Fast Food Rewards Optimizer

Screenshot of Fast Food Rewards Optimizer

Inspiration

After ordering, many fast food places reward you with points which you can redeem for more food. As a college student who often eats out at fast food restaurants in between breaks, I find the points to be very helpful. However, if you're trying to limit calories, get more protein, decrease fats, eat more fiber, etc. all at once, it can be hard to figure out how to spend your points in the best possible way. A tool like this would help me get the best meal out of my points, which is helpful if you have a short break and can't figure out what to order.

What it does

After selecting the restaurant, the user can decide to minimize or maximize for a variable such as points, calories, fiber, etc. Then, the user can place constraints on different variables. For example, if I want to minimize spending points at McDonald's while eating at least 40 grams of protein and less than 800 calories, this tool helps calculate the most optimal meal.

How we built it

Lucas built this using HTML, CSS, Javascript and React with help from ChatGPT and Gemini. The web app relies on the javascript-lp-solver library to calculate the optimal meal with the given constraints. Daniel used Python to get data from websites and APIs, and then parse the data into JSON files. This allowed our web app to easily handle the data.

Challenges we ran into

We faced challenges into collecting data from fast food restaurants. It was hard to parse the nutritional data which came from PDFs and websites, which had complex structures. Some websites didn't include some nutritional data like vitamins or sodium, so our web app had to consider the differences between each restaurant.

Accomplishments that we're proud of

We're proud of being able to navigate around messy data and build a working web app in less than two days

What we learned

We learned how to work collaboratively with git, how React works, and how to deploy websites.

What's next for Fast Food Rewards Optimizer

We want to add more restaurants, include the differing costs between restaurants, the ability to exclude certain foods, guidance to new users on how to select healthy nutrition constraints, and additional recommendations.