feat(allowlist): coverage projection + applied-count + post-accept projection (#7a/#7b)
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Cluster B, milestone #99. Backend for the allowlist tuning dashboard.

#7a: AllowlistService.coverage(tag_id, threshold) counts distinct images with
a prediction resolving to the tag (raw_name==tag.name OR (raw_name,category) in
the tag's aliases) scoring >= threshold — the gross candidate pool, mirroring
tasks.ml._confidence_for_tag resolution. list_all now carries applied_count
(grouped image_tag count) + coverage_count (at the row's threshold). New
GET /api/tags/<id>/allowlist/coverage?threshold= for the live what-if number.

#7b: /suggestions/accept + /alias return {allowlisted, tag_id, tag_name,
projected_count} (projection at the tag's threshold) instead of 204, so the UI
can show a non-blocking 'auto-applying to ~N images' toast. Apply still runs
async via apply_allowlist_tags — projected_count is an estimate.

Tests: coverage by threshold (direct + alias-with-category), list applied vs
coverage, coverage route (explicit/default/bad threshold), accept/alias payload
(newly-allowlisted vs already-on-list).

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01XCUHUGQLrBrkgyk1t49kpX
This commit is contained in:
2026-06-23 01:34:21 -04:00
parent 73dd301dbb
commit e206778a5c
6 changed files with 275 additions and 24 deletions
+78 -10
View File
@@ -5,11 +5,18 @@ image_tag AND to tag_allowlist; per-image removal/dismiss writes a rejection.
from collections.abc import Sequence
from dataclasses import dataclass
from sqlalchemy import delete, select
from sqlalchemy import and_, delete, distinct, func, or_, select
from sqlalchemy.dialects.postgresql import insert
from sqlalchemy.ext.asyncio import AsyncSession
from ...models import MLSettings, Tag, TagAllowlist, TagSuggestionRejection
from ...models import (
ImagePrediction,
MLSettings,
Tag,
TagAlias,
TagAllowlist,
TagSuggestionRejection,
)
from ...models.tag import image_tag
from .aliases import AliasService
@@ -20,6 +27,8 @@ class AllowlistRow:
tag_name: str
tag_kind: str
min_confidence: float
applied_count: int # image_tag rows currently carrying this tag
coverage_count: int # images a sweep WOULD cover at min_confidence
class AllowlistService:
@@ -116,6 +125,44 @@ class AllowlistService:
delete(TagAllowlist).where(TagAllowlist.tag_id == tag_id)
)
async def _coverage_match(self, tag: Tag):
"""The predicate over image_prediction rows that resolve to `tag`,
mirroring tasks.ml._confidence_for_tag's resolution: a prediction whose
raw_name equals the tag name (any category), OR an alias maps
(raw_name, category) -> this tag. Returns a SQLAlchemy boolean clause.
"""
alias_rows = (
await self.session.execute(
select(TagAlias.alias_string, TagAlias.alias_category).where(
TagAlias.canonical_tag_id == tag.id
)
)
).all()
name_clause = ImagePrediction.raw_name == tag.name
alias_clauses = [
and_(
ImagePrediction.raw_name == a,
ImagePrediction.category == c,
)
for a, c in alias_rows
]
return or_(name_clause, *alias_clauses) if alias_clauses else name_clause
async def coverage(self, tag_id: int, threshold: float) -> int:
"""How many distinct images a sweep WOULD cover for this tag at
`threshold`: images with a resolving prediction scoring >= threshold.
The gross candidate pool (NOT minus already-applied/rejected) — it's
the tuning signal for "lower the threshold and ~N more images qualify".
"""
tag = await self.session.get(Tag, tag_id)
if tag is None:
return 0
match = await self._coverage_match(tag)
stmt = select(
func.count(distinct(ImagePrediction.image_record_id))
).where(ImagePrediction.score >= threshold, match)
return (await self.session.execute(stmt)).scalar_one()
async def list_all(self) -> Sequence[AllowlistRow]:
stmt = (
select(
@@ -128,12 +175,33 @@ class AllowlistService:
.order_by(Tag.name.asc())
)
rows = (await self.session.execute(stmt)).all()
return [
AllowlistRow(
tag_id=r[0],
tag_name=r[1],
tag_kind=r[2].value if hasattr(r[2], "value") else str(r[2]),
min_confidence=r[3],
tag_ids = [r[0] for r in rows]
# Applied counts in ONE grouped query (vs N per-row counts).
applied: dict[int, int] = {}
if tag_ids:
applied = dict(
(
await self.session.execute(
select(image_tag.c.tag_id, func.count())
.where(image_tag.c.tag_id.in_(tag_ids))
.group_by(image_tag.c.tag_id)
)
).all()
)
for r in rows
]
result = []
for r in rows:
# Coverage is per-tag (alias set differs); allowlist is small.
cov = await self.coverage(r[0], r[3])
result.append(
AllowlistRow(
tag_id=r[0],
tag_name=r[1],
tag_kind=r[2].value if hasattr(r[2], "value") else str(r[2]),
min_confidence=r[3],
applied_count=applied.get(r[0], 0),
coverage_count=cov,
)
)
return result