Our frond-end designer has always been troubled to decide where to go for dinner. Frustrated and irritated, he decided to create an app that would present him with a range of choices that are totally random within walking distance. Hence the app Foodar. (First we came up with the name Tender, hence our domain is t3nd3r.com .)
What it does
Foodar's database stores over 200,000 pictures of food and restaurants from Yelp Open Dataset. Each picture is linked with a restaurant's profile, so when the users open our app, Foodar will present them with pictures of nearby restaurants. Users can choose to LIKE or PASS this picture by swiping right or left. After a few likes, Foodar will generate a list of restaurants the users prefer, and users can then filter and decide which one to go for dinner.
How we built it
- Google Cloud Platform -> setting up Compute Engine (VM)
- Yelp Open Dataset -> data cleansing -> setting up MySQL environment -> import tables -> design queries
- Google Auth -> express & mongoDB -> Domain.com -> login functionality
- Adobe XD -> building a prototype
- tried flutter -> using react-native to build the apps -> fetching data from back-end
- coming up with a name and logo
- writing the devpost
- preparing the presentation
Challenges we ran into
A LOT! Challenges are everywhere. We solved many and many remain. The biggest issue is that in a short period of time, we could not finish all the functionalities we promised during the designing phase.
Accomplishments that we're proud of
That we managed to build the entire app, after frustrating, clueless debugging! That we processed gigabytes of data and deployed a server handling those! That we cooperated with each other and had fun during the entire UncommonHacks! That we learned something new and built something cool!
What we learned
- react-native tricks and experiences
- using postman to test requests
- writing sql queries
- rapid prototyping
- version control within a team
What's next for Foodar
Hopefully we can enhance the user experience by designing a better, more intelligent choosing algorithm, and expand our database to include more useful info and more restaurants.