bvandeusen
bc6d43d3f2
Hygiene follow-up to the Camie retirement (#1189) — these were left inert to bound that change; nothing reads them now. Migration 0068 drops: - ml_settings: tagger_store_floor, tagger_model_version, suggestion_threshold_ character/general (already dead pre-retirement — scoring uses per-head thresholds), video_min_tag_frames (only the deleted video-prediction aggregator used it). - image_record: tagger_model_version (no writer), centroid_scores (dead JSON cache, no reader). Also: ml_admin _EDITABLE/GET/_validate pruned (dropped the store-floor invariant + video_min_tag_frames check); MLThresholdSliders trimmed to a video-embedding card (interval + max frames only); importer no longer resets the dropped cols; download_models drops the Camie fetch; stale CASCADE comments in cleanup_service no longer name the removed tables. Tests updated. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Ttrj5P7upUTueSfoJcxEqa
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
"""ML admin API: settings + backfill trigger."""
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from quart import Blueprint, jsonify, request
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from ..extensions import get_session
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from ..models import MLSettings
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ml_admin_bp = Blueprint("ml_admin", __name__, url_prefix="/api/ml")
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_EDITABLE = (
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"video_frame_interval_seconds",
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"video_max_frames",
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"head_min_positives",
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"head_auto_apply_precision",
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"head_auto_apply_enabled",
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"head_auto_apply_min_positives",
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"ccip_match_threshold",
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"ccip_auto_apply_enabled",
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"ccip_auto_apply_threshold",
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"embedder_model_name",
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"embedder_model_version",
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)
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@ml_admin_bp.route("/settings", methods=["GET"])
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async def get_settings():
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from sqlalchemy import select
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async with get_session() as session:
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s = (
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await session.execute(select(MLSettings).where(MLSettings.id == 1))
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).scalar_one()
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return jsonify(
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{
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"video_frame_interval_seconds": s.video_frame_interval_seconds,
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"video_max_frames": s.video_max_frames,
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"embedder_model_version": s.embedder_model_version,
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"head_min_positives": s.head_min_positives,
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"head_auto_apply_precision": s.head_auto_apply_precision,
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"head_auto_apply_enabled": s.head_auto_apply_enabled,
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"head_auto_apply_min_positives": s.head_auto_apply_min_positives,
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"ccip_match_threshold": s.ccip_match_threshold,
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"ccip_auto_apply_enabled": s.ccip_auto_apply_enabled,
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"ccip_auto_apply_threshold": s.ccip_auto_apply_threshold,
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"embedder_model_name": s.embedder_model_name,
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}
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)
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@ml_admin_bp.route("/settings", methods=["PATCH"])
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async def patch_settings():
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from sqlalchemy import select
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body = await request.get_json()
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if not isinstance(body, dict):
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return jsonify({"error": "body must be an object"}), 400
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async with get_session() as session:
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s = (
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await session.execute(select(MLSettings).where(MLSettings.id == 1))
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).scalar_one()
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# Merge the patch over current values, then validate the result as a
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# whole — the store-floor invariant couples three fields, so they
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# can't be checked one at a time.
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proposed = {f: getattr(s, f) for f in _EDITABLE}
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for field in _EDITABLE:
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if field in body:
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proposed[field] = body[field]
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err = _validate(proposed)
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if err is not None:
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return jsonify({"error": err}), 400
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for field in _EDITABLE:
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setattr(s, field, proposed[field])
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await session.commit()
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return await get_settings()
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def _validate(p: dict) -> str | None:
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"""Returns an error string if the proposed settings are invalid, else None."""
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# Video embedding (#747).
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if p["video_frame_interval_seconds"] <= 0:
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return "video_frame_interval_seconds must be > 0"
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if p["video_max_frames"] < 1:
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return "video_max_frames must be >= 1"
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# Head training (#114).
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if int(p["head_min_positives"]) < 1:
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return "head_min_positives must be >= 1"
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if not (0.5 <= float(p["head_auto_apply_precision"]) <= 0.999):
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return "head_auto_apply_precision must be between 0.5 and 0.999"
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if int(p["head_auto_apply_min_positives"]) < 1:
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return "head_auto_apply_min_positives must be >= 1"
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if not (0.5 <= float(p["ccip_match_threshold"]) <= 0.999):
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return "ccip_match_threshold must be between 0.5 and 0.999"
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if not (0.5 <= float(p["ccip_auto_apply_threshold"]) <= 0.999):
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return "ccip_auto_apply_threshold must be between 0.5 and 0.999"
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# Embedder model swap (#1190): both must be non-empty. Changing them means a
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# different embedding space — the operator must re-embed + retrain after.
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for key in ("embedder_model_name", "embedder_model_version"):
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if not str(p[key]).strip():
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return f"{key} must not be empty"
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return None
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@ml_admin_bp.route("/backfill", methods=["POST"])
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async def trigger_backfill():
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from ..tasks.ml import backfill
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r = backfill.delay()
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return jsonify({"celery_task_id": r.id}), 202
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