## Inspiration

As we all know the world has come to a halt in the last couple of years. Our motive behind this project was to help people come out of their shells and express themselves. Connecting various people around the world and making them feel that they are not the only ones fighting this battle was our main objective.

## What it does

People share their thought processes through speaking and our application identifies the problem the speaker is facing and connects him to a specialist in that particular domain so that his/her problem could be resolved. There is also a **Group Chat** option available where people facing similar issues can discuss their problems among themselves.
For example, if our application identifies that the topics spoken by the speaker are related to mental health, then it connects them to a specialist in the mental health field and also the user has an option to get into a group discussion which contains people who are also discussing mental health.

## How we built it

The front-end of the project was built by using HTML, CSS, Javascript, and Bootstrap. The back-end part was exclusively written in python and developed using the Django framework. We integrated the **Assembly AI** file created by using assembly ai functions to our back-end and were successful in creating a fully functional web application within 36 hours.

## Challenges we ran into

The first challenge was to understand the working of Assembly AI. None of us had used it before and it took us time to first understand it's working. Integrating the audio part into our application was also a major challenge. Apart from Assembly AI, we also faced issues while connecting our front-end to the back-end. Thanks to the internet and the mentors of **HackHarvard**, especially **Assembly AI Mentors** who were very supportive and helped us resolve our errors.

## Accomplishments that we're proud of

Firstly, we are proud of creating a fully functional application within 36 hours taking into consideration all the setbacks we had. We are also proud of building an application from which society can be benefitted. Finally and mainly we are proud of exploring and learning new things which is the very reason for hackathons.

## What we learned

We learned how working as a team can do wonders. Working under a time constraint can be a really challenging task, aspects such as time management, working under pressure, the never give up attitude and finally solving errors which we never came across are some of the few but very important things which we were successful in learning.
## Inspiration

As software engineers, we constantly seek ways to optimize efficiency and productivity. While we thrive on tackling challenging problems, sometimes we need assistance or a nudge to remember that support is available. Our app assists engineers by monitoring their states and employs Machine Learning to predict their efficiency in resolving issues.

## What it does

Our app leverages LLMs to predict the complexity of GitHub issues based on their title, description, and the stress level of the assigned software engineer. To gauge the stress level, we utilize a machine learning model that examines the developer’s sleep patterns, sourced from TerraAPI. The app provides task completion time estimates and periodically checks in with the developer, suggesting when to seek help. All this is integrated into a visually appealing and responsive front-end that fits effortlessly into a developer's routine.

## How we built it

A range of technologies power our app. The front-end is crafted with Electron and ReactJS, offering compatibility across numerous operating systems. On the backend, we harness the potential of webhooks, Terra API, ChatGPT API, Scikit-learn, Flask, NodeJS, and ExpressJS. The core programming languages deployed include JavaScript, Python, HTML, and CSS.

## Challenges we ran into

Constructing the app was a blend of excitement and hurdles due to the multifaceted issues at hand. Setting up multiple webhooks was essential for real-time model updates, as they depend on current data such as fresh Github issues and health metrics from wearables. Additionally, we ventured into sourcing datasets and crafting machine learning models for predicting an engineer's stress levels and employed natural language processing for issue resolution time estimates.

## Accomplishments that we're proud of

In our journey, we scripted close to 15,000 lines of code and overcame numerous challenges. Our preliminary vision had the front end majorly scripted in JavaScript, HTML, and CSS — a considerable endeavor in contemporary development. The pinnacle of our pride is the realization of our app, all achieved within a 3-day hackathon.

## What we learned

Our team was unfamiliar to one another before the hackathon. Yet, our decision to trust each other paid off as everyone contributed valiantly. We honed our skills in task delegation among the four engineers and encountered and overcame issues previously uncharted for us, like running multiple webhooks and integrating a desktop application with an array of server-side technologies.

## What's next for TBox 16 Pro Max (titanium purple)

The future brims with potential for this project. Our aspirations include introducing real-time stress management using intricate time-series models. User customization options are also on the horizon to enrich our time predictions. And certainly, front-end personalizations, like dark mode and themes, are part of our roadmap.
## 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.