diff --git a/app/celery_app.py b/app/celery_app.py
index ce1ca47..ab8f51b 100644
--- a/app/celery_app.py
+++ b/app/celery_app.py
@@ -75,6 +75,7 @@ def make_celery(app=None):
'app.tasks.maintenance.sync_character_fandoms_to_images': {'queue': 'maintenance'},
'app.tasks.maintenance.verify_media_integrity': {'queue': 'maintenance'},
'app.tasks.maintenance.verify_unverified_images': {'queue': 'maintenance'},
+ 'app.tasks.maintenance.apply_auto_accept_predictions': {'queue': 'maintenance'},
# Import tasks - handled by worker (heavy processing)
'app.tasks.import_file.*': {'queue': 'import'},
diff --git a/app/main.py b/app/main.py
index 750c3ef..f85cdcd 100644
--- a/app/main.py
+++ b/app/main.py
@@ -655,6 +655,16 @@ def trigger_sync_character_fandoms_to_images():
return redirect(url_for('main.settings', tab='maintenance'))
+@main.post('/settings/maintenance/apply-auto-accept-predictions')
+def trigger_apply_auto_accept_predictions():
+ """Sweep every image, apply general WD14 predictions above the auto-accept
+ threshold without requiring the user to open each modal. One-shot —
+ intended to be run after a backfill drains."""
+ from app.tasks.maintenance import apply_auto_accept_predictions
+ apply_auto_accept_predictions.apply_async(queue='maintenance')
+ return redirect(url_for('main.settings', tab='maintenance'))
+
+
@main.post('/settings/maintenance/verify-images')
def trigger_verify_unverified_images():
"""Sweep ImageRecords and enqueue per-image structural verification.
diff --git a/app/tasks/maintenance.py b/app/tasks/maintenance.py
index ea9e951..f45cb02 100644
--- a/app/tasks/maintenance.py
+++ b/app/tasks/maintenance.py
@@ -12,7 +12,7 @@ from celery import shared_task
from sqlalchemy import text
from app import db
-from app.models import ImageRecord, ImportTask, Tag
+from app.models import ImageRecord, ImportTask, SuggestionFeedback, Tag
from app.services.integrity import verify_path
log = logging.getLogger(__name__)
@@ -336,3 +336,103 @@ def sweep_blocklisted_tag_from_images(name: str) -> dict:
'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 (
+ _config, _existing_tag_names, get_suggestions,
+ )
+
+ cfg = _config()
+ existing_all = _existing_tag_names()
+
+ 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,
+ }
diff --git a/app/templates/settings.html b/app/templates/settings.html
index f645c6e..d5783a0 100644
--- a/app/templates/settings.html
+++ b/app/templates/settings.html
@@ -494,6 +494,24 @@
+
+ Apply auto-accepted predictions to all images
+
+ Walks every image and applies general-category WD14 predictions
+ whose confidence is at or above the auto-accept threshold. Same
+ side-effects as opening each modal individually (creates the
+ kind='user' tag if needed, attaches it to the image, logs a
+ SuggestionFeedback rejection-protected record). Idempotent — safe
+ to re-run. Useful after a fresh ML backfill so high-confidence
+ tags land without needing to view each image. Skips images flagged
+ as integrity-corrupt.
+
+
+
+
Auto-accept threshold (general tags)