bvandeusen
de33bab41c
So the work can be checked through an API as the agent fills in vectors (same pattern as /api/heads/metrics): - GET /api/ccip/overview: regions by kind, images with figure CCIP vectors, the per-character reference counts (which characters have enough examples to match on), and the embedding versions present. - GET /api/ccip/images/<id>: that image's stored regions (bbox, frame_time, has_ccip/has_siglip, versions) + the CCIP character matches it would get — for spot-checking detector + matcher output. Read-only, no GPU. (Queue depth is already at /api/gpu/status.) Tests: overview coverage counts + per-character refs; per-image regions + matches. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
107 lines
3.9 KiB
Python
107 lines
3.9 KiB
Python
"""CCIP / region observability API (#114) — read-only, analysis-shaped.
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So the work can be checked through an API as the agent fills in vectors: overall
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coverage (regions by kind, how many images have figure CCIP vectors, which
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characters have enough reference examples to match on) + a per-image drill-down
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(its regions + the CCIP character matches it would get). Mirrors the heads
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metrics endpoint; no GPU, just reads what's stored.
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"""
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from quart import Blueprint, jsonify
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from sqlalchemy import distinct, func, select
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from ..extensions import get_session
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from ..models import ImageRegion, Tag, TagKind
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from ..models.tag import image_tag
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from ..services.ml.ccip import match_image
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ccip_bp = Blueprint("ccip", __name__, url_prefix="/api/ccip")
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_FIGURE_KINDS = ("face", "figure")
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@ccip_bp.route("/overview", methods=["GET"])
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async def overview():
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async with get_session() as session:
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by_kind = dict(
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(
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await session.execute(
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select(ImageRegion.kind, func.count()).group_by(ImageRegion.kind)
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)
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).all()
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)
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images_with_figure_ccip = (
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await session.execute(
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select(func.count(distinct(ImageRegion.image_record_id)))
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.where(ImageRegion.kind.in_(_FIGURE_KINDS))
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.where(ImageRegion.ccip_embedding.is_not(None))
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)
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).scalar_one()
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# Per-character reference counts (no vectors loaded) — which characters
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# have enough examples to match on.
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ref_rows = (
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await session.execute(
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select(image_tag.c.tag_id, Tag.name, func.count())
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.select_from(ImageRegion)
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.join(
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image_tag,
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image_tag.c.image_record_id == ImageRegion.image_record_id,
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)
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.join(Tag, Tag.id == image_tag.c.tag_id)
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.where(Tag.kind == TagKind.character)
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.where(ImageRegion.kind.in_(_FIGURE_KINDS))
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.where(ImageRegion.ccip_embedding.is_not(None))
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.group_by(image_tag.c.tag_id, Tag.name)
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.order_by(func.count().desc())
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)
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).all()
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versions = [
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v for (v,) in (
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await session.execute(
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select(distinct(ImageRegion.embedding_version))
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)
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).all() if v
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]
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return jsonify({
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"regions_by_kind": by_kind,
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"images_with_figure_ccip": images_with_figure_ccip,
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"characters_with_references": len(ref_rows),
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"character_references": [
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{"tag_id": t, "name": n, "n_refs": c} for (t, n, c) in ref_rows
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],
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"embedding_versions": versions,
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})
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@ccip_bp.route("/images/<int:image_id>", methods=["GET"])
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async def image_detail(image_id: int):
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"""An image's stored regions + the CCIP character matches it would get —
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for spot-checking the agent's output + the matcher."""
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async with get_session() as session:
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regions = (
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await session.execute(
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select(ImageRegion)
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.where(ImageRegion.image_record_id == image_id)
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.order_by(ImageRegion.id)
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)
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).scalars().all()
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matches = await match_image(session, image_id)
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return jsonify({
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"image_id": image_id,
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"regions": [
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{
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"id": r.id,
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"kind": r.kind,
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"bbox": [r.rx, r.ry, r.rw, r.rh],
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"frame_time": r.frame_time,
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"score": r.score,
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"detector_version": r.detector_version,
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"embedding_version": r.embedding_version,
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"has_ccip": r.ccip_embedding is not None,
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"has_siglip": r.siglip_embedding is not None,
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}
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for r in regions
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],
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"ccip_matches": matches,
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})
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