Read cutover verified in prod (suggestions + allowlist read image_prediction;
backfill complete at 908k rows / 51k images). Removes the old JSON column and
everything that fed it:
- ImageRecord.tagger_predictions column removed; migration 0046 DROPs it.
tagger_model_version kept as the "tagged / current?" signal the backfill
sweep reads (needs-tagging check switched to tagger_model_version IS NULL).
- tag_and_embed no longer dual-writes the JSON — image_prediction is the only
write path.
- importer re-import reset drops the JSON line (image_prediction rows are
already deleted on re-import).
- Retired the one-time #768 backfill task + the #764 prune task, their admin
endpoints, and their Maintenance cards (Backfill/PrunePredictionsCard).
- Tests seed/assert via image_prediction; stale column refs removed.
Disk reclaim is NOT automatic: DROP COLUMN is a catalog change. Run
`VACUUM FULL image_record` off-hours afterward to return the ~100 GB to the OS
so DB backups go small (#739). image_prediction (~90 MB) stays in pg_dump — it's
the source of truth now.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Switch every prediction READER off the JSON column onto the normalized
image_prediction table. Parity by construction: each reader loads the same
{raw_name: {category, confidence}} dict it consumed before (via small
_load_predictions helpers), so all downstream threshold/alias/merge/consensus
logic is byte-identical — only the data source changed.
- suggestions.SuggestionService.for_image (and for_selection via it)
- ml.apply_allowlist_tags (iterates images that have prediction rows)
- importer re-import reset deletes the image's prediction rows
The tagger_predictions JSON column is still dual-written (step 1) so it stays
valid during transition; the backfill task's NULL check still works. Removing
the JSON write + DROP column + retiring the #764 prune is the cleanup
follow-up (needs a quiesced-worker window for the DROP lock).
Tests: shared tests/_prediction_helpers.seed_predictions seeds the table;
read-path tests (suggestions, bulk consensus, allowlist apply, API) seed there
instead of ImageRecord.tagger_predictions.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Consumer #4 of the store-floor change (#764). An allowlist tag can't
auto-apply more permissively than the ingest floor — predictions below
tagger_store_floor aren't stored, so a lower min_confidence behaves
identically to the floor. update_threshold now clamps to max(value, floor);
the AllowlistTable confidence input min-binds to the live floor and clamps
on edit. Keeps the stored threshold honest about actual apply behavior.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Promotes the prediction store-floor from the TAGGER_STORE_FLOOR env (default
0.05) to a DB-backed, Settings-UI-tunable ml_settings column (default 0.70).
Storing every tag down to 0.05 from a ~10k-tag tagger is what grew
image_record's TOAST to ~100 GB; the suggestion path already filters at 0.70
and the centroid/learned path covers lower-confidence preferred tags, so the
sub-0.70 tail is redundant. Foundation for plan-task #764 (backfill + reclaim
land next; this only changes the write gate for NEW imports).
- ml_settings.tagger_store_floor (migration 0044, default 0.70)
- tagger.Tagger.infer(store_floor=...); ml task passes settings.tagger_store_floor
- ML admin GET/PATCH expose it; PATCH rejects a category suggestion threshold
below the floor (nothing below the floor is stored, so the gap surfaces
nothing) — server backstop for the UI slider clamp
- Settings → ML: store-floor slider + caption; category sliders min-bound to it
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The typed dropdown sourced the threshold-filtered panel list (>= 0.70 general),
so low-confidence actions/features the model DID predict never appeared — forcing
hand-typed custom tags instead of accepting the model's canonical formatting.
Add a threshold override: SuggestionService.for_image(threshold_override=) and
GET /images/<id>/suggestions?min=<f> surface EVERY stored prediction (down to the
0.05 store floor), alias-resolved and normalized, still excluding applied/rejected
and unsurfaced categories. The suggestions store gains allByCategory + loadAll
(min=0); the dropdown searches that full set (cap 20), while the Suggestions panel
stays curated at the configured threshold. Accept/dismiss drop from both lists.
Operator-asked 2026-06-09. Test: a 0.30 general prediction is hidden by default
but surfaced with threshold_override=0.0; unsurfaced categories still excluded.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Rule 8 'no letters -> drop' was over-eager: bare digit tags like '2005'
returned None even though they're legitimate (booru year-tag shape).
Widen the keep-condition to any alphanumeric. Emoticons (':/', '^_^',
'+_+') still drop since they contain neither letters nor digits.
Camie's booru-style vocab strings (`uchiha_sasuke_(naruto)`,
`#unicus_(idolmaster)`, `1000-nen_ikiteru_(vocaloid)`, `:/`) were
surfacing raw in SuggestionsPanel — and worse, the SAME raw string was
written to tag.name on Accept, polluting the DB with `underscored_lowercase`
names that don't match the operator's "Title Case" tag convention.
Add backend/app/services/ml/tag_name.py with a single normalize()
applying nine rules (strip leading junk #/./+/;/~/_/ws, drop trailing
_(disambiguator) blocks iteratively, strip wrapping quotes, underscores
to spaces, space after colon, title-case each word's first char,
preserve hyphens/apostrophes/digits, drop entries with no letters).
Wire into SuggestionService.for_image:
- raw Camie key kept for alias_map lookup (alias rows are hand-curated
against raw keys; don't disturb)
- display_name = normalize(raw); None means drop the candidate
- existing-tag lookup widened to case-insensitive match against BOTH
raw and normalized forms so legacy underscore-named Tag rows accepted
before this change still surface as "existing" not "+ new"
Closes the last two findings from the 2026-06-02 audit (G5.1 + G5.4).
G5.1 — Centroid version no longer drifts:
CentroidService now reads MLSettings.embedder_model_version (the DB
row tag_and_embed already writes from) for both the centroid model-
version stamp and the drift-detection comparison. Previously the
centroid sites imported MODEL_VERSION from env, so the version stamped
on centroids could disagree with the version stamped on the embeddings
they were built from. By construction those now match, so list_drifted
won't silently miss the env-vs-DB drift case.
embedder.py keeps MODEL_VERSION as an env-driven constant for the
actual model loader — that's a different concern (which weights are
loaded) from the version-stamp that gets persisted alongside data.
G5.4 — Modal is a Pinia-only overlay:
The previous URL↔modal sync in GalleryView and ArtistGalleryTab
leaked the modal across route changes (RouterLink to /artist/<slug>
left the modal mounted on top of the new route) and re-opened it
on history back/forward with stale ?image=N entries.
Now: openImage() just calls modal.open(id) — no URL push.
GalleryView's dead closeImage helper is deleted. A route.name
watcher in App.vue closes the modal whenever the route changes,
which auto-fixes RouterLink-in-modal and back/forward.
Backward-compat: ?image=N is still honored on initial mount as a
one-shot deep-link opener, then router.replace strips the query so
the URL doesn't re-trigger and no extra history entry is added.
Existing bookmarks / shared URLs keep working; new opens stay
Pinia-only.
Four coupled operator-asked changes to the view modal (Scribe plan #509):
1. **Autofocus tag entry on modal open** — TagAutocomplete grabs focus
in onMounted/nextTick so the caret is in the input the moment the
modal renders. No click needed to start typing.
2. **General suggestions expanded by default** — SuggestionsPanel's
general-category group now mounts with `:default-open="true"`.
Operator can collapse if too noisy, but the v1 frame shows them.
3. **Lower general threshold default 0.95 → 0.50** — MLSettings.
suggestion_threshold_general default matches character. Alembic
0029 also bumps the existing singleton row's value if it's still
at the old 0.95. Operator can re-tune from Settings → ML.
4. **Retire `copyright` + `artist` as ML suggestion categories** —
neither feeds a Tag.kind (`artist` retired in FC-2d-vii-c, never
really existed as a copyright tag-kind). They were surfaced in the
suggestions pipeline + threshold settings UI but had no follow-
through. Drop from SURFACED_CATEGORIES, suggestions._threshold_for,
ml_admin GET/PATCH allowlist, MLSettings columns (alembic 0029
drops the two columns), frontend CATEGORY_ORDER + CATEGORY_LABELS,
SuggestionsPanel.peopleCats, AliasPickerDialog kind-check, and
MLThresholdSliders rows.
Out of scope (intentional): `tag_kind` Postgres enum still includes
`artist` for historic Tag row queryability (per the model comment);
no operator pain reported, no enum-shrink needed.
Tests:
- test_surfaced_categories asserts {character, general}, excludes
artist + copyright.
- test_threshold_for_artist_is_unsurfaced extended to cover copyright.
- test_get_and_patch_settings asserts new 0.50 default and the absent
artist + copyright keys in the GET payload.
The HF repo Camais03/camie-tagger-v2 has camie-tagger-v2.onnx (789 MB)
+ camie-tagger-v2-metadata.json (7.77 MB) at root, NOT model.onnx +
selected_tags.csv. Tags ship as nested JSON (dataset_info.tag_mapping)
not CSV. Per the published onnx_inference.py reference: input is NCHW
not NHWC, normalize with ImageNet mean/std, pad-square color (124,116,
104), sigmoid the second output (refined predictions) not the first.
Operator hit this during the IR migration ML backfill — download_models
silently fetched only 3 json files (allow_patterns matched nothing
useful), tagger.load() then raised RuntimeError. Fetched the actual
v2 layout via WebFetch, rewrote tagger to match published reference.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
onnxruntime is in requirements-ml.txt only (deliberately kept out of the
lean web image and CI). The top-level `import onnxruntime` broke pytest
collection of test_ml_tagger / test_ml_suggestions / test_tasks_ml even
though those are pure-logic/integration-marked, because collection
imports the module.
Mirrors the embedder's lazy-torch pattern: onnxruntime is imported inside
Tagger.load(), placed AFTER the file-existence checks so
test_load_raises_when_model_missing still gets RuntimeError (not
ModuleNotFoundError) in onnxruntime-less environments. self._session
annotation dropped to a comment to avoid an eval-time ort reference.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
AllowlistService: accept (apply ml_accepted + add to allowlist + clear
rejection; returns whether newly-added so API can kick retro-apply),
add_alias_and_accept, dismiss, reject_applied_tag (remove + record
rejection so the allowlist won't re-apply), threshold update, remove,
list_all.
TagService.rename: refuses on (name, kind, fandom_id) collision with a
message pointing at FC-2c merge. Tests marked integration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
The read path: load tagger_predictions, drop unsurfaced categories
(rating/meta/year), apply per-category thresholds, batch-resolve aliases,
skip applied + rejected, augment with centroid hits above the similarity
threshold, merge duplicate signals (take max score, mark source 'both'),
group by category, sort by score DESC. Tests marked integration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
recompute_for_tag (mean of member embeddings, eligible-kind + min-refs
gated, upsert), list_drifted (the delta-gate: member-count mismatch OR
missing OR wrong model version), find_similar_tags (pgvector cosine
distance, similarity = 1 - distance). Tests marked integration.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
resolve() / resolve_many() (batch, used by the suggestion read path),
idempotent create, remove, list_all. Category-scoped so 'naruto' as
character vs copyright map to different canonicals. Tests marked
integration (real DB).
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
Direct port of ImageRepo's siglip.py. Lazy torch/transformers import so
the web container can import the module (for enqueue logic) without the
torch cost. EMBED_DIM=1152 asserted against the schema's Vector(1152)
columns. Real inference runs in the local integration suite.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
CPU-only, lazy-loaded, process-singleton ONNX session. Parses Camie's
string-category selected_tags.csv (vs WD14's integer Danbooru ids).
STORE_FLOOR (0.05) keeps the stored predictions JSON compact;
SURFACED_CATEGORIES gates which categories the suggestion UI shows
(meta/rating/year stored but never surfaced).
Inference itself isn't unit-tested (1GB model not in CI); the missing-
model error path and pure-logic surface are. Full inference runs in the
local integration suite.
Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
bvandeusen