Recipe Radar

Inspiration

Our inspiration for Recipe Radar was that we wanted to help people save money and reduce food waste by using local grocery deals to create practical recipes. Combining AI with real-time flyer data seemed like a great way to make meal planning smarter and more accessible.

What it does

Recipe Radar generates AI-powered recipes based on current grocery flyer deals in your area. It automatically creates and updates shopping lists, highlighting sale items to help users save money and plan meals efficiently.

How we built it

We built a Flask backend integrating Flipp API and Google Gemini AI for recipe generation. The frontend is a React Native app using Expo for a smooth mobile experience. Data is stored in MongoDB Atlas, syncing recipes, deals, and user selections.

Challenges we ran into

Integrating Flipp’s API to reliably save data into a json file for Google Gemini AI and training it to generate useful recipes based on variable deals required iteration and testing. There was an initial learning curve integrating Google Gemini AI to the project which was a challenge for s as a group. Managing offline state and shopping list consolidation in React Native also proved to be more difficult than we expected.

Accomplishments that we're proud of

An accomplishment we are particularly proud of is our ability to use new technology. Specifically Google Gemini API and MongoDB. These technologies are new to our team and to learn them in such a short time was something to be proud of!

What we learned

The entire team improved our full-stack skills in API design and asynchronous data handling. We also gained valueable experience with AI integration for mobile applications which was a goal of ours this weekend.