## Inspiration

We recognized that a large time spent coding was spent troubleshooting and searching for answers on Stack Overflow. To counter this we made a library that, after being imported, gives the user access to Stack overflow without interuppting their code experience.

## What it does

Stack Workflow is a Python library that allows the user to interactively querry Stack overflow for answers without leaving their IDE. This streamlines the coding process and saves users time. After picking an answer, the user sees green text output to their screen and code in red that they can then copy and paste if they find it helpful.

## How we built it

We built this library in python, using beautiful-soup and requests libraries to scrape websites.

## Challenges we ran into

We planned initially to implement this as a Sublime Text 3 plugin. However, we were unable to do so. We then shoifted focus to building a library and in the end, made a more widely accessible program than if it were only a plugoin for Sublime Text 3.

## Accomplishments that we're proud of

We are proud of being able to produce a working program wiothin 24 hours, particularly with having to change focus halway through the project. We are also proud of creating a program that serves a real purpose and helps people.

## What's next for Stack Workflow

We will continue to work on Stack Workflow to make it run better and give it more features that are useful for programmers.
## Inspiration

Our inspiration for this project came from the issue we had in classrooms where many students would ask the same questions in slightly different ways, causing the teacher to use up valuable time addressing these questions instead of more pertinent and different ones.
Also, we felt that the bag of words embedding used to vectorize sentences does not make use of the sentence characteristics optimally, so we decided to create our structure in order to represent a sentence more efficiently.

## Overview

Our application allows students to submit questions onto a website which then determines whether this question is either:

1. The same as another question that was previously asked
2. The same topic as another question that was previously asked
3. A different topic entirely

The application does this by using the model proposed by the paper "Bilateral Multi-Perspective Matching for Natural Language Sentences" by Zhiguo et. al, with a new word structure input which we call "sentence tree" instead of a bag-of-words that outputs a prediction of whether the new question asked falls into one of the above 3 categories.

## Methodology

We built this project by splitting the task into multiple subtasks which could be done in parallel. Two team members worked on the web app while the other two worked on the machine learning model in order to our expertise efficiently and optimally. In terms of the model aspect, we split the task into getting the paper's code work and implementing our own word representation which we then combined into a single model.

## Challenges

Majorly, modifying the approach presented in the paper to suit our needs was challenging. On the web development side, we could not integrate the model in the web app easily as envisioned since we had customized our model.

## Accomplishments

We are proud that we were able to get accuracy close to the ones provided by the paper and for developing our own representation of a sentence apart from the classical bag of words approach.
Furthermore, we are excited to have created a novel system that eases the pain of classroom instructors a great deal.

## Takeaways

We learned how to implement research papers and improve on the results from these papers. Not only that, we learned more about how to use Tensorflow to create NLP applications and the differences between Tensorflow 1 and 2.
Going further, we also learned how to use the Stanford CoreNLP toolkit. We also learned more about web app design and how to connect a machine learning backend in order to run scripts from user input.

## What's next for AskMe.AI

We plan on finetuning the model to improve its accuracy and to also allow for questions that are multi sentence. Not only that, we plan to streamline our approach so that the tree sentence structure could be seamlessly integrated with other NLP models to replace bag of words and to also fully integrate the website with the backend.
## Inspiration

Determined to create a project that was able to make impactful change, we sat and discussed together as a group our own lived experiences, thoughts, and opinions. We quickly realized the way that the lack of thorough sexual education in our adolescence greatly impacted each of us as we made the transition to university. Furthermore, we began to really see how this kind of information wasn't readily available to female-identifying individuals (and others who would benefit from this information) in an accessible and digestible manner. We chose to name our idea 'Illuminate' as we are bringing light to a very important topic that has been in the dark for so long.

## What it does

This application is a safe space for women (and others who would benefit from this information) to learn more about themselves and their health regarding their sexuality and relationships. It covers everything from menstruation to contraceptives to consent. The app also includes a space for women to ask questions, find which products are best for them and their lifestyles, and a way to find their local sexual health clinics. Not only does this application shed light on a taboo subject but empowers individuals to make smart decisions regarding their bodies.

## How we built it

Illuminate was built using Flutter as our mobile framework in order to be able to support iOS and Android.
We learned the fundamentals of the dart language to fully take advantage of Flutter's fast development and created a functioning prototype of our application.

## Challenges we ran into

As individuals who have never used either Flutter or Android Studio, the learning curve was quite steep. We were unable to even create anything for a long time as we struggled quite a bit with the basics. However, with lots of time, research, and learning, we quickly built up our skills and were able to carry out the rest of our project.

## Accomplishments that we're proud of

In all honesty, we are so proud of ourselves for being able to learn as much as we did about Flutter in the time that we had. We really came together as a team and created something we are all genuinely really proud of. This will definitely be the first of many stepping stones in what Illuminate will do!

## What we learned

Despite this being our first time, by the end of all of this we learned how to successfully use Android Studio, Flutter, and how to create a mobile application!

## What's next for Illuminate

In the future, we hope to add an interactive map component that will be able to show users where their local sexual health clinics are using a GPS system.