Merge pull request 'Release v26.05.25.7 — animated-WebP worker fix + FC-Cleanup backend' (#21) from dev into main

alembic/versions/0020_library_audit_run.py

@@ -0,0 +1,65 @@
+"""fc-cleanup: library_audit_run table for async transparency/single_color audits
+
+Revision ID: 0020
+Revises: 0019
+Create Date: 2026-05-26
+
+The table backs the async audit lifecycle: rule + params snapshot, status
+state machine ('running' → 'ready' → 'applied'/'cancelled'/'error'), and
+the matched_ids JSONB array that the apply step deletes. Capped at 50k IDs
+per row by the scan task (oversize = rule too aggressive, operator narrows
+before re-running).
+"""
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+revision: str = "0020"
+down_revision: Union[str, None] = "0019"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "library_audit_run",
+        sa.Column("id", sa.Integer(), primary_key=True),
+        sa.Column("rule", sa.String(32), nullable=False),
+        sa.Column("params", postgresql.JSONB(astext_type=sa.Text()), nullable=False),
+        sa.Column(
+            "status", sa.String(16),
+            nullable=False, server_default="running",
+        ),
+        sa.Column(
+            "started_at", sa.DateTime(timezone=True),
+            nullable=False, server_default=sa.func.now(),
+        ),
+        sa.Column("finished_at", sa.DateTime(timezone=True), nullable=True),
+        sa.Column(
+            "scanned_count", sa.Integer(),
+            nullable=False, server_default="0",
+        ),
+        sa.Column(
+            "matched_count", sa.Integer(),
+            nullable=False, server_default="0",
+        ),
+        sa.Column(
+            "matched_ids", postgresql.JSONB(astext_type=sa.Text()),
+            nullable=False, server_default=sa.text("'[]'::jsonb"),
+        ),
+        sa.Column("error", sa.Text(), nullable=True),
+    )
+    op.create_index(
+        "ix_library_audit_run_rule", "library_audit_run", ["rule"],
+    )
+    op.create_index(
+        "ix_library_audit_run_status", "library_audit_run", ["status"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_library_audit_run_status", table_name="library_audit_run")
+    op.drop_index("ix_library_audit_run_rule", table_name="library_audit_run")
+    op.drop_table("library_audit_run")

backend/app/celery_app.py

@@ -33,6 +33,7 @@ def make_celery() -> Celery:
             "backend.app.tasks.download",
             "backend.app.tasks.backup",
             "backend.app.tasks.admin",
+            "backend.app.tasks.library_audit",
         ],
     )
     app.conf.update(
@@ -47,6 +48,7 @@ def make_celery() -> Celery:
             "backend.app.tasks.migration.*": {"queue": "maintenance"},
             "backend.app.tasks.backup.*": {"queue": "maintenance"},
             "backend.app.tasks.admin.*": {"queue": "maintenance"},
+            "backend.app.tasks.library_audit.*": {"queue": "maintenance"},
         },
         # Heavy ML tasks need fair dispatch — see ImageRepo's precedent.
         task_acks_late=True,

backend/app/models/__init__.py

@@ -11,6 +11,7 @@ from .image_record import ImageRecord
 from .import_batch import ImportBatch
 from .import_settings import ImportSettings
 from .import_task import ImportTask
+from .library_audit_run import LibraryAuditRun
 from .migration_run import MigrationRun
 from .ml_settings import MLSettings
 from .post import Post
@@ -43,6 +44,7 @@ __all__ = [
     "ImportBatch",
     "ImportTask",
     "ImportSettings",
+    "LibraryAuditRun",
     "MLSettings",
     "MigrationRun",
     "TagAlias",

backend/app/models/library_audit_run.py

@@ -0,0 +1,37 @@
+"""LibraryAuditRun — async transparency / single_color audit lifecycle.
+
+State machine: running → ready → applied / cancelled / error.
+matched_ids JSONB is appended-to by scan_library_for_rule; apply_audit_run
+reads it and routes through cleanup_service.delete_images.
+"""
+
+from datetime import datetime
+from typing import Any
+
+from sqlalchemy import DateTime, Integer, String, Text, func
+from sqlalchemy.dialects.postgresql import JSONB
+from sqlalchemy.orm import Mapped, mapped_column
+
+from .base import Base
+
+
+class LibraryAuditRun(Base):
+    __tablename__ = "library_audit_run"
+
+    id: Mapped[int] = mapped_column(Integer, primary_key=True)
+    rule: Mapped[str] = mapped_column(String(32), nullable=False, index=True)
+    params: Mapped[dict[str, Any]] = mapped_column(JSONB, nullable=False)
+    status: Mapped[str] = mapped_column(
+        String(16), nullable=False, default="running", index=True,
+    )
+    # running | ready | applied | cancelled | error
+    started_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now(),
+    )
+    finished_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True,
+    )
+    scanned_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    matched_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    matched_ids: Mapped[list[int]] = mapped_column(JSONB, nullable=False, default=list)
+    error: Mapped[str | None] = mapped_column(Text, nullable=True)

backend/app/services/audits/__init__.py

@@ -0,0 +1,6 @@
+"""Audit rule modules. Each module exposes evaluate(pil_image, **params) -> bool.
+
+The retroactive library-cleanup tab and (future) import-time filter logic
+both consume these. Importers should NOT inline rule logic going forward;
+add the rule here and call from both sides.
+"""

backend/app/services/audits/single_color.py

@@ -0,0 +1,53 @@
+"""Single-color audit: matches images where one color dominates beyond
+the threshold (within the given Euclidean RGB tolerance). The first
+canonical implementation — the import-side filter (SkipReason.single_color)
+was never wired; FC-Cleanup's audit module is the source of truth and a
+future spec can adopt it on the import path too.
+"""
+
+from PIL import Image
+
+_THUMB_SIZE = (64, 64)
+
+
+def evaluate(
+    pil_image,
+    *,
+    threshold: float,
+    tolerance: int,
+) -> bool:
+    """True iff the fraction of pixels within `tolerance` (Euclidean RGB
+    distance) of the dominant color exceeds `threshold`.
+
+    Downsamples to 64x64 for speed (~4ms regardless of source size).
+    Alpha channels are stripped; only RGB is considered. Animated images
+    use frame 0 (PIL's default after Image.open without seek).
+    """
+    im = pil_image
+    if im.mode == "RGBA":
+        im = im.convert("RGB")
+    elif im.mode not in ("RGB", "L"):
+        im = im.convert("RGB")
+    if im.size != _THUMB_SIZE:
+        im = im.resize(_THUMB_SIZE, Image.Resampling.BILINEAR)
+    pixels = list(im.getdata())
+    if not pixels:
+        return False
+    # Normalize L-mode pixels to RGB tuples for distance math.
+    if isinstance(pixels[0], int):
+        pixels = [(p, p, p) for p in pixels]
+    # Dominant color = mean RGB.
+    n = len(pixels)
+    sum_r = sum(p[0] for p in pixels)
+    sum_g = sum(p[1] for p in pixels)
+    sum_b = sum(p[2] for p in pixels)
+    dom = (sum_r / n, sum_g / n, sum_b / n)
+    tol_sq = tolerance * tolerance
+    within = 0
+    for r, g, b in pixels:
+        dr = r - dom[0]
+        dg = g - dom[1]
+        db = b - dom[2]
+        if dr * dr + dg * dg + db * db <= tol_sq:
+            within += 1
+    return (within / n) > threshold

backend/app/services/audits/transparency.py

@@ -0,0 +1,27 @@
+"""Transparency audit: matches images whose transparent-pixel fraction
+exceeds the threshold. Animated images short-circuit (skipped) to avoid
+the multi-frame PIL decode that hits Celery's hard time limit."""
+
+
+def evaluate(pil_image, *, threshold: float) -> bool:
+    """True iff the image's transparent-pixel fraction exceeds threshold.
+
+    False for non-alpha modes and animated images. Mirrors the import-side
+    Importer._transparency_pct logic so retroactive enforcement matches
+    prospective filtering.
+    """
+    if getattr(pil_image, "is_animated", False):
+        return False
+    if pil_image.mode not in ("RGBA", "LA") and not (
+        pil_image.mode == "P" and "transparency" in pil_image.info
+    ):
+        return False
+    im = pil_image
+    if im.mode != "RGBA":
+        im = im.convert("RGBA")
+    alpha = im.getchannel("A")
+    histogram = alpha.histogram()
+    transparent = histogram[0]
+    total = sum(histogram)
+    pct = transparent / total if total else 0.0
+    return pct > threshold

backend/app/services/cleanup_service.py

@@ -12,12 +12,14 @@ re-exports from this module and then delete the wrapper.
 """
 from __future__ import annotations
 
+from datetime import UTC, datetime
 from pathlib import Path
+from typing import Any
 
-from sqlalchemy import func, select
+from sqlalchemy import func, select, update
 from sqlalchemy.orm import Session
 
-from ..models import Artist, ImageRecord, Tag
+from ..models import Artist, ImageRecord, LibraryAuditRun, Tag
 from ..models.series_page import SeriesPage
 from ..models.tag import image_tag
 
@@ -365,3 +367,142 @@ def prune_unused_tags(session: Session, *, dry_run: bool = False) -> dict:
         )
         session.commit()
     return {"deleted": len(ids), "sample_names": sample}
+
+
+# ---------------------------------------------------------------------------
+# FC-Cleanup additions (2026-05-26): retroactive audit of import-filter rules.
+# ---------------------------------------------------------------------------
+
+_MIN_DIM_SAMPLE_CAP = 50
+
+
+def project_min_dimension_violations(
+    session: Session, *, min_width: int, min_height: int,
+) -> dict:
+    """Return {count, sample_ids} for image_record rows with width or
+    height below the thresholds. Synchronous SQL — no PIL inspection
+    needed since width/height are stored columns."""
+    base = select(ImageRecord.id).where(
+        (ImageRecord.width < min_width) | (ImageRecord.height < min_height)
+    )
+    count = session.execute(
+        select(func.count()).select_from(base.subquery())
+    ).scalar_one()
+    sample_ids = session.execute(
+        base.order_by(ImageRecord.id).limit(_MIN_DIM_SAMPLE_CAP)
+    ).scalars().all()
+    return {"count": count, "sample_ids": list(sample_ids)}
+
+
+def delete_min_dimension_violations(
+    session: Session, *, min_width: int, min_height: int, images_root: Path,
+) -> int:
+    """Delete every image_record where width<min_w OR height<min_h.
+    Routes through delete_images so file-unlink + cascading FKs
+    (image_tag / image_provenance / etc.) are handled uniformly."""
+    ids = session.execute(
+        select(ImageRecord.id).where(
+            (ImageRecord.width < min_width) | (ImageRecord.height < min_height)
+        )
+    ).scalars().all()
+    if not ids:
+        return 0
+    result = delete_images(
+        session, image_ids=list(ids), images_root=images_root,
+    )
+    return result["images_deleted"]
+
+
+# ---------------------------------------------------------------------------
+# Audit lifecycle (transparency + single_color async scans).
+# ---------------------------------------------------------------------------
+
+
+class AuditAlreadyRunning(Exception):
+    """Another audit_run is currently in status='running' — wait or
+    cancel it before starting a new one. Surfaces as HTTP 409 in the
+    /api/cleanup/audit POST endpoint."""
+
+
+class AuditNotReady(Exception):
+    """apply_audit_run called on an audit whose status is not 'ready'."""
+
+
+class ConfirmTokenMismatch(Exception):
+    """Operator-supplied confirm token did not match server-recomputed token."""
+
+
+_VALID_RULES = ("transparency", "single_color")
+
+
+def start_audit_run(
+    session: Session, *, rule: str, params: dict[str, Any],
+) -> int:
+    """Create a LibraryAuditRun row in status='running' and dispatch the
+    scan_library_for_rule Celery task. Returns the new audit_id.
+
+    Concurrent-runs guard: raises AuditAlreadyRunning if any audit_run
+    has status='running'. Operator must cancel or wait."""
+    if rule not in _VALID_RULES:
+        raise ValueError(f"unknown rule {rule!r}; expected one of {_VALID_RULES}")
+    existing = session.execute(
+        select(LibraryAuditRun.id).where(LibraryAuditRun.status == "running")
+    ).scalar_one_or_none()
+    if existing is not None:
+        raise AuditAlreadyRunning(existing)
+    audit = LibraryAuditRun(
+        rule=rule,
+        params=params,
+        status="running",
+        scanned_count=0,
+        matched_count=0,
+        matched_ids=[],
+    )
+    session.add(audit)
+    session.flush()
+    audit_id = audit.id
+    # Dispatch after flush so audit_id is populated; commit happens in
+    # the API handler so the audit row + dispatch are visible together.
+    from ..tasks.library_audit import scan_library_for_rule
+    scan_library_for_rule.delay(audit_id)
+    return audit_id
+
+
+def apply_audit_run(
+    session: Session, *, audit_id: int, confirm_token: str, images_root: Path,
+) -> int:
+    """Delete all images in audit_run.matched_ids after confirming token.
+    Marks audit status='applied'. Routes through delete_images so files
+    + cascading FK rows are handled uniformly."""
+    audit = session.execute(
+        select(LibraryAuditRun).where(LibraryAuditRun.id == audit_id)
+    ).scalar_one_or_none()
+    if audit is None:
+        raise ValueError(f"audit_run {audit_id} not found")
+    if audit.status != "ready":
+        raise AuditNotReady(audit.status)
+    expected = f"apply-audit-{audit_id}"
+    if confirm_token != expected:
+        raise ConfirmTokenMismatch(expected)
+    ids = list(audit.matched_ids or [])
+    deleted = 0
+    if ids:
+        result = delete_images(session, image_ids=ids, images_root=images_root)
+        deleted = result["images_deleted"]
+    session.execute(
+        update(LibraryAuditRun)
+        .where(LibraryAuditRun.id == audit_id)
+        .values(status="applied", finished_at=datetime.now(UTC))
+    )
+    return deleted
+
+
+def cancel_audit_run(session: Session, *, audit_id: int) -> None:
+    """Flip a running audit_run to 'cancelled'. The scan task checks
+    for status=='cancelled' between batches and exits cleanly."""
+    session.execute(
+        update(LibraryAuditRun)
+        .where(LibraryAuditRun.id == audit_id)
+        .where(LibraryAuditRun.status == "running")
+        .values(status="cancelled", finished_at=datetime.now(UTC))
+    )

backend/app/services/importer.py

@@ -860,8 +860,26 @@ class Importer:
                 pass
 
     def _transparency_pct(self, source: Path) -> float:
-        """Fraction of fully-transparent pixels in the image. 0.0 if no alpha."""
+        """Fraction of fully-transparent pixels in the image. 0.0 if no alpha.
+
+        For animated formats (multi-frame WebP / GIF / APNG), short-circuit
+        to 0.0 instead of decoding every frame. PIL's `getchannel("A")`
+        forces a full decode of all frames in an animated image, which for
+        a large animated WebP takes 5+ minutes and blows past the Celery
+        soft+hard time limits (300s/360s → SIGKILL). Operator-flagged
+        2026-05-26. Transparency analysis on a multi-frame image isn't
+        meaningful for art-curation purposes anyway — different frames
+        have different alpha — so the existing too_transparent skip rule
+        is bypassed entirely for animated content.
+        """
         with Image.open(source) as im:
+            if getattr(im, "is_animated", False):
+                log.info(
+                    "skipping transparency check for animated image %s "
+                    "(n_frames=%d) — avoids multi-frame decode timeout",
+                    source, getattr(im, "n_frames", 0),
+                )
+                return 0.0
             if im.mode not in ("RGBA", "LA") and not (
                 im.mode == "P" and "transparency" in im.info
             ):

backend/app/tasks/library_audit.py

@@ -0,0 +1,167 @@
+"""scan_library_for_rule Celery task — iterates image_record in keyset-
+paginated batches, evaluates the audit rule per image, populates
+LibraryAuditRun.matched_ids. Runs on the maintenance queue with a 2h soft
+time limit (plenty of margin for 100k+ image libraries at ~100ms PIL
+decode + histogram per image).
+
+State machine:
+  start: status='running'
+  end success: status='ready'
+  end error: status='error', error=traceback
+  oversize: status='error', error='matched too many images; tighten threshold'
+  external cancel: scan sees status='cancelled' between batches, exits.
+"""
+
+import logging
+import traceback
+from datetime import UTC, datetime
+
+from celery.exceptions import SoftTimeLimitExceeded
+from PIL import Image
+from sqlalchemy import select, update
+from sqlalchemy.exc import DBAPIError, OperationalError
+
+from ..celery_app import celery
+from ..models import ImageRecord, LibraryAuditRun
+from ..services.audits import single_color, transparency
+from ._sync_engine import sync_session_factory as _sync_session_factory
+
+log = logging.getLogger(__name__)
+
+_BATCH = 500
+_PROGRESS_TICK = 100
+_MAX_MATCHED = 50_000
+
+_RULES = {
+    "transparency": transparency.evaluate,
+    "single_color": single_color.evaluate,
+}
+
+
+@celery.task(
+    name="backend.app.tasks.library_audit.scan_library_for_rule",
+    bind=True,
+    autoretry_for=(OperationalError, DBAPIError),
+    retry_backoff=5,
+    retry_backoff_max=60,
+    retry_jitter=True,
+    max_retries=3,
+    soft_time_limit=7200,
+    time_limit=7500,
+)
+def scan_library_for_rule(self, audit_id: int) -> dict:
+    """See module docstring. Returns a small summary dict for eager-mode
+    test assertions (real workers ignore the return value)."""
+    SessionLocal = _sync_session_factory()
+    try:
+        with SessionLocal() as session:
+            audit = session.get(LibraryAuditRun, audit_id)
+            if audit is None:
+                return {"audit_id": audit_id, "status": "missing"}
+            evaluate = _RULES.get(audit.rule)
+            if evaluate is None:
+                _mark_error(session, audit_id, f"unknown rule {audit.rule!r}")
+                return {"audit_id": audit_id, "status": "error"}
+            params = dict(audit.params or {})
+            matched: list[int] = []
+            scanned = 0
+            last_id = 0
+            while True:
+                # Cancellation check between batches.
+                current_status = session.execute(
+                    select(LibraryAuditRun.status)
+                    .where(LibraryAuditRun.id == audit_id)
+                ).scalar_one()
+                if current_status == "cancelled":
+                    return {"audit_id": audit_id, "status": "cancelled"}
+                rows = session.execute(
+                    select(ImageRecord.id, ImageRecord.path)
+                    .where(ImageRecord.id > last_id)
+                    .where(ImageRecord.mime.like("image/%"))
+                    .order_by(ImageRecord.id.asc())
+                    .limit(_BATCH)
+                ).all()
+                if not rows:
+                    break
+                for image_id, image_path in rows:
+                    last_id = image_id
+                    scanned += 1
+                    try:
+                        with Image.open(image_path) as im:
+                            try:
+                                if evaluate(im, **params):
+                                    matched.append(image_id)
+                            except Exception as exc:  # noqa: BLE001
+                                log.warning(
+                                    "audit %s: rule evaluate failed on %s: %s",
+                                    audit_id, image_path, exc,
+                                )
+                    except FileNotFoundError:
+                        log.warning(
+                            "audit %s: image_record %s file missing at %s; skipping",
+                            audit_id, image_id, image_path,
+                        )
+                    except OSError as exc:
+                        log.warning(
+                            "audit %s: PIL load failed for %s: %s",
+                            audit_id, image_path, exc,
+                        )
+                    if len(matched) > _MAX_MATCHED:
+                        _mark_error(
+                            session, audit_id,
+                            f"matched > {_MAX_MATCHED} images; "
+                            "tighten threshold and re-run",
+                        )
+                        return {"audit_id": audit_id, "status": "error"}
+                    if scanned % _PROGRESS_TICK == 0:
+                        session.execute(
+                            update(LibraryAuditRun)
+                            .where(LibraryAuditRun.id == audit_id)
+                            .values(scanned_count=scanned)
+                        )
+                        session.commit()
+            # Final state.
+            session.execute(
+                update(LibraryAuditRun)
+                .where(LibraryAuditRun.id == audit_id)
+                .values(
+                    scanned_count=scanned,
+                    matched_count=len(matched),
+                    matched_ids=matched,
+                    status="ready",
+                    finished_at=datetime.now(UTC),
+                )
+            )
+            session.commit()
+            return {
+                "audit_id": audit_id,
+                "status": "ready",
+                "scanned": scanned,
+                "matched": len(matched),
+            }
+    except SoftTimeLimitExceeded:
+        with SessionLocal() as session:
+            _mark_error(session, audit_id, "soft_time_limit exceeded (>7200s)")
+        raise
+    except (OperationalError, DBAPIError):
+        # Retryable per the decorator; leave row in 'running' and let
+        # autoretry try again. Recovery sweep catches if all retries fail.
+        raise
+    except Exception:  # noqa: BLE001
+        tb = traceback.format_exc()
+        with SessionLocal() as session:
+            _mark_error(session, audit_id, tb)
+        raise
+
+
+def _mark_error(session, audit_id: int, error_msg: str) -> None:
+    session.execute(
+        update(LibraryAuditRun)
+        .where(LibraryAuditRun.id == audit_id)
+        .values(
+            status="error",
+            error=error_msg,
+            finished_at=datetime.now(UTC),
+        )
+    )
+    session.commit()

backend/app/utils/phash.py

@@ -13,8 +13,21 @@ HASH_SIZE = 8
 
 def compute_phash(pil_image) -> str | None:
     """Perceptual hash of an opened PIL image, as a hex string. None on any
-    failure (videos/unreadable/non-image)."""
+    failure (videos/unreadable/non-image).
+
+    For animated images (multi-frame WebP/GIF/APNG), explicitly seek to
+    frame 0 first. Without this, some PIL operations downstream of
+    imagehash.phash (convert("L"), resize) can iterate all frames and
+    blow past Celery's hard time limit on large animations
+    (operator-flagged 2026-05-26 against animated WebPs). The pHash of
+    frame 0 is the conventional choice for animated content.
+    """
     try:
+        if getattr(pil_image, "is_animated", False):
+            try:
+                pil_image.seek(0)
+            except Exception:
+                pass
         return str(imagehash.phash(pil_image, hash_size=HASH_SIZE))
     except Exception:
         return None

tests/test_audits_single_color.py

@@ -0,0 +1,42 @@
+"""Tests for the single-color audit rule.
+
+The rule downsamples + measures the fraction of pixels within `tolerance`
+(Euclidean RGB distance) of the dominant color. Matches if that fraction
+exceeds `threshold`. Single-color content is typically uploaded by
+mistake (placeholder/error/preview images) and should be flagged.
+"""
+
+from PIL import Image
+
+from backend.app.services.audits import single_color
+
+
+def test_single_color_evaluate_true_for_uniform_image():
+    im = Image.new("RGB", (50, 50), (128, 64, 200))
+    assert single_color.evaluate(im, threshold=0.9, tolerance=10) is True
+
+
+def test_single_color_evaluate_false_for_diverse_image():
+    # Half black, half white — no single color dominates.
+    im = Image.new("RGB", (50, 50), (0, 0, 0))
+    for x in range(25):
+        for y in range(50):
+            im.putpixel((x, y), (255, 255, 255))
+    assert single_color.evaluate(im, threshold=0.9, tolerance=10) is False
+
+
+def test_single_color_evaluate_respects_tolerance_widening():
+    # Gradient image: pixels span 0..50 in R channel. Tight tolerance
+    # rejects (no concentration), wide tolerance accepts (all near 25).
+    im = Image.new("RGB", (50, 50), (0, 0, 0))
+    for x in range(50):
+        for y in range(50):
+            im.putpixel((x, y), (x, 0, 0))
+    assert single_color.evaluate(im, threshold=0.9, tolerance=5) is False
+    assert single_color.evaluate(im, threshold=0.9, tolerance=50) is True
+
+
+def test_single_color_evaluate_handles_rgba_input():
+    # Alpha channel should be ignored — only RGB matters for the rule.
+    im = Image.new("RGBA", (50, 50), (100, 100, 100, 128))
+    assert single_color.evaluate(im, threshold=0.9, tolerance=10) is True

tests/test_audits_transparency.py

@@ -0,0 +1,46 @@
+"""Tests for the transparency audit rule.
+
+The rule mirrors `Importer._transparency_pct` semantics for retroactive
+enforcement: returns True iff the fraction of fully-transparent pixels
+exceeds the threshold. Animated images short-circuit to False to avoid
+the multi-frame PIL decode that triggered SoftTimeLimitExceeded
+2026-05-26 against animated WebPs.
+"""
+
+from PIL import Image
+
+from backend.app.services.audits import transparency
+
+
+def test_transparency_evaluate_true_when_fully_transparent():
+    im = Image.new("RGBA", (10, 10), (0, 0, 0, 0))
+    assert transparency.evaluate(im, threshold=0.5) is True
+
+
+def test_transparency_evaluate_false_when_fully_opaque():
+    im = Image.new("RGBA", (10, 10), (200, 100, 50, 255))
+    assert transparency.evaluate(im, threshold=0.5) is False
+
+
+def test_transparency_evaluate_respects_threshold_boundary():
+    # Half-transparent image: 50% alpha=0 pixels, 50% alpha=255.
+    im = Image.new("RGBA", (10, 10), (0, 0, 0, 0))
+    for x in range(5):
+        for y in range(10):
+            im.putpixel((x, y), (0, 0, 0, 255))
+    # 50% transparent. threshold=0.4 → True; threshold=0.6 → False.
+    assert transparency.evaluate(im, threshold=0.4) is True
+    assert transparency.evaluate(im, threshold=0.6) is False
+
+
+def test_transparency_evaluate_false_for_rgb_image_without_alpha():
+    im = Image.new("RGB", (10, 10), (128, 128, 128))
+    assert transparency.evaluate(im, threshold=0.5) is False
+
+
+def test_transparency_evaluate_false_for_animated_image():
+    im = Image.new("RGBA", (10, 10), (0, 0, 0, 0))
+    # Mark as animated (mimics PIL's WebP/GIF multi-frame attribute).
+    im.is_animated = True  # type: ignore[attr-defined]
+    im.n_frames = 5  # type: ignore[attr-defined]
+    assert transparency.evaluate(im, threshold=0.5) is False

tests/test_cleanup_service_audit.py

@@ -0,0 +1,175 @@
+"""Tests for cleanup_service's library-audit additions.
+
+Covers:
+  - project_min_dimension_violations: SQL-only query against width/height
+  - delete_min_dimension_violations: routes through existing delete_images
+  - audit lifecycle (start_audit_run / apply_audit_run / cancel_audit_run)
+
+Tests assert via column selects per reference-async-coredml-test-assertions
+(post-DML ORM entity access via session.get() raises MissingGreenlet on
+async sessions; we use db_sync here but the convention is preserved for
+consistency).
+"""
+
+from pathlib import Path
+
+import pytest
+from PIL import Image
+from sqlalchemy import func, select
+
+from backend.app.models import ImageRecord, LibraryAuditRun
+from backend.app.services import cleanup_service
+
+pytestmark = pytest.mark.integration
+
+
+def _make_image_record(db_sync, tmp_path, *, width, height, color):
+    """Helper: write a real PIL file to a UNIQUE path (reference-image-record-path-unique)
+    and insert an ImageRecord row with all required NOT-NULL columns set."""
+    path = tmp_path / f"img_{width}x{height}_{color[0]}.png"
+    Image.new("RGB", (width, height), color).save(path)
+    # sha256 column is varchar(64) — use a deterministic 64-char pseudo
+    # hash built from the unique inputs.
+    sha = f"{width:04d}{height:04d}{color[0]:03d}".ljust(64, "0")[:64]
+    rec = ImageRecord(
+        path=str(path),
+        sha256=sha,
+        size_bytes=path.stat().st_size,
+        mime="image/png",  # reference-image-record-required-columns
+        width=width,
+        height=height,
+        origin="imported_filesystem",  # feedback-check-existing-enums
+        integrity_status="ok",
+    )
+    db_sync.add(rec)
+    db_sync.flush()
+    return rec
+
+
+def test_project_min_dimension_violations_returns_count_and_samples(db_sync, tmp_path):
+    _make_image_record(db_sync, tmp_path, width=100, height=100, color=(10, 0, 0))
+    _make_image_record(db_sync, tmp_path, width=50, height=50, color=(20, 0, 0))
+    _make_image_record(db_sync, tmp_path, width=400, height=400, color=(30, 0, 0))
+    db_sync.commit()
+
+    result = cleanup_service.project_min_dimension_violations(
+        db_sync, min_width=200, min_height=200,
+    )
+    assert result["count"] == 2  # 100x100 and 50x50 violate
+    assert len(result["sample_ids"]) == 2
+
+
+def test_delete_min_dimension_violations_unlinks_and_cascades(db_sync, tmp_path):
+    rec_small = _make_image_record(db_sync, tmp_path, width=50, height=50, color=(40, 0, 0))
+    rec_big = _make_image_record(db_sync, tmp_path, width=500, height=500, color=(50, 0, 0))
+    small_path = Path(rec_small.path)
+    db_sync.commit()
+
+    # images_root is tmp_path here because the test fixtures stored files there;
+    # delete_images() uses it to unlink originals + thumbs from the right tree.
+    deleted = cleanup_service.delete_min_dimension_violations(
+        db_sync, min_width=200, min_height=200, images_root=tmp_path,
+    )
+    assert deleted == 1
+    # Verify via column selects per banked rule.
+    remaining_ids = db_sync.execute(
+        select(ImageRecord.id).order_by(ImageRecord.id)
+    ).scalars().all()
+    assert remaining_ids == [rec_big.id]
+    assert not small_path.exists()  # file unlinked
+
+
+# --- Audit lifecycle tests (Task 5) ---
+
+import backend.app.tasks.library_audit  # noqa: F401, E402 — celery registration
+
+
+def test_start_audit_run_creates_row_and_dispatches(db_sync, monkeypatch):
+    dispatched = []
+    from backend.app.tasks import library_audit as la_mod
+    monkeypatch.setattr(
+        la_mod.scan_library_for_rule, "delay",
+        lambda audit_id: dispatched.append(audit_id),
+    )
+    audit_id = cleanup_service.start_audit_run(
+        db_sync, rule="transparency", params={"threshold": 0.9},
+    )
+    db_sync.commit()
+    row_status = db_sync.execute(
+        select(LibraryAuditRun.status).where(LibraryAuditRun.id == audit_id)
+    ).scalar_one()
+    assert row_status == "running"
+    assert dispatched == [audit_id]
+
+
+def test_start_audit_run_rejects_when_another_is_running(db_sync, monkeypatch):
+    from backend.app.tasks import library_audit as la_mod
+    monkeypatch.setattr(
+        la_mod.scan_library_for_rule, "delay", lambda audit_id: None,
+    )
+    cleanup_service.start_audit_run(
+        db_sync, rule="transparency", params={"threshold": 0.9},
+    )
+    db_sync.commit()
+    with pytest.raises(cleanup_service.AuditAlreadyRunning):
+        cleanup_service.start_audit_run(
+            db_sync, rule="single_color",
+            params={"threshold": 0.95, "tolerance": 30},
+        )
+
+
+def test_apply_audit_run_with_correct_token_deletes_matched(db_sync, tmp_path):
+    rec = _make_image_record(
+        db_sync, tmp_path, width=100, height=100, color=(60, 0, 0),
+    )
+    db_sync.flush()
+    audit = LibraryAuditRun(
+        rule="transparency", params={"threshold": 0.9},
+        status="ready", scanned_count=1, matched_count=1,
+        matched_ids=[rec.id],
+    )
+    db_sync.add(audit)
+    db_sync.commit()
+
+    deleted = cleanup_service.apply_audit_run(
+        db_sync, audit_id=audit.id,
+        confirm_token=f"apply-audit-{audit.id}",
+        images_root=tmp_path,
+    )
+    assert deleted == 1
+    remaining_count = db_sync.execute(
+        select(func.count()).select_from(ImageRecord)
+    ).scalar_one()
+    assert remaining_count == 0
+    new_status = db_sync.execute(
+        select(LibraryAuditRun.status).where(LibraryAuditRun.id == audit.id)
+    ).scalar_one()
+    assert new_status == "applied"
+
+
+def test_apply_audit_run_with_wrong_token_raises(db_sync, tmp_path):
+    audit = LibraryAuditRun(
+        rule="transparency", params={"threshold": 0.9},
+        status="ready", matched_ids=[],
+    )
+    db_sync.add(audit)
+    db_sync.commit()
+    with pytest.raises(cleanup_service.ConfirmTokenMismatch):
+        cleanup_service.apply_audit_run(
+            db_sync, audit_id=audit.id,
+            confirm_token="wrong-token", images_root=tmp_path,
+        )
+
+
+def test_cancel_audit_run_flips_status(db_sync):
+    audit = LibraryAuditRun(
+        rule="transparency", params={"threshold": 0.9},
+        status="running", matched_ids=[],
+    )
+    db_sync.add(audit)
+    db_sync.commit()
+    cleanup_service.cancel_audit_run(db_sync, audit_id=audit.id)
+    new_status = db_sync.execute(
+        select(LibraryAuditRun.status).where(LibraryAuditRun.id == audit.id)
+    ).scalar_one()
+    assert new_status == "cancelled"

tests/test_tasks_library_audit.py

@@ -0,0 +1,100 @@
+"""Tests for scan_library_for_rule Celery task.
+
+Eager mode is used so the task runs synchronously in-test and we can
+assert state via column selects (post-DML ORM access banned per
+reference-async-coredml-test-assertions)."""
+
+import pytest
+from PIL import Image
+from sqlalchemy import select
+
+import backend.app.tasks.library_audit  # noqa: F401 — celery registration
+from backend.app import celery_app
+from backend.app.models import ImageRecord, LibraryAuditRun
+
+pytestmark = pytest.mark.integration
+
+
+def _mk_image(db_sync, tmp_path, *, mode, color, name):
+    path = tmp_path / name
+    Image.new(mode, (10, 10), color).save(path)
+    # sha256 column is varchar(64) — pad/truncate a per-file pseudo hash
+    # exactly to 64 chars. Each test fixture file must have a unique
+    # sha256 (reference-image-record-path-unique peer constraint).
+    sha = f"audit-{name}".ljust(64, "x")[:64]
+    rec = ImageRecord(
+        path=str(path),
+        sha256=sha,
+        size_bytes=path.stat().st_size,
+        mime="image/png",
+        width=10, height=10,
+        origin="imported_filesystem",
+        integrity_status="ok",
+    )
+    db_sync.add(rec)
+    db_sync.flush()
+    return rec, path
+
+
+def test_scan_library_for_rule_populates_matched_ids_for_transparency(
+    db_sync, tmp_path, monkeypatch,
+):
+    transparent_rec, _ = _mk_image(
+        db_sync, tmp_path, mode="RGBA", color=(0, 0, 0, 0), name="trans.png",
+    )
+    opaque_rec, _ = _mk_image(
+        db_sync, tmp_path, mode="RGBA", color=(200, 0, 0, 255), name="opaque.png",
+    )
+    audit = LibraryAuditRun(
+        rule="transparency", params={"threshold": 0.5},
+        status="running", matched_ids=[],
+    )
+    db_sync.add(audit)
+    db_sync.commit()
+    audit_id = audit.id
+
+    monkeypatch.setattr(celery_app.celery.conf, "task_always_eager", True)
+    from backend.app.tasks.library_audit import scan_library_for_rule
+    scan_library_for_rule.run(audit_id)
+    monkeypatch.setattr(celery_app.celery.conf, "task_always_eager", False)
+
+    matched = db_sync.execute(
+        select(LibraryAuditRun.matched_ids).where(LibraryAuditRun.id == audit_id)
+    ).scalar_one()
+    status = db_sync.execute(
+        select(LibraryAuditRun.status).where(LibraryAuditRun.id == audit_id)
+    ).scalar_one()
+    assert transparent_rec.id in matched
+    assert opaque_rec.id not in matched
+    assert status == "ready"
+
+
+def test_scan_library_for_rule_skips_missing_files_gracefully(
+    db_sync, tmp_path, monkeypatch,
+):
+    rec, path = _mk_image(
+        db_sync, tmp_path, mode="RGBA", color=(0, 0, 0, 0), name="ghost.png",
+    )
+    path.unlink()  # delete the file but leave the DB row
+    audit = LibraryAuditRun(
+        rule="transparency", params={"threshold": 0.5},
+        status="running", matched_ids=[],
+    )
+    db_sync.add(audit)
+    db_sync.commit()
+    audit_id = audit.id
+
+    monkeypatch.setattr(celery_app.celery.conf, "task_always_eager", True)
+    from backend.app.tasks.library_audit import scan_library_for_rule
+    scan_library_for_rule.run(audit_id)
+    monkeypatch.setattr(celery_app.celery.conf, "task_always_eager", False)
+
+    status = db_sync.execute(
+        select(LibraryAuditRun.status).where(LibraryAuditRun.id == audit_id)
+    ).scalar_one()
+    matched = db_sync.execute(
+        select(LibraryAuditRun.matched_ids).where(LibraryAuditRun.id == audit_id)
+    ).scalar_one()
+    # Missing file is skipped (warning logged), audit completes successfully.
+    assert status == "ready"
+    assert rec.id not in matched