JensandClaude Opus 5
c6837ec1b2
MapWorkspace.tsx was 3.157 lines: a props interface, 1.200 lines of derived state and handlers, and two complete render paths — the map-first explorer and the advanced workbench behind it. It is now five modules, and the container is nineteen lines that choose between the two. The obstacle was the props signature. The explorer reads 97 derived values and the workbench 40, so passing them individually would have produced a 97-field interface — worse than the file it replaced. Extracting the derived state into a hook that returns one object solves it: MapWorkspaceViewModel is ReturnType<typeof useMapWorkspaceViewModel>, so the shape is derived from what the hook actually produces and cannot drift from it. Each view then names two typed objects, and the JSX moved unchanged. The contract tests found the one place where widening a negative assertion is wrong. "The map workspace performs no transport" was true of the old file and false of the whole feature, because the hooks call the API by design. It is now scoped to the presentational modules, which is what it always meant. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
256 lines
8.6 KiB
Python
256 lines
8.6 KiB
Python
from __future__ import annotations
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from pathlib import Path
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from uuid import UUID, uuid4
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import pytest
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from fastapi.testclient import TestClient
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from geoalchemy2.shape import from_shape
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from shapely.geometry import box
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from app.core.errors import AppError
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from app.db.session import get_db
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from app.main import app
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from app.models import Detection, DetectionReview, QualityCheck, VectorFeature
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from app.schemas.detection_review import (
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DetectionReviewList,
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DetectionReviewRead,
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DetectionReviewSummary,
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DetectionReviewUpsert,
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)
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from app.services.detection_review_service import DetectionReviewService
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from tests.frontend_contract import read_feature
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ROOT = Path(__file__).resolve().parents[2]
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class FakeQuery:
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def __init__(self, rows):
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self.rows = list(rows)
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def filter(self, *criteria):
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for criterion in criteria:
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left = getattr(criterion, "left", None)
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right = getattr(criterion, "right", None)
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operator = getattr(criterion, "operator", None)
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name = getattr(left, "name", None)
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value = getattr(right, "value", right)
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if name and operator and operator.__name__ == "eq":
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self.rows = [row for row in self.rows if getattr(row, name) == value]
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return self
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def all(self):
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return list(self.rows)
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def first(self):
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return self.rows[0] if self.rows else None
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class FakeSession:
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def __init__(self, objects=None, query_rows=None) -> None:
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self.objects = objects or {}
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self.query_rows = query_rows or {}
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def get(self, model, item_id):
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return self.objects.get((model, item_id))
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def query(self, model):
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return FakeQuery(self.query_rows.setdefault(model, []))
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def add(self, row):
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rows = self.query_rows.setdefault(type(row), [])
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if row not in rows:
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rows.append(row)
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self.objects[(type(row), row.id)] = row
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def commit(self):
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return None
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def refresh(self, _row):
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return None
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def _review_context() -> tuple[FakeSession, UUID, UUID, Detection, VectorFeature]:
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project_id = uuid4()
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quality_check_id = uuid4()
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analysis_run_id = uuid4()
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candidate_dataset_id = uuid4()
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reference_dataset_id = uuid4()
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detection = Detection(
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id=uuid4(),
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project_id=project_id,
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dataset_id=candidate_dataset_id,
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analysis_run_id=analysis_run_id,
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model_name="yolo-configured",
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class_name="building",
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confidence=0.62,
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geometry=from_shape(box(5.0, 51.0, 5.001, 51.001), srid=4326),
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)
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reference = VectorFeature(
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id=uuid4(),
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dataset_id=reference_dataset_id,
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source_feature_id="grb-missed",
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feature_class="building",
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properties_json={},
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geometry=from_shape(box(5.002, 51.002, 5.003, 51.003), srid=4326),
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)
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quality_check = QualityCheck(
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id=quality_check_id,
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project_id=project_id,
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analysis_run_id=analysis_run_id,
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candidate_dataset_id=candidate_dataset_id,
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reference_dataset_id=reference_dataset_id,
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check_type="detections_vs_reference",
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status="ok",
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findings_json={
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"false_positive_evidence": [{"candidate_feature_id": str(detection.id)}],
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"false_negative_evidence": [{"reference_feature_id": str(reference.id)}],
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},
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)
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db = FakeSession(
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objects={
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(QualityCheck, quality_check_id): quality_check,
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(Detection, detection.id): detection,
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(VectorFeature, reference.id): reference,
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},
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query_rows={DetectionReview: []},
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)
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return db, project_id, quality_check_id, detection, reference
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def test_detection_review_model_and_migration_are_aligned() -> None:
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migration = (ROOT / "backend" / "alembic" / "versions" / "202607150001_detection_reviews.py").read_text(encoding="utf-8")
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columns = DetectionReview.__table__.columns
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for name in (
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"project_id",
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"quality_check_id",
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"analysis_run_id",
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"evidence_role",
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"evidence_feature_id",
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"detection_id",
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"reference_feature_id",
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"decision",
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"notes",
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"reviewed_by",
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"created_at",
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"updated_at",
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):
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assert name in columns
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assert f'"{name}"' in migration
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assert 'op.create_table(\n "detection_reviews"' in migration
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assert 'down_revision = "202607140001"' in migration
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def test_detection_review_queue_persists_only_valid_operator_decisions() -> None:
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db, project_id, quality_check_id, detection, _reference = _review_context()
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initial = DetectionReviewService.list_reviews(
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db,
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project_id=project_id,
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quality_check_id=quality_check_id,
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)
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assert initial.summary.total == 2
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assert initial.summary.reviewed == 0
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assert initial.summary.decision_counts == {"unreviewed": 2}
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saved = DetectionReviewService.upsert_review(
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db,
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project_id=project_id,
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quality_check_id=quality_check_id,
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payload=DetectionReviewUpsert(
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evidence_role="false_positive",
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evidence_feature_id=str(detection.id),
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decision="qa_alignment_mismatch",
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notes="Box overlaps the official footprint but is not a training negative.",
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),
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)
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assert saved.decision == "qa_alignment_mismatch"
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assert saved.detection_id == detection.id
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reviewed = DetectionReviewService.list_reviews(
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db,
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project_id=project_id,
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quality_check_id=quality_check_id,
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reviewed=True,
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)
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assert reviewed.total == 1
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assert reviewed.summary.reviewed == 1
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assert reviewed.summary.remaining == 1
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with pytest.raises(AppError) as exc:
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DetectionReviewService.upsert_review(
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db,
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project_id=project_id,
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quality_check_id=quality_check_id,
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payload=DetectionReviewUpsert(
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evidence_role="false_positive",
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evidence_feature_id=str(detection.id),
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decision="confirmed_model_false_negative",
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),
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)
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assert exc.value.code == "INVALID_DETECTION_REVIEW_DECISION"
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def test_detection_review_endpoints_use_canonical_envelopes(monkeypatch) -> None:
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project_id = uuid4()
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quality_check_id = uuid4()
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item = DetectionReviewRead(
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project_id=project_id,
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quality_check_id=quality_check_id,
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evidence_role="false_positive",
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evidence_feature_id=str(uuid4()),
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decision="unreviewed",
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)
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result = DetectionReviewList(
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items=[item],
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total=1,
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limit=50,
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offset=0,
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summary=DetectionReviewSummary(
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total=1,
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reviewed=0,
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remaining=1,
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false_positive_total=1,
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false_negative_total=0,
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decision_counts={"unreviewed": 1},
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),
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)
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monkeypatch.setattr(DetectionReviewService, "list_reviews", lambda *_args, **_kwargs: result)
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monkeypatch.setattr(DetectionReviewService, "upsert_review", lambda *_args, **_kwargs: item)
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app.dependency_overrides[get_db] = lambda: FakeSession()
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try:
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listed = TestClient(app).get(f"/api/v1/projects/{project_id}/quality-checks/{quality_check_id}/reviews")
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saved = TestClient(app).post(
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f"/api/v1/projects/{project_id}/quality-checks/{quality_check_id}/reviews",
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json={
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"evidence_role": "false_positive",
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"evidence_feature_id": item.evidence_feature_id,
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"decision": "unreviewed",
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},
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)
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finally:
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app.dependency_overrides.pop(get_db, None)
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assert listed.status_code == 200
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assert set(listed.json()) == {"data"}
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assert listed.json()["data"]["summary"]["remaining"] == 1
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assert saved.status_code == 200
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assert saved.json() == {"data": item.model_dump(mode="json")}
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def test_map_detection_qa_uses_documented_footprint_threshold_and_honest_labels() -> None:
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hook = (ROOT / "frontend" / "src" / "hooks" / "useMapOrthophotoAnalysis.ts").read_text(encoding="utf-8")
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app_source = read_feature("shell")
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evidence_service = (ROOT / "backend" / "app" / "services" / "quality_evidence_service.py").read_text(encoding="utf-8")
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assert "MAP_BUILDING_QA_IOU_THRESHOLD = 0.25" in hook
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assert "kandidaten" in hook
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assert "precision" in hook.lower()
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assert "false, iouThreshold" in app_source
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assert "AI-kandidaten" in read_feature("map_workspace")
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assert "VectorFeature.id.in_(uuid_identifiers)" in evidence_service
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assert "VectorFeature.source_feature_id.in_(identifiers)" in evidence_service
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assert "for row in db.query(VectorFeature).filter(VectorFeature.dataset_id" not in evidence_service
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