TUMuch

Introduction
Meal Ordering
Profile Editing
Unique experience

Inspiration

Inspired by our own experiences of ordering food online we set out to change the booking process from being TUMuch to being intuitive and easy.

What it does

Our database of Hello-Fresh recipes was tagged with certain "Moods" by an LLM. These moods are interpreted by our algorithm to recommend a mealplan to the user that matches their subconscious preferences. These are based upon the users initial reaction (like or dislike) to a wide list of AI-generated images that represent multiple "Moods".

How we built it

We built a No-SQL Database on AWS-DynamoDB that hosts the Recipe database and holds user dietary restrictions. A Lambda function which is triggered by a Flask API interacts with this database and performs the meal-mood-matching.

Challenges we ran into

X-Code Collaboration through GitHub was frustrating at times. It also took us quite a long time to decide on a challenge since we were very torn between two ideas.

Accomplishments that we're proud of

Building a fully functional MVP. Having our idea turn out even better than we expected.

What we learned

It is important to start coding as soon as possible. In addition, setting up the data infrastructure should not be underestimated. And on the technical side: This was our first time setting up an API with its own Database.