## Inspiration

After observing different hardware options, the dust sensor was especially outstanding in its versatility and struck us as exotic. Dust-particulates in our breaths are an ever present threat that is too often overlooked and the importance of raising awareness for this issue became apparent. But retaining interest in an elusive topic would require an innovative form of expression, which left us stumped. After much deliberation, we realized that many of us had a subconscious recognition for pets, and their demanding needs. Applying this concept, Pollute-A-Pet reaches a difficult topic with care and concern.

## What it does

Pollute-A-Pet tracks the particulates in a person's breaths and records them in the behavior of adorable online pets. With a variety of pets, your concern may grow seeing the suffering that polluted air causes them, no matter your taste in companions.

## How we built it

Beginning in two groups, a portion of us focused on connecting the dust sensor using Arduino and using python to connect Arduino using Bluetooth to Firebase, and then reading and updating Firebase from our website using javascript. Our other group first created gifs of our companions in Blender and Adobe before creating the website with HTML and data-controlled behaviors, using javascript, that dictated the pets’ actions.

## Challenges we ran into

The Dust-Sensor was a novel experience for us, and the specifications for it were being researched before any work began. Firebase communication also became stubborn throughout development, as javascript was counterintuitive to object-oriented languages most of us were used to. Not only was animating more tedious than expected, transparent gifs are also incredibly difficult to make through Blender. In the final moments, our team also ran into problems uploading our videos, narrowly avoiding disaster.

## Accomplishments that we're proud of

All the animations of the virtual pets we made were hand-drawn over the course of the competition. This was also our first time working with the feather esp32 v2, and we are proud of overcoming the initial difficulties we had with the hardware.

## What we learned

While we had previous experience with Arduino, we had not previously known how to use a feather esp32 v2. We also used skills we had only learned in beginner courses with detailed instructions, so while we may not have “learned” these things during the hackathon, this was the first time we had to do these things in a practical setting.

## What's next for Dustables

When it comes to convincing people to use a product such as this, it must be designed to be both visually appealing and not physically cumbersome. This cannot be said for our prototype for the hardware element of our project, which focused completely on functionality. Making this more user-friendly would be a top priority for team Dustables. We also have improvements to functionality that we could make, such as using Wi-Fi instead of Bluetooth for the sensors, which would allow the user greater freedom in using the device. Finally, more pets and different types of sensors would allow for more comprehensive readings and an enhanced user experience.
## Inspiration

snore or get pourd on yo pores
Coming into grade 12, the decision of going to a hackathon at this time was super ambitious. We knew coming to this hackathon we needed to be full focus 24/7. Problem being, we both procrastinate and push things to the last minute, so in doing so we created a project to help us

## What it does

It's a 3 stage project which has 3 phases to get the our attention. In the first stage we use a voice command and text message to get our own attention. If I'm still distracted, we commence into stage two where it sends a more serious voice command and then a phone call to my phone as I'm probably on my phone. If I decide to ignore the phone call, the project gets serious and commences the final stage where we bring out the big guns. When you ignore all 3 stages, we send a command that triggers the water gun and shoots the distracted victim which is myself, If I try to resist and run away the water gun automatically tracks me and shoots me wherever I go.

## How we built it

We built it using fully recyclable materials as the future innovators of tomorrow, our number one priority is the environment. We made our foundation fully off of trash cardboard, chopsticks, and hot glue. The turret was built using our hardware kit we brought from home and used 3 servos mounted on stilts to hold the water gun in the air. We have a software portion where we hacked a MindFlex to read off brainwaves to activate a water gun trigger. We used a string mechanism to activate the trigger and OpenCV to track the user's face.

## Challenges we ran into

Challenges we ran into was trying to multi-thread the Arduino and Python together. Connecting the MindFlex data with the Arduino was a pain in the ass, we had come up with many different solutions but none of them were efficient. The data was delayed, trying to read and write back and forth and the camera display speed was slowing down due to that, making the tracking worse. We eventually carried through and figured out the solution to it.

## Accomplishments that we're proud of

Accomplishments we are proud of is our engineering capabilities of creating a turret using spare scraps. Combining both the Arduino and MindFlex was something we've never done before and making it work was such a great feeling. Using Twilio and sending messages and calls is also new to us and such a new concept, but getting familiar with and using its capabilities opened a new door of opportunities for future projects.

## What we learned

We've learned many things from using Twilio and hacking into the MindFlex, we've learned a lot more about electronics and circuitry through this and procrastination. After creating this project, we've learned discipline as we never missed a deadline ever again.

## What's next for You snooze you lose. We dont lose

Coming into this hackathon, we had a lot of ambitious ideas that we had to scrap away due to the lack of materials. Including, a life-size human robot although we concluded with an automatic water gun turret controlled through brain control. We want to expand on this project with using brain signals as it's our first hackathon trying this out
## Inspiration

Did you know the average person spends about $1,800 a year on clothing, often discarding items that contribute to the 92 million tons of textile waste in landfills annually? Enter EcoCloset, the app revolutionizing how we value and recycle our wardrobes. With just a simple photo, our innovative AI-powered solution provides instant clothing valuation and recommendations for reselling, donating, or eco-friendly disposal. We support cultural clothing and gender expression while offering a gamified experience with rewards for sustainable practices. EcoCloset addresses critical issues: only 20% of textiles are collected for reuse or recycling globally, almost 60% of all clothing material is plastic, and clothing waste significantly contributes to air pollution and health risks. Our unique approach, combining multiple databases and AI, fills a gap identified by researchers in 2023. Users love us because we tap into the growing thrifting trend, help save and make money, build a community around sustainable fashion, and offer rewards for eco-friendly choices. Join EcoCloset in closing the loop on fashion waste while fostering a more sustainable and inclusive clothing ecosystem.

## What it does

Like no other app in the industry currently as functional and feature packed, EcoCloset is a sustainability-driven platform that helps users appraise and manage their clothing more effectively while promoting eco-conscious decisions. With EcoCloset, users can:

1. Easily Upload Photos: Users can quickly snap a picture of any garment they want to evaluate, enabling effortless interaction with the platform.
2. AI-Powered Analysis: Our app uses Machine Learning and AI to analyze the garment’s brand, age, condition, and material. Based on this analysis, the app determines whether the clothing should be resold, recycled, or discarded responsibly.
3. Appraisal and Value Estimation: EcoCloset provides users with an estimated resale value for their garments, helping them make informed decisions on whether the item is worth selling or donating.
4. Location-Based Drop-Off Suggestions: The app offers convenient location detection, helping users find nearby thrift stores or donation centers to drop off their clothes, making the process of donating and recycling more accessible.
5. Gamified Experience and Rewards: EcoCloset includes a leaderboard system that tracks user activity and rewards them with coupons for thrift stores as they donate or recycle more clothes, incentivizing sustainable behavior through gamification.
6. Educational Features: The app educates users about the environmental impact of different types of materials, particularly the effects of synthetic fibers on the planet. This encourages users to donate instead of disposing of items, thereby reducing their environmental footprint.
7. By combining convenience, education, and a rewards system, EcoCloset promotes a sustainable lifestyle while helping users declutter and extend the life of their clothing.

## How we built it

We built EcoCloset using two different versions, each tailored to specific user needs and technical requirements.

The primary version uses React and Next.js for a modern, responsive front-end that allows users to interact with the app across devices. On the backend, we use Flask and Node.js to handle real-time API requests and manage data processing efficiently. Our machine learning models for evaluating clothing condition were developed in Python using Sklearn, and deployed using ONNX for optimal performance and fast inference times. We integrated the OpenAI API to provide intelligent recommendations based on the condition of the clothing, and store all user data and assessments in MongoDB. We chose Vercel for seamless, scalable deployment, and version control is handled through GitHub.

In addition to this, we built an alternate version of EcoCloset fully on Streamlit, which uses Python from end-to-end. This version focuses on simplicity and rapid prototyping. It allows users to upload clothing images directly into the Streamlit app, where a pre-trained machine learning model—developed in Sklearn—assesses the item’s resale or donation value. We chose Streamlit for its ability to quickly spin up interactive web apps and deploy Python models directly, giving us the flexibility to iterate on features fast. This alternative version runs entirely in Python, allowing for easier modifications for future development, especially in ML-heavy scenarios.

By having two versions—one built with modern web technologies like React and one with Streamlit for rapid experimentation—we’re able to adapt the app to various user requirements and deployment contexts.

## Challenges we ran into

We faced several significant challenges while developing EcoCloset. One of the biggest obstacles was setting up cron jobs for scheduled tasks like database updates and notifications. We spent a considerable amount of time troubleshooting this issue, as managing time-sensitive tasks in our system was more complex than expected.

Another major hurdle was training our machine learning model. We lacked access to GPUs and had only about 2,000 images available, which wasn’t enough for the scale of model training we originally envisioned. This forced us to look for pre-trained models that could be adapted to our needs, but finding a model that fit our exact specifications was difficult. Eventually, we had to adjust our approach and leverage ONNX to make deployment smoother.

We also encountered problems with Streamlit, our initial framework choice for a simple and quick MVP. Streamlit doesn’t have built-in support for authentication and user logins, which was crucial for our app. This limitation forced us to switch to a Next.js setup for our main version, where we could implement more complex functionality like authentication and user management. Streamlit, though great for rapid prototyping, also had a few other limitations in terms of UI flexibility and scalability, which made us rethink our framework choices.

Integrating the backend with the frontend came with its own set of difficulties. Processing the AI responses from the Flask API into a user-friendly format on the frontend was challenging, and it took several iterations to get the communication between the backend and frontend smooth and efficient.

Additionally, we had a steep learning curve with Figma as we worked on the design of our user interface. While Figma is a powerful tool, understanding how to create responsive and intuitive designs that work well on different platforms took time and effort.

These challenges taught us the importance of flexibility in choosing technologies and helped us improve our problem-solving skills across the stack.

## Accomplishments that we're proud of

We’re incredibly proud of the fact that we built our own machine learning model to analyze clothing conditions rather than taking the easy route and relying solely on pre-built APIs or just wrapping ChatGPT for this task. By developing our own model, we gained far more control over the results and were able to tailor the solution to our specific problem, which gives EcoCloset a unique edge. This decision also allowed us to better understand the complexities of evaluating clothing for resale, donation, or recycling.

Another accomplishment we’re excited about is the development of two fully functional versions of EcoCloset: one built using Next.js for a more robust user experience and another using Streamlit for rapid prototyping and experimentation. Having both versions gives us flexibility and allows us to adapt the solution to different user needs or project requirements.

But perhaps our biggest point of pride is that we are solving a real-world sustainability problem that hasn't been addressed before at this scale. By creating a platform that helps reduce clothing waste and promotes eco-friendly practices, we’re tackling an issue with significant environmental impact. The fact that we’re contributing to a solution that encourages more sustainable fashion consumption and waste management is something we’re extremely proud of.

## What we learned

One of the biggest takeaways from this project is that cron jobs are a pain in the bum! Setting up scheduled tasks turned out to be more challenging than expected, and we quickly learned how important it is to have a solid understanding of scheduling tasks for backend operations.

We also learned that training a machine learning model from scratch is no easy feat, especially when you don’t have access to GPUs or a large dataset. Finding a pre-trained model or dataset that fits your needs can also be a huge challenge. It was eye-opening to realize how much work goes into developing models for niche applications like ours, and this experience has deepened our respect for those who specialize in AI and machine learning.

## What's next for EcoCloset

Meta RayBans Intergration for easy acess without pulling out your phone (Kinda works but not fully as no SDK)

Looking ahead, there’s a lot of potential for EcoCloset to grow and make an even greater impact. One key goal is expanding our international outreach. We want to bring EcoCloset’s sustainable mission to a global audience, ensuring that users from all over the world can engage with the platform and reduce their fashion waste. We also plan to enhance the app’s diversity aspect by expanding support for more cultural clothing donations and promoting inclusivity in fashion.

Partnering with more companies that prioritize sustainability is a huge next step for us. We aim to promote eco-friendly products and offer users more coupons and rewards for making sustainable choices. We’re also committed to providing better value for users by constantly refining the platform’s features and incentives.

In terms of technology, we’re focused on training a better, more accurate model that can provide users with precise estimates of what they can get for their clothing. The goal is to ensure that if the app quotes a resale value, users can walk into a store and reliably receive that amount. This will strengthen the app’s trustworthiness and provide real-world benefits to users.

Adobe Express Add-On for ECommerce+Marketing.
At EcoCloset, our goal is to create a vibrant e-commerce marketplace and community for thrifted and gently used clothing. This will allow users to appraise, sell, showcase, and share their sustainable fashion items within a connected community.

We plan to integrate Adobe Express as an add-on, enabling users to create e-commerce content directly from their uploaded clothing photos. After receiving an item analysis in the EcoCloset app, users can seamlessly export their images to Adobe Express to design custom marketing materials such as ads, social media posts, or product listings. Using stickers, icons, data visualization, and pre-built templates, users can create engaging, professional content to promote their items.

As part of this hackathon theme we have plans to develop a prototype add-on for Adobe Express. This would allow users to generate e-commerce assets with just a few clicks, featuring customizable templates, branding options, and tools for sustainable fashion marketing.

Through this integration, EcoCloset will foster a community-driven marketplace, where users can buy, sell, and showcase their thrifted fashion items, promoting both sustainability and creativity.