Sample Python Api Setup

A sample python-api service that retrieves data from The Metropolitan Museum of Art Collection API about objects on display in the museum.

Repo structure

• app - Flask app module
• helm - Helm charts for python-api deployment (via skaffold)

Basic usage

Available endpoints:

/ or /index - returns information about a default object on display in the museum - Japanese Hanging scroll "Quail and Millet" created by Kiyohara Yukinobu

Example output:

{
  "remote_endpoint_url": "https://collectionapi.metmuseum.org/public/collection/v1/objects/45734",
  "remote_entity": "Metropolitan Museum of Art",
  "result": {
    "GalleryNumber": "",
    "accessionNumber": "36.100.45",
    "accessionYear": "1936",
    "additionalImages": [
      "https://images.metmuseum.org/CRDImages/as/original/DP251138.jpg",
      "https://images.metmuseum.org/CRDImages/as/original/DP251120.jpg"
    ],
    "artistAlphaSort": "Kiyohara Yukinobu",
    "artistBeginDate": "1643",
    "artistDisplayBio": "Japanese, 1643\u20131682",
    "artistDisplayName": "Kiyohara Yukinobu",
    "artistEndDate": "1682",
    "artistGender": "Female",
    "artistNationality": "Japanese",
    ...
    "title": "Quail and Millet"
  },
  "status_code": 200
}

/object/{id} - returns information about the object with id {id} on display in the museum

Example output for the endpoint /object/10101:

{
  "remote_endpoint_url": "https://collectionapi.metmuseum.org/public/collection/v1/objects/10101",
  "remote_entity": "Metropolitan Museum of Art",
  "result": {
    "GalleryNumber": "774",
    "accessionNumber": "1978.513.7",
    "accessionYear": "1978",
    "additionalImages": [],
    "artistAlphaSort": "Ball, Thomas",
    "artistBeginDate": "1819",
    "artistDisplayBio": "American, Charlestown, Massachusetts 1819\u20131911 Montclair, New Jersey",
    "artistDisplayName": "Thomas Ball",
    "artistEndDate": "1911",
    "artistGender": "",
    "artistNationality": "American",
    ...
    "title": "La Petite Pens\u00e9e"
  },
  "status_code": 200
}

How to run the app locally with venv

Create and activate virtual environment and install dependecies:

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Run the following command to start Flask app:

flask run

Open your browser at http://localhost:8080 and browse the app webpage.

After the job is complete, run the following command to stop venv:

deactivate

How to run the app locally with Docker

On the repo root run the following command to build an image:

docker build -t localhost:python-api .

Run docker image:

docker run --rm -p 8080:8080 -d --name api localhost:python-api

Open your browser at http://localhost:8080 and browse the app webpage.

You can check the app logs by running:

docker logs api

After the job is complete, run the following command to stop docker container (it will be removed, since the container was launched with --rm flag):

docker stop api

How to deploy the app locally

Local deployment is done using minikube and skaffold for deploying helm charts to the cluster

• Minikube installation guide: https://minikube.sigs.k8s.io/docs/start/
• Skaffold installation guide: https://skaffold.dev/docs/install/

Setup

Start minikube with a custom profile and configure skaffold:

minikube start --profile custom
skaffold config set --global local-cluster true
eval $(minikube -p custom docker-env)

Run the following command to use Skaffold:

skaffold dev

Run on a separate terminal (Do not close the terminal window!):

minikube tunnel -p custom

Open your browser at http://localhost:8080 and browse the app webpage:

Or run curl command on your terminal:

curl -s http://localhost:8080/object/10101 | jq

Additionally you can test the helm release:

helm test python-api -n app

After the job is complete, press Ctrl+C to stop skaffold and press Ctrl+C to stop minikube tunnel.

Stop minikube:

minikube stop -p custom