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refactor(ml): retire the Camie tagger + allowlist bulk-apply (#1189)
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Browse Source 
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
This commit is contained in:
Bryan Van Deusen
2026-06-30 13:04:31 -04:00
parent 3d77a38a25
commit 485387ff0b
31 changed files with 159 additions and 1710 deletions
Download Patch File Download Diff File Expand all files Collapse all files
alembic/versions/0067_retire_camie_allowlist.py
+66
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@@ -0,0 +1,66 @@
"""retire the Camie tagger + allowlist bulk-apply (#1189)
The v2 pivot made heads + CCIP the tag source and head auto-apply the earned
propagation. The Camie tagger ran only to feed the allowlist bulk-apply (its
predictions had no other consumer), and the allowlist was a second, un-earned
auto-apply path parallel to heads. Both are retired — drop their storage.
(image_prediction = Camie's per-image predictions; tag_allowlist = the bulk-
apply allowlist. Nothing references INTO these tables, so the drop is clean.)
Revision ID: 0067
Revises: 0066
Create Date: 2026-06-30
"""
from typing import Sequence, Union
import sqlalchemy as sa
from alembic import op
revision: str = "0067"
down_revision: Union[str, None] = "0066"
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None
def upgrade() -> None:
op.drop_table("image_prediction")
op.drop_table("tag_allowlist")
def downgrade() -> None:
op.create_table(
"tag_allowlist",
sa.Column("tag_id", sa.Integer(), nullable=False),
sa.Column(
"min_confidence", sa.Float(), nullable=False, server_default="0.9"
),
sa.Column(
"created_at", sa.DateTime(timezone=True),
server_default=sa.func.now(), nullable=False,
),
sa.ForeignKeyConstraint(["tag_id"], ["tag.id"], ondelete="CASCADE"),
sa.PrimaryKeyConstraint("tag_id"),
sa.CheckConstraint(
"min_confidence >= 0 AND min_confidence <= 1",
name="ck_tag_allowlist_confidence_range",
),
)
op.create_table(
"image_prediction",
sa.Column("id", sa.Integer(), primary_key=True),
sa.Column("image_record_id", sa.Integer(), nullable=False),
sa.Column("raw_name", sa.String(length=255), nullable=False),
sa.Column("category", sa.String(length=32), nullable=False),
sa.Column("score", sa.Float(), nullable=False),
sa.ForeignKeyConstraint(
["image_record_id"], ["image_record.id"], ondelete="CASCADE"
),
)
op.create_index(
"ix_image_prediction_image", "image_prediction", ["image_record_id"]
)
op.create_index(
"ix_image_prediction_name_score", "image_prediction",
["raw_name", "score"],
)
backend/app/api/__init__.py
-2
View File
@@ -16,7 +16,6 @@ api_bp.add_url_rule("/health", view_func=health.get_health, methods=["GET"])
def all_blueprints() -> list[Blueprint]:
from .admin import admin_bp
from .aliases import aliases_bp
from .allowlist import allowlist_bp
from .artist import artist_bp
from .artists import artists_bp
from .attachments import attachments_bp
@@ -58,7 +57,6 @@ def all_blueprints() -> list[Blueprint]:
cleanup_bp,
import_admin_bp,
suggestions_bp,
allowlist_bp,
aliases_bp,
tag_eval_bp,
heads_bp,
backend/app/api/allowlist.py
-84
View File
@@ -1,84 +0,0 @@
"""Allowlist API: list, adjust threshold, remove."""
from quart import Blueprint, jsonify, request
from ..extensions import get_session
from ..models import TagAllowlist
from ..services.ml.allowlist import AllowlistService
allowlist_bp = Blueprint("allowlist", __name__, url_prefix="/api")
@allowlist_bp.route("/allowlist", methods=["GET"])
async def list_allowlist():
async with get_session() as session:
rows = await AllowlistService(session).list_all()
return jsonify(
[
{
"tag_id": r.tag_id,
"tag_name": r.tag_name,
"tag_kind": r.tag_kind,
"min_confidence": r.min_confidence,
"applied_count": r.applied_count,
"coverage_count": r.coverage_count,
}
for r in rows
]
)
@allowlist_bp.route("/tags/<int:tag_id>/allowlist/coverage", methods=["GET"])
async def coverage(tag_id: int):
"""Live "at threshold T, a sweep would cover ~N images" projection for the
allowlist tuning dashboard. Defaults to the tag's stored threshold."""
raw = request.args.get("threshold")
async with get_session() as session:
svc = AllowlistService(session)
if raw is not None:
try:
threshold = float(raw)
except ValueError:
return jsonify({"error": "threshold must be a float"}), 400
if not (0 < threshold <= 1):
return jsonify({"error": "threshold must be in (0, 1]"}), 400
else:
row = await session.get(TagAllowlist, tag_id)
if row is None:
return jsonify({"error": "not on allowlist"}), 404
threshold = row.min_confidence
count = await svc.coverage(tag_id, threshold)
return jsonify({"count": count, "threshold": threshold})
@allowlist_bp.route("/tags/<int:tag_id>/allowlist", methods=["GET"])
async def get_one(tag_id: int):
async with get_session() as session:
row = await session.get(TagAllowlist, tag_id)
if row is None:
return jsonify({"error": "not on allowlist"}), 404
return jsonify(
{"min_confidence": row.min_confidence, "added_at": row.added_at.isoformat()}
)
@allowlist_bp.route("/tags/<int:tag_id>/allowlist", methods=["PATCH"])
async def patch_threshold(tag_id: int):
body = await request.get_json()
if not body or "min_confidence" not in body:
return jsonify({"error": "min_confidence required"}), 400
mc = float(body["min_confidence"])
if not (0 < mc <= 1):
return jsonify({"error": "min_confidence must be in (0, 1]"}), 400
async with get_session() as session:
await AllowlistService(session).update_threshold(tag_id, mc)
await session.commit()
return "", 204
@allowlist_bp.route("/tags/<int:tag_id>/allowlist", methods=["DELETE"])
async def remove(tag_id: int):
async with get_session() as session:
await AllowlistService(session).remove(tag_id)
await session.commit()
return "", 204
backend/app/api/suggestions.py
+4 -37
View File
@@ -3,31 +3,12 @@
from quart import Blueprint, jsonify, request
from ..extensions import get_session
from ..models import Tag, TagAllowlist
from ..services.ml.allowlist import AllowlistService
from ..services.ml.suggestions import SuggestionService
suggestions_bp = Blueprint("suggestions", __name__, url_prefix="/api")
async def _accept_payload(session, svc, newly_added: bool, tag_id: int) -> dict:
"""Shape the accept/alias response. When accepting newly allowlists a tag,
include the coverage PROJECTION (at the tag's threshold) so the UI can show
a non-blocking "auto-applying to ~N images" toast — the actual apply runs
async via apply_allowlist_tags, so this is an estimate, not a post-hoc
count (#7)."""
payload = {"allowlisted": newly_added}
if newly_added:
tag = await session.get(Tag, tag_id)
row = await session.get(TagAllowlist, tag_id)
payload["tag_id"] = tag_id
payload["tag_name"] = tag.name if tag is not None else None
payload["projected_count"] = await svc.coverage(
tag_id, row.min_confidence if row is not None else 0.90,
)
return payload
@suggestions_bp.route("/images/<int:image_id>/suggestions", methods=["GET"])
async def get_suggestions(image_id: int):
# ?min=<float> overrides the configured per-category thresholds so the typed
@@ -83,15 +64,9 @@ async def accept_suggestion(image_id: int):
return jsonify({"error": "tag_id required"}), 400
tag_id = body["tag_id"]
async with get_session() as session:
svc = AllowlistService(session)
newly_added = await svc.accept(image_id, tag_id)
payload = await _accept_payload(session, svc, newly_added, tag_id)
await AllowlistService(session).accept(image_id, tag_id)
await session.commit()
if newly_added:
from ..tasks.ml import apply_allowlist_tags
apply_allowlist_tags.delay(tag_id=tag_id)
return jsonify(payload)
return jsonify({"accepted": True, "tag_id": tag_id})
@suggestions_bp.route(
@@ -104,22 +79,14 @@ async def alias_suggestion(image_id: int):
return jsonify({"error": f"required: {sorted(required)}"}), 400
canonical_tag_id = body["canonical_tag_id"]
async with get_session() as session:
svc = AllowlistService(session)
newly_added = await svc.add_alias_and_accept(
await AllowlistService(session).add_alias_and_accept(
image_id,
body["alias_string"],
body["alias_category"],
canonical_tag_id,
)
payload = await _accept_payload(
session, svc, newly_added, canonical_tag_id,
)
await session.commit()
if newly_added:
from ..tasks.ml import apply_allowlist_tags
apply_allowlist_tags.delay(tag_id=canonical_tag_id)
return jsonify(payload)
return jsonify({"accepted": True, "tag_id": canonical_tag_id})
@suggestions_bp.route(
backend/app/api/tags.py
+1 -9
View File
@@ -1,13 +1,12 @@
"""Tags API: autocomplete, create, list/add/remove for an image."""
from quart import Blueprint, jsonify, request
from sqlalchemy import exists, select
from sqlalchemy import select
from sqlalchemy.dialects.postgresql import insert as pg_insert
from sqlalchemy.exc import IntegrityError
from ..extensions import get_session
from ..models import Tag, TagKind, TagPositiveConfirmation
from ..models.tag_allowlist import TagAllowlist
from ..services.bulk_tag_service import BulkTagService
from ..services.ml.aliases import AliasService
from ..services.series_match_service import SeriesMatchService
@@ -297,13 +296,6 @@ async def merge_tag(source_id: int):
status = 404 if "not found" in msg else 400
return jsonify({"error": msg}), status
await session.commit()
target_allowlisted = await session.scalar(
select(exists().where(TagAllowlist.tag_id == result.target_id))
)
if target_allowlisted:
from ..tasks.ml import apply_allowlist_tags
apply_allowlist_tags.delay(tag_id=result.target_id)
return jsonify(
{
"target": {
backend/app/celery_app.py
-4
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@@ -101,10 +101,6 @@ def make_celery() -> Celery:
"task": "backend.app.tasks.ml.backfill",
"schedule": 86400.0,
},
"apply-allowlist-sweep-daily": {
"task": "backend.app.tasks.ml.apply_allowlist_tags",
"schedule": 86400.0,
},
"train-heads-nightly": {
"task": "backend.app.tasks.ml.scheduled_train_heads",
"schedule": 86400.0, # passive cadence; manual retrain stays available
backend/app/models/__init__.py
-4
View File
@@ -13,7 +13,6 @@ from .head_auto_apply_run import HeadAutoApplyRun
from .head_metric import HeadMetric
from .head_metrics_snapshot import HeadMetricsSnapshot
from .head_training_run import HeadTrainingRun
from .image_prediction import ImagePrediction
from .image_provenance import ImageProvenance
from .image_record import ImageRecord
from .image_region import ImageRegion
@@ -34,7 +33,6 @@ from .subscribestar_failed_media import SubscribeStarFailedMedia
from .subscribestar_seen_media import SubscribeStarSeenMedia
from .tag import Tag, TagKind, image_tag
from .tag_alias import TagAlias
from .tag_allowlist import TagAllowlist
from .tag_eval_run import TagEvalRun
from .tag_head import TagHead
from .tag_positive_confirmation import TagPositiveConfirmation
@@ -59,7 +57,6 @@ __all__ = [
"SeriesPage",
"SeriesSuggestion",
"ImageRecord",
"ImagePrediction",
"ImageProvenance",
"ImageRegion",
"Tag",
@@ -78,7 +75,6 @@ __all__ = [
"HeadMetricsSnapshot",
"HeadTrainingRun",
"TagAlias",
"TagAllowlist",
"TagEvalRun",
"TagHead",
"TagPositiveConfirmation",
backend/app/models/image_prediction.py
-37
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@@ -1,37 +0,0 @@
"""ImagePrediction — one row per (image, tagger vocab prediction).
Replaces the image_record.tagger_predictions JSON blob (#768). Storing the
raw Camie/booru vocab name (not a tag_id) preserves the suggestion read
path's semantics: raw_name → canonical Tag resolution happens at read time
via the alias map, and accepting a prediction can CREATE the Tag. The store
floor (ml_settings.tagger_store_floor) is applied at WRITE time, so only
predictions >= the floor land here.
"""
from sqlalchemy import Float, ForeignKey, Index, String, UniqueConstraint
from sqlalchemy.orm import Mapped, mapped_column
from .base import Base
class ImagePrediction(Base):
__tablename__ = "image_prediction"
__table_args__ = (
UniqueConstraint(
"image_record_id", "raw_name", name="image_raw_name",
),
# Per-image read (suggestion build) and the "images with tag X above
# Y" query the JSON blob never allowed.
Index("ix_image_prediction_image", "image_record_id"),
Index("ix_image_prediction_name_score", "raw_name", "score"),
)
id: Mapped[int] = mapped_column(primary_key=True)
image_record_id: Mapped[int] = mapped_column(
ForeignKey("image_record.id", ondelete="CASCADE"), nullable=False,
)
# The raw tagger vocab key (booru form) — NOT a tag_id. Resolved to a
# canonical Tag at read time, exactly as the old JSON keys were.
raw_name: Mapped[str] = mapped_column(String(255), nullable=False)
category: Mapped[str] = mapped_column(String(64), nullable=False)
score: Mapped[float] = mapped_column(Float, nullable=False)
backend/app/models/tag_allowlist.py
-32
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@@ -1,32 +0,0 @@
"""TagAllowlist — tags the operator opted-in to auto-apply via maintenance."""
from datetime import datetime
from sqlalchemy import CheckConstraint, DateTime, Float, ForeignKey, func
from sqlalchemy.orm import Mapped, mapped_column
from .base import Base
class TagAllowlist(Base):
__tablename__ = "tag_allowlist"
# Bare name — Base.metadata's naming convention prepends ck_<table>_,
# producing the final ck_tag_allowlist_confidence_range (matches migration 0003).
__table_args__ = (
CheckConstraint(
"min_confidence > 0 AND min_confidence <= 1",
name="confidence_range",
),
)
tag_id: Mapped[int] = mapped_column(
ForeignKey("tag.id", ondelete="CASCADE"), primary_key=True
)
# Default auto-apply threshold for a newly-accepted tag. 0.90 (lowered from
# 0.95 on operator evidence 2026-06-07: 0.95 was too strict and skipped
# confident-enough applications). Per-tag value is still tunable in the
# allowlist table; existing rows keep whatever they were stored with.
min_confidence: Mapped[float] = mapped_column(Float, nullable=False, default=0.90)
added_at: Mapped[datetime] = mapped_column(
DateTime(timezone=True), nullable=False, server_default=func.now()
)
backend/app/services/importer.py
-10
View File
@@ -1479,16 +1479,6 @@ class Importer:
existing.siglip_embedding = None
existing.siglip_model_version = None
existing.centroid_scores = None
# #768: predictions also live in the normalized image_prediction table
# now — clear them so a re-imported file re-derives a fresh set.
from sqlalchemy import delete as _delete
from ..models import ImagePrediction as _ImagePrediction
self.session.execute(
_delete(_ImagePrediction).where(
_ImagePrediction.image_record_id == existing.id
)
)
# created_at intentionally preserved; updated_at auto-bumps.
self.session.flush()
self.session.commit()
backend/app/services/ml/allowlist.py
+17 -152
View File
@@ -1,36 +1,20 @@
"""Allowlist semantics: accepting a suggestion adds the canonical tag to
image_tag AND to tag_allowlist; per-image removal/dismiss writes a rejection.
"""Suggestion actions: accept applies the canonical tag to an image (which
feeds head training); dismiss / reject record a per-image rejection.
(The Camie allowlist bulk-apply was retired #1189 — heads + CCIP are the tag
source, and head auto-apply is the earned propagation. Accept no longer
allowlists or fans a tag out across the library.)
"""
from collections.abc import Sequence
from dataclasses import dataclass
from sqlalchemy import and_, delete, distinct, func, or_, select
from sqlalchemy import delete
from sqlalchemy.dialects.postgresql import insert
from sqlalchemy.ext.asyncio import AsyncSession
from ...models import (
ImagePrediction,
MLSettings,
Tag,
TagAlias,
TagAllowlist,
TagSuggestionRejection,
)
from ...models import TagSuggestionRejection
from ...models.tag import image_tag
from .aliases import AliasService
@dataclass(frozen=True)
class AllowlistRow:
tag_id: int
tag_name: str
tag_kind: str
min_confidence: float
applied_count: int # image_tag rows currently carrying this tag
coverage_count: int # images a sweep WOULD cover at min_confidence
class AllowlistService:
def __init__(self, session: AsyncSession):
self.session = session
@@ -39,21 +23,11 @@ class AllowlistService:
async def _apply_image_tag(self, image_id: int, tag_id: int, source: str):
stmt = insert(image_tag).values(
image_record_id=image_id, tag_id=tag_id, source=source
)
stmt = stmt.on_conflict_do_nothing(
).on_conflict_do_nothing(
index_elements=["image_record_id", "tag_id"]
)
await self.session.execute(stmt)
async def _add_to_allowlist(self, tag_id: int) -> bool:
"""Returns True if newly added (caller should kick off retro-apply)."""
exists = await self.session.get(TagAllowlist, tag_id)
if exists is not None:
return False
self.session.add(TagAllowlist(tag_id=tag_id))
await self.session.flush()
return True
async def _clear_rejection(self, image_id: int, tag_id: int):
await self.session.execute(
delete(TagSuggestionRejection)
@@ -61,12 +35,11 @@ class AllowlistService:
.where(TagSuggestionRejection.tag_id == tag_id)
)
async def accept(self, image_id: int, tag_id: int) -> bool:
"""Accept a suggestion. Returns True if the tag was newly added to
the allowlist (the API layer enqueues apply_allowlist_tags then)."""
async def accept(self, image_id: int, tag_id: int) -> None:
"""Apply the accepted tag to this image (source='ml_accepted', a head
training positive) and clear any prior rejection."""
await self._apply_image_tag(image_id, tag_id, source="ml_accepted")
await self._clear_rejection(image_id, tag_id)
return await self._add_to_allowlist(tag_id)
async def add_alias_and_accept(
self,
@@ -74,17 +47,16 @@ class AllowlistService:
alias_string: str,
alias_category: str,
canonical_tag_id: int,
) -> bool:
) -> None:
await self.aliases.create(
alias_string, alias_category, canonical_tag_id
)
return await self.accept(image_id, canonical_tag_id)
await self.accept(image_id, canonical_tag_id)
async def dismiss(self, image_id: int, tag_id: int) -> None:
stmt = insert(TagSuggestionRejection).values(
image_record_id=image_id, tag_id=tag_id
)
stmt = stmt.on_conflict_do_nothing(
).on_conflict_do_nothing(
index_elements=["image_record_id", "tag_id"]
)
await self.session.execute(stmt)
@@ -96,118 +68,11 @@ class AllowlistService:
await self._clear_rejection(image_id, tag_id)
async def reject_applied_tag(self, image_id: int, tag_id: int) -> None:
"""Operator removed an applied tag from an image. Remove the
image_tag row AND record a rejection so the allowlist won't
re-apply it on the next maintenance sweep."""
"""Operator removed an applied tag from an image. Remove the image_tag
row AND record a rejection so head auto-apply won't re-apply it."""
await self.session.execute(
image_tag.delete()
.where(image_tag.c.image_record_id == image_id)
.where(image_tag.c.tag_id == tag_id)
)
await self.dismiss(image_id, tag_id)
async def _store_floor(self) -> float:
return (
await self.session.execute(
select(MLSettings.tagger_store_floor).where(MLSettings.id == 1)
)
).scalar_one()
async def update_threshold(
self, tag_id: int, min_confidence: float
) -> None:
row = await self.session.get(TagAllowlist, tag_id)
if row is not None:
# An allowlist tag can't auto-apply more permissively than the
# ingest store floor — predictions below tagger_store_floor aren't
# stored, so a lower min_confidence would behave identically to the
# floor. Clamp so the stored threshold matches actual behavior
# (#764).
floor = await self._store_floor()
row.min_confidence = max(min_confidence, floor)
async def remove(self, tag_id: int) -> None:
await self.session.execute(
delete(TagAllowlist).where(TagAllowlist.tag_id == tag_id)
)
async def _coverage_match(self, tag: Tag):
"""The predicate over image_prediction rows that resolve to `tag`,
mirroring tasks.ml._confidence_for_tag's resolution: a prediction whose
raw_name equals the tag name (any category), OR an alias maps
(raw_name, category) -> this tag. Returns a SQLAlchemy boolean clause.
"""
alias_rows = (
await self.session.execute(
select(TagAlias.alias_string, TagAlias.alias_category).where(
TagAlias.canonical_tag_id == tag.id
)
)
).all()
name_clause = ImagePrediction.raw_name == tag.name
alias_clauses = [
and_(
ImagePrediction.raw_name == a,
ImagePrediction.category == c,
)
for a, c in alias_rows
]
return or_(name_clause, *alias_clauses) if alias_clauses else name_clause
async def coverage(self, tag_id: int, threshold: float) -> int:
"""How many distinct images a sweep WOULD cover for this tag at
`threshold`: images with a resolving prediction scoring >= threshold.
The gross candidate pool (NOT minus already-applied/rejected) — it's
the tuning signal for "lower the threshold and ~N more images qualify".
"""
tag = await self.session.get(Tag, tag_id)
if tag is None:
return 0
match = await self._coverage_match(tag)
stmt = select(
func.count(distinct(ImagePrediction.image_record_id))
).where(ImagePrediction.score >= threshold, match)
return (await self.session.execute(stmt)).scalar_one()
async def list_all(self) -> Sequence[AllowlistRow]:
stmt = (
select(
TagAllowlist.tag_id,
Tag.name,
Tag.kind,
TagAllowlist.min_confidence,
)
.join(Tag, Tag.id == TagAllowlist.tag_id)
.order_by(Tag.name.asc())
)
rows = (await self.session.execute(stmt)).all()
tag_ids = [r[0] for r in rows]
# Applied counts in ONE grouped query (vs N per-row counts).
applied: dict[int, int] = {}
if tag_ids:
applied = dict(
(
await self.session.execute(
select(image_tag.c.tag_id, func.count())
.where(image_tag.c.tag_id.in_(tag_ids))
.group_by(image_tag.c.tag_id)
)
).all()
)
result = []
for r in rows:
# Coverage is per-tag (alias set differs); allowlist is small.
cov = await self.coverage(r[0], r[3])
result.append(
AllowlistRow(
tag_id=r[0],
tag_name=r[1],
tag_kind=r[2].value if hasattr(r[2], "value") else str(r[2]),
min_confidence=r[3],
applied_count=applied.get(r[0], 0),
coverage_count=cov,
)
)
return result
backend/app/services/ml/tagger.py
-210
View File
@@ -1,210 +0,0 @@
"""Camie-tagger-v2 ONNX wrapper (CPU).
Single-image at a time. Loaded lazily inside the ml-worker process; NOT
thread-safe — the ml queue worker runs --concurrency=1 per process (scale ML by
running multiple worker replicas, not threads).
v2 layout reference: HuggingFace Camais03/camie-tagger-v2 root has
camie-tagger-v2.onnx (789 MB) + camie-tagger-v2-metadata.json (7.77 MB)
+ config.json. Tags ship as nested JSON, not CSV. Preprocessing and
output handling follow the published onnx_inference.py reference:
ImageNet normalize, NCHW layout, sigmoid on refined logits (output[1]).
"""
import json
import os
from dataclasses import dataclass
from pathlib import Path
import numpy as np
from PIL import Image, ImageFile
# Cap inference threads (see Tagger.load) so each ml-worker replica is a bounded
# core consumer on a shared node — keep N_replicas × this within the cores
# allotted to ML so replicas don't oversubscribe the box / starve the DB.
_INTRA_OP_THREADS = 4
# onnxruntime lives in requirements-ml.txt only — it is NOT installed in the
# lean web image or in CI. Imported lazily inside Tagger.load() so this module
# imports fine without it (the suggestion service imports SURFACED_CATEGORIES
# from here in the web container, and CI collects the pure-logic tests).
# Tolerate minutely-truncated source images (same rationale as IR's wd14.py:
# a few missing bytes at the JPEG EOI shouldn't block tagging the whole image).
ImageFile.LOAD_TRUNCATED_IMAGES = True
MODEL_NAME = os.environ.get("CAMIE_MODEL_NAME", "camie-tagger-v2")
_MODEL_DIR = Path(os.environ.get("ML_MODEL_DIR", "/models")) / "camie"
_MODEL_FILE = f"{MODEL_NAME}.onnx"
_METADATA_FILE = f"{MODEL_NAME}-metadata.json"
# Ingest floor below which predictions aren't stored (keeps the JSON compact).
# DEFAULT/fallback only — the live value is DB-backed
# (ml_settings.tagger_store_floor) and passed into infer() per call by the ml
# task. 0.70: the suggestion path already filters there and the centroid path
# covers lower-confidence preferred tags, so the sub-0.70 tail is redundant
# (it had bloated image_record's TOAST to ~100 GB; plan-task #764).
DEFAULT_STORE_FLOOR = 0.70
# The categories FC-2b surfaces in the UI. Others (meta/rating/year) are
# still stored but the suggestion service filters them out.
# 'artist' retired in FC-2d-vii-c — artist identity is acquisition-derived
# (image_record.artist_id), never ML-inferred. 'copyright' retired
# 2026-06-01 — operator doesn't use the copyright tag-kind; fandom is
# this app's franchise/series concept (per TagsView.vue's doc comment).
# Raw predictions for both categories still get stored at STORE_FLOOR but
# don't surface in suggestions.
SURFACED_CATEGORIES = {"character", "general"}
# ImageNet preprocessing constants (per Camie v2 onnx_inference.py).
_IMAGENET_MEAN = np.array([0.485, 0.456, 0.406], dtype=np.float32)
_IMAGENET_STD = np.array([0.229, 0.224, 0.225], dtype=np.float32)
# Square-pad color ≈ ImageNet mean × 255 (matches reference inference).
_PAD_COLOR = (124, 116, 104)
@dataclass(frozen=True)
class TagPrediction:
name: str
category: str
confidence: float
class Tagger:
def __init__(self, model_dir: Path | None = None):
self._model_dir = model_dir or _MODEL_DIR
self._session = None # onnxruntime.InferenceSession once load()ed
self._tag_names: list[str] | None = None
self._tag_categories: list[str] | None = None
self._input_name: str | None = None
self._input_size: int = 512
def load(self) -> None:
if self._session is not None:
return
model_path = self._model_dir / _MODEL_FILE
meta_path = self._model_dir / _METADATA_FILE
if not model_path.is_file():
raise RuntimeError(
f"Camie {_MODEL_FILE} missing at {model_path}. "
f"Populate /models via the ml-worker downloader."
)
if not meta_path.is_file():
raise RuntimeError(
f"Camie {_METADATA_FILE} missing at {meta_path}. "
f"Populate /models via the ml-worker downloader."
)
with open(meta_path) as f:
metadata = json.load(f)
# Per Camie v2 onnx_inference.py: idx_to_tag is keyed by str(idx);
# tag_to_category maps tag_name -> category. Project to two parallel
# lists indexed by output position for O(1) lookup in the hot path.
ds = metadata["dataset_info"]
idx_to_tag = ds["tag_mapping"]["idx_to_tag"]
tag_to_category = ds["tag_mapping"]["tag_to_category"]
total = ds["total_tags"]
names: list[str] = []
cats: list[str] = []
for i in range(total):
name = idx_to_tag.get(str(i), f"unknown-{i}")
names.append(name)
cats.append(tag_to_category.get(name, "general"))
# Input size from metadata; fall back to 512 (the v2 default).
self._input_size = int(
metadata.get("model_info", {}).get("img_size", 512)
)
# Lazy import — kept after the file-existence checks so the
# missing-model RuntimeError still fires first in environments
# without onnxruntime (CI / lean web image).
import onnxruntime as ort
# Cap the intra-op thread pool. ONNX Runtime otherwise sizes it to ALL
# host cores, so on a shared node each ml-worker replica would grab every
# core and oversubscribe (and starve the co-located DB/web). Bounding it
# makes each replica a predictable core consumer — run N replicas where
# N × _INTRA_OP_THREADS stays within the cores you allot to ML.
opts = ort.SessionOptions()
opts.intra_op_num_threads = _INTRA_OP_THREADS
session = ort.InferenceSession(
str(model_path), sess_options=opts, providers=["CPUExecutionProvider"],
)
self._input_name = session.get_inputs()[0].name
# Assign sentinels last so a partial load isn't observable.
self._tag_names = names
self._tag_categories = cats
self._session = session
def _preprocess(self, image_path: Path) -> np.ndarray:
img = Image.open(image_path)
# Composite RGBA onto neutral so transparency doesn't bias the model.
if img.mode == "RGBA":
bg = Image.new("RGBA", img.size, (255, 255, 255, 255))
bg.paste(img, mask=img.split()[3])
img = bg.convert("RGB")
elif img.mode != "RGB":
img = img.convert("RGB")
# Pad to square with ImageNet-mean color, then bicubic resize.
w, h = img.size
side = max(w, h)
square = Image.new("RGB", (side, side), _PAD_COLOR)
square.paste(img, ((side - w) // 2, (side - h) // 2))
square = square.resize(
(self._input_size, self._input_size), Image.BICUBIC
)
arr = np.array(square, dtype=np.float32) / 255.0 # HWC, [0,1]
arr = (arr - _IMAGENET_MEAN) / _IMAGENET_STD # ImageNet normalize
arr = arr.transpose(2, 0, 1) # HWC -> CHW
return arr[np.newaxis, :, :, :] # NCHW
def infer(
self, image_path: Path, *, store_floor: float = DEFAULT_STORE_FLOOR,
) -> dict[str, TagPrediction]:
"""Run Camie v2 on one image. Returns {name: TagPrediction} with
confidence >= store_floor (across all categories — the suggestion
service does category filtering later). store_floor is the DB-backed
ml_settings.tagger_store_floor, passed in by the ml task.
v2 emits multiple outputs; we use the refined predictions
(output[1] per onnx_inference.py). Sigmoid is applied to raw
logits to produce [0,1] confidence scores.
"""
self.load()
x = self._preprocess(image_path)
outputs = self._session.run(None, {self._input_name: x})
# Refined predictions if present (v2 emits initial + refined),
# fall back to initial for single-output forks.
logits = outputs[1] if len(outputs) > 1 else outputs[0]
# Squeeze batch dim, apply sigmoid.
probs = 1.0 / (1.0 + np.exp(-logits[0]))
results: dict[str, TagPrediction] = {}
names = self._tag_names
cats = self._tag_categories
for idx, score in enumerate(probs):
conf = float(score)
if conf < store_floor:
continue
if idx >= len(names):
# Output longer than metadata declared — shouldn't happen but
# don't crash the import pipeline if v2 metadata desynchronizes.
continue
results[names[idx]] = TagPrediction(
name=names[idx], category=cats[idx], confidence=conf
)
return results
_default_tagger: Tagger | None = None
def get_tagger() -> Tagger:
"""Process-level singleton so the ONNX session loads once per worker."""
global _default_tagger
if _default_tagger is None:
_default_tagger = Tagger()
return _default_tagger
backend/app/services/tag_service.py
+5 -30
View File
@@ -10,7 +10,6 @@ from sqlalchemy.dialects.postgresql import insert as pg_insert
from sqlalchemy.ext.asyncio import AsyncSession
from ..models import HeadMetric, Tag, TagHead, TagKind, image_tag
from ..models.tag_allowlist import TagAllowlist
from .db_helpers import get_or_create
from .tag_query import fandom_join_alias, tag_columns
@@ -303,28 +302,22 @@ class TagService:
async def _keep_as_alias(self, tag_id: int) -> bool:
"""A merged-away tag's old name must survive as an alias iff the ML
pipeline has ever applied it OR could re-emit it (allowlisted) —
otherwise the proactive apply_allowlist_tags worker would silently
regenerate it. Purely-manual, ML-unknown tags are deleted outright (no
DB bloat)."""
pipeline has ever applied it (manual accept or head auto-apply) — so a
re-application or an alias remap resolves the canonical name. Purely-
manual, ML-unknown tags are deleted outright (no DB bloat)."""
is_machine = await self.session.scalar(
select(
exists().where(
and_(
image_tag.c.tag_id == tag_id,
image_tag.c.source.in_(
("ml_auto", "ml_accepted", "auto")
("ml_auto", "ml_accepted", "head_auto", "auto")
),
)
)
)
)
if is_machine:
return True
allowlisted = await self.session.scalar(
select(exists().where(TagAllowlist.tag_id == tag_id))
)
return bool(allowlisted)
return bool(is_machine)
async def rename(self, tag_id: int, new_name: str) -> Tag:
"""Rename a tag. Raises TagMergeConflict if the new name collides
@@ -564,7 +557,6 @@ class TagService:
merged_count = await self._repoint_image_tags(source_id, target_id)
await self._repoint_rejections(source_id, target_id)
await self._repoint_allowlist(source_id, target_id)
await self._repoint_aliases(source_id, target_id)
await self._repoint_fandom_children(
source_id, target_id, source_kind
@@ -630,23 +622,6 @@ class TagService:
.values(tag_id=tgt)
)
async def _repoint_allowlist(self, src: int, tgt: int) -> None:
tgt_has = await self.session.scalar(
select(exists().where(TagAllowlist.tag_id == tgt))
)
if tgt_has:
await self.session.execute(
text("DELETE FROM tag_allowlist WHERE tag_id = :src"),
{"src": src},
)
else:
await self.session.execute(
update(TagAllowlist)
.where(TagAllowlist.tag_id == src)
.values(tag_id=tgt)
)
async def _repoint_aliases(self, src: int, tgt: int) -> None:
from ..models.tag_alias import TagAlias
backend/app/tasks/ml.py
+24 -261
View File
@@ -1,20 +1,19 @@
"""ML Celery tasks: per-image inference, backfill discovery, head training,
allowlist auto-apply, model self-heal.
"""ML Celery tasks: per-image embedding, backfill discovery, head training,
model self-heal.
All run on the ml-worker (queue 'ml') except apply_allowlist_tags sweeps which
are 'maintenance' lane. Sync sessions (Celery workers are sync processes), same
pattern as FC-2a tasks.
All run on the ml-worker (queue 'ml'). Sync sessions (Celery workers are sync
processes), same pattern as FC-2a tasks.
"""
import logging
from pathlib import Path
from celery.exceptions import SoftTimeLimitExceeded
from sqlalchemy import delete, select
from sqlalchemy import select
from sqlalchemy.exc import DBAPIError, OperationalError
from ..celery_app import celery
from ..models import ImagePrediction, ImageRecord, MLSettings
from ..models import ImageRecord, MLSettings
from ._sync_engine import sync_session_factory as _sync_session_factory
log = logging.getLogger(__name__)
@@ -46,19 +45,16 @@ def _is_video(path: Path) -> bool:
time_limit=1200, # 20 min hard
)
def tag_and_embed(self, image_id: int) -> dict:
"""Run Camie + SigLIP on one image; store predictions + embedding;
then enqueue per-image allowlist application.
"""Compute + store one image's SigLIP embedding.
Video (#747): sample frames at a fixed cadence (ml_settings
video_frame_interval_seconds, capped at video_max_frames), keep a tag only if
it appears in >= video_min_tag_frames frames and average its confidence over
those frames (mean-pool, not max — kills one-frame noise); mean-pool the
SigLIP embeddings. On no-frames returns status='no_frames' (not an error).
video_frame_interval_seconds, capped at video_max_frames) and mean-pool the
per-frame SigLIP embeddings. On no-frames returns status='no_frames' (not an
error). (Camie tagging was retired #1189 — heads + CCIP are the tag source.)
"""
import time
from ..services.ml.embedder import get_embedder
from ..services.ml.tagger import get_tagger
# Phase + file context, so a timeout/crash names WHICH file and WHERE it
# died instead of a bare SoftTimeLimitExceeded() (operator-flagged 2026-06-08:
@@ -94,15 +90,13 @@ def tag_and_embed(self, image_id: int) -> dict:
return {"status": "file_missing", "image_id": image_id}
phase = "load_models"
tagger = get_tagger()
embedder = get_embedder(settings.embedder_model_name)
if is_vid:
# Layer-3 isolation: ffprobe (a separate process) validates
# the container before we burn ~20 GPU ops sampling frames
# from it. A corrupt video that would crash the frame
# decoder is rejected cleanly here instead of taking down
# the ml-worker. Operator-flagged 2026-05-28.
# the container before we burn GPU ops sampling frames from it.
# A corrupt video that would crash the frame decoder is rejected
# cleanly here instead of taking down the ml-worker.
phase = "video_probe"
from ..utils import safe_probe
vprobe = safe_probe.probe_video(src)
@@ -115,48 +109,23 @@ def tag_and_embed(self, image_id: int) -> dict:
"reason": vprobe.reason,
}
phase = "video_sample_frames"
t0 = time.monotonic()
frames = _sample_video_frames(
src,
interval=settings.video_frame_interval_seconds,
max_frames=settings.video_max_frames,
)
log.info(
"tag_and_embed sampled %d frame(s) in %.1fs: %s",
len(frames), time.monotonic() - t0, ctx,
)
if not frames:
return {"status": "no_frames", "image_id": image_id}
phase = "video_infer"
phase = "video_embed"
import numpy as np
preds = _aggregate_video_predictions(
[tagger.infer(f, store_floor=settings.tagger_store_floor)
for f in frames],
min_frames=settings.video_min_tag_frames,
)
# Mean-pool the per-frame SigLIP embeddings into one vector.
embedding = np.mean(
[embedder.infer(f) for f in frames], axis=0
).astype("float32")
log.info(
"tag_and_embed video aggregated %d tag(s) from %d frame(s) "
"(min_frames=%d): %s",
len(preds), len(frames), settings.video_min_tag_frames, ctx,
)
for f in frames:
f.unlink(missing_ok=True)
else:
phase = "tag"
t0 = time.monotonic()
raw = tagger.infer(src, store_floor=settings.tagger_store_floor)
log.info(
"tag_and_embed tagged in %.1fs (%d tags): %s",
time.monotonic() - t0, len(raw), ctx,
)
preds = {
name: {"category": p.category, "confidence": p.confidence}
for name, p in raw.items()
}
phase = "embed"
t0 = time.monotonic()
embedding = embedder.infer(src)
@@ -166,28 +135,9 @@ def tag_and_embed(self, image_id: int) -> dict:
)
phase = "persist"
record.tagger_model_version = settings.tagger_model_version
record.siglip_embedding = embedding.tolist()
record.siglip_model_version = settings.embedder_model_version
session.add(record)
# Write the normalized image_prediction rows (#768) — the sole home
# for predictions now (image_record.tagger_predictions was dropped in
# migration 0046). Delete-then-insert keeps a re-tag idempotent;
# tagger_store_floor was already applied in tagger.infer, so preds is
# the >=floor set.
session.execute(
delete(ImagePrediction).where(
ImagePrediction.image_record_id == image_id
)
)
session.add_all([
ImagePrediction(
image_record_id=image_id, raw_name=name,
category=p.get("category", "general"),
score=float(p.get("confidence", 0.0)),
)
for name, p in preds.items()
])
session.commit()
except SoftTimeLimitExceeded:
log.error(
@@ -210,11 +160,8 @@ def tag_and_embed(self, image_id: int) -> dict:
)
raise
log.info(
"tag_and_embed ok in %.1fs (%d tags): %s", _elapsed(), len(preds), ctx
)
apply_allowlist_tags.delay(image_id=image_id)
return {"status": "ok", "image_id": image_id, "tags": len(preds)}
log.info("tag_and_embed ok in %.1fs: %s", _elapsed(), ctx)
return {"status": "ok", "image_id": image_id}
def _sample_video_frames(
@@ -273,68 +220,24 @@ def _sample_video_frames(
return out
def _aggregate_video_predictions(per_frame: list[dict], *, min_frames: int) -> dict:
"""Aggregate per-frame {name: TagPrediction} into one prediction set (#747).
A tag is kept only if it appears (≥ the tagger store floor, already applied)
in at least `min_frames` of the sampled frames — because sampling is at a
fixed cadence, that means it was on screen for roughly min_frames×interval
seconds, so a single-frame flicker / scene-transition artifact is dropped
while a genuine scene-local tag in a long video survives. Confidence is the
MEAN over the frames where the tag appears (not max — max re-inflated the
one-frame noise this whole change exists to remove).
`min_frames` is clamped to the number of frames actually sampled so a very
short video (1–2 frames) still tags instead of dropping everything.
"""
n = len(per_frame)
if n == 0:
return {}
threshold = max(1, min(min_frames, n))
agg: dict[str, dict] = {}
for frame_preds in per_frame:
for name, p in frame_preds.items():
cur = agg.get(name)
if cur is None:
agg[name] = {"category": p.category, "sum": p.confidence, "count": 1}
else:
cur["sum"] += p.confidence
cur["count"] += 1
return {
name: {"category": v["category"], "confidence": v["sum"] / v["count"]}
for name, v in agg.items()
if v["count"] >= threshold
}
@celery.task(name="backend.app.tasks.ml.backfill", bind=True)
def backfill(self) -> int:
"""Enqueue tag_and_embed for images missing predictions/embeddings for
the current model versions. Keyset pagination by id ASC (restart-safe).
"""Enqueue tag_and_embed (embed-only) for images with no SigLIP embedding.
Keyset pagination by id ASC (restart-safe).
NB: a siglip MODEL-VERSION mismatch (an operator model swap, #1190) is NOT
re-embedded here — the CPU ml-worker can't churn the library at 384/512px;
the GPU agent owns version re-embeds via the 'embed' job.
"""
SessionLocal = _sync_session_factory()
enqueued = 0
last_id = 0
with SessionLocal() as session:
settings = session.execute(
select(MLSettings).where(MLSettings.id == 1)
).scalar_one()
while True:
rows = session.execute(
select(ImageRecord.id)
.where(ImageRecord.id > last_id)
.where(
(ImageRecord.tagger_model_version.is_(None))
| (
ImageRecord.tagger_model_version
!= settings.tagger_model_version
)
| (ImageRecord.siglip_embedding.is_(None))
# NB: a siglip MODEL-VERSION mismatch (an operator model swap,
# #1190) is intentionally NOT re-embedded here — the CPU
# ml-worker can't churn the whole library at 384/512px. The
# GPU agent owns version re-embeds via the 'embed' job.
)
.where(ImageRecord.siglip_embedding.is_(None))
.order_by(ImageRecord.id.asc())
.limit(500)
).scalars().all()
@@ -347,146 +250,6 @@ def backfill(self) -> int:
return enqueued
@celery.task(
name="backend.app.tasks.ml.apply_allowlist_tags",
bind=True,
# Audit 2026-06-02 — the full-sweep mode (neither tag_id nor image_id)
# is O(images × allowlist) and legitimately runs >5 min on large
# libraries. Cap matches the maintenance queue's recovery threshold.
soft_time_limit=1800, time_limit=2100,
)
def apply_allowlist_tags(self, tag_id: int | None = None,
image_id: int | None = None) -> int:
"""Retroactively apply allowlisted tags.
Modes:
- tag_id only : scan all images for this tag.
- image_id only : scan all allowlisted tags for this image.
- both : just the (image, tag) pair.
- neither : full sweep (daily beat).
Skips: already-applied, rejected (tag_suggestion_rejection), or
confidence below the tag's allowlist min_confidence. Applied with
source='ml_auto'.
"""
from sqlalchemy import and_
from sqlalchemy import select as sa_select
from sqlalchemy.dialects.postgresql import insert as pg_insert
from ..models import TagAllowlist, TagSuggestionRejection
from ..models.tag import image_tag
SessionLocal = _sync_session_factory()
applied = 0
with SessionLocal() as session:
allow_rows = session.execute(
sa_select(TagAllowlist.tag_id, TagAllowlist.min_confidence)
if tag_id is None
else sa_select(
TagAllowlist.tag_id, TagAllowlist.min_confidence
).where(TagAllowlist.tag_id == tag_id)
).all()
allow = {r[0]: r[1] for r in allow_rows}
if not allow:
return 0
# Images that have any predictions (#768: from image_prediction, not
# the old JSON column), optionally narrowed to one image.
img_ids_query = sa_select(ImagePrediction.image_record_id).distinct()
if image_id is not None:
img_ids_query = img_ids_query.where(
ImagePrediction.image_record_id == image_id
)
for (img_id,) in session.execute(img_ids_query).all():
preds = _load_predictions_sync(session, img_id)
for a_tag_id, min_conf in allow.items():
exists = session.execute(
sa_select(image_tag.c.tag_id).where(
and_(
image_tag.c.image_record_id == img_id,
image_tag.c.tag_id == a_tag_id,
)
)
).scalar_one_or_none()
if exists is not None:
continue
rej = session.get(
TagSuggestionRejection, (img_id, a_tag_id)
)
if rej is not None:
continue
from ..models import Tag
tag = session.get(Tag, a_tag_id)
if tag is None:
continue
conf = _confidence_for_tag(session, tag, preds)
if conf is None or conf < min_conf:
continue
stmt = pg_insert(image_tag).values(
image_record_id=img_id,
tag_id=a_tag_id,
source="ml_auto",
)
stmt = stmt.on_conflict_do_nothing(
index_elements=["image_record_id", "tag_id"]
)
session.execute(stmt)
applied += 1
session.commit()
return applied
def _load_predictions_sync(session, image_id: int) -> dict:
"""Predictions for one image from image_prediction (#768), in the
{raw_name: {category, confidence}} shape _confidence_for_tag consumes —
keeps the allowlist resolution logic unchanged."""
from sqlalchemy import select as sa_select
rows = session.execute(
sa_select(
ImagePrediction.raw_name,
ImagePrediction.category,
ImagePrediction.score,
).where(ImagePrediction.image_record_id == image_id)
).all()
return {
r.raw_name: {"category": r.category, "confidence": r.score}
for r in rows
}
def _confidence_for_tag(session, tag, preds: dict) -> float | None:
"""Highest confidence among predictions that resolve to `tag` —
either the prediction name equals the tag name, or an alias maps
(prediction name, category) -> tag.id.
"""
from sqlalchemy import select as sa_select
from ..models import TagAlias
best: float | None = None
direct = preds.get(tag.name)
if direct is not None:
best = float(direct.get("confidence", 0.0))
alias_rows = session.execute(
sa_select(TagAlias.alias_string, TagAlias.alias_category).where(
TagAlias.canonical_tag_id == tag.id
)
).all()
for alias_string, alias_category in alias_rows:
p = preds.get(alias_string)
if p is None:
continue
if p.get("category") != alias_category:
continue
c = float(p.get("confidence", 0.0))
if best is None or c > best:
best = c
return best
@celery.task(
name="backend.app.tasks.ml.tag_eval_run",
bind=True,
frontend/src/components/settings/AllowlistTable.vue
-120
View File
@@ -1,120 +0,0 @@
<template>
<MaintenanceTile
icon="mdi-playlist-check"
:title="`Allowlisted tags (${store.rows.length})`"
blurb="Tags auto-applied to images that score above their threshold. Tune the
threshold and see how many images it would cover."
>
<v-data-table-virtual
:headers="headers" :items="store.rows" :loading="store.loading"
height="360" density="compact" fixed-header
no-data-text="No tags on the allowlist yet — accept a suggestion to add one."
>
<template #item.applied_count="{ item }">
<span class="fc-num">{{ item.applied_count ?? '—' }}</span>
</template>
<template #item.min_confidence="{ item }">
<div class="fc-thr">
<v-text-field
:model-value="item.min_confidence" type="number"
density="compact" hide-details style="max-width: 100px;"
:min="floor" max="1" step="0.05"
:aria-label="`Auto-apply threshold for ${item.tag_name}`"
@update:model-value="(v) => onThreshold(item, v)"
/>
<span
v-if="proj[item.tag_id]"
class="fc-thr__proj"
:class="{ 'fc-thr__proj--loading': proj[item.tag_id].loading }"
:title="`At ${proj[item.tag_id].threshold}, a sweep would cover this many images`"
>≈ {{ proj[item.tag_id].count }} at {{ proj[item.tag_id].threshold }}</span>
</div>
</template>
<template #item.coverage_count="{ item }">
<span class="fc-num" :title="`Images a sweep covers at ${item.min_confidence}`">
{{ item.coverage_count ?? '—' }}
</span>
</template>
<template #item.actions="{ item }">
<v-btn
icon="mdi-delete" size="x-small" variant="text" color="error"
:aria-label="`Remove ${item.tag_name} from the allowlist`"
@click="store.remove(item.tag_id)"
/>
</template>
</v-data-table-virtual>
<p class="fc-muted text-caption mt-2">
<strong>Applied</strong> = images currently carrying the tag.
<strong>Covers</strong> = images a sweep would auto-apply it to at the
current threshold. Lower the threshold to cover more (less certain) images.
</p>
</MaintenanceTile>
</template>
<script setup>
import { computed, onMounted, reactive } from 'vue'
import { useAllowlistStore } from '../../stores/allowlist.js'
import { useMLStore } from '../../stores/ml.js'
import MaintenanceTile from '../common/MaintenanceTile.vue'
const store = useAllowlistStore()
const ml = useMLStore()
// min_confidence can't be set below the tagger store floor — predictions
// below it aren't stored, so a lower threshold would behave identically to
// the floor. The backend clamps too (#764).
const floor = computed(() => ml.settings?.tagger_store_floor ?? 0.70)
const headers = [
{ title: 'Tag', key: 'tag_name', sortable: true },
{ title: 'Kind', key: 'tag_kind', sortable: true, width: 100 },
{ title: 'Applied', key: 'applied_count', sortable: true, width: 90 },
{ title: 'Min confidence', key: 'min_confidence', sortable: false, width: 220 },
{ title: 'Covers', key: 'coverage_count', sortable: true, width: 90 },
{ title: '', key: 'actions', sortable: false, width: 56 }
]
// Per-row live projection while the operator drags a threshold:
// proj[tagId] = { threshold, count, loading }
const proj = reactive({})
onMounted(() => {
store.load()
if (!ml.settings) ml.loadSettings()
})
const debounces = {}
function onThreshold(item, value) {
const tagId = item.tag_id
const v = Math.max(parseFloat(value), floor.value)
if (!(v > 0 && v <= 1)) return
const shown = Number(v.toFixed(2))
// Optimistic live projection box (loading until the count returns).
proj[tagId] = { threshold: shown, count: proj[tagId]?.count ?? '…', loading: true }
if (debounces[tagId]) clearTimeout(debounces[tagId])
debounces[tagId] = setTimeout(async () => {
try {
const { count } = await store.coverage(tagId, v)
proj[tagId] = { threshold: shown, count, loading: false }
} catch {
delete proj[tagId] // drop the projection rather than show a wrong number
}
// Commit the new threshold (also refreshes the row's stored coverage_count).
store.updateThreshold(tagId, v)
}, 500)
}
</script>
<style scoped>
.fc-num { font-variant-numeric: tabular-nums; }
.fc-thr { display: flex; align-items: center; gap: 10px; }
.fc-thr__proj {
font-size: 12px;
font-variant-numeric: tabular-nums;
color: rgb(var(--v-theme-accent));
white-space: nowrap;
}
.fc-thr__proj--loading { color: rgb(var(--v-theme-on-surface-variant)); }
.fc-muted { color: rgb(var(--v-theme-on-surface-variant)); }
</style>
frontend/src/components/settings/MLBackfillCard.vue
+4 -3
View File
@@ -2,12 +2,13 @@
<MaintenanceTile
icon="mdi-refresh"
title="ML backfill"
blurb="Re-run tagging + embeddings on images missing them."
blurb="Compute SigLIP embeddings on images missing them."
:open="busy"
>
<p class="text-body-2 mb-3">
Re-run Camie + SigLIP on images missing predictions or embeddings
for the current model versions. Safe to re-run.
Compute the SigLIP embedding for any image that doesn't have one yet
(CPU). Safe to re-run. To re-embed under a NEW model, use the GPU
agent's "Re-embed library" instead.
</p>
<v-btn color="primary" rounded="pill" :loading="busy" @click="run">
<v-icon start>mdi-refresh</v-icon> Run backfill now
frontend/src/components/settings/MaintenancePanel.vue
+2 -4
View File
@@ -1,8 +1,8 @@
<template>
<div class="fc-maint">
<p class="fc-muted text-body-2 mb-5">
One-off backfills, tagging config and storage tools. The ML backfill runs
nightly; the allowlist auto-applies accepted tags. Click a tile to open it.
One-off backfills, tagging config and storage tools. Heads train nightly
and auto-apply earned tags. Click a tile to open it.
</p>
<section class="fc-section">
@@ -26,7 +26,6 @@
<MLThresholdSliders />
<HeadsCard />
<GpuAgentCard />
<AllowlistTable />
<AliasTable />
<TagEvalCard />
</div>
@@ -53,7 +52,6 @@ import DbMaintenanceCard from './DbMaintenanceCard.vue'
import MLThresholdSliders from './MLThresholdSliders.vue'
import HeadsCard from './HeadsCard.vue'
import GpuAgentCard from './GpuAgentCard.vue'
import AllowlistTable from './AllowlistTable.vue'
import AliasTable from './AliasTable.vue'
import TagEvalCard from './TagEvalCard.vue'
import BackupCard from './BackupCard.vue'
frontend/src/stores/allowlist.js
-44
View File
@@ -1,44 +0,0 @@
import { defineStore } from 'pinia'
import { ref } from 'vue'
import { useApi } from '../composables/useApi.js'
export const useAllowlistStore = defineStore('allowlist', () => {
const api = useApi()
const rows = ref([])
const loading = ref(false)
async function load() {
loading.value = true
try { rows.value = await api.get('/api/allowlist') }
finally { loading.value = false }
}
async function updateThreshold(tagId, minConfidence) {
await api.patch(`/api/tags/${tagId}/allowlist`, {
body: { min_confidence: minConfidence }
})
const r = rows.value.find(x => x.tag_id === tagId)
if (r) {
r.min_confidence = minConfidence
// The committed threshold changed the covered pool — refresh the row's
// coverage so the table stays truthful after a save.
try { r.coverage_count = (await coverage(tagId, minConfidence)).count }
catch { /* leave the stale count rather than blank it */ }
}
}
// Live "at threshold T, a sweep would cover ~N images" projection for the
// tuning dashboard. Returns { count, threshold }.
async function coverage(tagId, threshold) {
return api.get(`/api/tags/${tagId}/allowlist/coverage`, {
params: { threshold }
})
}
async function remove(tagId) {
await api.delete(`/api/tags/${tagId}/allowlist`)
rows.value = rows.value.filter(x => x.tag_id !== tagId)
}
return { rows, loading, load, updateThreshold, coverage, remove }
})
frontend/src/stores/suggestions.js
+9 -18
View File
@@ -113,7 +113,7 @@ export const useSuggestionsStore = defineStore('suggestions', () => {
})
tagId = created.id
}
const res = await api.post(`/api/images/${imageId}/suggestions/accept`, {
await api.post(`/api/images/${imageId}/suggestions/accept`, {
body: { tag_id: tagId }
})
// Only drop from THIS image's category list — if the user navigated,
@@ -121,23 +121,14 @@ export const useSuggestionsStore = defineStore('suggestions', () => {
if (currentImageId === imageId) {
_dropEverywhere(suggestion)
}
_acceptToast('Tagged', suggestion.display_name, res)
_acceptToast('Tagged', suggestion.display_name)
}
// One non-blocking toast for accept/alias. When the accept newly allowlisted
// the tag, surface the coverage PROJECTION (#7) so the operator sees the
// auto-apply reach without a blocking pre-accept preview — the apply itself
// runs async, hence "~N".
function _acceptToast(verb, displayName, res) {
if (res?.allowlisted) {
const n = res.projected_count
toast({
text: `${verb}: ${displayName} — allowlisted, auto-applying to ~${n} image${n === 1 ? '' : 's'}`,
type: 'success'
})
} else {
toast({ text: `${verb}: ${displayName}`, type: 'success' })
}
// One non-blocking toast for accept/alias. The accepted tag is applied to this
// image and feeds head training; head auto-apply handles propagation (earned),
// so there's no instant fan-out to project.
function _acceptToast(verb, displayName) {
toast({ text: `${verb}: ${displayName}`, type: 'success' })
}
async function aliasAccept(suggestion, canonicalTagId) {
@@ -149,7 +140,7 @@ export const useSuggestionsStore = defineStore('suggestions', () => {
// reappearing unaliased. raw_name is null only for centroid hits, which
// can't be aliased (the UI hides the action for them).
const aliasString = suggestion.raw_name ?? suggestion.display_name
const res = await api.post(`/api/images/${imageId}/suggestions/alias`, {
await api.post(`/api/images/${imageId}/suggestions/alias`, {
body: {
alias_string: aliasString,
alias_category: suggestion.category,
@@ -159,7 +150,7 @@ export const useSuggestionsStore = defineStore('suggestions', () => {
if (currentImageId === imageId) {
_dropEverywhere(suggestion)
}
_acceptToast('Aliased & tagged', suggestion.display_name, res)
_acceptToast('Aliased & tagged', suggestion.display_name)
}
// Remove the alias behind an aliased suggestion (the raw prediction reverts to
tests/_prediction_helpers.py
-21
View File
@@ -1,21 +0,0 @@
"""#768 test helper: seed image_prediction rows.
Read-path tests used to seed ImageRecord(tagger_predictions={...}); predictions
now live in the normalized image_prediction table, so seed there instead.
"""
from backend.app.models import ImagePrediction
async def seed_predictions(session, image_id: int, predictions: dict) -> None:
"""Insert image_prediction rows from a {raw_name: {category, confidence}}
dict (the old JSON shape). Caller commits/flushes as needed; this flushes."""
session.add_all([
ImagePrediction(
image_record_id=image_id,
raw_name=name,
category=p.get("category", "general"),
score=float(p.get("confidence", 0.0)),
)
for name, p in predictions.items()
])
await session.flush()
tests/test_api_allowlist.py
-88
View File
@@ -1,88 +0,0 @@
import pytest
from backend.app.models import ImagePrediction, ImageRecord, TagAllowlist, TagKind
from backend.app.services.tag_service import TagService
pytestmark = pytest.mark.integration
@pytest.mark.asyncio
async def test_list_and_patch_and_delete(client, db):
tag = await TagService(db).find_or_create("AL", TagKind.character)
db.add(TagAllowlist(tag_id=tag.id, min_confidence=0.95))
await db.commit()
resp = await client.get("/api/allowlist")
assert resp.status_code == 200
assert any(r["tag_id"] == tag.id for r in await resp.get_json())
resp = await client.patch(
f"/api/tags/{tag.id}/allowlist", json={"min_confidence": 0.80}
)
assert resp.status_code == 204
resp = await client.get(f"/api/tags/{tag.id}/allowlist")
assert (await resp.get_json())["min_confidence"] == pytest.approx(0.80)
resp = await client.delete(f"/api/tags/{tag.id}/allowlist")
assert resp.status_code == 204
resp = await client.get(f"/api/tags/{tag.id}/allowlist")
assert resp.status_code == 404
@pytest.mark.asyncio
async def test_patch_rejects_out_of_range(client, db):
tag = await TagService(db).find_or_create("AL2", TagKind.character)
db.add(TagAllowlist(tag_id=tag.id))
await db.commit()
resp = await client.patch(
f"/api/tags/{tag.id}/allowlist", json={"min_confidence": 1.5}
)
assert resp.status_code == 400
@pytest.mark.asyncio
async def test_coverage_endpoint(client, db):
tag = await TagService(db).find_or_create("Cover", TagKind.general)
db.add(TagAllowlist(tag_id=tag.id, min_confidence=0.90))
for i, score in enumerate((0.95, 0.60)):
img = ImageRecord(
path=f"/images/cov{i}.jpg", sha256=f"cv{i:062d}", size_bytes=1,
mime="image/jpeg", width=1, height=1,
origin="imported_filesystem", integrity_status="unknown",
)
db.add(img)
await db.flush()
db.add(ImagePrediction(
image_record_id=img.id, raw_name="Cover",
category="general", score=score,
))
await db.commit()
# Explicit threshold.
resp = await client.get(
f"/api/tags/{tag.id}/allowlist/coverage?threshold=0.90"
)
assert resp.status_code == 200
assert (await resp.get_json())["count"] == 1
# Lower what-if threshold widens coverage.
resp = await client.get(
f"/api/tags/{tag.id}/allowlist/coverage?threshold=0.50"
)
assert (await resp.get_json())["count"] == 2
# No threshold → uses the stored min_confidence (0.90).
resp = await client.get(f"/api/tags/{tag.id}/allowlist/coverage")
body = await resp.get_json()
assert body["count"] == 1
assert body["threshold"] == pytest.approx(0.90)
@pytest.mark.asyncio
async def test_coverage_rejects_bad_threshold(client, db):
tag = await TagService(db).find_or_create("Cover2", TagKind.general)
db.add(TagAllowlist(tag_id=tag.id))
await db.commit()
resp = await client.get(
f"/api/tags/{tag.id}/allowlist/coverage?threshold=2.0"
)
assert resp.status_code == 400
tests/test_api_suggestions.py
+19 -35
View File
@@ -15,9 +15,7 @@ def eager():
celery.conf.task_always_eager = False
async def _img(db, preds, sha="s" * 64):
from tests._prediction_helpers import seed_predictions
async def _img(db, sha="s" * 64):
img = ImageRecord(
path=f"/images/{sha}.jpg", sha256=sha, size_bytes=1,
mime="image/jpeg", width=1, height=1,
@@ -25,8 +23,6 @@ async def _img(db, preds, sha="s" * 64):
)
db.add(img)
await db.commit()
await seed_predictions(db, img.id, preds)
await db.commit()
return img
@@ -60,7 +56,7 @@ async def test_get_suggestions(client, db):
@pytest.mark.asyncio
async def test_accept_requires_tag_id(client, db):
img = await _img(db, {})
img = await _img(db)
resp = await client.post(
f"/api/images/{img.id}/suggestions/accept", json={}
)
@@ -68,43 +64,31 @@ async def test_accept_requires_tag_id(client, db):
@pytest.mark.asyncio
async def test_accept_then_applied(client, db):
img = await _img(db, {})
async def test_accept_applies_tag_to_image(client, db):
# Camie/allowlist retired (#1189): accept applies the tag to THIS image
# (source='ml_accepted', a head-training positive) — no bulk allowlist
# fan-out anymore.
from backend.app.models.tag import image_tag
img = await _img(db)
tag = await TagService(db).find_or_create("AcceptMe", TagKind.character)
await db.commit()
resp = await client.post(
f"/api/images/{img.id}/suggestions/accept", json={"tag_id": tag.id}
)
assert resp.status_code == 200
body = await resp.get_json()
# #7b: a fresh accept newly-allowlists → projection payload for the toast.
assert body["allowlisted"] is True
assert body["tag_id"] == tag.id
assert body["tag_name"] == "AcceptMe"
assert "projected_count" in body
@pytest.mark.asyncio
async def test_accept_already_allowlisted_reports_not_new(client, db):
img1 = await _img(db, {}, sha="c" * 64)
img2 = await _img(db, {}, sha="d" * 64)
tag = await TagService(db).find_or_create("Twice", TagKind.character)
await db.commit()
first = await client.post(
f"/api/images/{img1.id}/suggestions/accept", json={"tag_id": tag.id}
)
assert (await first.get_json())["allowlisted"] is True
second = await client.post(
f"/api/images/{img2.id}/suggestions/accept", json={"tag_id": tag.id}
)
body = await second.get_json()
assert body["allowlisted"] is False # already on the allowlist
assert "projected_count" not in body
assert (await resp.get_json())["accepted"] is True
src = (await db.execute(
select(image_tag.c.source)
.where(image_tag.c.image_record_id == img.id)
.where(image_tag.c.tag_id == tag.id)
)).scalar_one()
assert src == "ml_accepted"
@pytest.mark.asyncio
async def test_dismiss(client, db):
img = await _img(db, {})
img = await _img(db)
tag = await TagService(db).find_or_create("DismissMe", TagKind.general)
await db.commit()
resp = await client.post(
@@ -115,7 +99,7 @@ async def test_dismiss(client, db):
@pytest.mark.asyncio
async def test_undismiss_reverses_rejection(client, db):
img = await _img(db, {})
img = await _img(db)
tag = await TagService(db).find_or_create("UndismissMe", TagKind.general)
await db.commit()
await client.post(
@@ -134,7 +118,7 @@ async def test_undismiss_reverses_rejection(client, db):
@pytest.mark.asyncio
async def test_alias_requires_fields(client, db):
img = await _img(db, {})
img = await _img(db)
resp = await client.post(
f"/api/images/{img.id}/suggestions/alias", json={"alias_string": "x"}
)
tests/test_api_tag_merge.py
+1 -39
View File
@@ -68,15 +68,7 @@ async def test_rename_collision_returns_rich_409(client):
@pytest.mark.asyncio
async def test_merge_endpoint_moves_and_deletes(client, monkeypatch):
calls = []
from backend.app.tasks import ml as ml_tasks
monkeypatch.setattr(
ml_tasks.apply_allowlist_tags,
"delay",
lambda **kw: calls.append(kw),
)
async def test_merge_endpoint_moves_and_deletes(client):
tgt = await _mk(client, "Keep", "general")
src = await _mk(client, "Gone", "general")
resp = await client.post(
@@ -92,36 +84,6 @@ async def test_merge_endpoint_moves_and_deletes(client, monkeypatch):
assert r2.status_code == 200
@pytest.mark.asyncio
async def test_merge_enqueues_backfill_when_target_allowlisted(
client, monkeypatch
):
calls = []
from backend.app.tasks import ml as ml_tasks
monkeypatch.setattr(
ml_tasks.apply_allowlist_tags,
"delay",
lambda **kw: calls.append(kw),
)
tgt = await _mk(client, "AllowTgt", "general")
src = await _mk(client, "AllowSrc", "general")
# No public route adds a tag to the allowlist (it happens via
# accept-suggestion); set the row directly through the app session.
from backend.app.extensions import get_session
from backend.app.models.tag_allowlist import TagAllowlist
async with get_session() as s:
s.add(TagAllowlist(tag_id=tgt))
await s.commit()
resp = await client.post(
f"/api/tags/{src}/merge", json={"target_id": tgt}
)
assert resp.status_code == 200
assert calls == [{"tag_id": tgt}]
@pytest.mark.asyncio
async def test_merge_self_is_400(client):
t = await _mk(client, "Selfie", "general")
tests/test_image_prediction.py
-57
View File
@@ -1,57 +0,0 @@
"""#768: image_prediction table — model + constraints round-trip."""
import pytest
from sqlalchemy import select
from sqlalchemy.exc import IntegrityError
from backend.app.models import ImagePrediction, ImageRecord
pytestmark = pytest.mark.integration
async def _make_image(db, path="/img/p0.jpg", sha="0"):
rec = ImageRecord(
path=path, sha256=sha.ljust(64, "0")[:64], size_bytes=10,
mime="image/jpeg", origin="imported_filesystem",
)
db.add(rec)
await db.flush()
return rec
@pytest.mark.asyncio
async def test_image_prediction_round_trip(db):
rec = await _make_image(db)
db.add_all([
ImagePrediction(
image_record_id=rec.id, raw_name="blue_eyes",
category="general", score=0.92,
),
ImagePrediction(
image_record_id=rec.id, raw_name="hatsune_miku",
category="character", score=0.88,
),
])
await db.commit()
rows = (await db.execute(
select(ImagePrediction.raw_name, ImagePrediction.score)
.where(ImagePrediction.image_record_id == rec.id)
.order_by(ImagePrediction.score.desc())
)).all()
assert [r.raw_name for r in rows] == ["blue_eyes", "hatsune_miku"]
@pytest.mark.asyncio
async def test_image_prediction_unique_per_image_name(db):
rec = await _make_image(db, path="/img/p1.jpg", sha="1")
db.add(ImagePrediction(
image_record_id=rec.id, raw_name="dup",
category="general", score=0.9,
))
await db.commit()
db.add(ImagePrediction(
image_record_id=rec.id, raw_name="dup",
category="general", score=0.7,
))
with pytest.raises(IntegrityError):
await db.commit()
tests/test_migration_0003.py
-7
View File
@@ -5,14 +5,12 @@ from backend.app.models import (
ImageRecord,
MLSettings,
TagAlias,
TagAllowlist,
TagSuggestionRejection,
)
def test_new_tables_registered():
expected = {
"tag_allowlist",
"tag_suggestion_rejection",
"tag_alias",
"ml_settings",
@@ -40,11 +38,6 @@ def test_ml_settings_singleton_constraint():
assert "ck_ml_settings_singleton" in names
def test_tag_allowlist_confidence_check():
names = {c.name for c in TagAllowlist.__table__.constraints}
assert "ck_tag_allowlist_confidence_range" in names
def test_tag_suggestion_rejection_pk():
pk_cols = {c.name for c in TagSuggestionRejection.__table__.primary_key.columns}
assert pk_cols == {"image_record_id", "tag_id"}
tests/test_ml_allowlist.py
-182
View File
@@ -1,182 +0,0 @@
import pytest
from sqlalchemy import select
from backend.app.models import (
ImagePrediction,
TagAlias,
TagAllowlist,
TagKind,
TagSuggestionRejection,
)
from backend.app.models.tag import image_tag
from backend.app.services.ml.allowlist import AllowlistService
from backend.app.services.tag_service import TagService
pytestmark = pytest.mark.integration
async def _make_image(db, sha: str = "x" * 64):
from backend.app.models import ImageRecord
img = ImageRecord(
# Full sha in the path — the first 8 chars collide for sequential
# shas like c{i:063d}, and path is UNIQUE (uq_image_record_path).
path=f"/images/{sha}.jpg", sha256=sha, size_bytes=1,
mime="image/jpeg", width=1, height=1,
origin="imported_filesystem", integrity_status="unknown",
)
db.add(img)
await db.flush()
return img
async def _add_pred(db, image_id, raw_name, score, category="general"):
db.add(ImagePrediction(
image_record_id=image_id, raw_name=raw_name,
category=category, score=score,
))
await db.flush()
@pytest.mark.asyncio
async def test_accept_applies_and_allowlists(db):
img = await _make_image(db)
tag = await TagService(db).find_or_create("Hero", TagKind.character)
svc = AllowlistService(db)
newly_added = await svc.accept(img.id, tag.id)
assert newly_added is True
applied = (
await db.execute(
select(image_tag.c.source)
.where(image_tag.c.image_record_id == img.id)
.where(image_tag.c.tag_id == tag.id)
)
).scalar_one()
assert applied == "ml_accepted"
assert await db.get(TagAllowlist, tag.id) is not None
@pytest.mark.asyncio
async def test_accept_idempotent_allowlist(db):
img = await _make_image(db)
tag = await TagService(db).find_or_create("Hero2", TagKind.character)
svc = AllowlistService(db)
assert await svc.accept(img.id, tag.id) is True
assert await svc.accept(img.id, tag.id) is False
@pytest.mark.asyncio
async def test_reject_applied_tag_records_rejection(db):
img = await _make_image(db)
tag = await TagService(db).find_or_create("Removeme", TagKind.general)
svc = AllowlistService(db)
await svc.accept(img.id, tag.id)
await svc.reject_applied_tag(img.id, tag.id)
still_applied = (
await db.execute(
select(image_tag.c.tag_id)
.where(image_tag.c.image_record_id == img.id)
.where(image_tag.c.tag_id == tag.id)
)
).scalar_one_or_none()
assert still_applied is None
rej = await db.get(TagSuggestionRejection, (img.id, tag.id))
assert rej is not None
@pytest.mark.asyncio
async def test_dismiss_records_rejection(db):
img = await _make_image(db)
tag = await TagService(db).find_or_create("Dismissme", TagKind.general)
await AllowlistService(db).dismiss(img.id, tag.id)
assert await db.get(TagSuggestionRejection, (img.id, tag.id)) is not None
@pytest.mark.asyncio
async def test_add_alias_and_accept(db):
img = await _make_image(db)
canonical = await TagService(db).find_or_create(
"Canonical Char", TagKind.character
)
svc = AllowlistService(db)
await svc.add_alias_and_accept(
img.id, "model_char_name", "character", canonical.id
)
from backend.app.services.ml.aliases import AliasService
resolved = await AliasService(db).resolve("model_char_name", "character")
assert resolved.id == canonical.id
assert await db.get(TagAllowlist, canonical.id) is not None
@pytest.mark.asyncio
async def test_update_threshold_and_remove(db):
tag = await TagService(db).find_or_create("Thr", TagKind.general)
svc = AllowlistService(db)
img = await _make_image(db)
await svc.accept(img.id, tag.id)
await svc.update_threshold(tag.id, 0.80)
row = await db.get(TagAllowlist, tag.id)
assert abs(row.min_confidence - 0.80) < 1e-6
await svc.remove(tag.id)
assert await db.get(TagAllowlist, tag.id) is None
@pytest.mark.asyncio
async def test_coverage_by_threshold_direct_name(db):
tag = await TagService(db).find_or_create("Cov", TagKind.general)
svc = AllowlistService(db)
for i, score in enumerate((0.95, 0.80, 0.60)):
img = await _make_image(db, sha=f"c{i:063d}")
await _add_pred(db, img.id, "Cov", score)
assert await svc.coverage(tag.id, 0.90) == 1
assert await svc.coverage(tag.id, 0.70) == 2
assert await svc.coverage(tag.id, 0.50) == 3
@pytest.mark.asyncio
async def test_coverage_via_alias_respects_category(db):
tag = await TagService(db).find_or_create("Aliased", TagKind.character)
db.add(TagAlias(
alias_string="model_key", alias_category="character",
canonical_tag_id=tag.id,
))
await db.flush()
svc = AllowlistService(db)
hit = await _make_image(db, sha=f"a{0:063d}")
await _add_pred(db, hit.id, "model_key", 0.92, category="character")
# Same alias string but wrong category must NOT resolve to the tag.
miss = await _make_image(db, sha=f"a{1:063d}")
await _add_pred(db, miss.id, "model_key", 0.99, category="general")
assert await svc.coverage(tag.id, 0.90) == 1
@pytest.mark.asyncio
async def test_list_all_reports_applied_and_coverage(db):
tag = await TagService(db).find_or_create("Both", TagKind.general)
svc = AllowlistService(db)
applied_img = await _make_image(db, sha=f"b{0:063d}")
await svc.accept(applied_img.id, tag.id) # applies + allowlists
await _add_pred(db, applied_img.id, "Both", 0.95)
# A second image only has a qualifying prediction (covered, not applied).
cov_img = await _make_image(db, sha=f"b{1:063d}")
await _add_pred(db, cov_img.id, "Both", 0.95)
rows = await svc.list_all()
row = next(r for r in rows if r.tag_id == tag.id)
assert row.applied_count == 1 # only the accepted image
assert row.coverage_count == 2 # both have a ≥threshold pred
@pytest.mark.asyncio
async def test_update_threshold_clamped_to_store_floor(db):
# A min_confidence below the store floor (default 0.70) is clamped up —
# predictions below the floor aren't stored, so a lower threshold can't
# apply more permissively than the floor (#764).
tag = await TagService(db).find_or_create("Lowthr", TagKind.general)
svc = AllowlistService(db)
img = await _make_image(db)
await svc.accept(img.id, tag.id)
await svc.update_threshold(tag.id, 0.30)
row = await db.get(TagAllowlist, tag.id)
assert abs(row.min_confidence - 0.70) < 1e-6
tests/test_ml_artist_retired.py
-5
View File
@@ -3,11 +3,6 @@ import pytest
pytestmark = pytest.mark.integration
def test_artist_not_surfaced():
from backend.app.services.ml.tagger import SURFACED_CATEGORIES
assert "artist" not in SURFACED_CATEGORIES
def test_artist_not_head_eligible():
# Tagging-v2: suggestions come from heads, and heads are only trained for
# general/character concepts — so 'artist' (and any other kind) can't surface.
tests/test_ml_tagger.py
-54
View File
@@ -1,54 +0,0 @@
"""Tagger unit tests. The ONNX model isn't available in CI (it's a 1GB
download into /models), so these test the pure-logic surface:
DEFAULT_STORE_FLOOR constant, SURFACED_CATEGORIES set, TagPrediction
dataclass, and the load()-missing-file error path. Full inference is
exercised by the local integration suite against a real /models volume.
"""
import pytest
from backend.app.services.ml.tagger import (
DEFAULT_STORE_FLOOR,
SURFACED_CATEGORIES,
Tagger,
TagPrediction,
get_tagger,
)
def test_surfaced_categories():
# FC-2d-vii-c: 'artist' retired — artist identity is acquisition-
# derived (image_record.artist_id), never ML-inferred.
# 2026-06-01: 'copyright' retired — fandom serves as the franchise/
# copyright concept; operator doesn't use a separate copyright kind.
assert SURFACED_CATEGORIES == {"character", "general"}
assert "artist" not in SURFACED_CATEGORIES
assert "copyright" not in SURFACED_CATEGORIES
def test_default_store_floor():
# Raised 0.05 → 0.70 (plan-task #764): the suggestion path filters at
# 0.70 and the centroid path covers lower-confidence preferred tags, so
# storing the sub-0.70 tail was redundant (100 GB of TOAST). The live
# value is DB-backed (ml_settings.tagger_store_floor); this is the default.
assert DEFAULT_STORE_FLOOR == 0.70
def test_tag_prediction_dataclass():
p = TagPrediction(name="x", category="general", confidence=0.9)
assert p.name == "x"
assert p.category == "general"
assert p.confidence == 0.9
def test_get_tagger_singleton():
assert get_tagger() is get_tagger()
def test_load_raises_when_model_missing(tmp_path):
t = Tagger(model_dir=tmp_path / "nonexistent")
# Match the trailing "missing at <path>" rather than the specific
# filename, so a future model-version bump (camie-tagger-v3.onnx, etc.)
# doesn't bounce this test.
with pytest.raises(RuntimeError, match=r"\.onnx missing at "):
t.load()
tests/test_phash_dedup.py
-11
View File
@@ -11,7 +11,6 @@ from PIL import Image
from sqlalchemy import func, select
from backend.app.models import (
ImagePrediction,
ImageProvenance,
ImageRecord,
ImportSettings,
@@ -119,11 +118,6 @@ def test_smaller_existing_is_superseded(importer, import_layout):
image_record_id=eid, tag_id=tag.id, source="manual"
)
)
importer.session.add(
ImagePrediction(
image_record_id=eid, raw_name="x", category="general", score=0.9
)
)
old.siglip_embedding = [0.0] * 1152
old.integrity_status = "ok"
importer.session.commit()
@@ -141,11 +135,6 @@ def test_smaller_existing_is_superseded(importer, import_layout):
assert row.path != old_path
assert row.phash is not None
assert row.integrity_status == "unknown"
# #768: re-import clears the normalized predictions too
assert importer.session.execute(
select(func.count()).select_from(ImagePrediction)
.where(ImagePrediction.image_record_id == eid)
).scalar_one() == 0
assert row.siglip_embedding is None
linked = importer.session.execute(
select(image_tag.c.tag_id).where(
tests/test_tag_merge.py
+3 -43
View File
@@ -2,7 +2,6 @@ import pytest
from sqlalchemy import func, select
from backend.app.models import Tag, TagKind, image_tag
from backend.app.models.tag_allowlist import TagAllowlist
from backend.app.services.tag_service import (
MergeResult,
TagMergeConflict,
@@ -110,18 +109,6 @@ async def test_will_alias_true_when_machine_sourced(db):
assert ei.value.will_alias is True
@pytest.mark.asyncio
async def test_will_alias_true_when_allowlisted(db):
svc = TagService(db)
await svc.find_or_create("Canon2", TagKind.general)
source = await svc.find_or_create("Allowed", TagKind.general)
db.add(TagAllowlist(tag_id=source.id))
await db.flush()
with pytest.raises(TagMergeConflict) as ei:
await svc.rename(source.id, "Canon2")
assert ei.value.will_alias is True
@pytest.mark.asyncio
async def test_merge_rejects_self_merge(db):
svc = TagService(db)
@@ -250,35 +237,6 @@ async def test_merge_dedups_suggestion_rejections(db):
).first() is None
@pytest.mark.asyncio
async def test_merge_allowlist_target_has_keeps_target_threshold(db):
svc = TagService(db)
a = await svc.find_or_create("SrcAL", TagKind.general)
b = await svc.find_or_create("TgtAL", TagKind.general)
db.add(TagAllowlist(tag_id=a.id, min_confidence=0.5))
db.add(TagAllowlist(tag_id=b.id, min_confidence=0.9))
await db.flush()
await svc.merge(a.id, b.id)
rows = (await db.execute(select(TagAllowlist))).scalars().all()
assert len(rows) == 1
assert rows[0].tag_id == b.id
assert rows[0].min_confidence == 0.9
@pytest.mark.asyncio
async def test_merge_allowlist_source_only_moves_to_target(db):
svc = TagService(db)
a = await svc.find_or_create("SrcAL2", TagKind.general)
b = await svc.find_or_create("TgtAL2", TagKind.general)
db.add(TagAllowlist(tag_id=a.id, min_confidence=0.42))
await db.flush()
await svc.merge(a.id, b.id)
rows = (await db.execute(select(TagAllowlist))).scalars().all()
assert len(rows) == 1
assert rows[0].tag_id == b.id
assert rows[0].min_confidence == 0.42
@pytest.mark.asyncio
async def test_merge_repoints_existing_aliases(db):
from backend.app.models.tag_alias import TagAlias
@@ -372,7 +330,9 @@ async def test_alias_fallback_to_kind_when_no_predictions(db):
svc = TagService(db)
a = await svc.find_or_create("AllowNoPred", TagKind.character)
b = await svc.find_or_create("CanonF", TagKind.character)
db.add(TagAllowlist(tag_id=a.id))
# Machine-known via a prior accept (source='ml_accepted') → kept as alias.
img = await _img(db)
await svc.add_to_image(img, a.id, source="ml_accepted")
await db.flush()
result = await svc.merge(a.id, b.id)
assert result.alias_created is True
tests/test_tasks_ml.py
+4 -107
View File
@@ -1,15 +1,12 @@
"""tag_and_embed / backfill task tests. Models aren't in CI, so we test
the pure helpers (_aggregate_video_predictions, _is_video) as unit tests, and
the DB-touching backfill query as an integration test with monkeypatched
inference.
"""
"""tag_and_embed (embed-only) / backfill task tests. The pure _is_video helper
is a unit test; the DB-touching backfill query is an integration test with
monkeypatched dispatch."""
from pathlib import Path
import pytest
from backend.app.services.ml.tagger import TagPrediction
from backend.app.tasks.ml import _aggregate_video_predictions, _is_video
from backend.app.tasks.ml import _is_video
def test_is_video():
@@ -18,34 +15,6 @@ def test_is_video():
assert _is_video(Path("a.jpg")) is False
def _pred(name, conf, cat="general"):
return {name: TagPrediction(name, cat, conf)}
def test_aggregate_video_keeps_corroborated_and_means():
# #747: 4 frames; "smile" in 3, "sword" in 1 (noise). min_frames=2.
per_frame = [
{"smile": TagPrediction("smile", "general", 0.6),
"sword": TagPrediction("sword", "general", 0.9)},
_pred("smile", 0.8),
_pred("smile", 0.7),
{},
]
out = _aggregate_video_predictions(per_frame, min_frames=2)
assert "sword" not in out # one-frame flicker dropped
assert abs(out["smile"]["confidence"] - (0.6 + 0.8 + 0.7) / 3) < 1e-9 # mean, not max
def test_aggregate_video_clamps_min_frames_to_sample_count():
# Short video: 1 frame but min_frames=3 — clamp so it still tags.
out = _aggregate_video_predictions([_pred("solo", 0.8)], min_frames=3)
assert out["solo"]["confidence"] == 0.8
def test_aggregate_video_empty():
assert _aggregate_video_predictions([], min_frames=3) == {}
@pytest.mark.integration
@pytest.mark.asyncio
async def test_backfill_enqueues_missing(db, monkeypatch):
@@ -69,75 +38,3 @@ async def test_backfill_enqueues_missing(db, monkeypatch):
count = ml_tasks.backfill()
assert count >= 1
assert img.id in calls
@pytest.mark.integration
@pytest.mark.asyncio
async def test_apply_allowlist_applies_above_threshold(db):
from sqlalchemy import select
from backend.app.models import ImageRecord, TagAllowlist, TagKind
from backend.app.models.tag import image_tag
from backend.app.services.tag_service import TagService
from backend.app.tasks import ml as ml_tasks
from tests._prediction_helpers import seed_predictions
tag = await TagService(db).find_or_create("autohero", TagKind.character)
db.add(TagAllowlist(tag_id=tag.id, min_confidence=0.95))
img = ImageRecord(
path="/images/al.jpg", sha256="al" + "0" * 62, size_bytes=1,
mime="image/jpeg", width=1, height=1,
origin="imported_filesystem", integrity_status="unknown",
)
db.add(img)
await db.commit()
await seed_predictions(
db, img.id, {"autohero": {"category": "character", "confidence": 0.97}}
)
await db.commit()
n = ml_tasks.apply_allowlist_tags(tag_id=tag.id)
assert n >= 1
src = (
await db.execute(
select(image_tag.c.source)
.where(image_tag.c.image_record_id == img.id)
.where(image_tag.c.tag_id == tag.id)
)
).scalar_one()
assert src == "ml_auto"
@pytest.mark.integration
@pytest.mark.asyncio
async def test_apply_allowlist_skips_below_threshold(db):
from sqlalchemy import select
from backend.app.models import ImageRecord, TagAllowlist, TagKind
from backend.app.models.tag import image_tag
from backend.app.services.tag_service import TagService
from backend.app.tasks import ml as ml_tasks
from tests._prediction_helpers import seed_predictions
tag = await TagService(db).find_or_create("lowconf", TagKind.character)
db.add(TagAllowlist(tag_id=tag.id, min_confidence=0.95))
img = ImageRecord(
path="/images/lc.jpg", sha256="lc" + "0" * 62, size_bytes=1,
mime="image/jpeg", width=1, height=1,
origin="imported_filesystem", integrity_status="unknown",
)
db.add(img)
await db.commit()
await seed_predictions(
db, img.id, {"lowconf": {"category": "character", "confidence": 0.40}}
)
await db.commit()
ml_tasks.apply_allowlist_tags(tag_id=tag.id)
applied = (
await db.execute(
select(image_tag.c.tag_id)
.where(image_tag.c.image_record_id == img.id)
.where(image_tag.c.tag_id == tag.id)
)
).scalar_one_or_none()
assert applied is None