MIDAS API (uv)

This repository serves a Flask API and contains data pipeline scripts that rebuild the SQLite database used by the API.

Prerequisites

• Python 3.11
• uv installed: https://docs.astral.sh/uv/
• MIDAS API key in key.txt (required for data refresh scripts)
• Google Cloud SDK (gcloud) for deployment

Quick Start (uv)

From the repo root:

uv sync

Run the API locally:

echo "user=DEV" > .env
uv run flask --app app --debug run

Run tests:

uv run pytest -v

How To Update The Database

Run all commands from the repo root unless noted.

1. Ensure key.txt exists and contains a valid MIDAS API key.
2. Ensure .env points to local config:

echo "user=DEV" > .env

3. Pull latest upstream JSON data:

uv run python data_updates/update_midas_data.py

4. Rebuild the SQLite database (midasDB by default via config.ini):

uv run python data_updates/update_database.py

5. Optional validation: start API and hit an endpoint.

uv run flask --app app --debug run

Notes:

• config.ini controls database path by environment section (DEV and SERVER).
• The app reads .env variable user to select the section in config.ini.

Docker (uv-based)

Build image:

docker build -t midas-api:local .

Run locally:

docker run --rm -p 8080:8080 midas-api:local

Deploy To Google Cloud Run

This deployment path uses Artifact Registry + Cloud Run.

Set variables (edit values):

export PROJECT_ID="your-gcp-project-id"
export REGION="us-central1"
export REPO_NAME="midas-api"
export SERVICE_NAME="midas-api"

Authenticate and configure project:

gcloud auth login
gcloud config set project "$PROJECT_ID"
gcloud services enable run.googleapis.com artifactregistry.googleapis.com cloudbuild.googleapis.com

Create Artifact Registry repository (one-time):

gcloud artifacts repositories create "$REPO_NAME" \
	--repository-format=docker \
	--location="$REGION" \
	--description="MIDAS API container images"

Build and push image with Cloud Build:

gcloud builds submit \
	--tag "$REGION-docker.pkg.dev/$PROJECT_ID/$REPO_NAME/midas-api:latest"

Deploy to Cloud Run:

gcloud run deploy "$SERVICE_NAME" \
	--image "$REGION-docker.pkg.dev/$PROJECT_ID/$REPO_NAME/midas-api:latest" \
	--region "$REGION" \
	--platform managed \
	--allow-unauthenticated

Set runtime environment selector so app uses server config:

gcloud run services update "$SERVICE_NAME" \
	--region "$REGION" \
	--update-env-vars user=SERVER

Production Notes

• midasDB must be present in the container filesystem at runtime for query endpoints to work.
• If you rebuild midasDB, rebuild and redeploy the image so Cloud Run gets the updated database file.
• For larger or frequently updated datasets, consider moving database storage off-image (for example Cloud SQL or mounted volume patterns) and update config.ini accordingly.

Useful Commands

# install/update deps
uv sync

# local api
uv run flask --app app --debug run

# tests
uv run pytest -v

# data refresh
uv run python data_updates/update_midas_data.py
uv run python data_updates/update_database.py