Udemy Autocoupons

A python package that uses web scraping to obtain Udemy Coupons, and then enrolls in them using the provided account.

Currently supported coupons websites:

• tutorialbar.com

While similar projects exist, they are usually slow, cluttered with unnecessary options or easily blocked by Udemy.

This project aims to avoid those issues, and focuses on:

• Exhaustiveness: we should gather the largest possible amount of courses, usually by increasing the amount of coupon sources.
• Performance: parallelism and removing redundancy are the main concerns for the architecture of this project.
• Undetectability: we should take all measures to avoid being detected. This is why the project does not log into any account, instead using an existing profile.

Usage

It requires

• Python 3.11+
• Linux or Windows
• The latest Chrome version
• A Chrome profile already logged in. By default, the profile in the folder "Profile 1" is used, but this can be changed with --profile-directory. Note that "Profile 1" is automatically given by Chrome to the first manually created profile, it's not the default one.

Clone this repo and move into it

git clone https://github.com/Aplietexe/udemy-autocoupons && cd udemy-autocoupons

Create a virtual environment

python -m venv .venv

Activate the virtual environment

Linux:

source .venv/vin/activate

Windows (PowerShell):

.venv/Scripts/activate

Install the required dependencies

pip install -r requirements.txt

Run the package

python -m udemy_autocoupons

Once that is set up, you can run it again any time you want by moving into the directory with the project and running

source .venv/vin/activate && python -m udemy_autocoupons

CLI

--profile-directory

• The name of the directory in the Chrome install location with the profile that should be used.
• Default: Profile 1

--user_data_dir

• The path of the directory with the user data. The profile should be in this folder.
• Default:
  • Linux: ~/.config/google-chrome/
  • Windows: "%USERPROFILE%/AppData/Local/Google/Chrome/User Data/"

Contributing

Contributions are welcome, check CONTRIBUTING.

Architecture

Because of Cloudflare bot detection, the only consistent option is to use Selenium, which is synchronous. The solution then is to use multithreading to run the scrapers and Selenium in parallel, along with a thread-safe data structure to communicate between threads.

Also, we need to use an async-safe data structure to get the results of the scrapers, as they are run asynchronously.

The result can be synthesized in the following data flow diagram: