## Inspiration

We started with the commonly known problem of food waste in the US. There are millions of tons of food thrown away each year by restaurants, organizations, and small cafes simply because they do not have an outlet to easily donate the extras. As a specific example, all three of our team members are involved in Greek Life at our respective universities. We see tons of full and healthy meals discarded at the end of each day because not enough people showed up for lunch or dinner at the house and food is prepared daily for consumption. We thought... there's got to be a better way to deal with this waste problem. Furthermore, there are thousands of families in the US who struggle to put full and nutritious meals on the table. They often are not low income enough to qualify for food stamps, but struggle to provide meals home made from groceries. This app is targeted at those people. It provides a way to enjoy a large and healthy meal whenever making one themselves seems daunting and impossible to their funds.

## What it does

Our app links up families or individuals in need of meals with extra food produced by restaurants, cafes, or organizations with a chef. There are two routes that can be taken when logging in. Donators provide their location, a picture of their food (optional), a description of the meal, and a time that it can be picked up. Those looking to pick up food, of which we have named the Ride Overs, can then log in and are directed to a map or table view that is matched with their location via location services. They can then choose which of the meals they will pick up and the donator is made aware of this. The food is picked up at no cost to either party.

Our app is named Right Overz as a pun on Left Overs. Leftovers can have a negative connotation, so we wanted to put a fun spin on the concept.

## How we built it

We started with a blank app produced with XCode. We then brainstormed the general workflow that we wanted our app to have and started dividing tasks amongst ourselves. We would individually work on pieces and ask one another for help and advice, changing our original plans many times over. We started each aspect of the app by building up a simple UI for each page that were interconnected. We then attached functionality and underlying databases to these appropriate screens. Lastly we went in a made the UI look more attractive and easier to navigate for our uses.

## Challenges we ran into

There were many, many challenges faced throughout this process. We started with figuring our collaboration on git and how to effectively push and pull our contributions without wrecking havoc. This took some time to master as we are unfamiliar with involving multiple people on one git project. But by the end, we felt excited when we had a new push or pull to commit and could see what other members of our group had accomplished along side our own work.

The integration of the fire base database was extremely difficult. We had a lot of help from the sponsors from firebase to figure out how to effectively use it for our specific app and needs. This was a cool and eye opening process regarding the complexities of databases for each unique coding project.

## Accomplishments that we're proud of

The integration of the firebase database into our Xcode app. This allows us to keep track of users and their log ins within the app backend. It also allows this app to be distributed to many people in need because of the use of a database to store their information.

Furthermore, we were proud of our use of location services to know exactly where someone is when they log in and direct their searches according to this location. This makes it easier for both donators and users to work with the app quickly and efficiently. It is important for apps to contain features like this so that users do not have to as much on their end and stop using it because of effort.

We are also proud of the integration of our skill set to produce this app. Only one member had any background in iOS development, objective-C, Xcode, etc. We had to learn fast and adapt to challenges that we met along the way. We are proud of our finished product as it illustrates what we each uniquely contributed to this project.

## What we learned

This was our first time really working on a project as a group that could not be accomplished individually. We learned that each person brings unique perspectives and experiences to the table that, when combined, can accomplish amazing things!! We also learned a lot of technical skills along the way regarding Xcode, Objective C language, databases, git, etc.

I think the biggest take away is the need for collaboration over individualized work. So much more can be accomplished when you are encouraging and spurring one another towards growth and success. Thank you for this experience and for the opportunity to learn this!

## What's next for Right Overz

We think this app could be developed further and fully implemented, especially in big cities. There is a huge problem with both food waste and low income food access, thus this aims to fix both of them by providing a link with the other. If continued, the first step would be making sure we gather enough willing restaurants and organizations to participate in our endeavor. They would be instructed of how the pick up and packaging process will work. We then would focus on awareness of our app to families in need. This would be the most difficult aspect, the campaign how helpful food pickups would be for families who struggle to plate large healthy meals. There are several logistical issues and changes that would need to be made for full implementation, but thats just the fun of designing and creating an app! This could fully develop into a world wide non profit organization.
## Inspiration

Our inspiration was to create a fun and innovative way to choose what your next meal will be (instead of wasting hours debating with friends).

## What it does

It suggests to the user which meal at which restaurant the ai thinks is ideal for them. You can also input your information by using only your face!

## How we built it

For the face movement ai, we use the opencv2 library of python to detect the face and eyes, as well as write questions and response choices on the video screen. Eyes and Face features are defined using cascade xml files. When too many eyes are detected, the topmost eyes are kept, because usually, it is features of the nose or mouth that are confused as eyes. Face inclination is then calculated from the height difference vs width difference between the eyes. To request from the API, the python requests library was used. The project uses Artificial Intelligence to suggest the meal. It takes a few pieces of information about the user and predicts what the user would eat using a Nearest Neighbor model that was trained on a randomly generated dataset. We did the UI with JavaFX and CSS. This consisted of making an iPhone application just like big delivery companies have. We also implicated a live time clock on the top right of the screen to imitate a normal iPhone 10+.

## Challenges we ran into

For face recognition, seeing extra eyes where there were none was the main challenge we had to deal with. Many random bugs. The lack of an extensive database for the meal choosing ai. Make everything work together.

## Accomplishments that we're proud of

We are proud of our API. We are also proud to have made a working face-detection ai. We also have a very functional UI. Finally, we are very proud of how we managed to make such a relatively big project with so many files languages, and frameworks work altogether.

## What we learned

We also learned more about how facial recognition and API work. How artificial intelligence works. The software development and how to make everything work together.

## What's next for Raw-Dish

Add dietary restrictions. Next is to have access to a larger database, refine the facial recognition, and finally join the extension to Radish.
## What it does

You sit in front of the webcam and ask Alexa to give you a rating. In addition to your rating, it will give you a quality meme.

## How we built it

Our Alexa interaction is connected to an AWS lambda function. The lambda function then interacts with a python flask server that connects to a HTML/JavaScript front end. This front end accesses the webcam to take a picture which is sent back to the flask server and go through the faceplusplus API to get rated. This rating is then sent back to the lambda server where it matches appropriate memes for Alexa to tell you.

## Challenges we ran into

Meme girls sounds too much like Mean Girls and Alexa sometimes misunderstands us when we ask for a rate.