## Inspiration

We've all had that experience with our parents and grandparents asking us for help with the computer or smartphone, especially being at home in the pandemic with most meetings being virtual. This brought to mind the technological barriers between us and the older generation, as well as more so with lower socioeconomic groups who may not have access to the same privileges we have. Thus we got to thinking how can we increase accessibility of technology, and an idea is that people would need immediate help and would prefer an interface where they can talk to someone, but also more comprehensive educational materials are available to learn the technologies. Since what older people generally ask is 'can you help me with [insert technological issue]', the idea of CanYouHelpWith.Tech website was born.

## What it does

Our website has 4 main functions:

1. offer immediate tech support through search options to narrow your problem and video call and chat with our volunteers 24/7,
2. provide free live and recorded educational workshops to the general public,
3. become a volunteer and
4. make a donation.

## How we built it

Prototyping:
Figma

Website:

* React web app
* Node.js
* JavaScript, CSS, HTML
* Google Calendar API
* Goggle Auth2 User login
* Google Speech to text API that utilizes ML
* Twilio API
* Heroku Deployment
* Domain.com for a custom domain

## Challenges we ran into

The biggest challenge was that most of our teammates haven’t used the technologies such as React, Twilio and Google Authorization before, so we had to learn these as we used them. As expected, some errors also came up when running the code, which we discussed and worked through together to solve. Another issue that came up was with typescript files interfering with the javascript files, but this was resolved by removing the typescript.

## Accomplishments that we're proud of

* Built a mostly functional website!
* Learned React and Twilio
* Utilized features of Google Calendar, Google Speech to text and Twilio video call and chat

## What we learned

* How to build a website
* New Technologies: React, Twilio, Google technologies API
* How to merge conflicts with Git

## What's next for CanYouHelpWithMy.Tech

* Text-to-speech to aid those with vision issues
* Continue fixing bugs
* Optimize/update UI
* Recruit volunteers
* Advertising
## Inspiration

The inspiration came from the fact that modern democracy is flawed. Unfortunately, government lobbying, voter discrimination, misdirection, and undelivered promises are commonplace in democracies worldwide. This led to declining voter trust and less participation by the public during key moments that can seriously affect the power of the people over time.

## What it does

Our online web application allows the people's vote to matter once again. It is a platform used for voting on different subjects where the choices are carefully analyzed by experts before being sent to the voters. When an organizer creates a poll, the options are sent to specific experts who are not only experts in the field of that poll's subject but are also of a diverse group to minimize biases. Then, the experts will have the opportunity to commend the organizer's options, stating each choice's pros and cons. After the analysis, the poll will be sent to a representative pool of people that will rank the options using the comment of the experts. This will allow the average voter to understand the different stakes at play better and make a more intelligent decision. The organizer can then check the votes and see the winning side.

## How we built it

In the front end, we mostly used React-Bootstrap for the UI. We created multiple pages for the organizers, experts, and voters.
In the back end, we used firebase to store the vote data and the experts' advice. It is also connected to a web scrapper that searches for the most relevant experts and voters. That web scrapper uses the Twilio API to text people to make commenting/voting more user-friendly and intuitive.

## Challenges we ran into

We ran into a lot of UI issues using React-Bootstrap, especially when it came to connecting the front end with the database. We had to deal with multiple cases where the UI wouldn't load because of a single error between the UI and firebase.

## Accomplishments that we're proud of

The web scrapper connected to the Twilio API is one of the accomplishments we are the proudest of. It runs fast and has excellent interconnections with the Twilio API, allowing it to send dozens of participants within seconds.

## What we learned

We learned that being a perfectionist can be a hindrance during a hackathon. As we work increasingly on the front end, some of us debated the look of our web application. Although the discussion refined our UI, it also wasted valuable time that could have been used to implement more functionalities.

## What's next for Time2Vote

We will later implement different algorithms to count votes and add proper authentication to our system.
## Inspiration

COVID revolutionized the way we communicate and work by normalizing remoteness. **It also created a massive emotional drought -** we weren't made to empathize with each other through screens. As video conferencing turns into the new normal, marketers and managers continue to struggle with remote communication's lack of nuance and engagement, and those with sensory impairments likely have it even worse.

Given our team's experience with AI/ML, we wanted to leverage data to bridge this gap. Beginning with the idea to use computer vision to help sensory impaired users detect emotion, we generalized our use-case to emotional analytics and real-time emotion identification for video conferencing in general.

## What it does

In MVP form, empath.ly is a video conferencing web application with integrated emotional analytics and real-time emotion identification. During a video call, we analyze the emotions of each user sixty times a second through their camera feed.

After the call, a dashboard is available which displays emotional data and data-derived insights such as the most common emotions throughout the call, changes in emotions over time, and notable contrasts or sudden changes in emotion

**Update as of 7.15am: We've also implemented an accessibility feature which colors the screen based on the emotions detected, to aid people with learning disabilities/emotion recognition difficulties.**

**Update as of 7.36am: We've implemented another accessibility feature which uses text-to-speech to report emotions to users with visual impairments.**

## How we built it

Our backend is powered by Tensorflow, Keras and OpenCV. We use an ML model to detect the emotions of each user, sixty times a second. Each detection is stored in an array, and these are collectively stored in an array of arrays to be displayed later on the analytics dashboard. On the frontend, we built the video conferencing app using React and Agora SDK, and imported the ML model using Tensorflow.JS.

## Challenges we ran into

Initially, we attempted to train our own facial recognition model on 10-13k datapoints from Kaggle - the maximum load our laptops could handle. However, the results weren't too accurate, and we ran into issues integrating this with the frontend later on. The model's still available on our repository, and with access to a PC, we're confident we would have been able to use it.

## Accomplishments that we're proud of

We overcame our ML roadblock and managed to produce a fully-functioning, well-designed web app with a solid business case for B2B data analytics and B2C accessibility.

And our two last minute accessibility add-ons!

## What we learned

It's okay - and, in fact, in the spirit of hacking - to innovate and leverage on pre-existing builds. After our initial ML model failed, we found and utilized a pretrained model which proved to be more accurate and effective.

Inspiring users' trust and choosing a target market receptive to AI-based analytics is also important - that's why our go-to-market will focus on tech companies that rely on remote work and are staffed by younger employees.

## What's next for empath.ly

From short-term to long-term stretch goals:

* We want to add on AssemblyAI's NLP model to deliver better insights. For example, the dashboard could highlight times when a certain sentiment in speech triggered a visual emotional reaction from the audience.
* We want to port this to mobile and enable camera feed functionality, so those with visual impairments or other difficulties recognizing emotions can rely on our live detection for in-person interactions.
* We want to apply our project to the metaverse by allowing users' avatars to emote based on emotions detected from the user.