The headline bug: aliases created from the modal NEVER resolved. Create
sent the normalized display name ('Sword', 'Uchiha Sasuke') while
resolution keys on the raw booru model key ('sword', 'uchiha_sasuke',
case-sensitive) — so the mapping was stored under a key nothing looks up,
and the prediction kept reappearing unaliased. The raw key wasn't even in
the /suggestions response, so the modal couldn't send it.
- Suggestion now carries raw_name (the model key an alias must use) and
via_alias (surfaced via an operator alias); both serialized by the API.
- Modal alias-create sends raw_name, not display_name (the fix). Aliased
suggestions show an 'alias' badge and a 'Remove alias' action; 'Treat as
alias for…' is hidden for centroid hits (no model key) and already-aliased
rows.
- Tag-side management: TagCard ⋮ → 'Aliases…' opens a dialog listing the
model keys that fold into a tag, with remove (GET /api/tags/<id>/aliases +
AliasService.list_for_tag). Creation stays in the modal suggestion flow.
Tests: full API round-trip locking the raw-key contract (raw_name exposed →
alias authored with it → resolves + via_alias on a later image);
list_for_tag (service + API); via_alias/raw_name on the existing service
suggestion tests. No migration.
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>
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>
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"
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 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>
bvandeusen