This repository has been archived on 2026-05-31. You can view files and clone it. You cannot open issues or pull requests or push a commit.
Files
imagerepo/app/tasks/maintenance.py
T
bvandeusen b7d4998cdb feat(beat): self-gating schedules for ML backfill, centroids, auto-accept
Promotes three previously-manual maintenance tasks to Celery Beat schedules
so the user doesn't have to remember to run them:

- ml.backfill                              daily
- apply_auto_accept_predictions            daily
- recompute_all_centroids                  weekly

Cadences are env-overridable (ML_BACKFILL_EVERY_SECONDS,
AUTO_ACCEPT_EVERY_SECONDS, CENTROIDS_EVERY_SECONDS).

Each task self-gates so a scheduled run is a no-op when there's nothing
to do:

- ml.backfill: already self-gating — its first paginated query returns
  zero rows when no image is missing predictions/embeddings, the loop
  breaks, and the task returns. No code change.

- apply_auto_accept_predictions: adds a fast-path NOT EXISTS query that
  returns immediately when no WD14 prediction at/above the threshold
  exists for an unattached, non-rejected (image, tag) pair. The full
  walk only fires when fresh predictions have landed since the last run.
  Returns {'skipped_no_candidates': True} on the no-op path.

- recompute_all_centroids: tightens the aggregate query to LEFT JOIN
  tag_reference_embedding and skip tags whose stored reference_count
  already matches current image_tags membership count. Without this gate
  the daily-scheduled sweep would re-enqueue a recompute for every
  eligible tag every run, contending with tag_and_embed on the ml queue.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-04-28 00:06:09 -04:00

485 lines
17 KiB
Python

"""Maintenance-queue Celery tasks.
Currently holds the blocklist cleanup task that retroactively removes
blocklisted tag names from the library. The earlier sync_character_fandoms
task was removed on 2026-04-21 as part of the bare-name refactor.
"""
import logging
import re
from datetime import datetime, timezone
from celery import shared_task
from sqlalchemy import text
from app import db
from app.models import ImageRecord, ImportTask, SuggestionFeedback, Tag
from app.services.integrity import verify_path
log = logging.getLogger(__name__)
_FANDOM_SUFFIX_RE = re.compile(r'^(.+?) \(([^()]+)\)$')
@shared_task(
name='app.tasks.maintenance.sync_character_fandoms_to_images',
soft_time_limit=300,
time_limit=600,
)
def _heal_malformed_character_names() -> dict:
"""Find characters whose name still contains a '(Fandom)' suffix and
whose fandom_id is NULL (typically created by a pre-fix suggestion
accept that didn't parse the WD14 suffix). Parse the suffix, ensure
the fandom tag exists, and either promote the malformed row to
(name=bare, fandom_id=<fandom>) or merge it into an existing canonical
character with the same (bare_name, fandom_id) pair.
Same auto-merge semantics as migration j26042101 Phase 2. Uses raw
SQL throughout to avoid ORM autoflush ordering quirks between the
fandom INSERT and the subsequent tag UPDATE inside a single worker
task session.
"""
rows = db.session.execute(text("""
SELECT id, name FROM tag
WHERE kind = 'character' AND fandom_id IS NULL AND name LIKE '% (%)%'
""")).fetchall()
healed = 0
merged = 0
skipped = 0
for row in rows:
tag_id = row[0]
old_name = row[1]
m = _FANDOM_SUFFIX_RE.match(old_name)
if not m:
skipped += 1
continue
bare_name = m.group(1).strip()
fandom_name = m.group(2).strip()
if not bare_name or not fandom_name:
skipped += 1
continue
try:
# Find-or-create the fandom via raw SQL, commit so the FK from
# the subsequent character UPDATE can resolve.
fandom_row = db.session.execute(
text("SELECT id FROM tag WHERE kind='fandom' AND name=:n"),
{'n': fandom_name},
).fetchone()
if fandom_row is None:
fandom_id = db.session.execute(
text("INSERT INTO tag (kind, name) VALUES ('fandom', :n) RETURNING id"),
{'n': fandom_name},
).scalar_one()
else:
fandom_id = fandom_row[0]
db.session.commit()
# Collision check against the post-refactor partial index.
canonical_row = db.session.execute(
text("""
SELECT id FROM tag
WHERE kind='character' AND name=:n AND fandom_id=:f AND id<>:me
"""),
{'n': bare_name, 'f': fandom_id, 'me': tag_id},
).fetchone()
if canonical_row is not None:
canonical_id = canonical_row[0]
# Reassign image_tags onto the canonical, delete malformed's
# own image_tags rows.
db.session.execute(text("""
INSERT INTO image_tags (image_id, tag_id)
SELECT DISTINCT image_id, :to_id FROM image_tags WHERE tag_id = :from_id
ON CONFLICT DO NOTHING
"""), {'from_id': tag_id, 'to_id': canonical_id})
db.session.execute(
text("DELETE FROM image_tags WHERE tag_id = :tid"),
{'tid': tag_id},
)
_cascade_ref_embedding_rename(old_name, bare_name)
db.session.execute(
text("DELETE FROM tag WHERE id = :tid"),
{'tid': tag_id},
)
merged += 1
else:
_cascade_ref_embedding_rename(old_name, bare_name)
db.session.execute(
text("UPDATE tag SET name=:n, fandom_id=:f WHERE id=:tid"),
{'n': bare_name, 'f': fandom_id, 'tid': tag_id},
)
healed += 1
db.session.commit()
except Exception as e:
db.session.rollback()
log.warning(
"heal_malformed_character_names: tag %s (%r) failed: %s",
tag_id, old_name, e,
)
skipped += 1
return {'malformed_scanned': len(rows), 'healed': healed, 'merged': merged, 'skipped': skipped}
def _cascade_ref_embedding_rename(old_name: str, new_name: str) -> None:
"""Rename tag_reference_embedding rows from old_name to new_name where
new_name doesn't already have a row for the same model_version; delete
leftover old_name rows after. Matches the pattern used by migration
j26042101."""
if old_name == new_name:
return
db.session.execute(text("""
UPDATE tag_reference_embedding SET tag_name = :new_name
WHERE tag_name = :old_name
AND NOT EXISTS (
SELECT 1 FROM tag_reference_embedding w
WHERE w.tag_name = :new_name AND w.model_version = tag_reference_embedding.model_version
)
"""), {'old_name': old_name, 'new_name': new_name})
db.session.execute(
text("DELETE FROM tag_reference_embedding WHERE tag_name = :old_name"),
{'old_name': old_name},
)
def sync_character_fandoms_to_images() -> dict:
"""Idempotent two-pass maintenance:
Pass A — heal characters whose 'name' still embeds a '(Fandom)' suffix
and whose fandom_id is NULL. These come from pre-fix suggestion
accepts that didn't parse the WD14 output. Splits the suffix,
ensures the fandom tag exists, promotes or merges the row.
Pass B — for every character with a non-null fandom_id, attach the
fandom tag to every image that has the character attached.
Backfills the gap left by migration j26042101 (which populated
tag.fandom_id from old suffixes but didn't walk image_tags).
Additive for image_tags; destructive only for malformed character rows
that merge into a canonical row. Safe to re-run — both passes are
idempotent.
"""
pass_a = _heal_malformed_character_names()
chars = (
Tag.query
.filter_by(kind='character')
.filter(Tag.fandom_id.isnot(None))
.all()
)
total_links_added = 0
characters_processed = 0
failures = 0
for char in chars:
try:
result = db.session.execute(
text("""
INSERT INTO image_tags (image_id, tag_id)
SELECT it.image_id, :fandom_id
FROM image_tags it
WHERE it.tag_id = :char_id
ON CONFLICT DO NOTHING
"""),
{'char_id': char.id, 'fandom_id': char.fandom_id},
)
total_links_added += result.rowcount or 0
db.session.commit()
characters_processed += 1
except Exception as e:
db.session.rollback()
failures += 1
log.warning(
"sync_character_fandoms_to_images: char tag %s (%s) failed: %s",
char.id, char.name, e,
)
log.info(
"sync_character_fandoms_to_images: heal=%s, char_attach=%d processed, %d links added, %d failures",
pass_a, characters_processed, total_links_added, failures,
)
return {
'heal': pass_a,
'characters_scanned': len(chars),
'characters_processed': characters_processed,
'links_added': total_links_added,
'characters_failed': failures,
}
@shared_task(
name='app.tasks.maintenance.verify_media_integrity',
soft_time_limit=60,
time_limit=120,
)
def verify_media_integrity(image_id: int) -> dict:
"""Run structural verification on one image and persist the result.
Idempotent — safe to re-enqueue freely; each call re-checks the file
on disk and writes a fresh `integrity_checked_at` timestamp. Used both
by the post-import hook (so newly-imported rows land verified) and by
the sweep task (which re-verifies the existing library).
"""
image = ImageRecord.query.get(image_id)
if image is None:
return {'image_id': image_id, 'status': 'no_record'}
status, detail = verify_path(image.filepath)
image.integrity_status = status
image.integrity_checked_at = datetime.now(timezone.utc)
db.session.commit()
if status != 'ok':
log.warning(
"verify_media_integrity: image_id=%s path=%r%s (%s)",
image_id, image.filepath, status, detail,
)
return {'image_id': image_id, 'status': status, 'detail': detail}
@shared_task(
name='app.tasks.maintenance.verify_unverified_images',
soft_time_limit=300,
time_limit=600,
)
def verify_unverified_images(only_unknown: bool = True) -> dict:
"""Sweep every ImageRecord and enqueue per-image verify tasks.
Skips paths that are currently the target of an in-flight import — same
contract `deep_scan_directory` uses, so a half-written file mid-import
doesn't get false-flagged. By default only revisits rows whose status is
still 'unknown' (post-migration default + brand-new rows that haven't
been picked up by the import-time hook yet); pass only_unknown=False to
force a full re-verify across the library.
"""
active_paths = {
row[0] for row in db.session.execute(text("""
SELECT source_path FROM import_task
WHERE status IN ('pending', 'queued', 'processing')
""")).fetchall()
}
q = ImageRecord.query.filter(ImageRecord.filepath.isnot(None))
if only_unknown:
q = q.filter(ImageRecord.integrity_status == 'unknown')
enqueued = 0
skipped_active = 0
last_id = 0
BATCH = 500
while True:
rows = (
q.filter(ImageRecord.id > last_id)
.order_by(ImageRecord.id.asc())
.limit(BATCH)
.all()
)
if not rows:
break
for r in rows:
if r.filepath in active_paths:
skipped_active += 1
continue
verify_media_integrity.delay(r.id)
enqueued += 1
last_id = rows[-1].id
log.info(
"verify_unverified_images: enqueued=%d skipped_active=%d only_unknown=%s",
enqueued, skipped_active, only_unknown,
)
return {
'enqueued': enqueued,
'skipped_active': skipped_active,
'only_unknown': only_unknown,
}
@shared_task(
name='app.tasks.maintenance.sweep_blocklisted_tag_from_images',
soft_time_limit=60,
time_limit=120,
)
def sweep_blocklisted_tag_from_images(name: str) -> dict:
"""Remove a blocklisted tag name from every image that has it attached,
then delete the Tag row itself.
Scope is limited to kind='user' tags — that's the kind WD14's general
category gets materialized as when accepted, which is the vast majority
of blocklist hits. Character / fandom / artist / series / post / archive
tags sharing the same name are left alone: those are deliberate, curated
entities, and removing them silently because of a blocklist text match
would be destructive.
Returns a summary dict so the Celery result is introspectable.
"""
tag = Tag.query.filter_by(kind='user', name=name).first()
if tag is None:
log.info("sweep_blocklisted_tag_from_images: no kind='user' tag named %r; nothing to do", name)
return {'name': name, 'tag_found': False, 'image_tags_deleted': 0, 'tag_deleted': False}
result = db.session.execute(
text("DELETE FROM image_tags WHERE tag_id = :tid"),
{'tid': tag.id},
)
rowcount = result.rowcount or 0
db.session.delete(tag)
db.session.commit()
log.info(
"sweep_blocklisted_tag_from_images: removed %r (tag_id=%d) from %d images",
name, tag.id, rowcount,
)
return {
'name': name,
'tag_found': True,
'image_tags_deleted': rowcount,
'tag_deleted': True,
}
@shared_task(
name='app.tasks.maintenance.apply_auto_accept_predictions',
soft_time_limit=1800,
time_limit=2700,
)
def apply_auto_accept_predictions(batch_size: int = 100) -> dict:
"""Walk every image and apply general-category WD14 predictions whose
confidence is at or above the auto_accept_general_threshold config.
Mirrors the side effects of the modal's per-image auto-accept flow
(`get_image_suggestions`): for each candidate, find-or-create a
kind='user' Tag, attach it to the image, and log a SuggestionFeedback
row with decision='accepted'. Idempotent — re-running just skips
images whose existing tags + per-image rejections already cover the
candidates.
Designed for one-shot library-wide application after a backfill
completes, so the user doesn't have to open every modal to land
high-confidence WD14 tags. Skips integrity-flagged rows (the suggestion
service already returns empty for those) and amortizes the config
fetch + Tag-table scan across the loop.
"""
# Lazy-import the suggestion service so the maintenance worker doesn't
# pay its overhead on unrelated tasks.
from app.services.tag_suggestions import (
_DEFAULTS, _config, _existing_tag_names, get_suggestions,
)
from app.ml.wd14 import MODEL_VERSION as WD14_VER
cfg = _config()
existing_all = _existing_tag_names()
# Fast-path: if no WD14 prediction at/above the threshold exists for
# an image-tag combo that isn't already attached and isn't user-rejected,
# there's nothing to do. Daily-scheduled runs will hit this branch on
# most days once the library has settled, so the full walk only runs
# after fresh predictions land.
try:
threshold = float(cfg.get('auto_accept_general_threshold',
_DEFAULTS['auto_accept_general_threshold']))
except (TypeError, ValueError):
threshold = float(_DEFAULTS['auto_accept_general_threshold'])
pending_exists = db.session.execute(text("""
SELECT 1
FROM image_tag_prediction p
WHERE p.confidence >= :thr
AND p.tag_category = 'general'
AND p.model_version = :wd14_ver
AND NOT EXISTS (
SELECT 1 FROM image_tags it
JOIN tag t ON t.id = it.tag_id
WHERE it.image_id = p.image_id
AND t.name = p.tag_name
AND t.kind = 'user'
)
AND NOT EXISTS (
SELECT 1 FROM suggestion_feedback sf
WHERE sf.image_id = p.image_id
AND sf.tag_name = p.tag_name
AND sf.decision = 'rejected'
)
LIMIT 1
"""), {'thr': threshold, 'wd14_ver': WD14_VER}).first()
if pending_exists is None:
log.info(
"apply_auto_accept_predictions: no candidates above threshold=%.3f; skipping walk",
threshold,
)
return {
'scanned': 0,
'images_with_applies': 0,
'tags_applied': 0,
'skipped_no_candidates': True,
}
scanned = 0
images_with_applies = 0
tags_applied = 0
last_id = 0
while True:
rows = (
ImageRecord.query
.filter(ImageRecord.id > last_id)
.filter(ImageRecord.integrity_status.in_(('ok', 'unknown')))
.order_by(ImageRecord.id.asc())
.limit(batch_size)
.all()
)
if not rows:
break
for img in rows:
scanned += 1
grouped = get_suggestions(img.id, cfg=cfg, existing_all=existing_all)
candidates = grouped.get('auto_accept_candidates') or []
if not candidates:
continue
applied_for_image = 0
for cand in candidates:
name = cand['name']
tag = Tag.query.filter_by(kind='user', name=name).first()
if tag is None:
tag = Tag(kind='user', name=name)
db.session.add(tag)
db.session.flush()
if tag not in img.tags:
img.tags.append(tag)
applied_for_image += 1
db.session.add(SuggestionFeedback(
image_id=img.id,
tag_name=name,
suggestion_source=cand.get('source', 'wd14'),
confidence=cand.get('confidence', 0.0),
decision='accepted',
))
if applied_for_image:
images_with_applies += 1
tags_applied += applied_for_image
# Commit once per batch — keeps transactions short and lets the
# next batch see freshly-created Tag rows.
db.session.commit()
last_id = rows[-1].id
log.info(
"apply_auto_accept_predictions: progress scanned=%d "
"images_with_applies=%d tags_applied=%d last_id=%d",
scanned, images_with_applies, tags_applied, last_id,
)
log.info(
"apply_auto_accept_predictions: done scanned=%d "
"images_with_applies=%d tags_applied=%d",
scanned, images_with_applies, tags_applied,
)
return {
'scanned': scanned,
'images_with_applies': images_with_applies,
'tags_applied': tags_applied,
}