Inspiration

There are about 690 million people who go hungry every year and it is becoming a growing concern worldwide. Annually $1 trillion dollars worth of food is lost or wasted and if this wasn’t the case 2 billion people could be feed according to the U.N. Food and Agriculture Organization (UNFAO). To solve this problem we created NutriPick.

What it does

The goal of NutriPick is to give the food that would be wasted because it is close to its expiration date to the people who need it. The process is simple if you have packaged food that you are going to throw away, put it inside a contact-free dispenser for people to take. If you are looking for food, log onto the website and search for the food items you like or food items that are nearby. You can look at pictures and the calorie content provided by our machine learning system. Additionally, the process is mostly hands-free, to reduce transmission by COVID-19.

How we built it

NutriPick was made through the use of react and firebase (from google cloud). The UI/UX was created using various styling components and features. The functionality of the app was derived from Edamam through a Rest API. The user inputted items on the website are stored in firebase and are retrieved on the main page. We used machine learning to find the image and calorie content for each item. Radar.io was used to find distances between locations and reverse search the location in latitude, longitude to get the formatted address.

Challenges we ran into

One challenge we ran into was incorporation a Firebase system into our application and storing key-value pairs into a database. It required many imports of modules and various sets of values to be displayed onto the screen.

What we learned

During the designing and development of NutriPick, we learning how to use react and create an app that can be accessed in live time. We also learned how to implement a database and a Rest API to the backend of our project. Overall, this project allowed us to strengthen our skills in using a variety of programming languages and integrating them together for a great application.

What's next for NutriPick

The future of NutriPick is to add many additional features such as a live chat application for receivers and donors. We also would like to add a feature where items can be removed once they have been received in real-time. Lastly, we want to make the machine learning portion evolve over time by figuring out the item name and estimated value of the product.

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