## Inspiration

We've always wanted to be able to point our phone at an object and know what that object is in another language. So we built that app.

## What it does

Point your phone's camera towards an object, and it will identify that object for you, using the Inception neural network. We translate the object from a source language (English) to a target language, usually a language that the user wants to learn using Google Translation API. Using AR Kit, we depict the image name, in both English and a foreign language, on top of the object. In order to help you find the word, we help you see some different ways of using that word in a sentence.

All in all, the app is a great resource for learning how to pronounce and learn about different objects in different languages.

## How we built it

We built the frontend mobile app in Swift, used AR Kit to place words on top of an object, and used Google Cloud functions to access APIs.

## Challenges we ran into

Dealing with Swift frontend frames, and getting authentication keys to work properly for APIs.

## Accomplishments that we're proud of

We built an app looks awesome with AR Kit, and has great functionality. We took an app idea and worked together to make it come to life.

## What we learned

We learned in greater depth how Swift 4 works, how to use AR Kit, and how easy it is to use Google Cloud functions to offload a server-like computation away from your app without having to set up a server.

## What's next for TranslateAR

IPO in December
## Inspiration

Since the beginning of the hackathon, all of us were interested in building something related to helping the community. Initially we began with the idea of a trash bot, but quickly realized the scope of the project would make it unrealistic. We eventually decided to work on a project that would help ease the burden on both the teachers and students through technologies that not only make learning new things easier and more approachable, but also giving teachers more opportunities to interact and learn about their students.

## What it does

We built a Google Action that gives Google Assistant the ability to help the user learn a new language by quizzing the user on words of several languages, including Spanish and Mandarin. In addition to the Google Action, we also built a very PRETTY user interface that allows a user to add new words to the teacher's dictionary.

## How we built it

The Google Action was built using the Google DialogFlow Console. We designed a number of intents for the Action and implemented robust server code in Node.js and a Firebase database to control the behavior of Google Assistant. The PRETTY user interface to insert new words into the dictionary was built using React.js along with the same Firebase database.

## Challenges we ran into

We initially wanted to implement this project by using both Android Things and a Google Home. The Google Home would control verbal interaction and the Android Things screen would display visual information, helping with the user's experience. However, we had difficulty with both components, and we eventually decided to focus more on improving the user's experience through the Google Assistant itself rather than through external hardware. We also wanted to interface with Android things display to show words on screen, to strengthen the ability to read and write. An interface is easy to code, but a PRETTY interface is not.

## Accomplishments that we're proud of

None of the members of our group were at all familiar with any natural language parsing, interactive project. Yet, despite all the early and late bumps in the road, we were still able to create a robust, interactive, and useful piece of software. We all second guessed our ability to accomplish this project several times through this process, but we persevered and built something we're all proud of. And did we mention again that our interface is PRETTY and approachable?

Yes, we are THAT proud of our interface.

## What we learned

None of the members of our group were familiar with any aspects of this project. As a result, we all learned a substantial amount about natural language processing, serverless code, non-relational databases, JavaScript, Android Studio, and much more. This experience gave us exposure to a number of technologies we would've never seen otherwise, and we are all more capable because of it.

## What's next for Language Teacher

We have a number of ideas for improving and extending Language Teacher. We would like to make the conversational aspect of Language Teacher more natural. We would also like to have the capability to adjust the Action's behavior based on the student's level. Additionally, we would like to implement a visual interface that we were unable to implement with Android Things. Most importantly, an analyst of students performance and responses to better help teachers learn about the level of their students and how best to help them.
## 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.