A website that displays a list of courses at Brown known to have the lowest workloads to enable students to optimize their work/life balance

Burnt Out at Brown lists any courses that both appear on CAB for a given semester and have Critical Review data. First, we scrape CAB data, then we scrape Critical Review data, and then lastly, we compile the data and move it into a front end folder, where it is loaded on the static website. Users can use the frontend to filter and sort courses based on various metrics, such as number of hours per week, time of day, and whether they are first year seminars.

1. Create a Python virtual environment in the root directory (burnt-out-at-brown) by running python -m venv .venv.
2. Start the virtual environment:
   1. On Mac: source .venv/bin/activate
   2. On PC: .venv\Scripts\Activate.ps1 (powershell) or .venv\Scripts\activate.bat (cmd.exe)
3. Install requirements: pip install -r requirements.txt

1. Run setup above if you haven't already
2. Update semester (season and year) in both constants.py and constants.js in format 'spring2023'
   1. In python, it will determine which semesters are scraped
   2. In the javascript, it will determine which semesters are displayed
   3. If the front end has a semester that has not yet been scraped, it will produce an error (:frowny-face:)
3. Go to the Critical Review and log in. Get the value of the "connect.sid" cookie from the cookies section in the developer tools (Developer Tools > Application > Cookies). Put this into a file called cookie.py.
   1. If cookie.py does not already exist, make a copy of cookie_template.py, rename it cookie.py and insert the connect.sid where suggested.
4. Run python main.py which will scrape cab, scrape the critical review, and then compile data for the given semester you selected in constants.py. Repeat steps 2 and 4 for any additional semesters you wish to update.

The documentation folder contains further information about todos and solutions to common bugs.

The scrape-cab.py file uses CAB's API to get a list of courses offered a given semester at Brown and their corresponding data (course number, department, name, time, and professor), and formats the data as a json. scrape-cr.py then reads through the json that scrape-cab.py outputted. For each course in the json, scrape-cr.py goes to thecriticalreview.com for that page and scrapes additional data (the critical review link, the course and professor ratings, the average and max time commitment, the number of respondents, class size, and additional unpublished info). scrape-cr.py creates two json files from this aggregated data: (1) class_objs.json, which is a dictionary from course code to a list of course reviews (and corresponding data) for that course, and (2) prof_objs.json, which is a dictionary from the professor to the list of course reviews (and corresponding data) for courses they have taught. These two jsons will be compiled to allow for front-end manipulation of the data, such as sorting courses or professors by least workload. Currently, we do not use prof_objs.json.

Lastly, the data is compiled with compile_data.py, which aggregates the data into a form which our front end expects and into the burnt-out-app/src/data folder where the front end resides.