The inspiration behind Eco Eats comes from the shocking amount of food waste that occurs daily. Millions of tons of food are thrown away every year, while many people go hungry. We wanted to create a solution that reduces food waste and helps people make the most of the food they have. Additionally, we wanted to make it easier for people to eat sustainably and make environmentally conscious choices. With Eco Eats, we hope to not only reduce food waste but also encourage people to think more deeply about their impact on the planet.

What it does

Eco Eats is an innovative app that helps solve the problem of food waste. It allows users to input the ingredients they have on hand, and the app generates recipes for them to follow, reducing the likelihood of throwing away perfectly good food. Additionally, we developed a machine learning algorithm to determine the freshness of produce, so users can be confident they are eating safe and healthy food. By using Eco Eats, users can make the most of the food they have and reduce their environmental impact by wasting less. It's a win-win for both individuals and the planet.

How we built it

We built Eco Eats using a range of modern technologies to create a seamless user experience. For the front end, we utilized React and Material UI to create an intuitive and visually appealing interface. To power the back end, we used Node.js and Express, with MongoDB as our database of choice. The app's front end is hosted on GitLab Pages, while the back end is hosted on Digital Ocean, providing a reliable and scalable infrastructure. To ensure that we have access to a vast and diverse range of recipes, we sourced our recipe data from the EDMAM recipe API. By leveraging these powerful technologies, we were able to create a user-friendly and effective solution to the problem of food waste.

Challenges we ran into

One of the primary challenges we faced was with GitLab security cookie authentication, which caused some issues with user authentication. To address this, we instead leveraged local storage to store the JSON web tokens.
Another significant challenge was creating a relevancy metric for recipes, as well as scoring recipes based on their environmental impact.
We also ran into various issues with integrating our machine learning model into our web application. The model was built with Python and did not fit well into our web application tech stack. Given more time we would integrate the model directly into the user interface.

Accomplishments that we're proud of

Creating a full stack web application that makes a real world impact while staying efficient and easy to navigate. Implemented a food recognition system detecting various kinds of produce and the level of freshness using Machine Learning. We are proud of how easy to use and applicable to every day life this application is. All of the people we have talked with about Eco Eats say that this is a real problem that they face and that they would actually use our solution.

What's next for Eco Eats

If we had more time we would integrate a search / filter function to the web application. We would also create our own API for querying recipes instead of using the external API.

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