## 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 🌟

Nostalgia comes through small items that trigger our sweet old memories. It reminds us of simpler times when a sticky note was all we needed to remember something important and have fun. Those satisfying moments of peeling the last layer of the memo pad, the embarrassing history of putting online passwords around the computer, and the hilarious actions of putting a thousand window XP sticky notes on the home screen are tiny but significant memories. In today's digital age, we wanted to bring back that simplicity and tangible feeling of jotting down notes on a memo sticker, but with a twist. ScribbleSync is our homage to the past, reimagined with today's technology to enhance and organize our daily lives.

## What it does 📝

ScribbleSync takes the traditional office memo sticker into the digital era. It's an online interface where you can effortlessly scribble notes, ideas, or events. These digital sticky notes are then intelligently synced with your Google Calendar with Large Language Models and Computer Vision, turning your random notes into scheduled commitments and reminders, ensuring that the essence of the physical memo lives on in a more productive and organized digital format.

## How we built it 🛠️

We built ScribbleSync using a combination of modern web technologies and APIs. The front-end interface was designed with HTML/CSS/JS for simplistic beauty. For the backend, we used Flask mainly and integrated Google Calendar API to sync the notes with the user’s calendar. We use state-of-the-art models from Google Vision, Transformer models from Hugginface for image analysis and fine-tuned Cohere models with our custom dataset in a semi-supervised manner to achieve textual classification of tasks and time relation.

## Challenges we ran into 😓

One of our biggest challenges was mainly incorporating multiple ML models and making them communicate with the front end and back end. Meanwhile, all of us are very new to hackathons so we are still adapting to the high-intensity coding and eventful environment.

## Accomplishments that we're proud of 🏆

We're incredibly proud of developing a fully functional prototype within the limited timeframe. We managed to create an intuitive, cute UI and the real-time sync feature works and communicates flawlessly. Overcoming the technical challenges and seeing our idea come to life has been immensely rewarding.

## What we learned 📚

Throughout this journey, we've learned a great deal about API integration, real-time data handling, and creating user-centric designs. We also gained valuable insights into teamwork and problem-solving under pressure. Individually, we tried tech stacks that were unfamiliar to most of us such as Cohere and Google APIs, it is a long but fruitful process and we are now confident to explore other APIs provided by different companies.

## What's next for ScribbleSync 🚀

Our next step is to add practical and convenient functions such as allowing the sticky notes to set up drafts for email, schedules in Microsoft Teams and create Zoom links. We could also add features such as sticking to the home screen to enjoy those fun features from sticky notes in the good old days.
## Inspiration

The idea for IngredientAI came from my personal frustration with navigating complex and often misleading ingredient labels on beauty and personal care products. I realized that many consumers struggle to understand what these ingredients actually do and whether they are safe. The lack of accessible information often leaves people in the dark about what they are using daily. I wanted to create a tool that brings transparency to this process, empowering users to make healthier and more informed choices about the products they use.

## What it does

The idea for IngredientAI was born out of the frustration of navigating complex and often misleading ingredient labels on beauty and personal care products. I realized that many consumers struggle to understand what these ingredients actually do and whether they are safe. The lack of accessible information often leaves people in the dark about what they are using daily. I wanted to create a tool that brings transparency to this process, empowering users to make healthier and more informed choices about the products they use.

## How I built It

The frontend is built using React Native and ran using Expo. Users interact with a FlatList component that accesses a backend database powered by Convex. Text extracted from images as well as generates ingredient descriptions is all done through OpenAI's gpt-4o-mini large-language model.

## Challenges I ran into

A big challenge that I come across was figuring out how to extract text from images. Originally, I planned on setting up a server-side script that makes use of Tesseract.js's OCR capabilities. However, after some testing, I realized that I did not have enough time to fine tune Tesseract so that it extracts text from images under a variety of different lighting. For IngredientAI to be used by consumers, it must be able to work under a wide variety of circumstances. To solve this issue, I decided it would be best for me to use OpenAI's new Vision capabilities. I did not go with this originally because I wanted to minimize the amount of OpenAI API calls I made. However, under time constraints, this was the best option.

## Accomplishments that I'm proud of

I am extremely proud of how far my App Development has come. At a previous hackathon in March, I had used React Native for the very first time. At that hackathon, I was completely clueless with the technology. A lot of my code was copy/pasted from ChatGPT and I did not have a proper understanding of how it worked. Now, this weekend, I was able to create a fully functional mobile application that has organized (enough) code that allows me to expand on this project in the future.

## What I learned

Every hackathon, my goal is to learn at least one new technology. This weekend, I decided to use Convex for the very first time. I really appreciated the amount of resources that Convex provides for learning their technology. It was especially convenient that they had a dedicated page for hackathon projects. It made setting up my database extremely fast and convenient, and as we know, speed is key in a hackathon.

## What's next for IngredientAI

My aim is to eventually bring IngredientAI to app stores. This is an app I would personally find use for, and I would like to share that with others. Future improvements and features include:

* Categorization and visualization of ingredient data
* Suggested products section
* One-button multi-store checkout

I hope you all get the chance to try out IngredientAI in the near future!