## Our Inspiration

Have you ever looked inside the fridge, saw a particular piece of food, and wondered if it was still edible? Maybe you felt it, or looked it up online, or even gave it the old sniff test! Yet, at the end of the day, you still threw it away.

Did you know that US citizens waste an average of 200lbs of food per year, per person? Much of that waste can be traced back to food casually tossed out of the fridge without a second thought. Additionally, 80% of Americans rely on the printed ‘best by’ dates to determine if their food is still fresh, when in reality it is almost never a true indicator of expiration. We aim to address these problems and more.

## What do we do?

NoWaste.ai is a two-pronged approach to the same issue, allowing us to extend our support to users all over the world. At its core, NoWaste allows users to snap photos of an edible product, describe its context, and harness the power of generative AI to receive a gauge on the quality of their food. An example could be the following: "Pizza left on a warm counter for 6 hours, then refrigerated for 8" + [Picture of the pizza], which would return the AI's score of the food's safety.

In developed countries, everyone has a smartphone, which is why we provide a mobile application to make this process as convenient as possible. In order to create a product that users will be more likely to consistently use, the app has been designed to be extremely streamlined, rapidly responsive, and very lightweight.

However, in other parts of the world, this luxury is not so common. Hence, we have researched extensively to design and provide a cheap and scalable solution for communities around the world via Raspberry Pi Cameras. At just $100 per assembly and $200 per deployment, these individual servers have potential to be dispersed all across the world, saving thousands of lives, millions of pounds of waste, and billions of dollars.

## How we built it

Our hardware stack is hosted completely on the Raspberry Pi in a React/Flask environment. We utilize cloud AI models, such as LLAMA 70B on Together.ai and GPT4 Vision, to offload computation and make our solution as cheap and scalable as possible. Our software stack was built using Swift and communicates with similar APIs.

We began by brainstorming the potential services and technologies we could use to create lightweight applications as quickly as possible. Once we settled on a general idea, we split off and began implementing our own responsibilities: while some of us prototyped the frontend, others were experimenting with AI models or doing market research. This delegation of responsibility allowed us to work in parallel and design a comprehensive solution for a problem as large (yet seemingly so clear) as this.

Of course, our initial ideas were far from what we eventually settled on.

## Challenges we ran into

Finalizing and completing our stack was one of our greatest challenges. Our frontend technologies changed the most over the course of the weekend as we experimented with Django, Reflex, React, and Flask, not to mention the different APIs and LLM hosts that we researched. Additionally, we were ambitious in wanting to only use open-source solutions to further drive home the idea of world-wide collaboration for sustainability and the greater good, but we failed to identify a solution for our Vision model. We attempted to train LLAVA using Intel cloud machines, but our lack of time made it difficult as beginners. Our team also faced hardware issues, from broken cameras to faulty particle sensors. However, we were successful in remedying each of these issues in their own unique ways, and we are happy to present a product within the timeframe we had.

## Accomplishments that we're proud of

We are incredibly proud of what we were able to accomplish in such a short amount of time. We were passionate about both the underlying practicalities of our application as well as the core implementation. We created not only a webapp hosted on a Raspberry Pi, equipped with odor sensors and a camera, but a mobile app prototype and a mini business plan as well. We were able to target multiple audiences and clear areas where this issue prevails, and we have proposed solutions that suit all of them.

## What's next for NoWaste.ai

The technology that we propose is infinitely extensible. Beyond this weekend at TreeHacks, there is room for fine-tuning or training more models to produce more accurate results on rotting and spoiled food. Dedicated chips and board designs can bring the cost of production and extension down, making it even easier to provide solutions across the world. Augmented Reality is becoming more prevalent every day, and there is a clear spot for NoWaste to streamline our technology to work seamlessly with humans. The possibilities are endless, and we hope you can join and support us on our journey!
## Inspiration

Inspired by the Making the Mundane Fun prize category, our team immediately focused on helping users achieve long-term goals. We observed a surge in cooking at the start of lockdown, and wanted to keep that momentum going with a simple yet entertaining app to inspire users to enjoy cooking in the long term.

## What it does

With the login system, each user stores their own personalized information. The homepage allows users to track when they cooked and when they didn’t to show a visual of their daily cooking routine. Weekly challenges add interest to cooking, randomly generating an ingredient of the week to cook with, which earns you bonus points to go toward purchasing more plants. The garden is a fun game for users to design a personal space. The more you level up, the more plants you can add.

## How we built it

We built the app through Android Studio using Java. The UI images were created using Adobe Illustrator and the authentication was achieved through a Firebase database.

## Challenges we ran into

Although the project was relatively smooth sailing, we did run into some challenges along the way. The biggest challenge was implementing the Firebase database. We also experienced some difficulties with adding a clickable image grid to our garden pop-up.

## Accomplishments that we're proud of

First, our team was rather unfamiliar with Android Studio. We learned a lot about implementing a project in Android Studio. As well, Firebase was a new tool for our team. Although it was a challenge to get the implementation running we are proud of the authentication. Finally, this was the first time the team member who created the UI images had done so and we are proud of their achievements!

## What we learned

Overall, we learned a lot about Firebase, UI creation and implementing projects in Android Studio.

## What's next for Harvest

In the future, we hope to fully implement user-associated data and a platform to share latest creations with your friends to further personalize the app and include a broader community aspect.
## Inspiration:

Every year, the world wastes about 2.5 billion tons of food, with the United States alone discarding nearly 60 million tons. This staggering waste inspired us to create **eco**, an app focused on food sustainability and community generosity.

## What it does:

**eco** leverages advanced Computer Vision technology, powered by YOLOv8 and OpenCV, to detect fruits, vegetables, and groceries while accurately predicting their expiry dates. The app includes a Discord bot that notifies users of impending expirations and alerts them about unused groceries. Users can easily generate delicious recipes using OpenAI's API, utilizing ingredients from their fridge. Additionally, **eco** features a Shameboard to track and highlight instances of food waste, encouraging community members to take responsibility for their consumption habits.

## How we built it:

For the frontend, we chose React, Typescript, and TailwindCSS to create a sleek and responsive interface. The database is powered by Supabase Serverless, ensuring reliable and scalable data management. The heart of **eco** is its advanced Computer Vision model, developed with Python, OpenCV, and YOLOv8, allowing us to accurately predict expiry dates for fruits, vegetables, and groceries. We leveraged OpenAI's API to generate recipes based on expiring foods, providing users with practical and creative meal ideas. Additionally, we integrated a Discord bot using JavaScript for seamless communication and alerts within our Discord server.

## Challenges we ran into:

During development, we encountered significant challenges with WebSockets and training the Computer Vision model. These hurdles ignited our passion for problem-solving, driving us to think creatively and push the boundaries of innovation. Through perseverance and ingenuity, we not only overcame these obstacles but also emerged stronger, armed with newfound skills and a deepened resolve to tackle future challenges head-on.

## Accomplishments that we're proud of:

We take pride in our adaptive approach, tackling challenges head-on to deliver a fully functional app. Our successful integration of Computer Vision, Discord Bot functionality, and recipe generation showcases our dedication and skill in developing **eco**.

## What we learned:

Building **eco** was a transformative journey that taught us invaluable lessons in teamwork, problem-solving, and the seamless integration of technology. We immersed ourselves in the intricacies of Computer Vision, Discord bot development, and frontend/backend development, elevating our skills to new heights. These experiences have not only enriched our project but have also empowered us with a passion for innovation and a drive to excel in future endeavors.

**Eco** is not just an app; it's a movement towards a more sustainable and generous community. Join us in reducing food waste and fostering a sense of responsibility towards our environment with eco.