feat: redesign around phenotype-driven triage, not variant filtering

A table with filters made the user do the work. Rare disease triage is a different task:
which few variants could explain *this* patient's phenotype, and why. The app now answers
that, and lets a reviewer act on the answer.

Domain
- a case is a proband: a VCF plus the HPO terms observed in the patient (samples -> cases)
- HPO's gene-to-phenotype annotations are loaded as reference data (scripts/load-hpo.py)
- each candidate can be shortlisted or dismissed with a reason and a note

Ranking (app/services/triage.py, 21 tests)
- weighted sum of phenotype match, rarity, consequence severity and the model's score,
  with every component shown next to the candidate
- rarity and consequence filter; phenotype only ranks, because a real diagnosis can sit in
  a gene nobody has annotated yet and filtering on it would hide exactly that case
- ClinVar is deliberately not an input: it appears beside the result as independent
  confirmation, so nothing ranks highly merely because ClinVar already said pathogenic

UI
- the funnel is the headline: variants called -> rare -> coding candidates -> phenotype-matched
- ranked candidates with evidence chips, not a grid of everything; filters are demoted
- a variant panel showing the score breakdown, the matched HPO terms, the raw VEP record and
  links out to Ensembl/gnomAD/ClinVar, with the decision controls
- a printable case report: phenotype, funnel, shortlisted variants with reasons, provenance

API: /cases with phenotypes, /cases/{id}/candidates (funnel + ranked + weights),
/variants/{id}, /variants/{id}/decision, /cases/{id}/report, /phenotypes for the picker.
Scoring moved under the case and now answers 503 with the reason when no model registry is
reachable, instead of a 500.

Verified end to end on a simulated proband (scripts/make-demo-case.sh: real GIAB HG002
background + one real ClinVar 2-star pathogenic NF2 variant). 13 variants called -> 1 coding
candidate, and the planted variant ranks first at 0.80 on phenotype 1.00, rarity 1.00 and
consequence 1.00, with ClinVar agreeing afterwards.

Tests: api 75, ml 18, loader 16, web 27; ruff, mypy, svelte-check, terraform validate, both
kustomize overlays and the Nextflow stub run all clean.

api/app/services/candidates.py

@@ -0,0 +1,79 @@
+"""Assemble a case's ranked candidates: load the latest results, rank them, attach decisions.
+
+Everything for one job is loaded at once, which is fine for a gene panel or a chromosome — the
+size of case this demo handles. A whole genome would need the narrowing pushed into SQL.
+"""
+import uuid
+from dataclasses import dataclass
+
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+from sqlalchemy.orm import selectinload
+
+from app.models import Case, GenePhenotype, Job, JobStatus, Variant
+from app.services import triage
+
+EMPTY_FUNNEL = triage.Funnel(total=0, rare=0, candidates=0, phenotype_matched=0)
+
+
+@dataclass(frozen=True)
+class CaseView:
+    case: Case
+    job: Job | None
+    funnel: triage.Funnel
+    candidates: list[triage.Candidate]
+
+    @property
+    def labels(self) -> dict[str, str]:
+        return {p.hpo_id: p.label for p in self.case.phenotypes}
+
+
+async def get_case(session: AsyncSession, case_id: uuid.UUID) -> Case | None:
+    return await session.scalar(
+        select(Case).where(Case.id == case_id).options(selectinload(Case.phenotypes))
+    )
+
+
+async def latest_job(
+    session: AsyncSession, case_id: uuid.UUID, *, status: JobStatus | None = None
+) -> Job | None:
+    stmt = select(Job).where(Job.case_id == case_id).order_by(Job.created_at.desc()).limit(1)
+    if status is not None:
+        stmt = stmt.where(Job.status == status)
+    return await session.scalar(stmt)
+
+
+async def gene_terms_for(session: AsyncSession, genes: set[str]) -> dict[str, set[str]]:
+    """gene symbol -> the HPO terms annotated to it."""
+    if not genes:
+        return {}
+    rows = await session.execute(
+        select(GenePhenotype.gene_symbol, GenePhenotype.hpo_id).where(
+            GenePhenotype.gene_symbol.in_(genes)
+        )
+    )
+    index: dict[str, set[str]] = {}
+    for gene, hpo_id in rows:
+        index.setdefault(gene, set()).add(hpo_id)
+    return index
+
+
+async def build(session: AsyncSession, case: Case) -> CaseView:
+    job = await latest_job(session, case.id, status=JobStatus.succeeded)
+    if job is None:
+        return CaseView(case=case, job=None, funnel=EMPTY_FUNNEL, candidates=[])
+    variants = (
+        await session.scalars(
+            select(Variant)
+            .where(Variant.job_id == job.id)
+            .options(selectinload(Variant.prediction), selectinload(Variant.decision))
+        )
+    ).all()
+    case_terms = [p.hpo_id for p in case.phenotypes]
+    gene_terms = await gene_terms_for(session, {v.gene for v in variants if v.gene})
+    return CaseView(
+        case=case,
+        job=job,
+        funnel=triage.funnel(variants, case_terms, gene_terms),
+        candidates=triage.rank(variants, case_terms, gene_terms),
+    )