flask-login VS Flask

Are this modules good? https://github.com/flask-admin/flask-admin and https://github.com/maxcountryman/flask-login

flask-login: Provides access to the current username

I use Flask-login and implement role based authentication using decorators.

Flask-Login has a closed Issue #19 on GitHub but it actually doesn't answer my question :-/

You can try to look here https://github.com/maxcountryman/flask-login/blob/main/test_login.py

The example code provided by flask-login does it this way, but this only works because it's pulling the User objects from a global hard-coded dictionary, not as in a real-world scenario like a database, where the DB must be checked and User objects created after the user enters their login credentials. And I can't seem to find any other example code that illustrates using a database with flask-login.

What u/MikeDoesDo said. `flask-login` is rather built for the traditional approach to login and user management. It doesn't have a concept of auth tokens or JWT. It uses traditional cookies. The code that actually sets the cookie for the session isn't robust for a production app. Secure tokens have to be signed with robust keys. In addition to strong tokens, you also need measures that prevent brute-force attacks. Third party systems like auth0 bring all of this and more. Besides, with these systems you delegate the login process to the UI and the backend simply needs to verify incoming tokens. The crucial part is that you verify the token contents (audience and such) and the signature. For a production app with sensitive data and money behind it, don't settle for anything less than this.

https://github.com/maxcountryman/flask-login/blob/c77ba6b12ef5e3045df054cf8bf2d61c4d83f54f/flask_login/mixins.py#L35-L39

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.