## We got inspired by the lack of motivation household participation in local recycling programs. Many households have the ability to participate in curbside recycling and all have the ability to drop off recycling at a local recycling collection center. We thought if we provide a financial incentive or some form of gamification we could encourage users to recycle . We believe the government will assist us in this as in the long run it makes their jobs easier and costs them less money than large scale clean ups.

## What it does

The app takes pictures of garbage. Recognizes objects which are garbage, classifies it and gives users instructions on how to safely dispose or recycle the garbage and gives rewards for participating. The app also gives users the option of uploading to snapchat with captions and filters showing they recycled.

## How we built it

We built the app by using building an app and integrating various pieces together. We used various API's to help the app run smoothly such as Snapchat and Google Vision and camera kit. When we got all the necessary data we wrote the logic to classify this data with Toronto's waste database and pass back instructions to our users

## Challenges we ran into

## Accomplishments that we're proud of

We used google vision Api for the first time and properly identified objects. We also spent a great deal of time on the snap chat api so we were happy we got that to work.

## What we learned

We learned a lot about android studio and how to develop for android devices, we also significantly improved our debugging skills as android sdk came with a lot of problems , We had to refresh our memory of git and use proper version control as all four of us worked on different components and had to stay synced with each other.

## What's next for Enviro-bin

Partner with government for rewards system and have them also respond to pings from our app to pick up major garbage in location.
## Inspiration

We aimed to build smart cities by helping garbage truck drivers receive the most efficient route in near-real-time, minimizing waste buildup in prone areas. This approach directly addresses issues of inefficiency in traditional waste collection, which often relies on static daily or weekly routes.

You might be wondering, why not just let drivers follow standard routes? In densely populated areas, waste accumulates faster than in less populated zones. This means that a one-size-fits-all route approach leads to inefficient pickups, resulting in garbage buildup that contributes to air and water pollution. By targeting areas where waste accumulates more quickly, we can reduce contamination, improve air quality, and create greener, healthier environments. Additionally, optimizing waste collection can lead to more sustainable use of resources and reduce the strain on landfills.

## What it does

CleanSweep has sensors that collect real-time information about all the trash cans in the city, detecting different waste levels. This data is live-updated in the truck drivers' main portal while they drive, allowing them to receive the most efficient collection routes. These routes are optimized based on real-time data, including the percentage of trash bin capacity filled, traffic conditions between bins, and the number of days since the last pickup. As drivers collect trash, they can update their progress live to receive the next optimized route for the remaining bins.

## How we built it

For our hardware: We used two phone cameras in two similar scenarios that sent photos repeatedly to a computer. This image is then passed onto the Raspberry Pi where Python OpenCV was used to detect the trash level filled. Then a Node.JS script was used to display the levels (1 to 5) on a set of LEDs and then pass the information to a local server for further processing on the software side.

Backend: We received the reported trash level data from the hardware, information about the traffic in the surrounding vicinity as well as distance to the bin from the Google Maps API and Google Distance Matrix API, and pre-modeled values for time since last garbage retrieval and fed this into a Random Forest Classifier model on Databricks. Our model makes predictions on how to prioritize the bin routes in order to get a short path and distance–resulting in fewer emissions. An adjacency matrix was then used to retrieve the highest priority paths based on traffic and waste levels.

Frontend: We used React for the UI and TailwindCSS for styling to create the portal, deployed over Terraform. We brought all of this information together to display the recommended optimal routes based on how full the bins were (which can be changed by adding more trash to bin 1 and 2 in real life).

## Challenges we ran into

Measuring the level of the trash bin was actually a very challenging task for what seemed like a simple task. We first tried looking for pre-trained models that could do it for us, but other models only worked with object tracking. We went to OpenCV, but that only worked in certain conditions. Thus, one of the hardest challenges we experienced was making sure the lighting conditions, the setup for the cameras and hardware, and our OpenCV contour algorithm worked deterministically, as even a shadow could mess with our results.

## Accomplishments that we're proud of

None of us had ever worked with OpenCV or with using hardware at a hackathon. We wanted to take some of the skills we learned in our electrical classes and implement them with a cool solution, even if it was a bit tacky. We were super proud of learning how OpenCV worked and how we could use hardware as well as a network to create a complete interactive solution. Another major component of this hackathon was hardware as we’ve always wanted to use such a concept to visualize our project and make it easier to understand for everyone on what our goals are. We enjoyed our varied skills by stepping out of our comfort zone and using new technologies; each member used a new skill to implement in the project.

## What we learned

All of us learned about the parts of networks and hardware required to communicate including the security standards and communication frequency needed to keep all the data real time. We learned how to build a working website with a login system through the use of react and vite, as well as implementing the Google maps API for visualizing the route for the drivers. We also learned how GPIO works on a raspberry pi and how a simple JS script can control the power output for these pins to control LEDs. Another skill we found interesting was the use of OpenCV for image processing as it made computing the percentage a task of a second rather than a longer time of manual image processing algorithms. Finally, we were able to start using an ML model to train and help us find the best routes for drivers, this resulted in faster travel times as traffic was avoided and garbage collection optimized.

## What's next for Clean Sweep

Clean Sweep’s next goals would be to implement real time sensors in bins spread out all over the city to collect data and help train the model by providing the large amounts of data. This would then help implement this real time UI to efficiently visit all the necessary stops for the day. Such a system would also allow for coverage over rare cases such as a garbage truck missing a stop or allowing other trucks to take over a broken truck’s schedule as the route would update in real time to collect all garbage fast and efficiently.
**Inspiration**
Inspiration came from the growing concern about environmental sustainability and waste management. We were particularly struck by the amount of waste generated by single-use packaging and products from large corporations like PepsiCo. We wanted to find a way to inspire people to think creatively about trash, turning it into something valuable rather than discarding it. The idea of upcycling aligns perfectly with this goal, turning what would be waste into treasures with a new purpose.

**What it does**
TrashToTreasure is a sustainable showcase platform where users can upload pictures of PepsiCo packaging waste and receive creative upcycling ideas from our AI assistant, Gemini. After transforming the waste into something useful or artistic, users can upload pictures of their upcycled products. The community can then vote on the best upcycling transformations, encouraging both creativity and sustainable practices.

**How we built it**
We built TrashToTreasure using a combination of modern web technologies. The frontend is built with React, and we used Next.js for server-side rendering. We integrated Gemini, an AI assistant, to process user-submitted images and suggest upcycling ideas. The backend is powered by a NodeJS application with a MongoDB database, where we store user data, images, and voting information. We also incorporated voting functionality to allow users to rate upcycled creations. Images are processed using a combination of cloud storage and base64 encoding to facilitate smooth uploads and rendering.

**Challenges we ran into**
One of the biggest challenges was developing a seamless image processing pipeline, where users can upload pictures of their trash and upcycled items without losing quality or causing performance issues. Another challenge was training Gemini to suggest useful and creative upcycling ideas based on a wide range of packaging materials. Prompting was an important must here!

**Accomplishments that we're proud of**
For all but one, it's our first hackathon! We’re proud of creating a platform that promotes environmental sustainability and community engagement. We successfully integrated an AI that can provide,creative upcycling ideas, encouraging people to think outside the box when it comes to waste. Additionally, building a voting mechanism that allows the community to participate and celebrate the best ideas is something we’re really love.

**What we learned**
We learned the importance of seamless user experience, especially when dealing with images and voting mechanisms. Things can get complicated, and that's when users start leaving your page. We also deepened our understanding of AI-driven suggestion systems and how to fine-tune them for specific use cases like upcycling. Working with sustainability-focused applications taught us how small actions, like reimagining trash, can have a larger impact on both individuals and the environment.

What's next for TrashToTreasure - Sustainable Showcase
Moving forward, we want to expand the platform by integrating with other companies' packaging waste, not just PepsiCo’s. We plan to enhance Gemini's AI capabilities to suggest even more creative ideas and work on building a larger community where people can share, learn, and inspire others to upcycle. We’re also considering introducing challenges or competitions where users can win rewards for the most innovative upcycled creations, further incentivizing sustainability. Maybe a Pepsi Jet!