This tutorial shows you how to process text and then setup a task queue with Flask. In part one, we’ll set up a local development environment and then deploy both a staging environment and a production environment on Heroku.

Welcome! Today we’re going to start building a Flask app that calculates word-frequency pairs based on the text from a given URL. This is a full-stack tutorial.

1. Part One: Set up a local development environment and then deploy both a staging and a production environment on Heroku. (current)
2. Part Two: Set up a PostgreSQL database along with SQLAlchemy and Alembic to handle migrations.
3. Part Three: Add in the back-end logic to scrape and then process the word counts from a webpage using the requests, BeautifulSoup, and Natural Language Toolkit (NLTK) libraries.
4. Part Four: Implement a Redis task queue to handle the text processing.
5. Part Five: Set up Angular on the front-end to continuously poll the back-end to see if the request is done processing.
6. Part Six: Push to the staging server on Heroku - setting up  Redis and detailing how to run two processes (web and worker) on a single Dyno.
7. Part Seven: Update the front-end to make it more user-friendly.
8. Part Eight: Create a custom Angular Directive to display a frequency distribution chart using JavaScript and D3.

Need the code? Grab it from the repo.

Project Setup

We’ll start with a basic “Hello World” app on Heroku with staging (or pre-production) and production environments.

For the initial setup, you should have some familiarity with the following tools:

• Virtualenv - https://realpython.com/python-virtual-environments-a-primer/
• Flask - http://flask.pocoo.org/
• git/Github - https://realpython.com/python-git-github-intro/
• Heroku (basics) - https://devcenter.heroku.com/articles/getting-started-with-python

#flask #python #web-development