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.

pipeline/main.nf

@@ -7,9 +7,17 @@ include { LOAD_DB }   from './modules/load_db'
 
 workflow {
     if (!params.vcf) error "Provide --vcf"
-    vcf_ch = Channel.fromPath(params.vcf, checkIfExists: true)
+    if (workflow.profile.tokenize(',').contains('gcp') && !(params.project && params.bucket)) {
+        error "The gcp profile needs --project and --bucket (or GCP_PROJECT and GCS_BUCKET)"
+    }
+    // Stub runs (CI) have no VEP cache or plugin data on disk.
+    def must_exist = !workflow.stubRun
+
+    vcf_ch  = Channel.fromPath(params.vcf, checkIfExists: true)
+    cache   = file(params.vep_cache, checkIfExists: must_exist)
+    plugins = file(params.vep_plugin_data ?: "${projectDir}/assets/NO_FILE", checkIfExists: must_exist)
 
     NORMALISE(vcf_ch)
-    VEP(NORMALISE.out.vcf)
-    LOAD_DB(VEP.out.tsv, params.job_id ?: 'local', params.db_url ?: 'none')
+    VEP(NORMALISE.out.vcf, cache, plugins)
+    LOAD_DB(VEP.out.tsv, params.job_id ?: 'dry-run')
 }