It is a commonly heard fact that one in every three Americans are obese. This is an astounding number. As fast food becomes increasingly popular and people become increasingly busy, we worry that this number will only grow. This app was originally designed with college students in mind who are unaccustomed to buying and preparing meals. As we continued to develop this application, we realized that this was a tool that can help everyone eat what they enjoy while maintaining a healthy, personalized diet without fear of dietary restrictions.

What it does

'Meal Prep 4 U' keeps track of ingredients in your pantry through text and visual input and suggests new recipes based on given ingredients and dietary restrictions.

How we built it

We used Python for backend to implement the Google Cloud Vision AI library which we use to analyze pictures of groceries and pantries. Furthermore, we developed the website with HTML, CSS, and Javascript.

Challenges we ran into

Initially we were going to build an android app, but android not compatible with many of the APIs we wanted to work with. As a result, many features that we wanted to implement would not be able to be implemented, which is why we changed our project to a web application. We also automated grocery flyer parsing with UIPath, but we struggled to use the Orchestrator to fully automate the process that launched the parsing.

Accomplishments that we're proud of

Using the Google Cloud Vision AI API and creating frontend and backend from scratch with a sleek, modern website design.

What we learned

Usage of many different APIs, learning how to develop web application from start to finish that implements a backend to store data. We learned how to use Flask with Python and Javascript to host a web application. Furthermore, we learned how to implement a large scale project with several group members, successfully dividing work between us.

What's next for Meal Prep 4 U

We want to further integrate the Google Cloud machine learning and AI into our project through powerful recipe and search suggestions. We could also implement a database with Django to store all of the data that we collect. With more data, this would further help improve machine learning search suggestions.

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