cookiecutter VS Flask

Sometimes I use this to abstract boilerplate https://github.com/cookiecutter/cookiecutter

It can use a repo as a template.

It supports some interactive questions to choose options but mostly it is jinja templates.

Having libraries would be another option.

Install the cookiecutter package using the following command:

Consider taking a look at cookiecutter to generate projects from templates. There is also cookiecutter-django. As for your environment variables you should have an example .env file containing all the environment variables required by your project (without setting them) that can be safely pushed into your repository for you and other developers to copy into the actual .env file that'll be used by your project (add this file to .gitignore)

The Python Cookiecutter library revolutionizes project development by offering streamlined approach to creating template projects and improving developer experience.

We use cookie cutter templates (the Python project, https://github.com/cookiecutter/cookiecutter ), we prompt for the module & version etc

Why not use cookiecutter or a similar tool designed for making these sorts of project templates?

Is it common? I don't know. Is it useful? Absolutely. There is a tool called cookiecutter that allows you to define your own setup. For example, my cookiecutter setup for a python library is here. You can see what it's like by first installing the cookiecutter cli and then running

Also cookiecutter.

Visual Cookiecutter enhances the functionality of cookiecutter by offering unique features such as required fields, conditional input parameters, optional descriptions, and the ability to fix mistakes easily. This package seamlessly integrates with cookiecutter so that all existing templates work out-of-the-box.

I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:

https://github.com/pallets/flask

#!/usr/bin/python # # https://flask.palletsprojects.com/en/3.0.x/installation/ # from flask import Flask, jsonify, request contacts = [ { "id": "1", "firstname": "Lorem", "lastname": "Ipsum", "email": "[email protected]", }, { "id": "2", "firstname": "Mauris", "lastname": "Quis", "email": "[email protected]", }, { "id": "3", "firstname": "Donec Purus", "lastname": "Purus", "email": "[email protected]", } ] app = Flask(__name__, static_url_path='', static_folder='public',) @app.route("/contact//save", methods=["PUT"]) def save_contact(id): data = request.json contacts[id - 1] = data return jsonify(contacts[id - 1]) @app.route("/contact/", methods=["GET"]) @app.route("/contact//edit", methods=["GET"]) def get_contact(id): return jsonify(contacts[id - 1]) @app.route('/') def root(): return app.send_static_file('index.html') if __name__ == '__main__': app.run(debug=True)

I was looking at all the big projects like numpy, pytorch, flask, etc.

Explore here.

I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.

Now...

Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?

- [0]: https://flask.palletsprojects.com

- [1]: https://flask-admin.readthedocs.io/en/latest

- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery

- [3]: https://sentry.io

- [4]: https://posthog.com

- [5]: https://www.docker.com

In the lab to follow, we will be setting up an end-to-end DevOps workflow for a Flask microservice with GitHub Actions, using a self-managed custom runner for maximal control over the pipeline execution environment and automating deployments to a local Kubernetes cluster. Furthermore, we will construct separate pipelines for our "development" and "production" environments to further elaborate on the concepts of continuous deployment and delivery.

Flask Micro web Framework: You will use Flask to build a Flask web application.

In an earlier post I mentioned a bunch of Open Source web applications. Let's now focus on the ones written in Python using Flask the light-weight web framework.