a wide candidate pool (5x the requested limit, cap 200), then diversifies down to `limit`: - pHash near-duplicate collapse: drop candidates within 6 Hamming bits of the anchor or an already-kept candidate, so a repost (and the anchor's own clones) appears at most once. - MMR re-rank: greedily pick for closeness-to-anchor minus similarity-to-already -picked (lambda 0.55), so the result SPANS clusters instead of returning 40 variations of one image. Falls back to nearest-order on any failure / small pool, so existing nearest-first behaviour is unchanged when there's nothing to diversify. Frontend forwardTarget drops the now-redundant skip-nearest-third hack (the list is already diversified server-side) — plain random-over-unvisited gives the variance now. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusen
|
715e276c03 | Merge pull request 'feat(tagging): SigLIP concept crops + max-over-bag scoring (#114)' (#153) from feat/siglip-concept-crops into dev | ||
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bvandeusenandClaude Opus 4.8
|
55fa4656ff |
feat(agent): survive + auto-recover when curator is unreachable
For redeploying curator while away with nobody to restart the agent: - _process now distinguishes a TRANSPORT error (curator down/redeploying, 5xx, 401/403/408/409/429, or our lease reclaimed mid-flight) from a genuine job fault. On a transport error it hands the job back (best effort) and signals the loop to back off — instead of calling fail(), which would burn the job's server-side attempt budget (MAX_ATTEMPTS=3) and permanently error good jobs across a redeploy. Job-specific 4xx (404 image gone) still fail so they don't re-lease forever. - lease loop retries with capped exponential backoff (poll_idle → 60s) and resets on the first successful lease, so a long outage is gentle and recovery is automatic within ≤60s of curator returning. Sleeps are interruptible so Stop / pool-shrink stays responsive. - AUTO_START env (default on in compose) resumes the worker on container start, so a host reboot / crash-restart (restart: unless-stopped) self-heals with nobody at the desktop. - control UI shows a "waited out" counter + an "curator unreachable, holding work" banner so the recovering state reads as recovery, not failure. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
c6f38b0dac |
feat(tagging): SigLIP concept crops + max-over-bag scoring (#114)
Lift recall on small/local concepts (glasses, cum, stomach-bulge, xray, lactation) that the whole-image SigLIP vector washes out: the GPU agent now embeds figure crops with SigLIP too, stored as kind='concept' regions, and the suggestion rail scores each image as a BAG (whole-image + every concept crop), taking each head's MAX over the bag. The whole-image vector is always in the bag, so this can never score lower than before. Model-agnostic by construction: the server ANNOUNCES the embedding model (HF name + version) in the lease, so the agent loads whatever the heads were trained in and stays in lock-step — a model swap is a server setting + a re-embed migration, never an agent change. - agent: model-agnostic CropEmbedder (torch/transformers get_image_features, fp16 on CUDA, inference-locked); worker branches on job.task — 'ccip' emits figure(CCIP)+concept(SigLIP) in one pass, 'siglip' emits concept-only so the back-catalogue backfill never churns figure/CCIP regions; torch cu124 + transformers in the image. - server: lease announces embed_model_name/embed_version; score_image is max-over-bag (version-filtered region embeddings); enqueue_gpu_backfill 'siglip' gates on a missing concept region (drains the back-catalogue, retries failures, no double-enqueue); daily siglip-backfill beat; UI button; /api/ccip/overview reports images_with_concept_siglip. - v1 scope: suggestion rail only — auto-apply stays whole-image (conservative; heads' thresholds were calibrated on whole-image). Bulk-apply bag = follow-up. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
b91a230f12 |
feat(ccip): automation + reference quality — keep identity flowing hands-free (#114)
Works through the optional CCIP ideas + the "keep moving even if I forget" ask:
AUTOMATION (no button needed):
- Hourly beat auto-enqueues CCIP backfill — new images get embedded (and errored
ones retried) on their own; the queue never goes idle waiting for a click.
- CCIP auto-apply: a daily sweep tags confident matches (source='ccip_auto') so
identity tags keep flowing. ON by default (opt-out, like head auto-apply);
ml_settings.ccip_auto_apply_enabled + _threshold (0.92, above the suggest cut),
migration 0064. Vectorized (one matmul + reduceat per image), reversible, skips
already-applied/rejected. Switch + threshold in the GPU agent card; GET/PATCH
/api/ml/settings; auto_applied count in /api/ccip/overview.
REFERENCE QUALITY (the over-fire root cause):
- character_references now draws ONLY from single-character images — on a
multi-character image the tag is image-level, so every figure would otherwise
pollute each character's prototypes (a 2-char image tagged 'Velma' made
Daphne's figure a Velma reference). This is the contamination behind residual
over-firing.
- Cached on a cheap signature (char-tag count + ccip-region count/max-id) so the
reference load isn't redone on every modal open.
Tests: multi-character image not used as a reference; auto-apply tags a confident
match as ccip_auto.
NEXT (not done, confirmed): comic-panel cropping + SigLIP concept crops ("spot
interesting content").
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
|
74b7ceaf47 |
fix(tags): return focus to the tag input after reject/un-reject too
Accept already re-focused the tag input (so you keep typing without re-clicking); reject (✗) and un-reject (↶) went straight to the store and skipped it. Route them through onDismiss/onUndismiss which emit 'dismissed', and wire that to focusTagInput in TagPanel — same return-to-input behaviour as accept. TagPanel is shared, so this covers both the image modal and the Explore workspace. The field's mobile-focus guard is preserved. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
301f2de989 |
fix(explore): variance + no loop-back on → navigation (#94)
Two reports: → sometimes "loops back", and the walk gets stuck on near-identical images. Cause: forwardTarget picked a uniformly-random neighbour from the 24 NEAREST, so it (a) often landed on an image already in the trail — which snaps the cursor back into history and makes → bounce between visited nodes — and (b) only ever offered near-duplicates. forwardTarget now: excludes already-visited neighbours (→ opens something new, no snap-back), and skips the closest third of the (similarity-sorted) pool so the jump favours the more-varied remainder instead of lookalikes. Neighbour pool widened 24→40 for more variety to browse + jump into. The post-← browser-forward walk through visited crumbs is unchanged. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
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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bvandeusenandClaude Opus 4.8
|
b7fd69815e |
feat(agent): raise worker cap to 32 + size the HTTP pool for it (#114)
At 8 workers the GPU sat at ~5% util / <5GB VRAM — the pipeline is I/O-bound (downloading + decoding images over HTTP), so the GPU starves until many workers overlap that I/O. Raise MAX_CONCURRENCY 8→32 and make the UI worker control a number input (reaching 32 by ±1 was tedious); the cap is reported via /status so the UI clamps to it. Also size the shared requests pool (pool_maxsize=64) — the default 10 would have throttled 32 workers + spammed "connection pool is full". Verified by running; watch GPU util/VRAM climb as you dial up. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
3abbe58450 |
fix(agent): flatten transparency onto white before RGB (#114)
A naive convert('RGB') on a palette-with-transparency image (common: character
PNGs on a clear background) lets PIL guess the transparent pixels — black-ish
artifacts that bleed into the crop + the CCIP embedding (and the "should be
converted to RGBA" warning). to_rgb() composites over white first for a clean,
consistent background; used by both stills and video frames.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
|
4a1a9ec5a7 |
feat(agent): GPU load readout + live worker-count tuning (#114)
Control UI gains what the operator asked for: - GPU load (nvidia-smi): util %, VRAM used/total + bar, temp — so you can see how hard the card is working while you're at the desktop. - Worker count is now a live − / + control (POST /concurrency), not just an env: the worker is a pool of independent slots (shared model, so slots add concurrent inference, not N× VRAM). Dial up for speed, down to free the card. Replaces pause/resume with Start/Stop + the worker dial. - Graceful release on stop / pool-shrink: a slot hands its still-leased jobs back via client.release() so they're re-picked immediately (pairs with the server recovery sweep). Not CI-tested (agent/ outside CI) — verified by running. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
2cb0427868 |
feat(gpu): fast orphan recovery — graceful release + 60s sweep (#114)
So work an agent orphaned gets picked back up quickly, three layers: - GpuJobService.release(): a graceful agent stop hands its still-leased jobs back to pending instantly (POST /api/gpu/jobs/release), no waiting out the lease. - GpuJobService.recover_orphaned() + recover_orphaned_gpu_jobs Celery task on a 60s beat: resets expired leases (a hard-crashed agent) to pending and keeps the queue counts honest even when nothing is leasing. - Lease TTL 300→180s: still well above any single job (a capped-frame video embed is tens of seconds, and a live worker heartbeats), but a hard crash recovers faster once the sweep fires. Tests: release returns-to-pending (token-scoped), recover_orphaned resets only expired leases, release API round-trip. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
614b6bc52a |
docs(agent): note the NVIDIA Container Toolkit host prereq
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
7b10f4caab |
fix(agent): cuDNN base image so onnxruntime-gpu loads (#114)
onnxruntime-gpu needs cuDNN 9; the plain cuda:12.4.1-runtime image lacks it (libcudnn.so.9 missing → CUDAExecutionProvider falls back to CPU). Switch to the -cudnn-runtime variant which bundles cuDNN 9. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
b6b151a500 |
docs(agent): docker-compose for the GPU agent
compose file (pull the published image, GPU reservation, model-cache volume, .env for the token) so the agent runs with `docker compose up -d` instead of a long docker run. A copy + .env template also placed in ~/Documents/fc-gpu-agent. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
9449241fc2 |
ci(agent): publish the GPU agent image (build-agent job)
Build + push fabledcurator-agent alongside web/ml (own CUDA + onnxruntime-gpu image, context=agent/, same tag cadence: main → :main/:latest/:c-<sha>, tag → :<version>). So the operator PULLS + runs it on the GPU machine instead of building locally. README switched to docker pull. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
8419ebd761 |
feat(agent): desktop GPU agent container — CCIP + figure crops over HTTP (#114)
The last piece: a Dockerised desktop-GPU worker that talks to FC ONLY over HTTP (lease → fetch pixels → detect figures + CCIP-embed → submit), so Redis/Postgres stay private. New top-level agent/ (outside CI scope — verified by running it): - fc_agent/worker.py: the lease/compute/submit loop, concurrency 1, start/pause/ stop (stop frees the card; unprocessed leases expire + re-queue). - fc_agent/models.py: imgutils wrappers — detect_person (figures) + CCIP embed. The two API seams to verify against the installed dghs-imgutils (flagged). - fc_agent/media.py: stills + video frame sampling (ffmpeg) at FC's cadence → per-frame instances (the bag). - fc_agent/crops.py: vendored crop primitive. client.py: the FC HTTP client. - fc_agent/app.py: FastAPI localhost control UI (start/pause/stop + progress + queue depth). Dockerfile (CUDA + onnxruntime-gpu + ffmpeg) + requirements + README (token → build → run --gpus all → Start; CPU-fallback path). This completes the CCIP pipeline end to end: agent produces region CCIP vectors → RegionService stores → matcher suggests characters → rail. Verified by running on the desktop (not CI). README calls out the imgutils API + model-string checks. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
60f26247e9 |
style: alphabetize ccip_bp import (ruff I001)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
de33bab41c |
feat(ccip): read-only observability API for the crop/CCIP work (#114)
So the work can be checked through an API as the agent fills in vectors (same pattern as /api/heads/metrics): - GET /api/ccip/overview: regions by kind, images with figure CCIP vectors, the per-character reference counts (which characters have enough examples to match on), and the embedding versions present. - GET /api/ccip/images/<id>: that image's stored regions (bbox, frame_time, has_ccip/has_siglip, versions) + the CCIP character matches it would get — for spot-checking detector + matcher output. Read-only, no GPU. (Queue depth is already at /api/gpu/status.) Tests: overview coverage counts + per-character refs; per-image regions + matches. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
5faf34a3b5 |
feat(suggestions): overlay CCIP character matches onto the rail (#114)
SuggestionService.for_image now merges CCIP character matches with the SigLIP head suggestions — they're complementary, not exclusive: CCIP is the identity- specialized signal but needs a detected figure; the heads work whole-image but conflate identity with style. Merged by tag: 'both' when they corroborate (higher score wins), 'ccip' / 'head' otherwise. Cheap when no CCIP vectors exist yet (match_image returns early without a figure vector), so it's a no-op until the agent runs. Suggestion.source is now 'head' | 'ccip' | 'both'. Test: a character with a CCIP reference figure surfaces (source='ccip') on a new image whose figure matches. NEXT: the agent container (real CCIP/detector models, hands-on) that produces the vectors this consumes. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
d57ca847e7 |
feat(ccip): few-shot character matcher (#114 slice 5)
The server-side brain that turns stored CCIP vectors into character suggestions
— no GPU. character_references() gathers each character tag's prototype vectors
(figure/face-region CCIP embeddings on images carrying that tag); match_image()
cosine-matches an image's figure vectors against every character (multi-
prototype: best over a character's examples), surfacing those above a tunable
threshold as {tag_id, name, category:'character', score, source:'ccip'},
excluding already-applied characters. v1 = cosine on raw CCIP vectors; the exact
CCIP metric/threshold gets validated against the model in the hands-on eval.
Tests (synthetic vectors): same-character match across images, no-match for an
orthogonal figure, already-applied exclusion, no-figure-vectors empty.
NEXT: merge CCIP character suggestions into the rail; the agent container that
actually produces the vectors (hands-on, GPU — not CI-verifiable).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
|
d91eef7a4b |
feat(gpu): GPU agent admin card — token, queue, backfill (#114)
The FC-side control surface the operator asked for: Settings → Tagging → "GPU agent". Generate/reveal/copy/rotate the agent bearer token (with the FC URL to point the agent at), see the live job-queue depth (pending/in-flight/done/ errored, polled), and a "Queue character embedding (CCIP)" button that triggers the library backfill. Plain-HTTP-safe copy (copyText resolves on success, throws on fail). Closes the "how do I get the token in the UI" gap. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
558d965a1c |
fix(gpu): count backfill enqueues via RETURNING, not rowcount
result.rowcount is unreliable for INSERT…SELECT (returned -1), failing the idempotency assert. Use .returning(GpuJob.id) and count the rows. (run 1652) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
f247f9247c |
style(gpu): ruff — split as-import, dict(rows) over comprehension
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
6cabef07a4 |
feat(gpu): HTTP job API + token auth + backfill — the agent's server side (#114 slice 3b)
The thin HTTP surface over the queue so the desktop agent stays HTTP-only: - Agent endpoints (Authorization: Bearer <token>): POST /api/gpu/jobs/lease (returns jobs + image_url + mime + video frame cadence), /submit (stores regions via RegionService + closes the job; 409 on a stale lease), /heartbeat, /fail. Token validated against AppSetting (mirrors the extension-key pattern, constant-time compare). - Admin (browser): GET/POST /api/gpu/token[/rotate] (generate + show the agent token), GET /api/gpu/status (queue counts), POST /api/gpu/backfill → dispatches enqueue_gpu_backfill. - enqueue_gpu_backfill(task): one INSERT…SELECT enqueues a job per image lacking one for the task (scales to the full library; idempotent). Agent flow: lease over HTTP → fetch pixels via the normal FC image URL → compute on the GPU → submit. Redis/Postgres never exposed. Tests: bearer required (+ wrong-token 401), lease→submit round-trip (region+CCIP vector stored, job done via /status), stale-lease 409, backfill enqueue + idempotency. NEXT: the agent container + control UI, then the CCIP detector/embedder + matcher. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
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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bvandeusenandClaude Opus 4.8
|
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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bvandeusenandClaude Opus 4.8
|
e8d3400d22 |
feat(crops): shared crop primitive for the region/crop pipeline (#114)
The trunk of both crop jobs — CCIP figure-crops and SigLIP concept-crops call the SAME crop_region(): normalized-bbox crop with optional context padding, edge-clamping, and the lower-bound size floor (max of a fraction-of-short-side and an absolute pixel floor) below which a region is too small to embed and returns None. Only the proposer (where) and embedder (what) differ; the crop is shared. Pure Pillow — importable + testable anywhere (the GPU agent imports it for the crop step). Unit-lane tests (no DB): region pixels, floor rejection, edge clamp, pad expansion, out-size resize. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
f6e10ccc4f |
fix(explore): render videos with VideoCanvas, not ImageCanvas
The Explore center pane hardcoded ImageCanvas, so a video anchor (e.g. a 169 MB
MP4) tried to load the MP4 into an <img> and showed only the alt text — the
thumbnail worked but the "main image" never rendered. Branch on
mime.startsWith('video/') to VideoCanvas (with mime), exactly like the image
modal. The anchor payload already carries mime.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
|
ad2921b4a0 |
fix(tags): allow creating a same-named character in a different fandom
The autocomplete suppressed the Create row whenever any existing tag matched name+kind — but characters are unique by (name, kind, fandom), so a same-named character in a different fandom (e.g. another "Raven") is a valid distinct tag. allowCreate now always offers Create for the character kind; the fandom picker disambiguates and find_or_create is idempotent if the same fandom is re-picked. The Create row reads "Create another \"Raven\" character (different fandom)" when a same-name character already exists. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
1463794778 |
feat(heads): auto-apply UI on the Concept-heads card (#114 auto-apply C)
Surfaces earned auto-apply + its observability in Settings → Tagging → Concept heads: - Auto-apply section: an on/off switch (writes head_auto_apply_enabled), the precision-target + min-examples-to-fire tuning inputs, a Preview (dry-run → "would apply N", per-concept chips) and Apply-now button, with live run state. - "How auto-apply is landing": per-concept table from /api/heads/metrics — applied volume, misfires, realized misfire rate (green/amber/red), and missed (under-fires) — the signal to tune the precision target from. store: autoApply(dryRun) / autoApplyStatus() / metrics(). Card polls the sweep to completion, then refreshes counts + metrics. Completes the auto-apply task. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
a5a95320df |
fix(test): disable switch explicitly now that auto-apply defaults ON
test_auto_apply_disabled_blocks_real_run assumed head_auto_apply_enabled defaulted False; it now defaults True (opt-out), so a real sweep is accepted (202). Set the switch off in the test to exercise the disabled→400 path. (run 1629) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
9326a82b29 |
fix(heads): .all() before dict() in snapshot_head_metrics
dict(session.execute(...)) on a bare Result invokes the mapping protocol (a Result has .keys() = column names) and subscripts it → "CursorResult is not subscriptable". Materialize with .all() so dict() consumes rows as key-value pairs. The API path already did this; the snapshot task missed it. Caught by test_snapshot_records_timeseries_point (run 1628). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
48c8811d69 |
feat(heads): auto-apply observability + on by default (#114 auto-apply B)
Auto-apply is now ON by default (operator-asked: opt-OUT, not opt-in) — migration 0059 + model default flipped. The support (>=30) + measured-precision gates keep it safe and every auto-tag is reversible. Observability so the operator can tune from real data: - MISFIRE = an auto-applied (source='head_auto') tag the operator later removes. UNDER-FIRE = a tag with a head the operator adds by hand (the head missed it). Both captured at correction time in TagService.add_to_image/remove_from_image (source is lost on delete) into durable per-tag counters (head_metric), keyed by tag so they survive head retrain/prune. - Daily snapshot_head_metrics writes a per-concept time-series point (head_metrics_snapshot): auto-applied volume + cumulative misfires/under-fires + head quality; 180-day retention; daily beat. - GET /api/heads/metrics: per-concept current counts + realized misfire rate + head quality, plus the snapshot time-series — the report to tune the precision target + support floor. Migration 0060. Tests: misfire/under-fire counting (and the negatives — manual removal isn't a misfire, headless manual add isn't an under-fire), snapshot time-series, metrics API. What's the autofire threshold? There's no single number — each graduated head derives its OWN probability cutoff from its PR curve: the operating point that holds precision >= head_auto_apply_precision (0.97) at max recall. The global knobs are that target + the >=30 support floor. NEXT (slice 3): UI — enable toggle, dry-run preview, per-concept trends. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
01933c5b26 |
style(test): drop unused img in ungraduated-head sweep test (ruff F841)
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
74fef908d2 |
feat(heads): earned auto-apply — sweep mechanism, off by default (#114 auto-apply A)
Graduated heads can now apply their tag without a human — gated so it's safe:
- FIRING GATE: a head fires only when the master switch (head_auto_apply_enabled,
default OFF) is on AND it has >= head_auto_apply_min_positives (default 30)
clean labels. A precise-looking but under-supported low-N head can't spray tags.
- auto_apply_sweep (heads.py): streams every embedded image in chunks, scores
against the eligible heads (numpy, no sklearn), applies each head's tag where
score >= its auto_apply_threshold and the tag isn't already applied/rejected,
with source='head_auto' (distinguishable + reversible). dry_run counts only.
- HeadAutoApplyRun (migration 0059) tracks each sweep / preview; apply_head_tags
task (ml queue) + scheduled_apply_head_tags daily beat (no-op unless enabled)
+ recovery sweep + retention(20).
- API: POST /api/heads/auto-apply {dry_run} (202 / 409 running / 400 disabled),
GET /api/heads/auto-apply (recent runs + per-concept report). Settings
head_auto_apply_enabled + min_positives via /api/ml/settings.
Tests: sweep applies above threshold, dry-run writes nothing, skips under-
supported + ungraduated heads; API disabled/dry-run/conflict guards.
NEXT (slice 2): the observability the operator asked for — per-concept misfire
(auto-applied-then-removed) + under-fire tracking, time-series snapshots, and a
reporting API to tune. Slice 3: the UI (enable, preview, trends).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
|
77baee49fd |
feat(heads): nightly auto-retrain + inline Retrain button in Explore
Two cadences for keeping heads in sync with your tagging: - PASSIVE: a nightly `scheduled_train_heads` beat (skips if a run is already in flight; creates+commits the run row before dispatching train_heads so the ml worker always finds it). Folds the day's accepts/rejects + newly-eligible concepts into the heads without anyone clicking. - ACTIVE: a "Retrain heads" button in the Explore trail bar — bank the +/- feedback you just gave while walking content, without a trip to Settings. Shared logic in a new useHeadTraining composable (trigger + poll + start/finish toasts), used by the Explore button; reflects an already-running run (incl. the nightly one) on mount. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
353b5d8087 |
feat(explore): ← / → keyboard navigation through the walk
Arrow keys walk the Explore breadcrumb trail: ← steps back, → goes forward to an already-visited item or — with no forward history — jumps to a RANDOM neighbour to keep the rabbit-hole going (operator-asked). The trail gains a cursor (browser back/forward semantics): stepping back no longer trims the forward branch, so → can return to it; a genuinely new walk off a back-step truncates the stale branch then appends. The crumb-bar "current" highlight follows the cursor, not the tip. Arrows are ignored while typing a tag, but still navigate when the tag input is focused-but-empty (it auto-focuses after every walk, so otherwise arrow-nav would dead-end after one step). Modifier-key combos pass through untouched. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
ca1c17446c |
feat(suggestions): heads are the suggestion source — Camie + centroid removed (#114 C)
The rail's Suggestions now come from the trained per-concept heads. SuggestionService.for_image scores the image's frozen SigLIP embedding against every head (heads.score_image) and surfaces concepts above each head's own suggest threshold; the typed-dropdown's min=0 "show everything" mode maps to a flat floor so any head-scored concept can still be picked. Already-applied tags drop; rejected tags stay flagged + reversible (unchanged). REMOVED from the suggestion path (rule 22, no fallback): the Camie ImagePrediction candidate/alias/merge pipeline and the per-tag centroid augmentation, plus the now-dead SuggestionService internals (_load_predictions, _threshold_for, _settings, self.aliases, self.centroids). Head suggestions are always canonical tags, so raw_name/via_alias are null/false and the rail's alias kebab is inert by data (its removal + the Camie ingest-tagger rip are the flagged follow-up). for_selection (bulk consensus) now aggregates head suggestions unchanged. Tests rewritten to the head path: test_ml_suggestions (surfaces/applied/ rejected-reversible/override/no-embedding/no-heads), test_suggestions_bulk (consensus), test_api_suggestions (get + dropped the Camie-alias roundtrip), and test_ml_artist_retired (artist not head-eligible via _HEAD_KINDS). DEPLOY NOTE: after this lands, the rail is empty until you run Train heads (Settings → Tagging → Concept heads) — deploy, train, then the rail populates. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
06d5e83da4 |
feat(heads): admin card to train + inspect concept heads (#114 B)
The UI for the heads subsystem: Settings → Tagging → "Concept heads". Shows head count, auto-apply-ready count, and last-trained; a Train/Retrain button (one run at a time, polls while running, surfaces a failed run's error); an empty state guiding the operator to tag first; and a per-concept table (name, category, +tags, AP, P, R, auto-apply ⚡) sorted strongest-first so weak/under- tagged concepts are obvious. Rehydrates status from GET /api/heads on mount so it survives navigation. Pulls head_min_positives from ML settings for copy. Slice C (swap the rail's suggestions to heads, remove Camie + centroid) is next. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
|
1ed0895e8d |
style(heads): fix import ordering (ruff I001)
Alphabetize HeadTrainingRun in models/__init__ + maintenance imports (H before I), and drop the inline comment that split heads.py's import block. Pure import ordering — no behavior change. (run 1601 lint) Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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291b90803d |
fix(test): match rejected suggestion by id, not display casing
test_rejected_tag_surfaced_flagged_then_reversible asserted "Rejectme" but an
existing tag keeps its stored name ("rejectme"), so the suggestion's
display_name is lowercase. Match by canonical_tag_id instead (casing-robust).
The feature was correct — only the assertion was wrong (run 1595 integration).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
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bvandeusenandClaude Opus 4.8
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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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bvandeusenandClaude Opus 4.8
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179c1a9dcc |
feat(suggestions): visible, reversible rejection in the modal rail
A red-✗ dismissal no longer makes the suggestion vanish. The rejected tag stays in the rail — dimmed, struck-through, with a "rejected" pill and a one-click undo (↶) in place of the ✗ — so a misclick is recoverable and the operator can see what they've said no to (operator-asked 2026-06-27). Backend: SuggestionService.for_image now KEEPS rejected tags, flagged rejected=True, sorted to the bottom of their category, instead of dropping them. New AllowlistService.undismiss + POST /suggestions/undismiss clears the TagSuggestionRejection. Rejected items are still excluded from bulk consensus (for_selection) and the type-to-add dropdown, whose jobs are unchanged. Frontend: store.dismiss flags in place (canonical tags) rather than dropping; new store.undismiss reverts. SuggestionItem renders the rejected state and swaps ✗→↶; ✓ still accepts (which clears the rejection server-side). Tests: rejected-surfaced-flagged-then-reversible (service) + undismiss endpoint idempotency (API). Completes #1134's reversible-rejection half. Heads-as-suggestion-source is the remaining piece. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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1d39afa3b6 |
feat(modal): green ✓ / red ✗ verdict pair on suggestion rows
Replace the single "Accept" pill in the modal Suggestions rail with the eval card's green ✓ / red ✗ language: ✓ accepts the tag (positive), ✗ dismisses it for this image — which already persists a TagSuggestionRejection (hard negative the heads train on). The pair occupies ~the footprint of the old pill, so per-image rejection becomes a one-click peer of accepting instead of being buried in the kebab. Dismiss moves off the 3-dot menu, so the kebab now only carries alias actions and is hidden when none apply (centroid hits with no alias option). Toward #1134 (native per-image negatives in the rail). The bigger piece — heads as a suggestion source feeding this panel — is still ahead. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa |
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bvandeusenandClaude Opus 4.8
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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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bvandeusenandClaude Opus 4.8
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4fd8790c85 |
fix(tag-eval): don't re-suggest already-rejected items every run
"head would suggest" drew from the whole negative pool, which INCLUDES the images the operator rejected. A rejected near-miss (e.g. an orc under "goblin") is a hard negative that still scores high, so it kept resurfacing as a fresh suggestion every run (operator-flagged: "same items keep appearing"). Exclude already-rejected ids from the suggest list — once you've said no, it's gone. (head doubts = lowest-scoring positives is unchanged; genuinely-hard true positives legitimately recur there.) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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5143f4c34f |
feat(tag-eval): auto-apply operating point + server-side top-N concept discovery
Two additions driven by "what's the commit threshold?" + "find more tags":
1. High-precision operating point (Bar 4). Per concept, report the threshold that
maximizes recall while holding precision >= a target (default 0.97, configurable
via `precision_target`) — i.e. "could this fire without a human, and how much
would it catch?" `head.auto_apply` = {target, threshold, precision, recall} or
null if the target is unreachable. Surfaced on the card.
2. Server-side concept auto-discovery. `auto_top_n` param unions the explicit
concept list with the N most-tagged general tags (one fast DB query) so the
eval can broaden itself without hand-listing — replaces the slow HTTP directory
paging. Card gains "+ auto-add top-N" and precision-target inputs.
No migration; numpy/sklearn stay lazy. Existing _normalize_params test still
holds (new keys additive; None still falls back to DEFAULT_CONCEPTS).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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bvandeusenandClaude Opus 4.8
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fc64f130b8 |
fix(tag-eval): thumbnail click opens the view modal, not Explore
Clicking an example in the maintenance card navigated to /explore/<id> — heavier than wanted (operator: just want a bigger look). Open the existing app-wide ImageViewer modal via modal.open(id) instead: bigger image + tags in place, no navigation away from Settings. The ✓/✗ actions are unaffected (separate overlay buttons). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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13d297b881 |
feat(tag-eval): inline confirm/reject actions on example thumbnails
Closes the learn-from-tags loop directly on the eval lists (operator-flagged: no surface to confirm/refine the head's suggestions). Each thumbnail gets a green ✓ / red ✗ that writes the SAME tables the head trains on: - suggest + ✓ → apply tag (new positive, POST /images/<id>/tags) - suggest + ✗ → record rejection (hard negative, suggestions/dismiss) - doubt + ✗ → remove tag + record rejection (kill bad positive, add negative) - doubt + ✓ → keep (stays a positive, no write) Acted thumbs grey out with a badge; re-run to see the head sharpen. Thumb still links to /explore/<id>. All endpoints already existed — no backend change. Inline is the starting point; longer-term the modal Suggestions rail gets the red "No" (negative) so per-image rejection is native there too (next slice). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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4974b7cf77 |
feat(tag-eval): bigger, clickable example thumbnails (label-review queue)
The 56px example thumbs were too small to judge a label (operator-flagged). Bump to 120px and wrap each in a link to /explore/<id> (new tab) so the "head doubts / would suggest" galleries double as a review-and-fix queue — click a doubted positive, land on it in Explore, correct the tag, re-run. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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6cd7281af5 |
feat(settings): tag-eval admin card — trigger + persisted report (survives nav)
Frontend for #1130. A maintenance tile in Settings → Tagging: - Editable concept list + "Run eval" → POST /api/tag-eval (one running at a time). - Rehydrates on mount via the persisted run (getRun by latest id) and polls while running — so the report SURVIVES navigation (operator-flagged); the task runs backend-side regardless and the card reconnects to its row. - Renders the saved report: per-concept head-vs-centroid metrics table (AP/F1/ precision/recall) with Δ AP, the learning curve (AP @ N positives), and thumbnail galleries (head-would-suggest / head-doubts-positive) for eyeballing. Backend: _examples now stores thumbnail_urls (not just ids) so the report is a self-contained artifact that renders without per-id lookups on reload. No new top-level surface — slots into the existing maintenance area. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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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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bvandeusenandClaude Opus 4.8
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958378312c |
fix(settings): sticky headers on the virtual data tables
Allowlist / Alias / ImportTask tables scroll their bodies (height=360/480) but the column headers scrolled away with the rows, so you lost the column labels (operator-flagged 2026-06-27). Add Vuetify `fixed-header` so the header row stays pinned while the body scrolls. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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d63dfa511a |
fix(explore): bound the 3-pane grid row so a tall rail can't scroll the page
The panes grid had no explicit row, so the implicit `auto` row sized to its tallest pane's content. With Provenance + Tags + a long Suggestions list, the rail outgrew the fixed-height workspace, spilled over and made the WHOLE page scrollable — showing as a weird empty gap at the top (operator-flagged 2026-06-26). grid-template-rows: minmax(0, 1fr) bounds the row to the container so each pane's own overflow-y:auto scrolls internally instead. Reset to `none` in the stacked (<=1100px) layout where the page is meant to scroll. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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e34f79fc56 |
feat(explore): show Provenance in the tag rail (post often names the character)
The post title/description frequently names the character, so surface it while tagging in Explore (operator-asked 2026-06-26). ProvenancePanel gains optional imageId/image props (default = modal store, so the modal is unchanged) since provenance is its own system loaded by id; ExploreView renders it above TagPanel in the right rail, hosted on the anchor. Self-collapses when the image has no provenance. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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c8a8e23050 |
feat(explore/tags): return focus to the tag input after every action
Explore is a rapid walk-and-tag surface, so focus must keep returning to the tag input with no extra click (operator-asked 2026-06-26). Two gaps closed: - Navigation hardening: refocus on every focused-image change (neighbour click, breadcrumb, Random image, seed) now runs nextTick → requestAnimationFrame, so it lands AFTER the post-navigation re-render/paint instead of being stolen back by the neighbour-grid re-render. - All tag actions refocus, in both Explore and the modal: tag add (existing/new) and remove now hand focus back like accept-suggestion already did; and the rename + fandom-assignment dialogs refocus on @after-leave (fires after Vuetify's own focus-return to the activator, so ours wins). TagAutocomplete's mobile guard is preserved throughout (no soft-keyboard pop on touch). Modal behaviour gains the same stickier focus — consistent, low-risk. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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e3855a5ae0 |
chore(tags): remove orphaned cluster tag-gaps route + service method
The cluster tag-gap feature's only UI (Explore's TagGapPanel) was removed in the 3-pane rework, leaving the backend that fed it with no caller. Surgical removal: - drop the POST /api/images/cluster/tag-gaps route (cluster_tag_gaps) - drop BulkTagService.tag_gaps (+ the now-unused `import math`) - drop the tag_gaps tests (test_bulk_tag_service, test_api_bulk_tags) BulkTagService's common_tags / bulk_add / bulk_remove stay — they still back the gallery bulk editor. Pure deletion, no behaviour change. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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5be6b9cada |
feat(explore): auto-focus the tag input on every image change
The workspace is built for rapid walk-and-tag, but the tag field was only focused once (TagAutocomplete's on-mount autofocus) — walking to a neighbour left focus behind, so the operator had to click the field each time (operator-asked 2026-06-26). TagPanel now exposes focusTagInput; ExploreView watches the focused image id and re-focuses the field on seed + every walk via nextTick. Reuses the existing focus path, so TagAutocomplete's mobile guard (no soft-keyboard pop on touch) is preserved. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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4a1f255164 |
fix(modal): place meta + save block under Provenance, above Tags
Operator-clarified 2026-06-26: the dimensions/size/type + save (floppy) block should sit DIRECTLY above the Tags section — i.e. just under Provenance — not at the very top of the rail. Reorder the rail's main scroll area to Provenance → ImageMetaBar → TagPanel (Related stays pinned at the bottom). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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1728b43167 |
fix(modal): pin Related to rail bottom, floppy-disk download, drop suggestions cap
Right-rail layout fixes (operator-flagged 2026-06-26 — the prior change wasn't
the intended improvement):
- Pin the Related strip to the BOTTOM of the rail: the side becomes a flex
column with a scrolling main area (meta + provenance + tags + suggestions)
and a pinned Related footer (capped at 45% of the rail, scrolls past that).
Related now stays reachable no matter how long Tags/Suggestions run, and
self-collapses (no footer space) when there's nothing to show.
- Remove the 320px suggestions scroll cap (
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bvandeusenandClaude Opus 4.8
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2d1cddd9b7 |
feat(explore): 3-pane tagging workspace — gallery | viewer | tag rail
Reworks Explore from "anchor + neighbour grid + cluster tag-gap rail" into a persistent 3-pane workspace that unfolds the image modal so you can tag while rabbit-holing (operator concept 2026-06-26): - LEFT neighbour grid (larger thumbs), click = walk; breadcrumb retained. - CENTER light viewer — reuses ImageCanvas + ImageMetaBar(:image) for the focused image; "Open full viewer" still launches the overlay modal. - RIGHT the modal's TagPanel, hosted on the anchor for modal-parity tagging (chips, autocomplete, suggestions + Accept, fandom-on-chip, T/"/" focus). Reuse without destabilising the audited modal store: TagPanel and SuggestionsPanel gain an optional `host` prop (default = modal store, so the image modal is unchanged); the explore store implements the same small tag-CRUD surface (current/currentImageId + reloadTags/addExistingTag/ removeTag/createAndAdd) over the anchor. ImageMetaBar gains an optional `image` prop for the same reason. Drops the mass/cluster tagger (TagGapPanel deleted; clusterIds/thumbById removed) — per-image tagging feeds the per-tag reference-embedding centroid better than bulk ops. Nav: keep the Explore tab but bare /explore now SEEDS a random image (GET /api/showcase?limit=1 → /explore/:id) so the tab kick-starts a rabbit hole; explicit meta.navOrder pins nav order (Explore after Gallery) since router.getRoutes() doesn't preserve declaration order. Note: the backend cluster tag-gaps route/service (#94a) is now frontend-orphaned — left in place; flag for a separate cleanup. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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bvandeusenandClaude Opus 4.8
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1aadf3267b |
fix(tags): correct directory image_count — fandom leg must correlate the outer tag
The directory card count regressed to a globally-inflated number (~every card showed the same ~469): the fandom leg used a doubly-nested correlated subquery — image_tag.tag_id IN (SELECT member.id WHERE member.fandom_id == Tag.id) — whose inner predicate did not correlate the outer Tag, so it matched EVERY character that has any fandom and counted all their images for every tag. The gallery scope and cleanup count were unaffected (they pass a literal tag id, a single-level subquery), which is why only the card diverged from the gallery. Rewrite the count as a single-level correlated scalar subquery: join `member` (the tag applied to the image) and match image_tag.tag_id == Tag.id (direct) OR member.fandom_id == Tag.id (a character of this fandom). Strengthen the directory test with a second unrelated fandom/character so a non-correlating fandom leg fails (count would read 4 instead of 3). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |