feat(wip): soft title tier — sketch/doodle vocab + ring-loud audit (#1474)
Extends WIP title-tagging to lower-precision cues (sketch/doodle/scribble) safely. - wip_title.py: soft matcher (word-anchored; sketchbook/kadoodle don't trip it); WIP_TITLE_SOFT_SOURCE + soft SQL prefilter; apply_wip_image_tags takes a source arg. - training_data._AUTO_SOURCES += 'wip_title_soft' → the soft tier is PROVISIONAL and never trains the wip head (a finished "sketch" can't pollute it). Only the hard tier (wip_title) + manual train. - ImportSettings.wip_soft_title_tagging_enabled (OFF by default, opt-in). Migration 0087. - importer: hard tier wins, soft is the fallback (source wip_title_soft). - backfill: refactored into a shared _backfill_wip_tier; hard always, soft when enabled. - heads.soft_wip_conflict_audit + daily beat: score soft-tagged images against content heads, flag ring-loud ones (PresentationReview mode=process) for the review strip — the operator's "measure if they got falsely tagged" safety. - api settings toggle; ImportFiltersForm soft toggle. - tests: soft matcher pos/neg; soft source not a training positive; audit flags ring-loud + spares quiet. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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@@ -136,17 +136,53 @@ def test_process_sweep_flags_conflict_with_process_mode(db_sync):
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def test_process_auto_source_never_trains_head(db_sync):
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# The runaway break: a wip tag the process sweep applied (source='process_auto')
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# is NOT a training positive; a title-heuristic / manual one IS. So the head
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# learns only from trusted labels, never its own output.
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# The runaway break: provisional wip tags (process sweep 'process_auto', soft
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# title 'wip_title_soft') are NOT training positives; a HARD title-heuristic /
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# manual one IS. So the head learns only from trusted labels, never its own
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# output or the low-precision sketch/doodle tier (#1464 + #1474).
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wip = _system_tag(db_sync, "wip")
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auto_img = _img(db_sync, "f" * 64, _emb(0))
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soft_img = _img(db_sync, "9" * 64, _emb(2))
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title_img = _img(db_sync, "0" * 64, _emb(1))
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db_sync.execute(image_tag.insert().values(
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image_record_id=auto_img.id, tag_id=wip.id, source="process_auto"))
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db_sync.execute(image_tag.insert().values(
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image_record_id=soft_img.id, tag_id=wip.id, source="wip_title_soft"))
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db_sync.execute(image_tag.insert().values(
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image_record_id=title_img.id, tag_id=wip.id, source="wip_title"))
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db_sync.commit()
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positives = set(_ids_with_tag(db_sync, wip.id))
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assert title_img.id in positives # trusted label trains the head
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assert title_img.id in positives # trusted HARD label trains the head
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assert auto_img.id not in positives # its own auto-applied output does NOT
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assert soft_img.id not in positives # low-precision soft tier does NOT
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def test_soft_wip_conflict_audit_flags_ring_loud(db_sync):
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# A soft-tagged image (sketch/doodle title) that ALSO scores high on a content
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# head is probably finished art mis-tagged — flagged for review; a quiet one is not.
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from backend.app.services.ml.heads import soft_wip_conflict_audit
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s = db_sync.execute(select(MLSettings).where(MLSettings.id == 1)).scalar_one()
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s.process_conflict_threshold = 0.6
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wip = _system_tag(db_sync, "wip")
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content = Tag(name="looksreal", kind=TagKind.general)
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db_sync.add(content)
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db_sync.flush()
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_head(db_sync, content.id, 0, weight=1.0) # sigmoid(1)=0.73 > 0.6 conflict
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ring = _img(db_sync, "1" * 64, _emb(0)) # scores on the content head
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quiet = _img(db_sync, "2" * 64, _emb(5)) # orthogonal → 0.5 < 0.6
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for img in (ring, quiet):
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db_sync.execute(image_tag.insert().values(
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image_record_id=img.id, tag_id=wip.id, source="wip_title_soft"))
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db_sync.commit()
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res = soft_wip_conflict_audit(db_sync)
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assert res["n_flagged"] == 1
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flag = db_sync.execute(
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select(PresentationReview).where(PresentationReview.image_record_id == ring.id)
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).scalar_one()
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assert flag.mode == "process"
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assert flag.conflict_tag_id == content.id
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assert db_sync.execute(
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select(PresentationReview).where(PresentationReview.image_record_id == quiet.id)
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).scalar_one_or_none() is None
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