## Inspiration

How many times have you went on a website, looked at the length of the content and decided that it was not worth your time to read the entire page? The inspiration for my project comes from this exact problem. Imagine how much easier it would be if website owners could automatically generate a summary of their webpage for their users to consume.

## What it does

Allows the website owner to easily generate a summary of their webpage and place it anywhere on there webpage.

## How I built it

I used CloudFlare so that it would be available to the many website owners very easily as an app. I used javascript to fetch the summary from a 3rd party API and to update the html of the page.

## Challenges I ran into

It was hard trying to find a summarizer that was suitable for my needs. Many of them only accept URLs as input and many are just not good enough. Once I found one that was suitable, I also had difficulty incorporating it within my app.

I had also never done anything with html or css before so it was hard trying to create the view that the user would see.

## Accomplishments that I'm proud of

It works, looks nice, and is easy to use

## What I learned

How to make a CloudFlare app, html, css

## What's next for TL;DR

Add more options for the app (maybe show relevant tweets?) and possibly create a chrome web extension that does that same thing.
## Inspiration

Although each of us came from different backgrounds, we each share similar experiences/challenges during our high school years: it was extremely hard to visualize difficult concepts, much less understand the the various complex interactions. This was most prominent in chemistry, where 3D molecular models were simply nonexistent, and 2D visualizations only served to increase confusion. Sometimes, teachers would use a combination of Styrofoam balls, toothpicks and pens to attempt to demonstrate, yet despite their efforts, there was very little effect. Thus, we decided to make an application which facilitates student comprehension by allowing them to take a picture of troubling text/images and get an interactive 3D augmented reality model.

## What it does

The app is split between two interfaces: one for text visualization, and another for diagram visualization. The app is currently functional solely with Chemistry, but can easily be expanded to other subjects as well.

If the text visualization is chosen, an in-built camera pops up and allows the user to take a picture of the body of text. We used Google's ML-Kit to parse the text on the image into a string, and ran a NLP algorithm (Rapid Automatic Keyword Extraction) to generate a comprehensive flashcard list. Users can click on each flashcard to see an interactive 3D model of the element, zooming and rotating it so it can be seen from every angle. If more information is desired, a Wikipedia tab can be pulled up by swiping upwards.

If diagram visualization is chosen, the camera remains perpetually on for the user to focus on a specific diagram. An augmented reality model will float above the corresponding diagrams, which can be clicked on for further enlargement and interaction.

## How we built it

Android Studio, Unity, Blender, Google ML-Kit

## Challenges we ran into

Developing and integrating 3D Models into the corresponding environments.
Merging the Unity and Android Studio mobile applications into a single cohesive interface.

## What's next for Stud\_Vision

The next step of our mobile application is increasing the database of 3D Models to include a wider variety of keywords. We also aim to be able to integrate with other core scholastic subjects, such as History and Math.
## Inspiration

We were originally considering creating an application that would take a large amount of text and summarize it using natural language processing. As well, Shirley Wang feels an awkward obligation to incorporate IBM's Watson into the project. As a result, we came up with the concept of putting in an image and getting a summary from the Wikipedia article.

## What it does

You can input the url of a picture into the webapp, and it will return a brief summary in bullet point form of the Wikipedia article on the object identified within the picture.

## How we built it

We considered originally using Android Studio, but then a lot of problems occurred trying to make the software work with it, so we switched over to Google App Engine. Then we used Python to build the underlaying logic, along with using IBM's Watson to identify and classify photos, the Wikipedia API to get information from Wikipedia articles, and Google's Natural Language API to get only the key sentences and shorten them down to bullet point form while maintaining its original meaning.

## Challenges we ran into

We spent over 2 hours trying to fix a Google Account Authentication problem, which occurred because we didn't know how to properly write the path to a file, and Pycharm running apps is different from Pycharm running apps in its own terminal. We also spent another 2 hours trying to deploy the app, because Pycharm had a screwed up import statement and requirements file that messed up a lot of it.

## Accomplishments that we're proud of

This is our first hackathon and our first time creating a web app, and we're really happy that we managed to actually successfully create something that works.

## What I learned

Sometimes reading the API carefully will save you over half of your debugging time in the long run.

## What's next for Image Summarizer

Maybe we'll be able to make a way for the users to input a photo directly from their camera or their computer saved photos.