circleci-demo-python-flask - A demo application to learn how to use CircleCI

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This is a working application that you can use to learn how to build, test and deploy with CircleCI 2.0. Follow the Project Walkthrough guide here. Original Author Credit: This is directly based on Miguel Grinberg's excellent Flasky application.



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This is the public repository for, a static website generated by Jekyll. If you find any errors in our docs or have suggestions, please follow our Contributing Guide to submit an issue or pull request. For minor changes like typos, you can click Suggest an edit to this page, located at the bottom of each article. This will take you to the source file on GitHub, where you can submit a pull request for your change through the UI.

flaskbb - A classic Forum Software in Python using Flask.

  •    Python

FlaskBB is a Forum Software written in Python using the micro framework Flask. Feel free to checkout it's feature on our testing instance over at You can use the demo user (demo//demo) to avoid the registration process.

django-react-boilerplate - Django, React, Bootstrap 4 with Python 3 and webpack project boilerplate

  •    Python

For continuous integration, a CircleCI configuration .circleci/config.yml is included. This is a good starting point for modern Python/JavaScript web projects.

Flask-Blogging - A Markdown Based Python Blog Engine as a Flask Extension.

  •    Python

This is a Flask extension for adding blog support to your site using Markdown. Please see Flask-Blogging documentation for more details. You can extend Flask-Blogging by using plugins from here. Check out the Serverless Blog demo running on AWS Lambda.

Flask-Large-Application-Example - This is how I structure my large Flask applications.

  •    Python

PyPI Portal is a small demo app used as an example of a potentially large Flask application with several views and Celery tasks. This is how I structure my large Flask applications. In this README I'll explain my design choices with several aspects of the project. For information on how to deploy this application to different production environments, visit the project's wiki.

tensorflow-101 - learn code with tensorflow

  •    Python

In folder finetuning, we use tf.slim to finetuning the pretrain model (I use the same method in my porn detection) and use flask to buid a very simple inference system. I deploy a image classification in demo page. It is based on Tensorflow and Flask. Feel free to try.

flaskr-tdd - Flaskr: Intro to Flask, Test Driven Development (TDD), and jQuery

  •    Python

As many of you know, Flaskr - a mini-blog-like-app - is the app you build for the official tutorial for Flask, the awesome, Python-based micro web framework. I've gone through the tutorial more times than I care to admit. Anyway, I wanted to take the tutorial a step further by adding test driven development and a bit of jQuery. This post is that tutorial. Enjoy.Test Driven Development (TDD) is an iterative development cycle that emphasizes writing automated tests before writing the actual feature of function. Put another way, TDD combines building and testing. This process not only helps ensure correctness of the code - but also helps to indirectly evolve the design and architecture of the project at hand.

heroku-buildpack-python - The official Heroku buildpack for Python apps.

  •    Shell

This is the official Heroku buildpack for Python apps, powered by Pipenv, pip and other excellent software. Recommended web frameworks include Django and Flask. The recommended webserver is Gunicorn. There are no restrictions around what software can be used (as long as it's pip-installable). Web processes must bind to $PORT, and only the HTTP protocol is permitted for incoming connections.

flask-marshmallow - Flask + marshmallow for beautiful APIs

  •    Python

Flask-Marshmallow is a thin integration layer for Flask (a Python web framework) and marshmallow (an object serialization/deserialization library) that adds additional features to marshmallow, including URL and Hyperlinks fields for HATEOAS-ready APIs. It also (optionally) integrates with Flask-SQLAlchemy. Create your app.

flask-heroku - Heroku environment variable configurations for Flask.

  •    Python

Heroku environment variable configurations for Flask.

node-express-mongoose-demo - A simple demo app using express, mongoose, passport for beginners

  •    Javascript

This is a demo node.js application illustrating various features used in everyday web development, with a fine touch of best practices. The demo app is a blog application where users (signing up using facebook, twitter, github and simple registrations) can create an article, delete an article and add comments on the article. and replace the values there. In production env, it is not safe to keep the ids and secrets in a file, so you need to set it up via commandline. If you are using heroku checkout how environment variables are set here.

squanchy-android - Open source Android app for your conferences

  •    Kotlin

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Flask-Foundation - A solid foundation for your flask app

  •    Python

There is a cookiecutter version of this repo at Flask Foundation is a solid foundation for flask applications, built with best practices, that you can easily construct your website/webapp off of. Flask Foundation is different from most Flask frameworks as it does not assume anything about your development or production environments. Flask Foundation is platform agnostic in this respect.

microblog - A microblogging web application written in Python and Flask that I developed as part of my Flask Mega-Tutorial series

  •    Python

This is an example application featured in my Flask Mega-Tutorial. See the tutorial for instructions on how to work with it.


  •    Python

This is a simple Python/Flask application intended to provide a working example of Uber's external API. The goal of these endpoints is to be simple, well-documented and to provide a base for developers to develop other applications off of.After creating your app on Heroku, you have to configure the redirect URL for your Uber OAuth app. Use a https://{your-app-name} URL. You will also want to configure the heroku environment variable FLASK_DEBUG=False in order to properly serve SSL traffic.

MyFlaskTutorial - A Flask Tutorial for people who don't know any web programming

  •    Python

This is a Flask tutorial for people who don't know any web programming. I started learning Flask on June 4th, 2012. I was unhappy with the Flaskr tutorial, probably because I don't have a web programming background. I am writing this tutorial for people like me.

flask_heroku - An easy-to-use Flask template for Heroku.

  •    CSS

A template to get your Flask app running on Heroku as fast as possible. For added convenience, the templates use Twitter's Bootstrap project to help reduce the amount of time it's takes you as a developer to go from an idea to a working site. All of the CSS stylesheets are written using the Less CSS syntax (even Bootstrap's CSS). If you're using Mac OS X for development, make sure to check out incident57's

flask-mongoengine - MongoEngine flask extension with WTF model forms support

  •    Python

Flask-MongoEngine is a Flask extension that provides integration with MongoEngine. It handles connection management for your app. You can also use WTForms as model forms for your models. To run the test suite, ensure you are running a local copy of Flask-MongoEngine and run: python nosetests.

spark-movie-lens - An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset

  •    Jupyter

This Apache Spark tutorial will guide you step-by-step into how to use the MovieLens dataset to build a movie recommender using collaborative filtering with Spark's Alternating Least Saqures implementation. It is organised in two parts. The first one is about getting and parsing movies and ratings data into Spark RDDs. The second is about building and using the recommender and persisting it for later use in our on-line recommender system. This tutorial can be used independently to build a movie recommender model based on the MovieLens dataset. Most of the code in the first part, about how to use ALS with the public MovieLens dataset, comes from my solution to one of the exercises proposed in the CS100.1x Introduction to Big Data with Apache Spark by Anthony D. Joseph on edX, that is also publicly available since 2014 at Spark Summit. Starting from there, I've added with minor modifications to use a larger dataset, then code about how to store and reload the model for later use, and finally a web service using Flask.