fix: localize dashboard evidence, explain blocked vehicles, clarify pending odometers
Three content defects found by a live reviewer:
- Dashboard attention subtext was raw, untranslated evidence.summary text, and for
11 of 15 seeded issues that text was literally "Synthetic deterministic seed
issue". AttentionItem now exposes evidence_signals (stable code + params, same
shape as the issue detail page) instead of a detail string; the frontend renders
them through a shared describeEvidenceSignal() used by both the dashboard and the
issue detail page. Every previously-placeholder seed row now cites a real,
per-rule-type fact (a genuinely crossed service threshold, a genuinely blank
field, or a real pair of booking odometer readings) instead of invented prose.
- 5 of 7 blocked vehicles had no quality issue at all and one had only a resolved
one, so "needs attention" led nowhere. Each now has a real open
missing_required_field issue backed by a genuinely blank field (no schema change,
no migration -- reuses the existing data-quality pipeline).
- Booking odometer fields showing a bare "-" for 25 reserved + 1 active booking now
show a localized explanation ("trip hasn't started yet" / "not yet closed").
MO-024's rented-but-service-overdue contradiction was already caught by the
vehicle-status evaluator (DQ-SCAN, vehicle.manual_review_required) -- added a
regression test rather than new logic.
Also fixed a related bug the above exposed: the vehicle entity_snapshot omitted
registration_number entirely, so the "provide missing fields" form always showed
it blank regardless of the real value.
Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
This commit is contained in:
NuklearRabbit
co-authored by
Claude Sonnet 5
Claude Sonnet 5
parent
3808bbe132
commit
4faac24b5a
@@ -19,6 +19,7 @@ from app.schemas import (
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AutomationRunOut,
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CurrentUser,
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DashboardOut,
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EvidenceSignalOut,
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TodayItem,
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)
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from app.services.operations import compute_metrics
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@@ -61,12 +62,21 @@ def get_dashboard(
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entity = customers_by_id.get(issue.entity_id)
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link_type = "customer"
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link_ref = entity.public_ref if entity else ""
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# The backend never emits prose for the attention queue -- only stable signal
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# codes + raw data params, exactly like the issue detail page's evidence list
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# (see app/services/data_quality.py::_open_issue). The frontend is the one place
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# that turns these into the operator's selected language; `evidence_json["summary"]`
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# is a technical fallback only, never rendered here.
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signals = [
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EvidenceSignalOut(code=s["code"], params=s.get("params", {}))
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for s in issue.evidence_json.get("signals", [])
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]
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attention_items.append(
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AttentionItem(
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kind="quality_issue",
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severity=issue.severity,
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rule_type=issue.rule_type,
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detail=issue.evidence_json.get("summary", ""),
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evidence_signals=signals,
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link_type=link_type,
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link_ref=link_ref,
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issue_ref=issue.public_ref,
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@@ -115,6 +115,7 @@ def _snapshot(entity_type: str, ref: str, db: Session) -> dict | None:
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return {
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"entity_type": "vehicle",
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"public_ref": vehicle.public_ref,
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"registration_number": vehicle.registration_number,
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"make": vehicle.make,
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"model": vehicle.model,
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"location": vehicle.location,
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@@ -350,11 +350,16 @@ class DashboardMetrics(BaseModel):
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pending_or_failed_workflows: int
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class EvidenceSignalOut(BaseModel):
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code: str
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params: dict[str, Any] = Field(default_factory=dict)
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class AttentionItem(BaseModel):
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kind: Literal["quality_issue", "vehicle"]
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severity: str
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rule_type: str
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detail: str
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evidence_signals: list[EvidenceSignalOut] = Field(default_factory=list)
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link_type: Literal["vehicle", "booking", "customer"]
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link_ref: str
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issue_ref: str | None = None
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+109
-31
@@ -139,47 +139,49 @@ def load_seed(db: Session) -> SeedResult:
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vehicle_id_by_ref: dict[str, uuid.UUID] = {}
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vehicle_rows = []
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vehicle_row_by_ref: dict[str, dict] = {}
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for row in _read_csv("vehicles.csv"):
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vid = uuid.uuid4()
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vehicle_id_by_ref[row["public_ref"]] = vid
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vehicle_rows.append(
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{
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"id": vid,
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"public_ref": row["public_ref"],
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"make": row["make"],
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"model": row["model"],
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"model_year": int(row["model_year"]),
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"registration_number": row["registration_number"],
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"location": row["location"],
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"operational_status": row["operational_status"],
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"odometer_km": int(row["odometer_km"]),
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"next_service_km": int(row["next_service_km"]),
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"active": _parse_bool(row["active"]),
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"version": 1,
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}
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)
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vehicle_row = {
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"id": vid,
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"public_ref": row["public_ref"],
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"make": row["make"],
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"model": row["model"],
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"model_year": int(row["model_year"]),
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"registration_number": row["registration_number"],
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"location": row["location"],
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"operational_status": row["operational_status"],
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"odometer_km": int(row["odometer_km"]),
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"next_service_km": int(row["next_service_km"]),
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"active": _parse_bool(row["active"]),
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"version": 1,
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}
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vehicle_rows.append(vehicle_row)
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vehicle_row_by_ref[row["public_ref"]] = vehicle_row
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db.execute(insert(Vehicle), vehicle_rows)
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counts["vehicles"] = len(vehicle_rows)
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booking_id_by_ref: dict[str, uuid.UUID] = {}
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booking_rows = []
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booking_row_by_ref: dict[str, dict] = {}
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for row in _read_csv("bookings.csv"):
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bid = uuid.uuid4()
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booking_id_by_ref[row["public_ref"]] = bid
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booking_rows.append(
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{
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"id": bid,
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"public_ref": row["public_ref"],
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"customer_id": customer_id_by_ref[row["customer_ref"]],
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"vehicle_id": vehicle_id_by_ref[row["vehicle_ref"]],
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"starts_at": _parse_dt(row["starts_at"]) + shift,
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"ends_at": _parse_dt(row["ends_at"]) + shift,
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"status": row["status"],
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"start_odometer_km": _parse_optional_int(row["start_odometer_km"]),
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"end_odometer_km": _parse_optional_int(row["end_odometer_km"]),
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"requirements_complete": _parse_bool(row["requirements_complete"]),
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}
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)
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booking_row = {
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"id": bid,
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"public_ref": row["public_ref"],
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"customer_id": customer_id_by_ref[row["customer_ref"]],
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"vehicle_id": vehicle_id_by_ref[row["vehicle_ref"]],
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"starts_at": _parse_dt(row["starts_at"]) + shift,
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"ends_at": _parse_dt(row["ends_at"]) + shift,
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"status": row["status"],
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"start_odometer_km": _parse_optional_int(row["start_odometer_km"]),
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"end_odometer_km": _parse_optional_int(row["end_odometer_km"]),
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"requirements_complete": _parse_bool(row["requirements_complete"]),
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}
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booking_rows.append(booking_row)
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booking_row_by_ref[row["public_ref"]] = booking_row
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db.execute(insert(Booking), booking_rows)
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counts["bookings"] = len(booking_rows)
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@@ -225,6 +227,82 @@ def load_seed(db: Session) -> SeedResult:
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return "customer", customer_id_by_ref[entity_ref]
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return "vehicle", vehicle_id_by_ref[entity_ref]
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def _vehicle_conflict_facts(vehicle_ref: str, *, service_threshold_reached: bool) -> dict:
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# Mirrors app.services.vehicle_status.VehicleStatusFacts.as_dict() for the
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# handful of seed-only rows below -- none of them carry an active rental or a
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# real booking conflict (verified against the fixed seed dataset), only a
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# genuinely-crossed service threshold or none at all, so those two fields are
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# the only ones that vary per vehicle.
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vehicle = vehicle_row_by_ref[vehicle_ref]
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return {
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"active_booking_refs": [],
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"overlapping_booking_pairs": [],
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"service_threshold_reached": service_threshold_reached,
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"odometer_km": vehicle["odometer_km"],
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"next_service_km": vehicle["next_service_km"],
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"open_booking_overlap_issue_ref": None,
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}
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def _odometer_regression_signal(later_ref: str, earlier_ref: str) -> list[dict]:
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later = booking_row_by_ref[later_ref]
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earlier = booking_row_by_ref[earlier_ref]
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return [
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{
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"code": "odometer.regression",
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"params": {
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"later_ref": later_ref,
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"later_km": later["end_odometer_km"],
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"earlier_ref": earlier_ref,
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"earlier_km": earlier["end_odometer_km"],
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},
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}
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]
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def _missing_field_signal(field: str) -> list[dict]:
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return [{"code": "missing_field", "params": {"field": field}}]
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# Every seed-only row below (i.e. not one of the four named DQ-DEMO-* scenarios)
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# used to carry no structured signal at all -- just the placeholder summary
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# "Synthetic deterministic seed issue". Each now cites a real fact about its actual
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# entity (a genuinely-crossed service threshold, a genuinely-blank field, or a real
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# pair of booking odometer readings engineered into seed/bookings.csv), using the
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# exact same signal vocabulary the live scan (app.services.data_quality) already
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# renders through -- see docs/fleet-ops-correction/current-gap-audit.md §6.
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_SEED_SIGNALS_BY_REF: dict[str, list[dict]] = {
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"DQ-0005": [
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{
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"code": "vehicle.service_threshold_reached",
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"params": _vehicle_conflict_facts("MO-036", service_threshold_reached=True),
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}
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],
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"DQ-0006": _missing_field_signal("location"),
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"DQ-0007": _odometer_regression_signal("BK-H-0007", "BK-H-0057"),
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"DQ-0008": [
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{
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"code": "vehicle.rental_ended",
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"params": _vehicle_conflict_facts("MO-007", service_threshold_reached=False),
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}
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],
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"DQ-0009": _missing_field_signal("location"),
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"DQ-0010": _odometer_regression_signal("BK-H-0010", "BK-H-0060"),
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"DQ-0011": [
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{
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"code": "vehicle.service_threshold_reached",
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"params": _vehicle_conflict_facts("MO-028", service_threshold_reached=True),
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}
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],
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"DQ-0012": _missing_field_signal("registration_number"),
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"DQ-0013": _missing_field_signal("location"),
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"DQ-0014": _missing_field_signal("registration_number"),
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"DQ-0015": _missing_field_signal("location"),
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"DQ-0016": _missing_field_signal("location"),
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"DQ-0017": _missing_field_signal("location"),
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"DQ-0018": _missing_field_signal("registration_number"),
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"DQ-0019": _missing_field_signal("location"),
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"DQ-0020": _missing_field_signal("location"),
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"DQ-0021": _missing_field_signal("location"),
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}
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def _seed_signals(public_ref: str, entity_ref: str, related_refs: list[str]) -> list[dict]:
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# The four named DQ-DEMO-* rows anchor the guided demo's scripted scenarios, so
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# they carry real, accurate structured signals (not just a legacy English
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@@ -253,7 +331,7 @@ def load_seed(db: Session) -> SeedResult:
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"params": {"booking_ref": related_refs[0] if related_refs else ""},
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}
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]
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return []
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return _SEED_SIGNALS_BY_REF.get(public_ref, [])
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dq_rows = []
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now = datetime.now(UTC)
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