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/predictions.py

@@ -1,16 +1,22 @@
 import uuid
 
-from fastapi import APIRouter, Depends
-from sqlalchemy.ext.asyncio import AsyncSession
+from fastapi import APIRouter, HTTPException
 
-from app.db import get_session
+from app.db import SessionDep
+from app.models import Job, JobStatus
+from app.schemas import ScoreOut
 from app.services.scoring import score_job
 
 router = APIRouter()
 
 
-@router.post("/score/{job_id}")
-async def score(job_id: uuid.UUID, session: AsyncSession = Depends(get_session)) -> dict:
-    """Load the registered MLflow model and score every variant of a job."""
-    n = await score_job(job_id, session)
-    return {"job_id": str(job_id), "scored": n}
+@router.post("/score/{job_id}", response_model=ScoreOut)
+async def score(job_id: uuid.UUID, session: SessionDep) -> ScoreOut:
+    """Score every variant of a finished job with the model behind the registry alias."""
+    job = await session.get(Job, job_id)
+    if job is None:
+        raise HTTPException(404, "job not found")
+    if job.status != JobStatus.succeeded:
+        raise HTTPException(409, f"job is {job.status.value}; only succeeded jobs can be scored")
+    n, version = await score_job(job_id, session)
+    return ScoreOut(job_id=job_id, scored=n, model_version=version)