Initial release: rarelens platform skeleton (AGPL-3.0)

End-to-end variant interpretation platform for rare genetic disease research: SvelteKit UI, FastAPI + PostgreSQL API, Nextflow/Ensembl VEP pipeline, LightGBM pathogenicity scoring with MLflow, K8s/ArgoCD/GCP infrastructure. Public test data only; no clinical claims.
2026-09-11 16:55:35 +01:00
commit 5463f489a3
74 changed files with 4597 additions and 0 deletions
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 name: ci
 on:
  pull_request:
  push:
    branches: [main]
 jobs:
  api:
    runs-on: ubuntu-latest
    services:
      postgres:
        image: postgres:16-alpine
        env: { POSTGRES_USER: rarelens, POSTGRES_PASSWORD: rarelens, POSTGRES_DB: rarelens }
        ports: ["5432:5432"]
        options: --health-cmd "pg_isready -U rarelens" --health-interval 5s --health-retries 10
    steps:
      - uses: actions/checkout@v4
      - uses: astral-sh/setup-uv@v3
      - run: uv pip install --system -e "api[dev]"
      - run: cd api && ruff check . && mypy app
      - run: cd api && pytest -q
        env: { DATABASE_URL: postgresql+asyncpg://rarelens:rarelens@localhost:5432/rarelens }
  web:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: actions/setup-node@v4
        with: { node-version: 22, cache: npm, cache-dependency-path: web/package-lock.json }
      - run: cd web && npm ci && npm run check && npm test && npm run build
  pipeline:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: nf-core/setup-nextflow@v2
      - run: cd pipeline && nextflow run main.nf -profile docker --vcf ../data/example.vcf.gz -stub-run
  terraform:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: hashicorp/setup-terraform@v3
      - run: cd infra/terraform && terraform init -backend=false && terraform fmt -check && terraform validate
  images:
    if: github.ref == 'refs/heads/main'
    needs: [api, web, pipeline]
    runs-on: ubuntu-latest
    permissions: { contents: read, id-token: write }
    strategy:
      matrix: { component: [api, web, pipeline, ml] }
    steps:
      - uses: actions/checkout@v4
      - uses: google-github-actions/auth@v2
        with:
          workload_identity_provider: ${{ secrets.GCP_WIF_PROVIDER }}
          service_account: ${{ secrets.GCP_CI_SA }}
      - run: gcloud auth configure-docker europe-west2-docker.pkg.dev --quiet
      - uses: docker/build-push-action@v6
        with:
          context: ${{ matrix.component }}
          push: true
          tags: europe-west2-docker.pkg.dev/${{ secrets.GCP_PROJECT }}/rarelens/${{ matrix.component }}:${{ github.sha }}
@@ -0,0 +1,22 @@
 # GitOps: CI only bumps image tags in the gcp overlay; ArgoCD does the deploy.
 name: bump-images
 on:
  workflow_run:
    workflows: [ci]
    types: [completed]
    branches: [main]
 jobs:
  bump:
    if: ${{ github.event.workflow_run.conclusion == 'success' }}
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4
      - uses: imranismail/setup-kustomize@v2
      - run: |
          cd infra/k8s/overlays/gcp
          for c in api web; do
            kustomize edit set image rarelens/$c=europe-west2-docker.pkg.dev/${{ secrets.GCP_PROJECT }}/rarelens/$c:${{ github.event.workflow_run.head_sha }}
          done
      - uses: stefanzweifel/git-auto-commit-action@v5
        with: { commit_message: "chore: bump images to ${{ github.event.workflow_run.head_sha }}" }
@@ -0,0 +1,17 @@
 __pycache__/
 *.pyc
 .venv/
 .env
 !web/.env
 node_modules/
 web/.svelte-kit/
 web/build/
 pipeline/work/
 pipeline/.nextflow*
 pipeline/results/
 mlruns/
 *.tfstate
 *.tfstate.backup
 .terraform/
 data/*.vcf*
 !data/README.md
@@ -0,0 +1,661 @@
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  Each version is given a distinguishing version number.  If the
 Program specifies that a certain numbered version of the GNU Affero General
 Public License "or any later version" applies to it, you have the
 option of following the terms and conditions either of that numbered
 version or of any later version published by the Free Software
 Foundation.  If the Program does not specify a version number of the
 GNU Affero General Public License, you may choose any version ever published
 by the Free Software Foundation.
  If the Program specifies that a proxy can decide which future
 versions of the GNU Affero General Public License can be used, that proxy's
 public statement of acceptance of a version permanently authorizes you
 to choose that version for the Program.
  Later license versions may give you additional or different
 permissions.  However, no additional obligations are imposed on any
 author or copyright holder as a result of your choosing to follow a
 later version.
  15. Disclaimer of Warranty.
  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
 APPLICABLE LAW.  EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT
 HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY
 OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO,
 THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
 PURPOSE.  THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM
 IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
 ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
  16. Limitation of Liability.
  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
 WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
 THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
 GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
 USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
 DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
 PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
 EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
 SUCH DAMAGES.
  17. Interpretation of Sections 15 and 16.
  If the disclaimer of warranty and limitation of liability provided
 above cannot be given local legal effect according to their terms,
 reviewing courts shall apply local law that most closely approximates
 an absolute waiver of all civil liability in connection with the
 Program, unless a warranty or assumption of liability accompanies a
 copy of the Program in return for a fee.
                     END OF TERMS AND CONDITIONS
            How to Apply These Terms to Your New Programs
  If you develop a new program, and you want it to be of the greatest
 possible use to the public, the best way to achieve this is to make it
 free software which everyone can redistribute and change under these terms.
  To do so, attach the following notices to the program.  It is safest
 to attach them to the start of each source file to most effectively
 state the exclusion of warranty; and each file should have at least
 the "copyright" line and a pointer to where the full notice is found.
    <one line to give the program's name and a brief idea of what it does.>
    Copyright (C) <year>  <name of author>
    This program is free software: you can redistribute it and/or modify
    it under the terms of the GNU Affero General Public License as published by
    the Free Software Foundation, either version 3 of the License, or
    (at your option) any later version.
    This program is distributed in the hope that it will be useful,
    but WITHOUT ANY WARRANTY; without even the implied warranty of
    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
    GNU Affero General Public License for more details.
    You should have received a copy of the GNU Affero General Public License
    along with this program.  If not, see <https://www.gnu.org/licenses/>.
 Also add information on how to contact you by electronic and paper mail.
  If your software can interact with users remotely through a computer
 network, you should also make sure that it provides a way for users to
 get its source.  For example, if your program is a web application, its
 interface could display a "Source" link that leads users to an archive
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  You should also get your employer (if you work as a programmer) or school,
 if any, to sign a "copyright disclaimer" for the program, if necessary.
 For more information on this, and how to apply and follow the GNU AGPL, see
 <https://www.gnu.org/licenses/>.
@@ -0,0 +1,25 @@
 .PHONY: up down migrate test pipeline kind lint
 up:
 	docker compose up -d --build
 down:
 	docker compose down -v
 migrate:
 	docker compose exec api alembic upgrade head
 test:
 	cd api && uv run pytest -q
 	cd web && npm test
 lint:
 	cd api && uv run ruff check . && uv run mypy app
 	cd web && npm run check
 pipeline:
 	cd pipeline && nextflow run main.nf -profile docker --vcf ../data/example.vcf.gz --outdir results
 kind:
 	kind create cluster --name rarelens || true
 	kubectl apply -k infra/k8s/overlays/local
@@ -0,0 +1,54 @@
 # rarelens
 A small, end-to-end variant interpretation platform for rare genetic disease research.
 Scientists upload a VCF, a Nextflow workflow annotates it with Ensembl VEP, a machine
 learning model scores each variant, and results are browsable in a web app.
 This repository is a **self-training lab**. It exists so that one engineer can learn, in
 public, how a modern life-sciences platform is built end to end: full-stack application,
 scientific pipeline, ML serving, and cloud infrastructure, all in one monorepo. It is not a
 clinical tool and makes no diagnostic claims.
 ## What is in the box
 | Layer      | Technology                                             | Directory            |
 |------------|--------------------------------------------------------|----------------------|
 | Pipeline   | Nextflow DSL2, bcftools, Ensembl VEP, Docker           | `pipeline/`          |
 | API        | FastAPI, Pydantic v2, SQLAlchemy 2.0 (async), Alembic  | `api/`               |
 | Database   | PostgreSQL 16                                          | `docker-compose.yml` |
 | Frontend   | SvelteKit, TypeScript                                  | `web/`               |
 | ML         | LightGBM pathogenicity scorer, MLflow tracking         | `ml/`                |
 | Orchestration | Argo Workflows (pipeline), Pub/Sub (events)         | `infra/argo-workflows/` |
 | Platform   | Kubernetes (Kustomize), ArgoCD (GitOps)                | `infra/k8s/`, `infra/argocd/` |
 | Cloud      | GCP: GKE Autopilot, Cloud SQL, GCS, Artifact Registry  | `infra/terraform/`   |
 | CI/CD      | GitHub Actions, Workload Identity Federation           | `.github/workflows/` |
 ## Quick start (local)
 ```bash
 make up          # postgres + api + web via docker-compose
 make migrate     # alembic upgrade head
 make pipeline    # nextflow run pipeline/main.nf -profile docker --vcf data/example.vcf.gz
 make kind        # spin up a local kind cluster and apply infra/k8s/overlays/local
 ```
 Then open http://localhost:5173.
 ## Architecture
 See [docs/architecture.md](docs/architecture.md) for the diagram and the reasoning behind
 each choice.
 ## Status
 Work in progress. Milestones, in order:
 1. Skeleton, Postgres, FastAPI, Nextflow VEP annotation on a public VCF, CI green
 2. SvelteKit UI: sample list, variant table with filters, job status
 3. Kubernetes manifests, kind, Argo Workflows trigger
 4. Terraform for GCP, ArgoCD GitOps deploy
 5. Pathogenicity model, MLflow registry, prediction endpoint
 ## Licence
 AGPL-3.0. Test data are public (ClinVar, gnomAD subsets); no patient data are used or accepted.
@@ -0,0 +1,8 @@
 FROM python:3.12-slim
 WORKDIR /app
 RUN pip install --no-cache-dir uv
 COPY pyproject.toml .
 RUN uv pip install --system -e .
 COPY . .
 EXPOSE 8000
 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
@@ -0,0 +1,36 @@
 [alembic]
 script_location = alembic
 sqlalchemy.url = postgresql+asyncpg://rarelens:rarelens@db:5432/rarelens
 [loggers]
 keys = root,sqlalchemy,alembic
 [handlers]
 keys = console
 [formatters]
 keys = generic
 [logger_root]
 level = WARN
 handlers = console
 [logger_sqlalchemy]
 level = WARN
 handlers =
 qualname = sqlalchemy.engine
 [logger_alembic]
 level = INFO
 handlers =
 qualname = alembic
 [handler_console]
 class = StreamHandler
 args = (sys.stderr,)
 level = NOTSET
 formatter = generic
 [formatter_generic]
 format = %(levelname)-5.5s [%(name)s] %(message)s
 datefmt = %H:%M:%S
@@ -0,0 +1,32 @@
 import asyncio
 from logging.config import fileConfig
 from alembic import context
 from sqlalchemy.ext.asyncio import async_engine_from_config
 from sqlalchemy import pool
 from app.config import settings
 from app.models import Base
 config = context.config
 config.set_main_option("sqlalchemy.url", settings.database_url)
 if config.config_file_name:
    fileConfig(config.config_file_name)
 target_metadata = Base.metadata
 def run_migrations(connection):
    context.configure(connection=connection, target_metadata=target_metadata)
    with context.begin_transaction():
        context.run_migrations()
 async def run_async():
    engine = async_engine_from_config(
        config.get_section(config.config_ini_section, {}), poolclass=pool.NullPool
    )
    async with engine.connect() as conn:
        await conn.run_sync(run_migrations)
 asyncio.run(run_async())
@@ -0,0 +1,26 @@
 """${message}
 Revision ID: ${up_revision}
 Revises: ${down_revision | comma,n}
 Create Date: ${create_date}
 """
 from typing import Sequence, Union
 from alembic import op
 import sqlalchemy as sa
 ${imports if imports else ""}
 # revision identifiers, used by Alembic.
 revision: str = ${repr(up_revision)}
 down_revision: Union[str, None] = ${repr(down_revision)}
 branch_labels: Union[str, Sequence[str], None] = ${repr(branch_labels)}
 depends_on: Union[str, Sequence[str], None] = ${repr(depends_on)}
 def upgrade() -> None:
    ${upgrades if upgrades else "pass"}
 def downgrade() -> None:
    ${downgrades if downgrades else "pass"}
@@ -0,0 +1,90 @@
 """initial schema
 Revision ID: a3e9ead256a5
 Revises: 
 Create Date: 2026-09-11 15:36:04.335440
 """
 from typing import Sequence, Union
 from alembic import op
 import sqlalchemy as sa
 from sqlalchemy.dialects import postgresql
 # revision identifiers, used by Alembic.
 revision: str = 'a3e9ead256a5'
 down_revision: Union[str, None] = None
 branch_labels: Union[str, Sequence[str], None] = None
 depends_on: Union[str, Sequence[str], None] = None
 def upgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
    op.create_table('samples',
    sa.Column('id', sa.UUID(), nullable=False),
    sa.Column('name', sa.String(length=120), nullable=False),
    sa.Column('vcf_uri', sa.Text(), nullable=False),
    sa.Column('assembly', sa.String(length=10), nullable=False),
    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
    sa.PrimaryKeyConstraint('id'),
    sa.UniqueConstraint('name')
    )
    op.create_table('jobs',
    sa.Column('id', sa.UUID(), nullable=False),
    sa.Column('sample_id', sa.UUID(), nullable=False),
    sa.Column('status', sa.Enum('queued', 'running', 'succeeded', 'failed', name='jobstatus'), nullable=False),
    sa.Column('workflow_ref', sa.String(length=200), nullable=True),
    sa.Column('vep_version', sa.String(length=40), nullable=True),
    sa.Column('log', sa.Text(), nullable=True),
    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
    sa.Column('finished_at', sa.DateTime(timezone=True), nullable=True),
    sa.ForeignKeyConstraint(['sample_id'], ['samples.id'], ondelete='CASCADE'),
    sa.PrimaryKeyConstraint('id')
    )
    op.create_table('variants',
    sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
    sa.Column('job_id', sa.UUID(), nullable=False),
    sa.Column('chrom', sa.String(length=10), nullable=False),
    sa.Column('pos', sa.Integer(), nullable=False),
    sa.Column('ref', sa.Text(), nullable=False),
    sa.Column('alt', sa.Text(), nullable=False),
    sa.Column('gene', sa.String(length=60), nullable=True),
    sa.Column('consequence', sa.String(length=120), nullable=True),
    sa.Column('impact', sa.String(length=20), nullable=True),
    sa.Column('hgvsc', sa.Text(), nullable=True),
    sa.Column('hgvsp', sa.Text(), nullable=True),
    sa.Column('gnomad_af', sa.Float(), nullable=True),
    sa.Column('clinvar_sig', sa.String(length=120), nullable=True),
    sa.Column('annotations', postgresql.JSONB(astext_type=sa.Text()), nullable=False),
    sa.ForeignKeyConstraint(['job_id'], ['jobs.id'], ondelete='CASCADE'),
    sa.PrimaryKeyConstraint('id')
    )
    op.create_index(op.f('ix_variants_chrom'), 'variants', ['chrom'], unique=False)
    op.create_index(op.f('ix_variants_gene'), 'variants', ['gene'], unique=False)
    op.create_index(op.f('ix_variants_job_id'), 'variants', ['job_id'], unique=False)
    op.create_index(op.f('ix_variants_pos'), 'variants', ['pos'], unique=False)
    op.create_table('predictions',
    sa.Column('id', sa.Integer(), autoincrement=True, nullable=False),
    sa.Column('variant_id', sa.Integer(), nullable=False),
    sa.Column('model_name', sa.String(length=80), nullable=False),
    sa.Column('model_version', sa.String(length=40), nullable=False),
    sa.Column('score', sa.Float(), nullable=False),
    sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
    sa.ForeignKeyConstraint(['variant_id'], ['variants.id'], ondelete='CASCADE'),
    sa.PrimaryKeyConstraint('id'),
    sa.UniqueConstraint('variant_id')
    )
    # ### end Alembic commands ###
 def downgrade() -> None:
    # ### commands auto generated by Alembic - please adjust! ###
    op.drop_table('predictions')
    op.drop_index(op.f('ix_variants_pos'), table_name='variants')
    op.drop_index(op.f('ix_variants_job_id'), table_name='variants')
    op.drop_index(op.f('ix_variants_gene'), table_name='variants')
    op.drop_index(op.f('ix_variants_chrom'), table_name='variants')
    op.drop_table('variants')
    op.drop_table('jobs')
    op.drop_table('samples')
    # ### end Alembic commands ###
@@ -0,0 +1,15 @@
 from pydantic_settings import BaseSettings, SettingsConfigDict
 class Settings(BaseSettings):
    model_config = SettingsConfigDict(env_file=".env", extra="ignore")
    database_url: str = "postgresql+asyncpg://rarelens:rarelens@localhost:5432/rarelens"
    mlflow_tracking_uri: str = "http://localhost:5000"
    model_name: str = "rarelens-pathogenicity"
    model_stage: str = "Production"
    gcs_bucket: str | None = None          # set in GCP; local uses ./data
    pubsub_topic: str | None = None        # "vcf-uploaded" in GCP; local runs pipeline inline
 settings = Settings()
@@ -0,0 +1,13 @@
 from collections.abc import AsyncIterator
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
 from app.config import settings
 engine = create_async_engine(settings.database_url, pool_pre_ping=True)
 SessionLocal = async_sessionmaker(engine, expire_on_commit=False)
 async def get_session() -> AsyncIterator[AsyncSession]:
    async with SessionLocal() as session:
        yield session
@@ -0,0 +1,28 @@
 from contextlib import asynccontextmanager
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 from app.routers import jobs, predictions, samples, variants
@asynccontextmanager
 async def lifespan(app: FastAPI):
    # Warm the model cache here once ml/ is wired in.
    yield
 app = FastAPI(title="rarelens API", version="0.1.0", lifespan=lifespan)
 app.add_middleware(
    CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]
 )
 app.include_router(samples.router, prefix="/samples", tags=["samples"])
 app.include_router(jobs.router, prefix="/jobs", tags=["jobs"])
 app.include_router(variants.router, prefix="/variants", tags=["variants"])
 app.include_router(predictions.router, prefix="/predictions", tags=["predictions"])
@app.get("/health", tags=["ops"])
 async def health() -> dict[str, str]:
    return {"status": "ok"}
@@ -0,0 +1,77 @@
 """SQLAlchemy 2.0 declarative models.
 One sample -> many jobs; one job -> many variants; one variant -> one prediction (latest).
 """
 from datetime import datetime
 import enum
 import uuid
 from sqlalchemy import DateTime, Enum, Float, ForeignKey, Integer, String, Text, func
 from sqlalchemy.dialects.postgresql import JSONB, UUID
 from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column, relationship
 class Base(DeclarativeBase):
    pass
 class JobStatus(str, enum.Enum):
    queued = "queued"
    running = "running"
    succeeded = "succeeded"
    failed = "failed"
 class Sample(Base):
    __tablename__ = "samples"
    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    name: Mapped[str] = mapped_column(String(120), unique=True)
    vcf_uri: Mapped[str] = mapped_column(Text)
    assembly: Mapped[str] = mapped_column(String(10), default="GRCh38")
    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
    jobs: Mapped[list["Job"]] = relationship(back_populates="sample")
 class Job(Base):
    __tablename__ = "jobs"
    id: Mapped[uuid.UUID] = mapped_column(UUID(as_uuid=True), primary_key=True, default=uuid.uuid4)
    sample_id: Mapped[uuid.UUID] = mapped_column(ForeignKey("samples.id", ondelete="CASCADE"))
    status: Mapped[JobStatus] = mapped_column(Enum(JobStatus), default=JobStatus.queued)
    workflow_ref: Mapped[str | None] = mapped_column(String(200))   # Argo workflow name / nf run id
    vep_version: Mapped[str | None] = mapped_column(String(40))
    log: Mapped[str | None] = mapped_column(Text)
    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
    finished_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True))
    sample: Mapped[Sample] = relationship(back_populates="jobs")
    variants: Mapped[list["Variant"]] = relationship(back_populates="job")
 class Variant(Base):
    __tablename__ = "variants"
    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    job_id: Mapped[uuid.UUID] = mapped_column(ForeignKey("jobs.id", ondelete="CASCADE"), index=True)
    chrom: Mapped[str] = mapped_column(String(10), index=True)
    pos: Mapped[int] = mapped_column(Integer, index=True)
    ref: Mapped[str] = mapped_column(Text)
    alt: Mapped[str] = mapped_column(Text)
    gene: Mapped[str | None] = mapped_column(String(60), index=True)
    consequence: Mapped[str | None] = mapped_column(String(120))
    impact: Mapped[str | None] = mapped_column(String(20))
    hgvsc: Mapped[str | None] = mapped_column(Text)
    hgvsp: Mapped[str | None] = mapped_column(Text)
    gnomad_af: Mapped[float | None] = mapped_column(Float)
    clinvar_sig: Mapped[str | None] = mapped_column(String(120))
    annotations: Mapped[dict] = mapped_column(JSONB, default=dict)   # full VEP CSQ record
    job: Mapped[Job] = relationship(back_populates="variants")
    prediction: Mapped["Prediction | None"] = relationship(back_populates="variant", uselist=False)
 class Prediction(Base):
    __tablename__ = "predictions"
    id: Mapped[int] = mapped_column(Integer, primary_key=True, autoincrement=True)
    variant_id: Mapped[int] = mapped_column(ForeignKey("variants.id", ondelete="CASCADE"), unique=True)
    model_name: Mapped[str] = mapped_column(String(80))
    model_version: Mapped[str] = mapped_column(String(40))
    score: Mapped[float] = mapped_column(Float)         # P(pathogenic)
    created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), server_default=func.now())
    variant: Mapped[Variant] = relationship(back_populates="prediction")
@@ -0,0 +1,18 @@
 import uuid
 from fastapi import APIRouter, Depends, HTTPException
 from sqlalchemy.ext.asyncio import AsyncSession
 from app.db import get_session
 from app.models import Job
 from app.schemas import JobOut
 router = APIRouter()
@router.get("/{job_id}", response_model=JobOut)
 async def get_job(job_id: uuid.UUID, session: AsyncSession = Depends(get_session)):
    job = await session.get(Job, job_id)
    if job is None:
        raise HTTPException(404, "job not found")
    return job
@@ -0,0 +1,16 @@
 import uuid
 from fastapi import APIRouter, Depends
 from sqlalchemy.ext.asyncio import AsyncSession
 from app.db import get_session
 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}
@@ -0,0 +1,49 @@
 import uuid
 from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 from app.db import get_session
 from app.models import Job, Sample
 from app.schemas import JobOut, SampleCreate, SampleOut
 from app.services.events import publish_vcf_uploaded
 router = APIRouter()
@router.get("", response_model=list[SampleOut])
 async def list_samples(session: AsyncSession = Depends(get_session)):
    result = await session.scalars(select(Sample).order_by(Sample.created_at.desc()))
    return result.all()
@router.post("", response_model=SampleOut, status_code=status.HTTP_201_CREATED)
 async def create_sample(payload: SampleCreate, session: AsyncSession = Depends(get_session)):
    sample = Sample(**payload.model_dump())
    session.add(sample)
    await session.commit()
    await session.refresh(sample)
    return sample
@router.get("/{sample_id}/jobs", response_model=list[JobOut])
 async def list_jobs(sample_id: uuid.UUID, session: AsyncSession = Depends(get_session)):
    result = await session.scalars(
        select(Job).where(Job.sample_id == sample_id).order_by(Job.created_at.desc())
    )
    return result.all()
@router.post("/{sample_id}/annotate", response_model=JobOut, status_code=status.HTTP_202_ACCEPTED)
 async def annotate(sample_id: uuid.UUID, session: AsyncSession = Depends(get_session)):
    sample = await session.get(Sample, sample_id)
    if sample is None:
        raise HTTPException(404, "sample not found")
    job = Job(sample_id=sample.id)
    session.add(job)
    await session.commit()
    await session.refresh(job)
    # Emits to Pub/Sub in GCP; runs the Nextflow pipeline inline for local dev.
    await publish_vcf_uploaded(job_id=job.id, vcf_uri=sample.vcf_uri)
    return job
@@ -0,0 +1,43 @@
 import uuid
 from fastapi import APIRouter, Depends, Query
 from sqlalchemy import func, select
 from sqlalchemy.ext.asyncio import AsyncSession
 from sqlalchemy.orm import selectinload
 from app.db import get_session
 from app.models import Prediction, Variant
 from app.schemas import VariantPage
 router = APIRouter()
@router.get("", response_model=VariantPage)
 async def list_variants(
    job_id: uuid.UUID,
    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),
 ):
    stmt = select(Variant).where(Variant.job_id == job_id)
    if gene:
        stmt = stmt.where(Variant.gene == gene.upper())
    if impact:
        stmt = stmt.where(Variant.impact == impact)
    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)
    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)
        .limit(limit)
        .offset(offset)
    )
    return VariantPage(items=rows.all(), total=total or 0, limit=limit, offset=offset)
@@ -0,0 +1,63 @@
 from datetime import datetime
 import uuid
 from pydantic import BaseModel, ConfigDict, Field
 from app.models import JobStatus
 class ORMModel(BaseModel):
    model_config = ConfigDict(from_attributes=True)
 class SampleCreate(BaseModel):
    name: str = Field(min_length=1, max_length=120)
    vcf_uri: str
    assembly: str = "GRCh38"
 class SampleOut(ORMModel):
    id: uuid.UUID
    name: str
    vcf_uri: str
    assembly: str
    created_at: datetime
 class JobOut(ORMModel):
    id: uuid.UUID
    sample_id: uuid.UUID
    status: JobStatus
    workflow_ref: str | None
    vep_version: str | None
    created_at: datetime
    finished_at: datetime | None
 class PredictionOut(ORMModel):
    model_name: str
    model_version: str
    score: float
 class VariantOut(ORMModel):
    id: int
    chrom: str
    pos: int
    ref: str
    alt: str
    gene: str | None
    consequence: str | None
    impact: str | None
    hgvsc: str | None
    hgvsp: str | None
    gnomad_af: float | None
    clinvar_sig: str | None
    prediction: PredictionOut | None = None
 class VariantPage(BaseModel):
    items: list[VariantOut]
    total: int
    limit: int
    offset: int
@@ -0,0 +1,31 @@
 """Event publishing.
 GCP: publish a JSON message to Pub/Sub; an Argo Events sensor turns it into an Argo Workflow.
 Local: run Nextflow directly in a background task so `docker compose up` gives a working demo.
 """
 import asyncio
 import json
 import uuid
 from app.config import settings
 async def publish_vcf_uploaded(job_id: uuid.UUID, vcf_uri: str) -> None:
    message = {"job_id": str(job_id), "vcf_uri": vcf_uri}
    if settings.pubsub_topic:
        from google.cloud import pubsub_v1  # optional dependency, installed in the GCP image
        publisher = pubsub_v1.PublisherClient()
        publisher.publish(settings.pubsub_topic, json.dumps(message).encode())
        return
    cmd = [
        "nextflow", "run", "/pipeline/main.nf", "-profile", "docker",
        "--vcf", vcf_uri, "--job_id", str(job_id), "--db_url", settings.database_url,
    ]
    try:
        await asyncio.create_subprocess_exec(*cmd)
    except FileNotFoundError:
        # Nextflow is not installed in this environment (no Java toolchain yet);
        # leave the job queued instead of crashing the request.
        print(f"[rarelens] nextflow not found; cannot start pipeline for job {job_id}", flush=True)
@@ -0,0 +1,45 @@
 import uuid
 import mlflow
 import pandas as pd
 from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 from app.config import settings
 from app.models import Prediction, Variant
 _model = None
 def load_model():
    global _model
    if _model is None:
        mlflow.set_tracking_uri(settings.mlflow_tracking_uri)
        _model = mlflow.pyfunc.load_model(f"models:/{settings.model_name}/{settings.model_stage}")
    return _model
 def featurise(variants: list[Variant]) -> pd.DataFrame:
    # Mirror ml/rarelens_ml/features.py exactly; shared package later.
    return pd.DataFrame(
        {
            "impact": [v.impact for v in variants],
            "consequence": [v.consequence for v in variants],
            "gnomad_af": [v.gnomad_af if v.gnomad_af is not None else 0.0 for v in variants],
            "cadd_phred": [v.annotations.get("CADD_PHRED") for v in variants],
            "am_pathogenicity": [v.annotations.get("am_pathogenicity") for v in variants],
        }
    )
 async def score_job(job_id: uuid.UUID, session: AsyncSession) -> int:
    variants = (await session.scalars(select(Variant).where(Variant.job_id == job_id))).all()
    if not variants:
        return 0
    model = load_model()
    scores = model.predict(featurise(variants))
    for v, s in zip(variants, scores):
        session.add(Prediction(variant_id=v.id, model_name=settings.model_name,
                               model_version=settings.model_stage, score=float(s)))
    await session.commit()
    return len(variants)
@@ -0,0 +1,28 @@
 [project]
 name = "rarelens-api"
 version = "0.1.0"
 description = "FastAPI backend for rarelens"
 requires-python = ">=3.12"
 dependencies = [
  "fastapi>=0.115",
  "uvicorn[standard]>=0.30",
  "sqlalchemy[asyncio]>=2.0",
  "asyncpg>=0.29",
  "alembic>=1.13",
  "pydantic>=2.8",
  "pydantic-settings>=2.4",
  "httpx>=0.27",
  "mlflow-skinny>=2.16",
  "lightgbm>=4.5",
  "pandas>=2.2",
 ]
 [project.optional-dependencies]
 dev = ["pytest", "pytest-asyncio", "ruff", "mypy", "aiosqlite"]
 [tool.ruff]
 line-length = 100
 target-version = "py312"
 [tool.pytest.ini_options]
 asyncio_mode = "auto"
@@ -0,0 +1,12 @@
 import pytest
 from httpx import ASGITransport, AsyncClient
 from app.main import app
@pytest.mark.asyncio
 async def test_health():
    async with AsyncClient(transport=ASGITransport(app=app), base_url="http://test") as c:
        r = await c.get("/health")
    assert r.status_code == 200
    assert r.json() == {"status": "ok"}
@@ -0,0 +1,16 @@
 # Test data
 No patient data. Use public sources only:
 - ClinVar VCF (GRCh38): https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/
 - gnomAD exomes subset for allele frequencies
 - A small HG002 (GIAB) chr22 slice for a realistic germline sample
 ```bash
 # Example: 2,000 ClinVar variants on chr22 as a smoke-test VCF
 wget -O clinvar.vcf.gz https://ftp.ncbi.nlm.nih.gov/pub/clinvar/vcf_GRCh38/clinvar.vcf.gz
 tabix -p vcf clinvar.vcf.gz
 bcftools view -r 22 clinvar.vcf.gz | bcftools view -H | head -2000 > body.vcf
 (bcftools view -h clinvar.vcf.gz; cat body.vcf) | bgzip > example.vcf.gz
 tabix -p vcf example.vcf.gz
 ```
@@ -0,0 +1,45 @@
 services:
  db:
    image: postgres:16-alpine
    environment:
      POSTGRES_USER: rarelens
      POSTGRES_PASSWORD: rarelens
      POSTGRES_DB: rarelens
    ports: ["5432:5432"]
    volumes: [pgdata:/var/lib/postgresql/data]
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U rarelens"]
      interval: 5s
      retries: 10
  api:
    build: ./api
    environment:
      DATABASE_URL: postgresql+asyncpg://rarelens:rarelens@db:5432/rarelens
      MLFLOW_TRACKING_URI: http://mlflow:5000
    ports: ["8000:8000"]
    depends_on:
      db: { condition: service_healthy }
    volumes: ["./api:/app"]
    command: uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload
  web:
    build:
      context: ./web
      target: build
    environment:
      PUBLIC_API_URL: http://localhost:8000
    ports: ["5173:5173"]
    depends_on: [api]
    volumes: ["./web:/app", "/app/node_modules"]
    command: npm run dev -- --host
  mlflow:
    image: ghcr.io/mlflow/mlflow:v2.16.0
    command: mlflow server --host 0.0.0.0 --backend-store-uri sqlite:///mlflow.db --default-artifact-root /mlruns
    ports: ["5000:5000"]
    volumes: [mlruns:/mlruns]
 volumes:
  pgdata:
  mlruns:
@@ -0,0 +1,51 @@
 # Architecture
 ```mermaid
 flowchart LR
  U[Scientist] -->|browser| W[SvelteKit web]
  W -->|REST| A[FastAPI]
  A --> P[(PostgreSQL / Cloud SQL)]
  A -->|publish vcf-uploaded| Q[Pub/Sub]
  Q --> E[Argo Events sensor]
  E --> AW[Argo Workflow]
  AW --> NF[Nextflow: bcftools norm, VEP, load_db]
  NF -->|reads VCF| G[(GCS bucket)]
  NF -->|writes variants| P
  A -->|models:/rarelens-pathogenicity| M[MLflow registry]
  T[ml/train.py on GKE, optional GPU] --> M
  GH[GitHub Actions] -->|images via WIF| AR[Artifact Registry]
  GH -->|bumps overlay tags| R[(git: infra/k8s/overlays/gcp)]
  R --> CD[ArgoCD] --> K[GKE Autopilot]
 ```
 ## Why these choices
 **One monorepo.** The four components share a schema (`variants` table, feature columns) and the
 point of the exercise is to see them evolve together. Separate repos would hide the coupling.
 **Nextflow for the science, Argo Workflows for the trigger.** Nextflow is the lingua franca for
 bioinformatics pipelines and has a native Kubernetes executor. Argo is what the platform team
 already runs. So Argo owns *when* a pipeline runs; Nextflow owns *what* it does. The API never
 talks to Kubernetes directly; it publishes an event and gets on with its life.
 **FastAPI + Pydantic v2 + SQLAlchemy 2.0 async.** Typed at both boundaries: request/response models
 and ORM models are separate on purpose so the database can change without breaking the frontend
 contract. Alembic owns the schema; the loader script writes raw SQL against that schema, not the ORM,
 because the pipeline container should not import the API.
 **SvelteKit.** Small runtime, no virtual DOM, and Svelte 5 runes make server-driven state simple.
 The UI has exactly two pages; the goal is a table a scientist actually wants to filter, not a dashboard.
 **GKE Autopilot + Cloud SQL, not self-managed.** The lab is about the platform patterns (Workload
 Identity, GitOps, Kustomize overlays, GPU node selection), not about running etcd.
 **GitOps.** CI builds and tests; it never runs `kubectl apply`. It edits image tags in the `gcp` overlay
 and ArgoCD reconciles. Rollback is `git revert`.
 **MLflow registry as the model contract.** The API loads `models:/rarelens-pathogenicity/Production`.
 Training writes there; serving reads there. Feature engineering lives in one module that both sides import.
 ## What is deliberately missing
 Authentication, PHI handling, audit logs, clinical validation. This is a learning platform on public
 data. Adding Identity-Aware Proxy in front of the ingress is the first step if that ever changes.
@@ -0,0 +1,37 @@
 # Triggered by an Argo Events sensor listening on the Pub/Sub topic "vcf-uploaded".
 apiVersion: argoproj.io/v1alpha1
 kind: WorkflowTemplate
 metadata: { name: annotate-vcf, namespace: rarelens }
 spec:
  entrypoint: nextflow
  arguments:
    parameters:
      - { name: job_id }
      - { name: vcf_uri }
  templates:
    - name: nextflow
      inputs:
        parameters: [{ name: job_id }, { name: vcf_uri }]
      serviceAccountName: rarelens-pipeline
      container:
        image: europe-west2-docker.pkg.dev/PROJECT/rarelens/pipeline:latest
        command: [nextflow]
        args:
          - run
          - /pipeline/main.nf
          - -profile
          - gcp
          - --vcf
          - "{{inputs.parameters.vcf_uri}}"
          - --job_id
          - "{{inputs.parameters.job_id}}"
          - --db_url
          - "$(DATABASE_URL)"
        envFrom: [{ secretRef: { name: api-secrets } }]
        resources: { requests: { cpu: "2", memory: 4Gi } }
    - name: score
      # Optional GPU step for the deep-learning baseline; Autopilot schedules on an L4 node.
      nodeSelector: { cloud.google.com/gke-accelerator: nvidia-l4 }
      container:
        image: europe-west2-docker.pkg.dev/PROJECT/rarelens/ml:latest
        resources: { limits: { nvidia.com/gpu: 1 } }
@@ -0,0 +1,13 @@
 apiVersion: argoproj.io/v1alpha1
 kind: Application
 metadata: { name: rarelens, namespace: argocd }
 spec:
  project: default
  source:
    repoURL: https://github.com/lynchaos/rarelens
    targetRevision: main
    path: infra/k8s/overlays/gcp
  destination: { server: https://kubernetes.default.svc, namespace: rarelens }
  syncPolicy:
    automated: { prune: true, selfHeal: true }
    syncOptions: [CreateNamespace=true]
@@ -0,0 +1,26 @@
 apiVersion: apps/v1
 kind: Deployment
 metadata: { name: api }
 spec:
  replicas: 2
  selector: { matchLabels: { app: api } }
  template:
    metadata: { labels: { app: api } }
    spec:
      serviceAccountName: rarelens-api
      containers:
        - name: api
          image: rarelens/api
          ports: [{ containerPort: 8000 }]
          envFrom: [{ secretRef: { name: api-secrets } }]
          readinessProbe: { httpGet: { path: /health, port: 8000 }, periodSeconds: 5 }
          resources: { requests: { cpu: 250m, memory: 512Mi }, limits: { cpu: "1", memory: 1Gi } }
 ---
 apiVersion: v1
 kind: Service
 metadata: { name: api }
 spec: { selector: { app: api }, ports: [{ port: 80, targetPort: 8000 }] }
 ---
 apiVersion: v1
 kind: ServiceAccount
 metadata: { name: rarelens-api }
@@ -0,0 +1,9 @@
 apiVersion: networking.k8s.io/v1
 kind: Ingress
 metadata: { name: rarelens }
 spec:
  rules:
    - http:
        paths:
          - { path: /api, pathType: Prefix, backend: { service: { name: api, port: { number: 80 } } } }
          - { path: /,    pathType: Prefix, backend: { service: { name: web, port: { number: 80 } } } }
@@ -0,0 +1,4 @@
 apiVersion: kustomize.config.k8s.io/v1beta1
 kind: Kustomization
 namespace: rarelens
 resources: [namespace.yaml, api.yaml, web.yaml, ingress.yaml]
@@ -0,0 +1,3 @@
 apiVersion: v1
 kind: Namespace
 metadata: { name: rarelens }
@@ -0,0 +1,20 @@
 apiVersion: apps/v1
 kind: Deployment
 metadata: { name: web }
 spec:
  replicas: 2
  selector: { matchLabels: { app: web } }
  template:
    metadata: { labels: { app: web } }
    spec:
      containers:
        - name: web
          image: rarelens/web
          ports: [{ containerPort: 3000 }]
          env: [{ name: PUBLIC_API_URL, value: /api }]
          resources: { requests: { cpu: 100m, memory: 128Mi }, limits: { cpu: 500m, memory: 256Mi } }
 ---
 apiVersion: v1
 kind: Service
 metadata: { name: web }
 spec: { selector: { app: web }, ports: [{ port: 80, targetPort: 3000 }] }
@@ -0,0 +1,21 @@
 apiVersion: kustomize.config.k8s.io/v1beta1
 kind: Kustomization
 resources: [../../base]
 images:
  - { name: rarelens/api, newName: europe-west2-docker.pkg.dev/PROJECT/rarelens/api, newTag: latest }
  - { name: rarelens/web, newName: europe-west2-docker.pkg.dev/PROJECT/rarelens/web, newTag: latest }
 patches:
  - target: { kind: ServiceAccount, name: rarelens-api }
    patch: |
      - op: add
        path: /metadata/annotations
        value: { iam.gke.io/gcp-service-account: [email protected] }
  - target: { kind: Deployment, name: api }
    patch: |
      - op: add
        path: /spec/template/spec/containers/-
        value:
          name: cloud-sql-proxy
          image: gcr.io/cloud-sql-connectors/cloud-sql-proxy:2.13.0
          args: ["--structured-logs", "--port=5432", "PROJECT:europe-west2:rarelens-pg"]
          securityContext: { runAsNonRoot: true }
@@ -0,0 +1,9 @@
 apiVersion: kustomize.config.k8s.io/v1beta1
 kind: Kustomization
 resources: [../../base, postgres.yaml]
 images:
  - { name: rarelens/api, newName: rarelens-api, newTag: dev }
  - { name: rarelens/web, newName: rarelens-web, newTag: dev }
 secretGenerator:
  - name: api-secrets
    literals: [DATABASE_URL=postgresql+asyncpg://rarelens:rarelens@postgres:5432/rarelens]
@@ -0,0 +1,20 @@
 apiVersion: apps/v1
 kind: Deployment
 metadata: { name: postgres }
 spec:
  selector: { matchLabels: { app: postgres } }
  template:
    metadata: { labels: { app: postgres } }
    spec:
      containers:
        - name: postgres
          image: postgres:16-alpine
          env:
            - { name: POSTGRES_USER, value: rarelens }
            - { name: POSTGRES_PASSWORD, value: rarelens }
            - { name: POSTGRES_DB, value: rarelens }
 ---
 apiVersion: v1
 kind: Service
 metadata: { name: postgres }
 spec: { selector: { app: postgres }, ports: [{ port: 5432 }] }
@@ -0,0 +1,29 @@
 resource "google_sql_database_instance" "pg" {
  name             = "rarelens-pg"
  database_version = "POSTGRES_16"
  region           = var.region
  deletion_protection = false
  settings {
    tier              = "db-f1-micro" # lab budget; bump for real use
    availability_type = "ZONAL"
    backup_configuration { enabled = true }
    ip_configuration {
      ipv4_enabled    = false
      private_network = google_compute_network.vpc.id
    }
  }
 }
 resource "google_sql_database" "rarelens" {
  name     = "rarelens"
  instance = google_sql_database_instance.pg.name
 }
 resource "google_sql_user" "api" {
  name     = "rarelens"
  instance = google_sql_database_instance.pg.name
  password = random_password.pg.result
 }
 resource "random_password" "pg" { length = 32 }
@@ -0,0 +1,9 @@
 resource "google_container_cluster" "rarelens" {
  name             = "rarelens"
  location         = var.region
  enable_autopilot = true
  deletion_protection = false
  workload_identity_config { workload_pool = "${var.project}.svc.id.goog" }
  release_channel { channel = "REGULAR" }
 }
@@ -0,0 +1,50 @@
 # Workload Identity Federation: GitHub Actions pushes images without long-lived keys.
 resource "google_iam_workload_identity_pool" "github" {
  workload_identity_pool_id = "github"
 }
 resource "google_iam_workload_identity_pool_provider" "github" {
  workload_identity_pool_id          = google_iam_workload_identity_pool.github.workload_identity_pool_id
  workload_identity_pool_provider_id = "github"
  attribute_mapping = {
    "google.subject"       = "assertion.sub"
    "attribute.repository" = "assertion.repository"
  }
  attribute_condition = "assertion.repository == \"${var.github_repo}\""
  oidc { issuer_uri = "https://token.actions.githubusercontent.com" }
 }
 resource "google_service_account" "ci" { account_id = "rarelens-ci" }
 resource "google_service_account_iam_member" "ci_wif" {
  service_account_id = google_service_account.ci.name
  role               = "roles/iam.workloadIdentityUser"
  member             = "principalSet://iam.googleapis.com/${google_iam_workload_identity_pool.github.name}/attribute.repository/${var.github_repo}"
 }
 resource "google_artifact_registry_repository_iam_member" "ci_push" {
  repository = google_artifact_registry_repository.images.name
  location   = var.region
  role       = "roles/artifactregistry.writer"
  member     = "serviceAccount:${google_service_account.ci.email}"
 }
 # Runtime identities (bound to k8s ServiceAccounts via GKE Workload Identity)
 resource "google_service_account" "api"      { account_id = "rarelens-api" }
 resource "google_service_account" "pipeline" { account_id = "rarelens-pipeline" }
 resource "google_project_iam_member" "api_sql" {
  project = var.project
  role    = "roles/cloudsql.client"
  member  = "serviceAccount:${google_service_account.api.email}"
 }
 resource "google_project_iam_member" "api_pubsub" {
  project = var.project
  role    = "roles/pubsub.publisher"
  member  = "serviceAccount:${google_service_account.api.email}"
 }
 resource "google_storage_bucket_iam_member" "pipeline_data" {
  bucket = google_storage_bucket.data.name
  role   = "roles/storage.objectAdmin"
  member = "serviceAccount:${google_service_account.pipeline.email}"
 }
@@ -0,0 +1,4 @@
 resource "google_compute_network" "vpc" {
  name                    = "rarelens-vpc"
  auto_create_subnetworks = true
 }
@@ -0,0 +1,5 @@
 output "cluster_name"   { value = google_container_cluster.rarelens.name }
 output "sql_connection" { value = google_sql_database_instance.pg.connection_name }
 output "data_bucket"    { value = google_storage_bucket.data.name }
 output "wif_provider"   { value = google_iam_workload_identity_pool_provider.github.name }
 output "ci_sa"          { value = google_service_account.ci.email }
@@ -0,0 +1,20 @@
 resource "google_storage_bucket" "data" {
  name                        = "${var.project}-rarelens-data"
  location                    = var.region
  uniform_bucket_level_access = true
  lifecycle_rule {
    condition {
      age            = 30
      matches_prefix = ["work/"]
    }
    action { type = "Delete" }
  }
 }
 resource "google_artifact_registry_repository" "images" {
  repository_id = "rarelens"
  location      = var.region
  format        = "DOCKER"
 }
 resource "google_pubsub_topic" "vcf_uploaded" { name = "vcf-uploaded" }
@@ -0,0 +1,9 @@
 variable "project" { type = string }
 variable "region" {
  type    = string
  default = "europe-west2" # London: keeps public genomic test data and the Cambridge team in one jurisdiction
 }
 variable "github_repo" {
  type    = string
  default = "lynchaos/rarelens"
 }
@@ -0,0 +1,13 @@
 terraform {
  required_version = ">= 1.8"
  required_providers {
    google = { source = "hashicorp/google", version = "~> 6.0" }
    random = { source = "hashicorp/random", version = "~> 3.6" }
  }
  backend "gcs" { bucket = "REPLACE-tfstate", prefix = "rarelens" }
 }
 provider "google" {
  project = var.project
  region  = var.region
 }
@@ -0,0 +1,7 @@
 FROM python:3.12-slim
 WORKDIR /ml
 RUN pip install --no-cache-dir uv
 COPY pyproject.toml .
 RUN uv pip install --system -e .
 COPY rarelens_ml ./rarelens_ml
 ENTRYPOINT ["python", "-m", "rarelens_ml.train"]
@@ -0,0 +1,8 @@
 [project]
 name = "rarelens-ml"
 version = "0.1.0"
 requires-python = ">=3.12"
 dependencies = ["lightgbm>=4.5", "mlflow>=2.16", "pandas", "scikit-learn", "sqlalchemy", "psycopg[binary]"]
 [project.optional-dependencies]
 gpu = ["torch"]   # for the optional deep-learning baseline on GPU
@@ -0,0 +1,16 @@
 """Feature engineering shared by training and serving. Keep this identical to api/app/services/scoring.py."""
 import pandas as pd
 IMPACT_ORDER = {"MODIFIER": 0, "LOW": 1, "MODERATE": 2, "HIGH": 3}
 CATEGORICAL = ["consequence"]
 NUMERIC = ["impact_rank", "gnomad_af", "cadd_phred", "am_pathogenicity"]
 def build(df: pd.DataFrame) -> pd.DataFrame:
    out = pd.DataFrame()
    out["impact_rank"] = df["impact"].map(IMPACT_ORDER).fillna(0)
    out["gnomad_af"] = pd.to_numeric(df["gnomad_af"], errors="coerce").fillna(0.0)
    out["cadd_phred"] = pd.to_numeric(df["cadd_phred"], errors="coerce")
    out["am_pathogenicity"] = pd.to_numeric(df["am_pathogenicity"], errors="coerce")
    out["consequence"] = df["consequence"].astype("category")
    return out
@@ -0,0 +1,55 @@
 """Train a pathogenicity classifier on ClinVar labels (Pathogenic/Likely pathogenic vs Benign/Likely benign).
 Label leakage warning: CLIN_SIG must never be a feature. This is a learning exercise, not a clinical model.
 Usage: python -m rarelens_ml.train --tsv results/clinvar.vep.tsv
 """
 import argparse
 import lightgbm as lgb
 import mlflow
 import mlflow.lightgbm
 import pandas as pd
 from sklearn.metrics import average_precision_score, roc_auc_score
 from sklearn.model_selection import train_test_split
 from rarelens_ml.features import build
 POS = {"Pathogenic", "Likely_pathogenic", "Pathogenic/Likely_pathogenic"}
 NEG = {"Benign", "Likely_benign", "Benign/Likely_benign"}
 def load(tsv: str) -> tuple[pd.DataFrame, pd.Series]:
    df = pd.read_csv(tsv, sep="\t", comment="#", header=None, dtype=str)
    with open(tsv) as fh:
        df.columns = next(l for l in fh if l.startswith("#Uploaded")).lstrip("#").rstrip().split("\t")
    df = df.rename(columns={"IMPACT": "impact", "Consequence": "consequence", "gnomADe_AF": "gnomad_af",
                            "CADD_PHRED": "cadd_phred"})
    y = df["CLIN_SIG"].map(lambda s: 1 if s in POS else 0 if s in NEG else None)
    keep = y.notna()
    return build(df[keep]), y[keep].astype(int)
 def main() -> None:
    p = argparse.ArgumentParser()
    p.add_argument("--tsv", required=True)
    p.add_argument("--register", action="store_true")
    a = p.parse_args()
    X, y = load(a.tsv)
    Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)
    mlflow.set_experiment("rarelens-pathogenicity")
    with mlflow.start_run():
        params = {"n_estimators": 400, "learning_rate": 0.05, "num_leaves": 31, "class_weight": "balanced"}
        mlflow.log_params(params)
        model = lgb.LGBMClassifier(**params).fit(Xtr, ytr)
        proba = model.predict_proba(Xte)[:, 1]
        mlflow.log_metrics({"auroc": roc_auc_score(yte, proba), "auprc": average_precision_score(yte, proba)})
        mlflow.lightgbm.log_model(
            model, "model",
            registered_model_name="rarelens-pathogenicity" if a.register else None,
        )
 if __name__ == "__main__":
    main()
@@ -0,0 +1,5 @@
 # Loader image: pandas + psycopg for LOAD_DB
 FROM python:3.12-slim
 RUN pip install --no-cache-dir pandas sqlalchemy "psycopg[binary]"
 COPY bin/load_db.py /usr/local/bin/load_db.py
 RUN chmod +x /usr/local/bin/load_db.py
@@ -0,0 +1,61 @@
 #!/usr/bin/env python
 """Load a VEP --tab output into the rarelens Postgres schema and mark the job succeeded."""
 import argparse
 import json
 import sys
 import pandas as pd
 from sqlalchemy import create_engine, text
 def main() -> None:
    p = argparse.ArgumentParser()
    p.add_argument("--tsv", required=True)
    p.add_argument("--job-id", required=True)
    p.add_argument("--db-url", required=True)
    a = p.parse_args()
    df = pd.read_csv(a.tsv, sep="\t", comment="#", header=None, dtype=str)
    with open(a.tsv) as fh:
        header = next(l for l in fh if l.startswith("#Uploaded_variation")).lstrip("#").rstrip().split("\t")
    df.columns = header
    chrom_pos = df["Location"].str.split(":", expand=True)
    rows = []
    for i, r in df.iterrows():
        ref, _, alt = r["Uploaded_variation"].partition("/") if "/" in r["Uploaded_variation"] else ("", "", r["Allele"])
        rows.append({
            "job_id": a.job_id,
            "chrom": chrom_pos.iloc[i, 0],
            "pos": int(chrom_pos.iloc[i, 1].split("-")[0]),
            "ref": ref or "-",
            "alt": r["Allele"],
            "gene": r.get("SYMBOL") if r.get("SYMBOL") != "-" else None,
            "consequence": r["Consequence"],
            "impact": r["IMPACT"],
            "hgvsc": None if r.get("HGVSc") == "-" else r.get("HGVSc"),
            "hgvsp": None if r.get("HGVSp") == "-" else r.get("HGVSp"),
            "gnomad_af": None if r.get("gnomADe_AF", "-") == "-" else float(r["gnomADe_AF"]),
            "clinvar_sig": None if r.get("CLIN_SIG", "-") == "-" else r["CLIN_SIG"],
            "annotations": json.dumps({k: v for k, v in r.items() if v != "-"}),
        })
    if a.db_url == "none":
        print(f"{len(rows)} variants parsed (dry run, no DB)")
        return
    engine = create_engine(a.db_url.replace("+asyncpg", "+psycopg"))
    with engine.begin() as conn:
        conn.execute(text("""
            INSERT INTO variants (job_id, chrom, pos, ref, alt, gene, consequence, impact,
                                  hgvsc, hgvsp, gnomad_af, clinvar_sig, annotations)
            VALUES (:job_id, :chrom, :pos, :ref, :alt, :gene, :consequence, :impact,
                    :hgvsc, :hgvsp, :gnomad_af, :clinvar_sig, CAST(:annotations AS jsonb))
        """), rows)
        conn.execute(text("UPDATE jobs SET status='succeeded', finished_at=now() WHERE id=:id"),
                     {"id": a.job_id})
    print(f"loaded {len(rows)} variants for job {a.job_id}", file=sys.stderr)
 if __name__ == "__main__":
    main()
@@ -0,0 +1,15 @@
 #!/usr/bin/env nextflow
 nextflow.enable.dsl = 2
 include { NORMALISE } from './modules/normalise'
 include { VEP }       from './modules/vep'
 include { LOAD_DB }   from './modules/load_db'
 workflow {
    if (!params.vcf) error "Provide --vcf"
    vcf_ch = Channel.fromPath(params.vcf, checkIfExists: true)
    NORMALISE(vcf_ch)
    VEP(NORMALISE.out.vcf)
    LOAD_DB(VEP.out.tsv, params.job_id ?: 'local', params.db_url ?: 'none')
 }
@@ -0,0 +1,13 @@
 process LOAD_DB {
    tag "$job_id"
    input:
        path tsv
        val job_id
        val db_url
    output: stdout
    script:
    """
    load_db.py --tsv $tsv --job-id $job_id --db-url '$db_url'
    """
 }
@@ -0,0 +1,11 @@
 process NORMALISE {
    tag "$vcf.simpleName"
    input:  path vcf
    output: path "${vcf.simpleName}.norm.vcf.gz", emit: vcf
    script:
    """
    bcftools norm -m -both -Oz -o ${vcf.simpleName}.norm.vcf.gz $vcf
    bcftools index -t ${vcf.simpleName}.norm.vcf.gz
    """
 }
@@ -0,0 +1,16 @@
 process VEP {
    tag "$vcf.simpleName"
    publishDir params.outdir, mode: 'copy'
    input:  path vcf
    output:
        path "${vcf.simpleName}.vep.tsv",      emit: tsv
        path "${vcf.simpleName}.vep_summary.html"
    script:
    """
    vep -i $vcf -o ${vcf.simpleName}.vep.tsv --tab \\
        --assembly ${params.assembly} --cache --dir_cache ${params.vep_cache} --offline \\
        --everything --pick --af_gnomade --plugin CADD --plugin AlphaMissense \\
        --stats_file ${vcf.simpleName}.vep_summary.html --fork ${task.cpus}
    """
 }
@@ -0,0 +1,24 @@
 params {
    vcf        = null
    job_id     = null
    db_url     = null
    outdir     = "results"
    assembly   = "GRCh38"
    vep_cache  = "${projectDir}/cache/vep"     // download once with `vep_install`; or use --offline false
    vep_plugins = "CADD,AlphaMissense"
 }
 profiles {
    docker { docker.enabled = true }
    gcp {
        process.executor = 'k8s'               // runs inside GKE via Argo; Nextflow k8s executor
        workDir = "gs://${params.bucket}/work"
        google.project = params.project
    }
 }
 process {
    withName: VEP        { container = 'ensemblorg/ensembl-vep:release_113.0'; cpus = 4; memory = '8 GB' }
    withName: NORMALISE  { container = 'quay.io/biocontainers/bcftools:1.20--h8b25389_0' }
    withName: LOAD_DB    { container = 'ghcr.io/lynchaos/rarelens-loader:latest' }
 }
@@ -0,0 +1 @@
 PUBLIC_API_URL=http://localhost:8000
@@ -0,0 +1,14 @@
 FROM node:22-alpine AS build
 WORKDIR /app
 COPY package*.json ./
 RUN npm ci
 COPY . .
 RUN npm run build
 FROM node:22-alpine
 WORKDIR /app
 COPY --from=build /app/build ./build
 COPY --from=build /app/package*.json ./
 RUN npm ci --omit=dev
 EXPOSE 3000
 CMD ["node", "build"]
@@ -0,0 +1,23 @@
 {
  "name": "rarelens-web",
  "version": "0.1.0",
  "private": true,
  "type": "module",
  "scripts": {
    "dev": "vite dev",
    "build": "vite build",
    "preview": "vite preview",
    "check": "svelte-kit sync && svelte-check --tsconfig ./tsconfig.json",
    "test": "vitest run"
  },
  "devDependencies": {
    "@sveltejs/adapter-node": "^5.2.0",
    "@sveltejs/kit": "^2.5.0",
    "@sveltejs/vite-plugin-svelte": "^4.0.0",
    "svelte": "^5.0.0",
    "svelte-check": "^4.0.0",
    "typescript": "^5.5.0",
    "vite": "^5.4.0",
    "vitest": "^2.0.0"
  }
 }
@@ -0,0 +1,35 @@
 /* Palette: pale slate paper, ink navy, and one plum accent reserved for pathogenicity. */
 :root {
  --paper: #f3f5f7;
  --ink: #16243a;
  --ink-soft: #52627a;
  --line: #cfd6df;
  --plum: #7a1f5c;
  --plum-soft: #f0dbe8;
  --amber: #b86a00;
  --mono: 'JetBrains Mono', ui-monospace, monospace;
  --sans: 'Source Sans 3', system-ui, sans-serif;
 }
 html { background: var(--paper); color: var(--ink); font-family: var(--sans); font-size: 17px; }
 body { margin: 0; }
 main { max-width: 1100px; margin: 0 auto; padding: 2.5rem 1.5rem; }
 h1 { font-weight: 600; font-size: 2rem; letter-spacing: -0.01em; margin: 0 0 0.5rem; }
 h2 { font-weight: 600; font-size: 1.25rem; margin: 2rem 0 0.75rem; }
 p.lede { color: var(--ink-soft); max-width: 60ch; margin: 0 0 2rem; }
 a { color: var(--plum); text-underline-offset: 3px; }
 a:focus-visible, button:focus-visible, input:focus-visible, select:focus-visible { outline: 3px solid var(--plum); outline-offset: 2px; }
 button { font: inherit; padding: 0.5rem 1rem; border: 1.5px solid var(--ink); background: var(--ink); color: white; border-radius: 4px; cursor: pointer; }
 button.quiet { background: transparent; color: var(--ink); }
 input, select { font: inherit; padding: 0.45rem 0.6rem; border: 1.5px solid var(--line); border-radius: 4px; background: white; }
 table { width: 100%; border-collapse: collapse; background: white; border: 1px solid var(--line); }
 th, td { text-align: left; padding: 0.55rem 0.75rem; border-bottom: 1px solid var(--line); vertical-align: top; }
 th { font-weight: 600; color: var(--ink-soft); }
 td.coord, td.hgvs { font-family: var(--mono); font-size: 0.85rem; }  /* aligned digits matter here */
 .score { display: inline-block; min-width: 3.2rem; text-align: right; padding: 0.1rem 0.4rem; border-radius: 3px; font-variant-numeric: tabular-nums; }
 .score.high { background: var(--plum); color: white; }
 .score.mid { background: var(--plum-soft); color: var(--plum); }
 .empty { padding: 2rem; border: 1.5px dashed var(--line); color: var(--ink-soft); }
 .status { font-weight: 600; }
 .status.failed { color: #9a1b1b; }
 .status.running, .status.queued { color: var(--amber); }
@media (prefers-reduced-motion: no-preference) { button { transition: background 120ms; } }
@@ -0,0 +1,13 @@
 <!doctype html>
 <html lang="en">
  <head>
    <meta charset="utf-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1" />
    <link rel="preconnect" href="https://fonts.googleapis.com" />
    <link href="https://fonts.googleapis.com/css2?family=Source+Sans+3:wght@400;600&family=JetBrains+Mono:wght@400&display=swap" rel="stylesheet" />
    %sveltekit.head%
  </head>
  <body data-sveltekit-preload-data="hover">
    <div style="display: contents">%sveltekit.body%</div>
  </body>
 </html>
@@ -0,0 +1,25 @@
 import { PUBLIC_API_URL } from '$env/static/public';
 export type Sample = { id: string; name: string; vcf_uri: string; assembly: string; created_at: string };
 export type Job = { id: string; sample_id: string; status: 'queued' | 'running' | 'succeeded' | 'failed'; created_at: string; finished_at: string | null };
 export type Variant = {
  id: number; chrom: string; pos: number; ref: string; alt: string; gene: string | null;
  consequence: string | null; impact: string | null; hgvsc: string | null; hgvsp: string | null;
  gnomad_af: number | null; clinvar_sig: string | null; prediction: { score: number; model_version: string } | null;
 };
 export type VariantPage = { items: Variant[]; total: number; limit: number; offset: number };
 async function req<T>(path: string, init?: RequestInit): Promise<T> {
  const r = await fetch(`${PUBLIC_API_URL}${path}`, { headers: { 'content-type': 'application/json' }, ...init });
  if (!r.ok) throw new Error(`${r.status} ${r.statusText} on ${path}`);
  return r.json() as Promise<T>;
 }
 export const api = {
  samples: () => req<Sample[]>('/samples'),
  createSample: (body: Pick<Sample, 'name' | 'vcf_uri'>) => req<Sample>('/samples', { method: 'POST', body: JSON.stringify(body) }),
  jobsForSample: (sampleId: string) => req<Job[]>(`/samples/${sampleId}/jobs`),
  annotate: (sampleId: string) => req<Job>(`/samples/${sampleId}/annotate`, { method: 'POST' }),
  job: (id: string) => req<Job>(`/jobs/${id}`),
  variants: (q: Record<string, string | number>) => req<VariantPage>(`/variants?${new URLSearchParams(q as Record<string, string>)}`)
 };
@@ -0,0 +1,6 @@
 <script lang="ts">
  import '../app.css';
  let { children } = $props();
 </script>
 <main>{@render children()}</main>
@@ -0,0 +1,48 @@
 <script lang="ts">
  import { onMount } from 'svelte';
  import { api, type Sample } from '$lib/api';
  let samples = $state<Sample[]>([]);
  let name = $state('');
  let vcfUri = $state('');
  let error = $state<string | null>(null);
  async function refresh() { samples = await api.samples(); }
  onMount(refresh);
  async function add() {
    error = null;
    try { await api.createSample({ name, vcf_uri: vcfUri }); name = ''; vcfUri = ''; await refresh(); }
    catch (e) { error = (e as Error).message; }
  }
 </script>
 <h1>rarelens</h1>
 <p class="lede">Annotate a VCF with Ensembl VEP, score each variant, and browse what came back. Public test data only.</p>
 <h2>Add a sample</h2>
 <div style="display:flex; gap:0.5rem; flex-wrap:wrap">
  <input placeholder="Sample name" bind:value={name} aria-label="Sample name" />
  <input placeholder="gs://bucket/sample.vcf.gz or /data/example.vcf.gz" bind:value={vcfUri} aria-label="VCF path" style="flex:1; min-width: 20rem" />
  <button onclick={add} disabled={!name || !vcfUri}>Add sample</button>
 </div>
 {#if error}<p role="alert">Could not add the sample: {error}</p>{/if}
 <h2>Samples</h2>
 {#if samples.length === 0}
  <div class="empty">No samples yet. Add one above to run the annotation pipeline.</div>
 {:else}
  <table>
    <thead><tr><th>Name</th><th>VCF</th><th>Assembly</th><th>Added</th></tr></thead>
    <tbody>
      {#each samples as s (s.id)}
        <tr>
          <td><a href="/samples/{s.id}">{s.name}</a></td>
          <td class="hgvs">{s.vcf_uri}</td>
          <td>{s.assembly}</td>
          <td>{new Date(s.created_at).toLocaleDateString('en-GB')}</td>
        </tr>
      {/each}
    </tbody>
  </table>
 {/if}
@@ -0,0 +1,82 @@
 <script lang="ts">
  import { onMount } from 'svelte';
  import { api, type Job, type VariantPage } from '$lib/api';
  let { data } = $props();
  let job = $state<Job | null>(null);
  let page = $state<VariantPage | null>(null);
  let gene = $state('');
  let impact = $state('');
  let maxAf = $state('0.01');
  let busy = $state(false);
  onMount(async () => {
    const jobs = await api.jobsForSample(data.sampleId);
    const done = jobs.find(j => j.status === 'succeeded');
    if (done) { job = done; await loadVariants(); }
    else if (jobs.length) job = jobs[0];
  });
  async function runAnnotation() {
    busy = true;
    job = await api.annotate(data.sampleId);
    const poll = setInterval(async () => {
      if (!job) return;
      job = await api.job(job.id);
      if (job.status === 'succeeded' || job.status === 'failed') { clearInterval(poll); busy = false; if (job.status === 'succeeded') await loadVariants(); }
    }, 3000);
  }
  async function loadVariants() {
    if (!job) return;
    const q: Record<string, string> = { job_id: job.id, max_af: maxAf, limit: '100' };
    if (gene) q.gene = gene;
    if (impact) q.impact = impact;
    page = await api.variants(q);
  }
  const scoreClass = (s: number) => (s >= 0.8 ? 'score high' : s >= 0.5 ? 'score mid' : 'score');
 </script>
 <a href="/">All samples</a>
 <h1>Sample {data.sampleId.slice(0, 8)}</h1>
 {#if !job}
  <button onclick={runAnnotation} disabled={busy}>Run VEP annotation</button>
 {:else}
  <p>Job <span class="hgvs">{job.id.slice(0, 8)}</span>: <span class="status {job.status}">{job.status}</span></p>
 {/if}
 {#if job?.status === 'succeeded'}
  <h2>Variants</h2>
  <div style="display:flex; gap:0.5rem; flex-wrap:wrap; margin-bottom:0.75rem">
    <input placeholder="Gene symbol" bind:value={gene} aria-label="Gene" />
    <select bind:value={impact} aria-label="Impact">
      <option value="">Any impact</option><option>HIGH</option><option>MODERATE</option><option>LOW</option><option>MODIFIER</option>
    </select>
    <label>Max gnomAD AF <input type="number" step="0.001" min="0" max="1" bind:value={maxAf} style="width:6rem" /></label>
    <button class="quiet" onclick={loadVariants}>Apply filters</button>
  </div>
  {#if page && page.items.length === 0}
    <div class="empty">No variants match these filters. Raise the allele frequency cap or clear the gene filter.</div>
  {:else if page}
    <p style="color:var(--ink-soft)">{page.total} variants, showing {page.items.length}</p>
    <table>
      <thead><tr><th>Position</th><th>Gene</th><th>Consequence</th><th>HGVS</th><th>gnomAD AF</th><th>ClinVar</th><th>Score</th></tr></thead>
      <tbody>
        {#each page.items as v (v.id)}
          <tr>
            <td class="coord">{v.chrom}:{v.pos} {v.ref}>{v.alt}</td>
            <td>{v.gene ?? ''}</td>
            <td>{v.consequence ?? ''}<br /><small style="color:var(--ink-soft)">{v.impact ?? ''}</small></td>
            <td class="hgvs">{v.hgvsp ?? v.hgvsc ?? ''}</td>
            <td>{v.gnomad_af?.toExponential(2) ?? 'absent'}</td>
            <td>{v.clinvar_sig ?? ''}</td>
            <td>{#if v.prediction}<span class={scoreClass(v.prediction.score)}>{v.prediction.score.toFixed(2)}</span>{:else}<span style="color:var(--ink-soft)">unscored</span>{/if}</td>
          </tr>
        {/each}
      </tbody>
    </table>
  {/if}
 {/if}
@@ -0,0 +1,2 @@
 import type { PageLoad } from './$types';
 export const load: PageLoad = ({ params }) => ({ sampleId: params.id });
@@ -0,0 +1,7 @@
 import adapter from '@sveltejs/adapter-node';
 import { vitePreprocess } from '@sveltejs/vite-plugin-svelte';
 export default {
  preprocess: vitePreprocess(),
  kit: { adapter: adapter() }
 };
@@ -0,0 +1,4 @@
 {
  "extends": "./.svelte-kit/tsconfig.json",
  "compilerOptions": { "strict": true, "moduleResolution": "bundler" }
 }
@@ -0,0 +1,3 @@
 import { sveltekit } from '@sveltejs/kit/vite';
 import { defineConfig } from 'vite';
 export default defineConfig({ plugins: [sveltekit()], server: { port: 5173 } });
		`@@ -0,0 +1,2 @@`
							`import type { PageLoad } from './$types';`
							`export const load: PageLoad = ({ params }) => ({ sampleId: params.id });`