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
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
Cluster B, milestone #99. Backend for the allowlist tuning dashboard.
#7a: AllowlistService.coverage(tag_id, threshold) counts distinct images with
a prediction resolving to the tag (raw_name==tag.name OR (raw_name,category) in
the tag's aliases) scoring >= threshold — the gross candidate pool, mirroring
tasks.ml._confidence_for_tag resolution. list_all now carries applied_count
(grouped image_tag count) + coverage_count (at the row's threshold). New
GET /api/tags/<id>/allowlist/coverage?threshold= for the live what-if number.
#7b: /suggestions/accept + /alias return {allowlisted, tag_id, tag_name,
projected_count} (projection at the tag's threshold) instead of 204, so the UI
can show a non-blocking 'auto-applying to ~N images' toast. Apply still runs
async via apply_allowlist_tags — projected_count is an estimate.
Tests: coverage by threshold (direct + alias-with-category), list applied vs
coverage, coverage route (explicit/default/bad threshold), accept/alias payload
(newly-allowlisted vs already-on-list).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XCUHUGQLrBrkgyk1t49kpX
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>
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>
bvandeusen