Inspiration

We looked for issues that the new advances in machine learning could tackle. We browsed through countless statistics and analyzed data in order to find out an issue that has not yet been tackled by others.

How We Built It

For the front end, we used Next.js and React to improve the user experience and make their interactions with data interactive. Additionally, through the usage of TailwindCSS, we drastically improved the user experience by styling the app beautifully. Finally, we used Clerk to authenticate the user into the marketplace and recipe generator. A full demo of the frontend is displayed in the demo video above.

Challenges We Ran Into

While developing the app, there were countless minor issues that we had to solve, but here are the major issues:

• Overfitting of the machine learning model: When training our machine learning model using TensorFlow, we had to be extremely careful about overfitting. Therefore, we had to carefully watch our accuracy and the number of parameters that our model had in order to mitigate it while using techniques such as splitting our data and reducing model complexity layers.

Accomplishments That We're Proud Of

We accomplished everything that we planned and brainstormed with the short amount of time we had. Our execution when integrating Machine Learning and AI while having an interactive user interface was almost perfect: the user experience along with the accuracy of the model boosted the performance of our app. We are proud to have an app that works and works extremely well, and it is an enormous leap for tackling food waste.

Built With

• next.js
• react