22cdf0f334
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>
144 lines
4.8 KiB
Python
144 lines
4.8 KiB
Python
import pytest
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from backend.app.models import ImageRecord, TagKind
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from backend.app.models.tag import image_tag
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from backend.app.services.ml.aliases import AliasService
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from backend.app.services.ml.suggestions import SuggestionService
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from backend.app.services.tag_service import TagService
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pytestmark = pytest.mark.integration
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def _img(sha: str) -> ImageRecord:
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return ImageRecord(
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path=f"/images/{sha}.jpg",
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sha256=sha,
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size_bytes=1,
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mime="image/jpeg",
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width=1,
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height=1,
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origin="imported_filesystem",
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integrity_status="unknown",
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)
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async def _seed_img(db, sha: str, predictions: dict) -> ImageRecord:
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"""#768: create an image + seed its predictions into image_prediction
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(the read path's source), returning the flushed record."""
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from tests._prediction_helpers import seed_predictions
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img = _img(sha)
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db.add(img)
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await db.flush()
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await seed_predictions(db, img.id, predictions)
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return img
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@pytest.mark.asyncio
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async def test_threshold_filters_low_confidence_general(db):
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# Default general threshold is 0.50 (alembic 0029 lowered it from
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# 0.95). Use 0.30/0.60 to keep the test asserting threshold behavior
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# rather than the exact cutoff number.
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img = await _seed_img(
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db,
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"a" * 64,
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{
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"lowconf": {"category": "general", "confidence": 0.30},
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"sword": {"category": "general", "confidence": 0.97},
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},
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)
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sl = await SuggestionService(db).for_image(img.id)
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names = [s.display_name for s in sl.by_category.get("general", [])]
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# display_name is normalized (tag_name.normalize) before surfacing.
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assert "Sword" in names
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assert "Lowconf" not in names
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@pytest.mark.asyncio
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async def test_threshold_override_surfaces_low_confidence(db):
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# The typed-dropdown "show everything the model saw" mode: threshold_override
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# surfaces stored predictions below the configured threshold (in canonical
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# formatting) so they can be picked instead of hand-typed (2026-06-09).
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img = await _seed_img(
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db,
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"d" * 64,
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{
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"lowconf": {"category": "general", "confidence": 0.30},
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"sword": {"category": "general", "confidence": 0.97},
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},
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)
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sl = await SuggestionService(db).for_image(img.id, threshold_override=0.0)
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names = [s.display_name for s in sl.by_category.get("general", [])]
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assert "Sword" in names
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assert "Lowconf" in names # below the configured threshold, surfaced anyway
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# Unsurfaced categories are still excluded even with the override.
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img2 = await _seed_img(
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db, "e" * 64, {"safe": {"category": "rating", "confidence": 0.99}}
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)
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sl2 = await SuggestionService(db).for_image(img2.id, threshold_override=0.0)
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assert "rating" not in sl2.by_category
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@pytest.mark.asyncio
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async def test_unsurfaced_category_dropped(db):
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img = await _seed_img(
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db,
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"b" * 64,
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{"safe": {"category": "rating", "confidence": 0.99}},
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)
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sl = await SuggestionService(db).for_image(img.id)
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assert "rating" not in sl.by_category
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@pytest.mark.asyncio
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async def test_alias_resolution(db):
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tags = TagService(db)
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canonical = await tags.find_or_create("Sasuke Uchiha", TagKind.character)
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await AliasService(db).create("uchiha_sasuke", "character", canonical.id)
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img = await _seed_img(
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db,
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"c" * 64,
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{"uchiha_sasuke": {"category": "character", "confidence": 0.96}},
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)
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sl = await SuggestionService(db).for_image(img.id)
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chars = sl.by_category["character"]
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assert len(chars) == 1
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assert chars[0].display_name == "Sasuke Uchiha"
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assert chars[0].canonical_tag_id == canonical.id
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assert chars[0].creates_new_tag is False
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@pytest.mark.asyncio
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async def test_raw_tag_creates_new(db):
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img = await _seed_img(
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db,
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"d" * 64,
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{"brand_new_tag": {"category": "character", "confidence": 0.96}},
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)
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sl = await SuggestionService(db).for_image(img.id)
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chars = sl.by_category["character"]
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# display_name is the normalized Camie name (underscores -> spaces,
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# title-cased), not the raw vocab key.
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assert chars[0].display_name == "Brand New Tag"
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assert chars[0].creates_new_tag is True
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assert chars[0].canonical_tag_id is None
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@pytest.mark.asyncio
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async def test_applied_tag_not_suggested(db):
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tags = TagService(db)
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tag = await tags.find_or_create("alreadyhere", TagKind.character)
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img = await _seed_img(
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db,
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"e" * 64,
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{"alreadyhere": {"category": "character", "confidence": 0.96}},
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)
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await db.execute(
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image_tag.insert().values(
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image_record_id=img.id, tag_id=tag.id, source="manual"
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)
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)
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sl = await SuggestionService(db).for_image(img.id)
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assert "character" not in sl.by_category or not sl.by_category["character"]
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