485387ff0b
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
150 lines
5.6 KiB
Python
150 lines
5.6 KiB
Python
"""Suggestions API: per-image ranked suggestions + accept/alias/dismiss."""
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from quart import Blueprint, jsonify, request
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from ..extensions import get_session
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from ..services.ml.allowlist import AllowlistService
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from ..services.ml.suggestions import SuggestionService
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suggestions_bp = Blueprint("suggestions", __name__, url_prefix="/api")
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@suggestions_bp.route("/images/<int:image_id>/suggestions", methods=["GET"])
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async def get_suggestions(image_id: int):
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# ?min=<float> overrides the configured per-category thresholds so the typed
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# tag-input dropdown can surface EVERY stored prediction (min=0), including
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# low-confidence actions/features, in canonical formatting. Omitted → the
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# curated above-threshold list the Suggestions panel uses.
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override = None
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raw_min = request.args.get("min")
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if raw_min is not None:
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try:
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override = min(1.0, max(0.0, float(raw_min)))
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except ValueError:
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return jsonify({"error": "min must be a float in [0,1]"}), 400
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async with get_session() as session:
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sl = await SuggestionService(session).for_image(
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image_id, threshold_override=override
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)
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return jsonify(
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{
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"by_category": {
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cat: [
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{
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"canonical_tag_id": s.canonical_tag_id,
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"display_name": s.display_name,
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"category": s.category,
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"score": round(s.score, 4),
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"source": s.source,
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"creates_new_tag": s.creates_new_tag,
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# raw model key (alias is stored under this) + whether an
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# operator alias produced this suggestion — drive the
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# modal's "Treat as alias"/"Remove alias" affordances.
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"raw_name": s.raw_name,
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"via_alias": s.via_alias,
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# operator dismissed this tag for this image — surfaced
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# (not dropped) so the rail can show it rejected + offer
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# one-click un-reject.
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"rejected": s.rejected,
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}
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for s in items
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]
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for cat, items in sl.by_category.items()
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}
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}
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)
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@suggestions_bp.route(
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"/images/<int:image_id>/suggestions/accept", methods=["POST"]
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)
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async def accept_suggestion(image_id: int):
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body = await request.get_json()
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if not body or "tag_id" not in body:
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return jsonify({"error": "tag_id required"}), 400
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tag_id = body["tag_id"]
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async with get_session() as session:
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await AllowlistService(session).accept(image_id, tag_id)
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await session.commit()
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return jsonify({"accepted": True, "tag_id": tag_id})
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@suggestions_bp.route(
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"/images/<int:image_id>/suggestions/alias", methods=["POST"]
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)
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async def alias_suggestion(image_id: int):
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body = await request.get_json()
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required = {"alias_string", "alias_category", "canonical_tag_id"}
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if not body or not required.issubset(body):
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return jsonify({"error": f"required: {sorted(required)}"}), 400
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canonical_tag_id = body["canonical_tag_id"]
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async with get_session() as session:
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await AllowlistService(session).add_alias_and_accept(
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image_id,
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body["alias_string"],
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body["alias_category"],
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canonical_tag_id,
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)
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await session.commit()
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return jsonify({"accepted": True, "tag_id": canonical_tag_id})
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@suggestions_bp.route(
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"/images/<int:image_id>/suggestions/dismiss", methods=["POST"]
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)
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async def dismiss_suggestion(image_id: int):
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body = await request.get_json()
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if not body or "tag_id" not in body:
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return jsonify({"error": "tag_id required"}), 400
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async with get_session() as session:
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await AllowlistService(session).dismiss(image_id, body["tag_id"])
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await session.commit()
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return "", 204
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@suggestions_bp.route(
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"/images/<int:image_id>/suggestions/undismiss", methods=["POST"]
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)
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async def undismiss_suggestion(image_id: int):
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"""Reverse a per-image dismissal (reject-recovery). Idempotent — undoing a
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tag that isn't rejected is a no-op delete."""
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body = await request.get_json()
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if not body or "tag_id" not in body:
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return jsonify({"error": "tag_id required"}), 400
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async with get_session() as session:
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await AllowlistService(session).undismiss(image_id, body["tag_id"])
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await session.commit()
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return "", 204
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@suggestions_bp.route("/suggestions/bulk", methods=["POST"])
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async def bulk_suggestions():
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body = await request.get_json()
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if not body or "image_ids" not in body:
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return jsonify({"error": "image_ids required"}), 400
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raw = body["image_ids"]
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if not isinstance(raw, list) or not raw:
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return jsonify({"error": "image_ids must be a non-empty list"}), 400
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try:
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ids = [int(x) for x in raw]
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except (TypeError, ValueError):
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return jsonify({"error": "image_ids must be integers"}), 400
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if len(ids) > 200:
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return jsonify({"error": "selection too large (max 200)"}), 400
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try:
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threshold = float(body.get("threshold", 0.8))
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except (TypeError, ValueError):
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threshold = 0.8
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threshold = min(1.0, max(0.0, threshold))
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async with get_session() as session:
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suggestions = await SuggestionService(session).for_selection(
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ids, threshold=threshold
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)
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return jsonify(
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{
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"suggestions": suggestions,
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"evaluated": len(ids),
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"threshold": threshold,
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}
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)
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