Flask by Example – Text Processing with Requests, BeautifulSoup, and NLTK

Flask by Example – Text Processing with Requests, BeautifulSoup, and NLTK

Flask by Example – Text Processing with Requests, BeautifulSoup, and NLTK. 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.

In this part of the series, we’re going to scrape the contents of a webpage and then process the text to display word counts.

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. (current)
  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.

flask python web-development

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