This part of the tutorial details how to implement a Redis task queue to handle text processing.

Remember: Here’s what we’re building—A Flask app that calculates word-frequency pairs based on the text from a given URL.

1. Part One: Set up a local development environment and then deploy both a staging and a production environment on Heroku.
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. (current)
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.

#flask #redis #python #web-development