fix: overhaul the platform skeleton, add a serverless deployment track

An end-to-end audit found the repo could not build, test or run as shipped. This
fixes every finding, then adds a Cloud Run track so the demo costs about £1/month
idle instead of ~£150.

CI (red on its first run)
- api: setuptools could not build the package (flat layout with app/ and alembic/)
- web: missing @types/node; `vitest run` exited 1 with no test files
- pipeline: the stub run needed a gitignored VCF, and no process had a stub block
- ruff pinned, mypy configured, DB tests on real Postgres (pgserver locally, service in CI)

ML serving (scores were meaningless)
- the registered model now carries its own feature engineering and returns predict_proba,
  so serving sends raw columns and cannot drift from training
- resolve by registry alias (stages are deprecated in MLflow 3) and record the real
  version; re-scoring upserts instead of failing on the unique constraint
- ClinVar labels parsed from VEP's lowercase terms

Pipeline
- exact ref/alt recovered from a CHROM_POS_REF_ALT VCF ID; loading is idempotent
- job status reaches running/failed/succeeded, so the UI stops polling dead jobs
- DATABASE_URL travels in the environment or a Nextflow secret, never on a command line
- VEP cache and plugins staged as inputs; the gcp profile runs tasks on Google Batch

Deployment
- the API serves /api (matching the ingress); the web app reads its API URL at runtime
- migrations run in an init container under a Postgres advisory lock
- terraform: custom VPC shared with Batch, private Cloud SQL, API enablement, Workload
  Identity bindings, Secret Manager, deletion protection
- serverless track, now the default: Cloud Run services scaling to zero, a Cloud Run job
  for the Nextflow driver, and Neon or Cloud SQL behind one DATABASE_URL secret. GKE and
  Argo remain, behind -var deploy_kubernetes=true. See docs/cloud.md.

Correctness and security
- 409 on duplicate sample names, 422 on bad paging, natural chromosome ordering, wider
  VEP text columns, enum dropped on downgrade, the sample's assembly actually used
- vcf_uri restricted to gs:// objects or files under the data root, blocking option injection
- CORS restricted to configured origins; `make down` no longer deletes volumes

Data
- docs/data.md records the peer-reviewed, openly licensed sources (GIAB HG002, ClinVar,
  gnomAD) with citations and an honest evaluation plan; `make data` fetches a chr22 slice

Verified: api 50 tests, ml 18, loader 16, web 12; ruff, mypy, svelte-check, terraform
validate and both kustomize overlays clean.

api/app/routers/variants.py

@@ -1,28 +1,37 @@
 import uuid
 
-from fastapi import APIRouter, Depends, Query
-from sqlalchemy import func, select
-from sqlalchemy.ext.asyncio import AsyncSession
+from fastapi import APIRouter, Query
+from sqlalchemy import Integer, case, cast, func, select
 from sqlalchemy.orm import selectinload
 
-from app.db import get_session
+from app.db import SessionDep
 from app.models import Prediction, Variant
-from app.schemas import VariantPage
+from app.schemas import VariantOut, VariantPage
 
 router = APIRouter()
 
+# Karyotype order (1..22, X, Y, MT) instead of text order, where "10" sorts before "2".
+_chrom = func.regexp_replace(Variant.chrom, "^chr", "", "i")
+CHROM_ORDER = case(
+    (_chrom.regexp_match("^[0-9]+$"), cast(_chrom, Integer)),
+    (_chrom == "X", 23),
+    (_chrom == "Y", 24),
+    (_chrom.in_(["M", "MT"]), 25),
+    else_=26,
+)
+
 
 @router.get("", response_model=VariantPage)
 async def list_variants(
     job_id: uuid.UUID,
+    session: SessionDep,
     gene: str | None = None,
     impact: str | None = Query(None, pattern="^(HIGH|MODERATE|LOW|MODIFIER)$"),
     max_af: float | None = Query(None, ge=0, le=1),
     min_score: float | None = Query(None, ge=0, le=1),
-    limit: int = Query(50, le=500),
-    offset: int = 0,
-    session: AsyncSession = Depends(get_session),
-):
+    limit: int = Query(50, ge=1, le=500),
+    offset: int = Query(0, ge=0),
+) -> VariantPage:
     stmt = select(Variant).where(Variant.job_id == job_id)
     if gene:
         stmt = stmt.where(Variant.gene == gene.upper())
@@ -31,13 +40,21 @@ async def list_variants(
     if max_af is not None:
         stmt = stmt.where((Variant.gnomad_af.is_(None)) | (Variant.gnomad_af <= max_af))
     if min_score is not None:
-        stmt = stmt.join(Prediction, Prediction.variant_id == Variant.id).where(Prediction.score >= min_score)
+        stmt = stmt.join(Prediction, Prediction.variant_id == Variant.id).where(
+            Prediction.score >= min_score
+        )
 
     total = await session.scalar(select(func.count()).select_from(stmt.subquery()))
     rows = await session.scalars(
         stmt.options(selectinload(Variant.prediction))
-        .order_by(Variant.chrom, Variant.pos)
+        # id breaks ties between split multiallelics at one position, keeping pages stable.
+        .order_by(CHROM_ORDER, Variant.chrom, Variant.pos, Variant.id)
         .limit(limit)
         .offset(offset)
     )
-    return VariantPage(items=rows.all(), total=total or 0, limit=limit, offset=offset)
+    return VariantPage(
+        items=[VariantOut.model_validate(v) for v in rows],
+        total=total or 0,
+        limit=limit,
+        offset=offset,
+    )