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FabledCurator/backend/app/api/suggestions.py
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bvandeusen c999c64cbe
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feat(suggestions): tag-input dropdown searches the full prediction set
The typed dropdown sourced the threshold-filtered panel list (>= 0.70 general),
so low-confidence actions/features the model DID predict never appeared — forcing
hand-typed custom tags instead of accepting the model's canonical formatting.

Add a threshold override: SuggestionService.for_image(threshold_override=) and
GET /images/<id>/suggestions?min=<f> surface EVERY stored prediction (down to the
0.05 store floor), alias-resolved and normalized, still excluding applied/rejected
and unsurfaced categories. The suggestions store gains allByCategory + loadAll
(min=0); the dropdown searches that full set (cap 20), while the Suggestions panel
stays curated at the configured threshold. Accept/dismiss drop from both lists.

Operator-asked 2026-06-09. Test: a 0.30 general prediction is hidden by default
but surfaced with threshold_override=0.0; unsurfaced categories still excluded.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-09 20:22:24 -04:00

133 lines
4.6 KiB
Python

"""Suggestions API: per-image ranked suggestions + accept/alias/dismiss."""
from quart import Blueprint, jsonify, request
from ..extensions import get_session
from ..services.ml.allowlist import AllowlistService
from ..services.ml.suggestions import SuggestionService
suggestions_bp = Blueprint("suggestions", __name__, url_prefix="/api")
@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
# tag-input dropdown can surface EVERY stored prediction (min=0), including
# low-confidence actions/features, in canonical formatting. Omitted → the
# curated above-threshold list the Suggestions panel uses.
override = None
raw_min = request.args.get("min")
if raw_min is not None:
try:
override = min(1.0, max(0.0, float(raw_min)))
except ValueError:
return jsonify({"error": "min must be a float in [0,1]"}), 400
async with get_session() as session:
sl = await SuggestionService(session).for_image(
image_id, threshold_override=override
)
return jsonify(
{
"by_category": {
cat: [
{
"canonical_tag_id": s.canonical_tag_id,
"display_name": s.display_name,
"category": s.category,
"score": round(s.score, 4),
"source": s.source,
"creates_new_tag": s.creates_new_tag,
}
for s in items
]
for cat, items in sl.by_category.items()
}
}
)
@suggestions_bp.route(
"/images/<int:image_id>/suggestions/accept", methods=["POST"]
)
async def accept_suggestion(image_id: int):
body = await request.get_json()
if not body or "tag_id" not in body:
return jsonify({"error": "tag_id required"}), 400
tag_id = body["tag_id"]
async with get_session() as session:
newly_added = 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 "", 204
@suggestions_bp.route(
"/images/<int:image_id>/suggestions/alias", methods=["POST"]
)
async def alias_suggestion(image_id: int):
body = await request.get_json()
required = {"alias_string", "alias_category", "canonical_tag_id"}
if not body or not required.issubset(body):
return jsonify({"error": f"required: {sorted(required)}"}), 400
async with get_session() as session:
newly_added = await AllowlistService(session).add_alias_and_accept(
image_id,
body["alias_string"],
body["alias_category"],
body["canonical_tag_id"],
)
await session.commit()
if newly_added:
from ..tasks.ml import apply_allowlist_tags
apply_allowlist_tags.delay(tag_id=body["canonical_tag_id"])
return "", 204
@suggestions_bp.route(
"/images/<int:image_id>/suggestions/dismiss", methods=["POST"]
)
async def dismiss_suggestion(image_id: int):
body = await request.get_json()
if not body or "tag_id" not in body:
return jsonify({"error": "tag_id required"}), 400
async with get_session() as session:
await AllowlistService(session).dismiss(image_id, body["tag_id"])
await session.commit()
return "", 204
@suggestions_bp.route("/suggestions/bulk", methods=["POST"])
async def bulk_suggestions():
body = await request.get_json()
if not body or "image_ids" not in body:
return jsonify({"error": "image_ids required"}), 400
raw = body["image_ids"]
if not isinstance(raw, list) or not raw:
return jsonify({"error": "image_ids must be a non-empty list"}), 400
try:
ids = [int(x) for x in raw]
except (TypeError, ValueError):
return jsonify({"error": "image_ids must be integers"}), 400
if len(ids) > 200:
return jsonify({"error": "selection too large (max 200)"}), 400
try:
threshold = float(body.get("threshold", 0.8))
except (TypeError, ValueError):
threshold = 0.8
threshold = min(1.0, max(0.0, threshold))
async with get_session() as session:
suggestions = await SuggestionService(session).for_selection(
ids, threshold=threshold
)
return jsonify(
{
"suggestions": suggestions,
"evaluated": len(ids),
"threshold": threshold,
}
)