## Inspiration

Food is capable of uniting all of us together, no matter which demographic we belong to or which cultures we identify with. Our team recognized that there was a problem with how challenging it can be for groups to choose a restaurant that accommodated everyone's preferences. Furthermore, food apps like Yelp and Zomato can often cause 'analysis paralysis' as there are too many options to choose from. Because of this, we wanted to build a platform to facilitate the process of coming together for food, and make the process as simple and convenient as possible.

## What it does

Bonfire is an intelligent food app that takes into account the food preferences of multiple users and provides a fast, reliable, and convenient recommendation based on the aggregate inputs of the group. To remove any friction while decision-making, Bonfire is even able to make a reservation on behalf of the group using Google's Dialogflow.

## How we built it

We used Android Studio to build the mobile application and connected it to a Python back-end. We used Zomato's API for locating restaurants and data collection, and Google Sheets API and Google Apps scripts to decide the optimal restaurant recommendation given the user's preferences. We then used Adobe XD to create detailed wireframes to visualize the app's UI/UX.

## Challenges we ran into

We found that Integrating all the API's into our app was quite challenging as some required Partner access privileges and restricted the amount of information we could request. In addition, choosing a framework to connect the back-end was a difficult.

## Accomplishments that we're proud of

As our team is comprised of students studying bioinformatics, statistics, and kinesiology, we are extremely proud to have been able to bring an idea to fruition, and we are excited to continue working on this project as we think it has promising applications.

## What we learned

We learned that trying to build a full-stack application in 24 hours is no easy task. We managed to build a functional prototype and a wireframe to visualize what the UI/UX experience should be like.

## What's next for Bonfire: the Intelligent Food App

For the future of Bonfire, we are aiming to include options for dietary restrictions and incorporating Google Duplex into our app for a more natural-sounding linguistic profile. Furthermore, we want to further polish the UI to enhance the user experience. To improve the quality of the recommendations, we plan to implement machine learning for the decision-making process, which will also take into account the user's past food preferences and restaurant reviews.
## Inspiration

We love the playing the game and were disappointed in the way that there wasnt a nice web implementation of the game that we could play with each other remotely. So we fixed that.

## What it does

Allows between 5 and 10 players to play Avalon over the web app.

## How we built it

We made extensive use of Meteor and forked a popular game called [Spyfall](https://github.com/evanbrumley/spyfall) to build it out. This game had a very basic subset of rules that were applicable to Avalon. Because of this we added a lot of the functionality we needed on top of Spyfall to make the Avalon game mechanics work.

## Challenges we ran into

Building realtime systems is hard. Moreover, using a framework like Meteor that makes a lot of things easy by black boxing them is also difficult by the same token. So a lot of the time we struggled with making things work that happened to not be able to work within the context of the framework we were using. We also ended up starting the project over again multiple times since we realized that we were going down a path in which it was impossible to build that application.

## Accomplishments that we're proud of

It works. Its crisp. Its clean. Its responsive. Its synchronized across clients.

## What we learned

Meteor is magic. We learned how to use a lot of the more magical client synchronization features to deal with race conditions and the difficulties of making a realtime application.

## What's next for Avalon

Fill out the different roles, add a chat client, integrate with a video chat feature.
## Inspiration

We wanted to explore what GCP has to offer more in a practical sense, while trying to save money as poor students

## What it does

The app tracks you, and using Google Map's API, calculates a geofence that notifies the restaurants you are within vicinity to, and lets you load coupons that are valid.

## How we built it

React-native, Google Maps for pulling the location, python for the webscraper (*<https://www.retailmenot.ca/>*), Node.js for the backend, MongoDB to store authentication, location and coupons

## Challenges we ran into

React-Native was fairly new, linking a python script to a Node backend, connecting Node.js to react-native

## What we learned

New exposure APIs and gained experience on linking tools together

## What's next for Scrappy.io

Improvements to the web scraper, potentially expanding beyond restaurants.