## Inspiration

E-cigarette use, specifically Juuling, has become an increasing public health concern among young adults and teenagers over the past few years. While e-cigarettes are often viewed as a safe alternative to traditional tobacco cigarettes, e-cigarettes have been proven to have negative health effects on both the user and second-hand smokers, as shown in multiple CDC and Surgeon General reviewed studies. E-cigarettes also still contain the active ingredient nicotine, which is a well know addictive drug. Yet, students across the United States on high school and college campuses continue to vape. For us, high school students, it is common sight to see classmates skipping class and “Juul-ing” in the bathroom. The Juul is one of the most popular e-cigarettes as it has a sleek design and looks like a USB drive. This design coupled with the fact that there is no lasting smell or detectable smoke, it makes it easy for users to go undetected in the high school environment. Moreover, this results in students not receiving help for their addition or even realizing they do have an addition.

With an increasing use of e-cigarettes among millennials, there has been a creation of vape culture filled with vape gods preforming vape porn, displayig the artistic style of their smoke creations. Users often post pictures and videos of themself Juuling on social media platforms, specifically Instagram and Facebook. With this in mind, we set out to create a research-based solution that could identify e-cigarette users and deter them from future use, a process school administration have attempted and failed at. Juuly the Bear was created as the mascot leading the war on teenage e-cigarette use.

## What it does

Juuly the Bear is intended to fight the growth of vape culture by creating a counter culture that actively discourages Juuling while informing users of dangers. It does this by using computer vision to analyze the Instagram account of an inputted user. The program flags images it detects to be of a person using an e-cigarette. If more than 40% of the images analyzed are of a person vaping, the user is classified as a “frequent e-cigarette” as defined by a study by Jung Ah Lee (2017), and categorized as high-risk for nicotine addiction. Juuly will then automatically message the high-risk user on Facebook Messenger informing them of their status and suggestions on how to cut down on their Juul use. Juuly will also provide external resources that the user can utilize.

## How I built it

We built Juuly’s computer vision using the Clarify API in Python. First, we trained a machine learning model with images of e-cigarette users actively vaping. We then tested images of other vaping people to evaluate and further train the model until a sufficient accuracy level was reached. Then, we used the library to create a data scraping program for Instagram. When a username is inputed, the program gathers the most recent posts which are then fed into the computer vision program, analyzing the images with the previously trained model. If more than 40% of the images are of vaping, a Facebook Messenger bot automatically messages the user with warnings and resources.

## Challenges I ran into

We ran into many challenges with implementing Juuly the Bear, especially because the technology was initially foreign to us. As high school students, we did not have a huge background in computer vision or machine learning. Initially, we had to completely learn the Clarify API and the Facebook Messenger API. We also had a hard time finding the design and thinking of a way to maximize our outreach. We decided that adding a bit of humor into the design would better resonate with teenagers, the average age at which people Juul.

In addition, we were unable to successfully when trying to combine the backend Juuly program with our frontend. We initially wanted to create a fully functional website where one can enter Instagram and Facebook profiles to analyze, but when we had both the front and back ends completed, we had a hard time seamlessly integrating the two. In the end, we had to scrap the front-end in favor of a more functional backend.

## Accomplishments that I'm proud of

As a group of high school students, we were able to use many new tools that we had never encountered before. The tools described above were extremely new to us before the hackathon, however, by working with various mentors and continually striving to learn these tools, we were able to create a successful program.

The most successful part of the project was creating a powerful backend that was able to detect people Juuling. By training a machine learning model with the Clarify API, we were able to reaching over a 80% accuracy rate for the set of images we had, while initially we had barely any knowledge in machine learning.

Another very successful part was our scraping program. This was completely new to us and we were able to create a program that perfectly fit our application. Scraping was also a very powerful tool, and by learning how to scrape social media pages, we had a lot more data than we wouldn’t have had otherwise.

## What's next for Juuly the Bear

Our immediate next step would be combining our already designed front end website with our backend. We spent a lot of time trying to understand how to do this successfully, but we ultimately just ran out of time.
In the future, we would optimally partner up with major social media organizations including Facebook and Twitter to create a large scale implementation of Juuly. This will have a much larger impact on vape culture as people are able to become more informed. This can have major impacts on public health, adolescent behavior/culture, and also increase the quality of life of all as the number of vapers are reduced.
## Inspiration

The majority of cleaning products in the United States contain harmful chemicals. Although many products pass EPA regulations, it is well known that many products still contain chemicals that can cause rashes, asthma, allergic reactions, and even cancer. It is important that the public has easy access to information of the chemicals that may be harmful to them as well as the environment.

## What it does

Our app allows users to scan a product's ingredient label and retrieves information on which ingredients to avoid for the better of the environment as well as their own health.

## How we built it

We used Xcode and Swift to design the iOS app. We then used Vision for iOS to detect text based off a still image. We used a Python scrapper to collect data from ewg.org, providing the product's ingredients as well as the side affects of certain harmful additives.

## Challenges we ran into

We had very limited experience for the idea that we have in developing an iOS app but, we wanted to challenge ourselves. The challenges the front end had were incorporating the camera feature and the text detector into a single app. As well as trying to navigate the changes between the newer version of Swift 11 from older versions. For our backend members, they had difficulties incorporating databases from Microsoft/Google, but ended up using JSON.

## Accomplishments that we're proud of

We are extremely proud of pushing ourselves to do something we haven't done before. Initially, we had some doubt in our project because of how difficult it was. But, as a team we were able to help each other along the way. We're very proud of creating a single app that can do both a camera feature and Optical Character Recognition because as we found out, it's very complicated and error prone. Additionally, for data scrapping, even though the HTML code was not consistent we managed to successfully scrap the necessary data by taking all corner cases into consideration with 100% success rate from more than three thousand HTML files and we are very proud of it.

## What we learned

Our teammates working in the front end learned how to use Xcode and Swift in under 24 hours. Our backend team members learned how to scrap the data from a website for the first time as well. Together, we learned how to alter our original expectations of our final product based on time constraint.

## What's next for Ingredient Label Scanner

Currently our project is specific to cleaning products, however in the future we would like to incorporate other products such as cosmetics, hair care, skin care, medicines, and food products. Additionally, we hope to alter the list of ingredients in a more visual way so that users can clearly understand which ingredients are more dangerous than others.
## Inspiration

Irresponsible substance use causes fatalities due to accidents and related incidents. Driving under the influence is one of the top 5 leading causes of death in young adults. 37 people die every day from a DUI-related accident--that's one person every 38 minutes. We aspire to build a convenient and accessible app that people can use to accurately determine whether they are in good condition to drive.

## What it does

BACScanner is a computer vision mobile app that compares iris–pupil ratio before and after substance intake for safer usage.

## How we built it

We built the mobile app with SwiftUI and the CV model using Pytorch and OpenCV. Our machine learning model was linked to the frontend by deploying a Flask API.

## Challenges we ran into

We were originally hoping to be able to figure out your sobriety based on one video of your eyes. However, found that it was fundamental to take a sober image as a control image to compare to and we had to amend our app to support taking a "before" image and an "after" image, comparing the two.

## Accomplishments that we're proud of

We implemented eye tracking and the segmentation neural network with 92% accuracy. We also made an elegant UI for the mobile app.

## What we learned

We learned about building full-stack apps that involve ML. Prior to this, we didn't know how to attach an ML model to a frontend app. We thus learned how to deploy our ML model to an API and link it to our front end using Flask.

## What's next for BACScanner

We hope to be able to add better recognition for narcotic usage, as right now our app can only accurately detect BAC.