## 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

There are variety of factors that contribute to *mental health* and *wellbeing*. For many students, the stresses of remote learning have taken a toll on their overall sense of peace. Our group created **Balance Pad** as a way to serve these needs. Thus, Balance Pads landing page gives users access to various features that aim to improve their wellbeing.

# What it does

Balance Pad is a web-based application that gives users access to **several resources** relating to mental health, education, and productivity. Its initial landing page is a dashboard tying everything together to make a clear and cohesive user experience.

### Professional Help

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> 1. *Chat Pad:* The first subpage of the application has a built in *Chatbot* offering direct access to a **mental heath professional** for instant messaging.
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### Productivity

>
> 1. *Class Pad:* With the use of the Assembly API, users can convert live lecture content into text based notes. This feature will allow students to focus on live lectures without the stress of taking notes. Additionally, this text to speech aide will increase accessibility for those requiring note takers.
> 2. *Work Pad:* Timed working sessions using the Pomodoro technique and notification restriction are also available on our webpage. The Pomodoro technique is a proven method to enhance focus on productivity and will benefit students
> 3. *To Do Pad:* Helps users stay organized
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>
>

### Positivity and Rest

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> 1. *Affirmation Pad:* Users can upload their accomplishments throughout their working sessions. Congratulatory texts and positive affirmations will be sent to the provided mobile number during break sessions!
> 2. *Relaxation Pad:* Offers options to entertain students while resting from studying. Users are given a range of games to play with and streaming options for fun videos!
>
>
>

### Information and Education

>
> 1. *Information Pad:* is dedicated to info about all things mental health
> 2. *Quiz Pad:* This subpage tests what users know about mental health. By taking the quiz, users gain valuable insight into how they are and information on how to improve their mental health, wellbeing, and productivity.
>
>
>

# How we built it

**React:** Balance Pad was built using React. This allowed for us to easily combine the different webpages we each worked on.

**JavaScript, HTML, and CSS:** React builds on these languages so it was necessary to gain familiarity with them

**Assembly API:** The assembly API was used to convert live audio/video into text

**Twilio:** This was used to send instant messages to users based on tracked accomplishments

# Challenges we ran into

>
> * Launching new apps with React via Visual Studio Code
> * Using Axios to run API calls
> * Displaying JSON information
> * Domain hosting of Class Pad
> * Working with Twilio
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>
>

# Accomplishments that we're proud of

*Pranati:* I am proud that I was able to learn React from scratch, work with new tech such as Axios, and successfully use the Assembly API to create the Class Pad (something I am passionate about). I was able to persevere through errors and build a working product that is impactful. This is my first hackathon and I am glad I had so much fun.

*Simi:* This was my first time using React, Node.js, and Visual Studio. I don't have a lot of CS experience so the learning curve was steep but rewarding!

*Amitesh:* Got to work with a team to bring a complicated idea to life!

# What we learned

*Amitesh:* Troubleshooting domain creation for various pages, supporting teammates and teaching concepts

*Pranati:* I learned how to use new tech such as React, new concepts such API calls using Axios, how to debug efficiently, and how to work and collaborate in a team

*Simi:* I learned how APIs work, basic html, and how React modularizes code. Also learned the value of hackathons as this was my first

# What's next for Balance Pad

*Visualizing Music:* Our group hopes to integrate BeatCaps software to our page in the future. This would allow a more interactive music experience for users and also allow hearing impaired individuals to experience music
*Real Time Transcription:* Our group hopes to implement in real time transcription in the Class Pad to make it even easier for students.
Shashank Ojha, Sabrina Button, Abdellah Ghassel, Joshua Gonzales

#



![](https://drive.google.com/uc?export=view&id=1admSn1s1K2eioqtCisLGZD4zXZjGWsy8)



"Reduce Reuse Recoin"


## Theme Covered:

The themes covered in this project include post pandemic restoration for both the environment, small buisnesses, and personal finance! The app pitched uses an extensivly trained AI system to detect trash and sort it to the proper bin from your smartphone. While using the app, users will be incentivized to use the app and recover the environment through the opportunity to earn points, which will be redeemable in partnering stores.

## Problem Statment:

As our actions continue to damage the environment, it is important that we invest in solutions that help restore our community in more sustainable practices. Moreover, an average person creates over 4 pounds of trash a day, and the EPA has found that over 75% of the waste we create are recyclable. As garbage sorting is so niche from town-to-town, students have reportable agreed to the difficulty of accurately sorting garbage, thus causing this significant misplacement of garbage.

Our passion to make our community globally and locally more sustainable has fueled us to use artificial intelligence to develop an app that not only makes sorting garbage as easy as using Snapchat, but also rewards individuals for sorting their garbage properly.

For this reason, we would like to introduce Recoin. This intuitive app allows a person to scan any product and easily find the bin that the trash belongs based off their location. Furthermore, if they attempt to sell their product, or use our app, they will earn points which will be redeemable in partnering stores that advocate for the environment. The more the user uses the app, the more points they receive, resulting in better items to redeem in stores. With this app we will not only help recover the environment, but also increase sales in small businesses which struggled during the pandemic to recover.

## About the App:

### Incentive Breakdown:

![](https://drive.google.com/uc?export=view&id=1CU2JkOJqplaTxNo7B8s_UXN_UaEydsd3)

Please note that these expenses are estimated expectations for potential benefit packages but are not defined yet.

We are proposing a $1 discount for participating small businesses when 100 coffee/drink cups are returned to participating restaurants. This will be easy for small companies to uphold financially, while providing a motivation for individuals to use our scanner.

Amazon costs around $0.5 to $2 on packaging, so we are proposing that Amazon provides a $15 gift card per 100 packages returned to Amazon. As the 100 packages can cost from $50 to $200, this incentive will save Amazon resources by 5 to 100 times the amount, while providing positive public perception for reusing.

As recycling plastic for 3D filament is an up-and-coming technology that can revolutionize environment sustainability, we would like to create a system where providing materials for such causes can give the individuals benefits.

Lastly, as metals become more valuable, we hope to provide recyclable metals to companies to reduce their expenses through our platform.

The next steps to this endeavor will be to provide benefits for individuals that provide batteries and electronics with some sort of incentive as well.

## User Interface:

![](https://drive.google.com/uc?export=view&id=1QR2fNvrkpB7q_PAI_5iZfL3M7i9nV6m5)

## Technological Specifics and Next Steps:

![](https://drive.google.com/uc?export=view&id=1vlfSjhtg-_JZZzVITaRMiOsXC-9TwyNS)

### Frontend

![](https://drive.google.com/uc?export=view&id=1TSAcKAPLFtZdJrn8OZVRfkBvS_29Y8Dk)

We used to React.JS to develop components for the webcam footage and capture screen shots. It was also utilized to create the rest of the overall UI design.

### Backend

#### Waste Detection AI:

![](https://drive.google.com/uc?export=view&id=1Fx8uAW3I_OntNn74aZXYQbThnfPkZs56)
On Pytorch, we utilized an open-source trash detection AI software and data, to train the trash detection system originally developed by IamAbhinav03. The system utilizes over 2500 images to train, test, and validate the system. To improve the system, we increased the number of epochs to 8 rather than 5 (number of passes the training system has completed). This allowed the accuracy to increase by 4% more than the original system. We also modified the test/train ratio and split amounts to 70%, 10%, and 20% respectively, as more prominent AI studies have found this distribution to receive the best results.

Currently, the system is predicted to have a 94% accuracy, but in the future, we plan on using reinforcement learning in our beta testing to continuously improve our algorithm. Reinforcement learning allows for the data to be more accurate, through learning from user correction. This will allow AI to become more precise as it gains more popularity.

A flask server is used to make contact with the waste detection neural network; an image is sent from the front end as a post request, the Flask server generates a tensor and runs that through the neural net, then sends the response from the algorithm back to the front end. This response is the classification of the waste as either cardboard, glass, plastic, metal, paper or trash.

#### Possible next steps:

By using Matbox API and the Google Suite/API, we will be creating maps to find recycling locations and an extensively thorough Recoin currency system that can easily be transferred to real time money for consumers and businesses (as shown in the user interface above).

## Stakeholders:

After the completion of this project, we intend to continue to pursue the app to improve our communities’ sustainability. After looking at the demographic of interest in our school itself, we know that students will be interested in this app, not only from convenience but also through the reward system. Local cafes and Starbucks already have initiatives to improve public perspective and support the environment (i.e., using paper straws and cups), therefore supporting this new endeavor will be an interest to them. As branding is everything in a business, having a positive public perspective will increase sales.

![](https://drive.google.com/uc?export=view&id=189sA5C0KDT8VIaRdD6jQQtnN32qO87h8)

## Amazon:

As Amazon continues to be the leading online marketplace, more packages will continue to be made, which can be detrimental to the world's limited resources. We will be training the UI to track packages that are Amazon based. With such training, we would like to be able to implement a system where the packaging can be sent back to Amazon to be reused for credit. This will allow Amazon to form a more environmentally friendly corporate image, while also saving on resources.

## Small Businesses:
As the pandemic has caused a significant decline in small business revenue, we intend to mainly partner with small businesses in this project. The software will also help increase small business sales as by supporting the app, students will be more inclined to go to their store due to a positive public image, and the additive discounts will attract more customers. In the future, we wish to train AI to also detect trash of value (i.e.. Broken smartphones, precious metals), so that consumers can sell it in a bundle to local companies that can benefit from the material (ex: 3D-printing companies that convert used plastic to filament)

## Timeline:
The following timeline will be used to ensure that our project will be on the market as soon as possible:

![](https://drive.google.com/uc?export=view&id=1jA6at4g31KpTOJdwna-10UwP_U-z4JUp)

## About the Team:

We are first and second year students from Queen's University who are very passionate about sustainbility and designing of innovative solutions to modern day problems. We all have the mindset to give any task our all and obtain the best results. We have a diverse skillset in the team and throughout the hackathon we utlized it to work efficienty. We are first time hackathoners, so even though we all had respective expierence in our own fields, this whole expierence was very new and educationally rewarding for us. We would like to thank the organisers and mentors for all thier support and for organizing the event.

## Code References

• <https://medium.datadriveninvestor.com/deploy-your-pytorch-model-to-production-f69460192217>

• <https://narainsreehith.medium.com/upload-image-video-to-flask-backend-from-react-native-app-expo-app-1aac5653d344>

• <https://pytorch.org/tutorials/beginner/saving_loading_models.html>

• <https://pytorch.org/tutorials/intermediate/flask_rest_api_tutorial.html>

• <https://pytorch.org/get-started/locally/>

• <https://www.kdnuggets.com/2019/03/deploy-pytorch-model-production.html>

## References for Information

• <https://www.rubicon.com/blog/trash-reason-statistics-facts/>

• <https://www.dosomething.org/us/facts/11-facts-about-recycling>

• <https://www.forbes.com/sites/forbesagencycouncil/2016/10/31/why-brand-image-matters-more-than-you-think/?sh=6a4b462e10b8>

• <https://www.channelreply.com/blog/view/ebay-amazon-packaging-costs>