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Personal TV Guide

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About

Scrape your watchlist data from JustWatch and turn it into HTML to serve as your own Personal TV Guide!

screenshot

Background

Remember the weekly TV guide that came with the newspaper? It was the only useful thing in there - aside from the comic section.

1987

How to get it working

Install the required Python modules

pip install -r requirements.txt

Make my_data folder

Create this folder. This is where your private info goes

Make .env file

To get started, make a copy of /sample_files/.env-sample and name it /.env. This is where to set up your variables before continuing with the other steps.

Variable Required? Description
WHEN_TO_START y The first hour in your guide's timeline
HOURS_TO_PRINT y How many hours worth of data to include in timeline
STYLESHEET_PATH y Where to find the CSS stylesheet
OUTFILE y The name of the HTML file you want to generate
USE_KEYWORD_LIST n set to True to enable keyword lists.
genre_KEYWORDS n If creating custom rows in the table, enter a comma-separated list of strings to match. This is currently case-sensitive.
DEV_MODE n Set to False for normal use. When developing and testing, set to True to limit the number of titles read from the source.

Run it

Each of these will pause and wait for you to sign in, as mentioned above. Once they finish, they will write the data to .bin files. These will be used in the next step.

A note on credentials

The first time you run the program, you will be prompted to enter your JustWatch credentials. These get stored in a local file in your my_data folder that will be ignored in any new commits you might make to the repository.

python run.py

If all goes well, you'll have an output file (probably in my_data) that will show your Personal TV Guide!

Acknowledgements

Theme

The default stylesheet is using the Nord theme.

Badges

Custom activity badges are inspired by Shields.io, which I probably saw originally on awesome-badges.

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🎨 We use gitmoji to enhance our commit statements. 🚀

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Python-based JustWatch scraper

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  • Python 84.5%
  • CSS 15.5%