## Inspiration

Our idea was inspired by Duolingo. We noticed that the main component that the platform was missing was a tool for practicing your conversation skills. Upon learning about AssemblyAI's Speech-to-Text API, we decided that it might be possible to realize this idea ourselves.

## What it does

GrammarParrot allows users to practice their English speaking skills through simulated conversations. The website sends audio files recorded by the user to Assembly, which returns a transcript. The GrammarBot API uses this transcript and returns a list of grammatical errors for the user to review.

## How we built it

The front-end of this website was built using HTML, CSS and JavaScript, and is hosted on Netlify. The back-end was developed with Python's Flask and is hosted on the Google Cloud platform.

## Challenges we ran into

Hosting a Flask server on Google Cloud, making API calls.

## Accomplishments that we're proud of

We're proud of our fantastic front-end design and creating a fully-functioning back-end for the first time!

## What we learned

Hosting a Flask server on Google Cloud, making API calls, making animations with JavaScript and CSS, front-end design.

## What's next for GrammarParrot

Expanding to different languages, using a more powerful grammar API, using user-submitted questions, tracking user progress.
## What it does

XEN SPACE is an interactive web-based game that incorporates emotion recognition technology and the Leap motion controller to create an immersive emotional experience that will pave the way for the future gaming industry.

# How we built it

We built it using three.js, Leap Motion Controller for controls, and Indico Facial Emotion API. We also used Blender, Cinema4D, Adobe Photoshop, and Sketch for all graphical assets.
## Inspiration

We were inspired by hard working teachers and students. Although everyone was working hard, there was still a disconnect with many students not being able to retain what they learned. So, we decided to create both a web application and a companion phone application to help target this problem.

## What it does

The app connects students with teachers in a whole new fashion. Students can provide live feedback to their professors on various aspects of the lecture, such as the volume and pace. Professors, on the other hand, get an opportunity to receive live feedback on their teaching style and also give students a few warm-up exercises with a built-in clicker functionality.
The web portion of the project ties the classroom experience to the home. Students receive live transcripts of what the professor is currently saying, along with a summary at the end of the lecture which includes key points. The backend will also generate further reading material based on keywords from the lecture, which will further solidify the students’ understanding of the material.

## How we built it

We built the mobile portion using react-native for the front-end and firebase for the backend. The web app is built with react for the front end and firebase for the backend. We also implemented a few custom python modules to facilitate the client-server interaction to ensure a smooth experience for both the instructor and the student.

## Challenges we ran into

One major challenge we ran into was getting and processing live audio and giving a real-time transcription of it to all students enrolled in the class. We were able to solve this issue through a python script that would help bridge the gap between opening an audio stream and doing operations on it while still serving the student a live version of the rest of the site.

## Accomplishments that we’re proud of

Being able to process text data to the point that we were able to get a summary and information on tone/emotions from it. We are also extremely proud of the

## What we learned

We learned more about React and its usefulness when coding in JavaScript. Especially when there were many repeating elements in our Material Design. We also learned that first creating a mockup of what we want will facilitate coding as everyone will be on the same page on what is going on and all thats needs to be done is made very evident. We used some API’s such as the Google Speech to Text API and a Summary API. We were able to work around the constraints of the API’s to create a working product. We also learned more about other technologies that we used such as: Firebase, Adobe XD, React-native, and Python.

## What's next for Gradian

The next goal for Gradian is to implement a grading system for teachers that will automatically integrate with their native grading platform so that clicker data and other quiz material can instantly be graded and imported without any issues. Beyond that, we can see the potential for Gradian to be used in office scenarios as well so that people will never miss a beat thanks to the live transcription that happens.