## Inspiration

In the work from home era, many are missing the social aspect of in-person work. And what time of the workday most provided that social interaction? The lunch break. culina aims to bring back the social aspect to work from home lunches. Furthermore, it helps users reduce their food waste by encouraging the use of food that could otherwise be discarded and diversifying their palette by exposing them to international cuisine (that uses food they already have on hand)!

## What it does

First, users input the groceries they have on hand. When another user is found with a similar pantry, the two are matched up and displayed a list of healthy, quick recipes that make use of their mutual ingredients. Then, they can use our built-in chat feature to choose a recipe and coordinate the means by which they want to remotely enjoy their meal together.

## How we built it

The frontend was built using React.js, with all CSS styling, icons, and animation made entirely by us. The backend is a Flask server. Both a RESTful API (for user creation) and WebSockets (for matching and chatting) are used to communicate between the client and server. Users are stored in MongoDB. The full app is hosted on a Google App Engine flex instance and our database is hosted on MongoDB Atlas also through Google Cloud. We created our own recipe dataset by filtering and cleaning an existing one using Pandas, as well as scraping the image URLs that correspond to each recipe.

## Challenges we ran into

We found it challenging to implement the matching system, especially coordinating client state using WebSockets. It was also difficult to scrape a set of images for the dataset. Some of our team members also overcame technical roadblocks on their machines so they had to think outside the box for solutions.

## Accomplishments that we're proud of.

We are proud to have a working demo of such a complex application with many moving parts – and one that has impacts across many areas. We are also particularly proud of the design and branding of our project (the landing page is gorgeous 😍 props to David!) Furthermore, we are proud of the novel dataset that we created for our application.

## What we learned

Each member of the team was exposed to new things throughout the development of culina. Yu Lu was very unfamiliar with anything web-dev related, so this hack allowed her to learn some basics of frontend, as well as explore image crawling techniques. For Camilla and David, React was a new skill for them to learn and this hackathon improved their styling techniques using CSS. David also learned more about how to make beautiful animations. Josh had never implemented a chat feature before, and gained experience teaching web development and managing full-stack application development with multiple collaborators.

## What's next for culina

Future plans for the website include adding videochat component to so users don't need to leave our platform. To revolutionize the dating world, we would also like to allow users to decide if they are interested in using culina as a virtual dating app to find love while cooking. We would also be interested in implementing organization-level management to make it easier for companies to provide this as a service to their employees only. Lastly, the ability to decline a match would be a nice quality-of-life addition.
## Inspiration

We love cooking and watching food videos. From the Great British Baking Show to Instagram reels, we are foodies in every way. However, with the 119 billion pounds of food that is wasted annually in the United States, we wanted to create a simple way to reduce waste and try out new recipes.

## What it does

lettuce enables users to create a food inventory using a mobile receipt scanner. It then alerts users when a product approaches its expiration date and prompts them to notify their network if they possess excess food they won't consume in time. In such cases, lettuce notifies fellow users in their network that they can collect the surplus item. Moreover, lettuce offers recipe exploration and automatically checks your pantry and any other food shared by your network's users before suggesting new purchases.

## How we built it

lettuce uses React and Bootstrap for its frontend and uses Firebase for the database, which stores information on all the different foods users have stored in their pantry. We also use a pre-trained image-to-text neural network that enables users to inventory their food by scanning their grocery receipts. We also developed an algorithm to parse receipt text to extract just the food from the receipt.

## Challenges we ran into

One big challenge was finding a way to map the receipt food text to the actual food item. Receipts often use annoying abbreviations for food, and we had to find databases that allow us to map the receipt item to the food item.

## Accomplishments that we're proud of

lettuce has a lot of work ahead of it, but we are proud of our idea and teamwork to create an initial prototype of an app that may contribute to something meaningful to us and the world at large.

## What we learned

We learned that there are many things to account for when it comes to sustainability, as we must balance accessibility and convenience with efficiency and efficacy. Not having food waste would be great, but it's not easy to finish everything in your pantry, and we hope that our app can help find a balance between the two.

## What's next for lettuce

We hope to improve our recipe suggestion algorithm as well as the estimates for when food expires. For example, a green banana will have a different expiration date compared to a ripe banana, and our scanner has a universal deadline for all bananas.
## Inspiration

I was cooking at home one day and I kept noticing we had half a carrot, half an onion, and like a quarter of a pound of ground pork lying around all the time. More often than not it was from me cooking a fun dish that my mother have to somehow clean up over the week. So I wanted to create an app that would help me use those ingredients that I have neglected so that even if both my mother and I forget about it we would not contribute to food waste.

## What it does

Our app uses a database to store our user's fridge and keeps track of the food in their fridge. When the user wants a food recipe recommendation our app will help our user finish off their food waste. Using the power of chatGPT our app is super flexable and all the unknown food and food that you are too lazy to measure the weight of can be quickly put into a flexible and delicious recipe.

## How we built it

Using figma for design, react.JS bootstrap for frontend, flask backend, a mongoDB database, and openAI APIs we were able to create this stunning looking demo.

## Challenges we ran into

We messed up our database schema and poor design choices in our APIs resulting in a complete refactor. Our group also ran into problems with react being that we were relearning it. OpenAI API gave us inconsistency problems too. We pushed past these challenges together by dropping our immediate work and thinking of a solution together.

## Accomplishments that we're proud of

We finished our demo and it looks good. Our dev-ops practices were professional and efficient, our kan-ban board saved us a lot of time when planning and implementing tasks. We also wrote plenty of documentations where after our first bout of failure we planned out everything with our group.

## What we learned

We learned the importance of good API design and planning to save headaches when implementing out our API endpoints. We also learned much about the nuance and intricacies when using CORS technology. Another interesting thing we learned is how to write detailed prompts to retrieve formatted data from LLMs.

## What's next for Food ResQ : AI Recommended Recipes To Reduce Food Waste

We are planning to add a receipt scanning feature so that our users would not have to manually add in each ingredients into their fridge. We are also working on a feature where we would prioritize ingredients that are closer to expiry. Another feature we are looking at is notifications to remind our users that their ingredients should be used soon to drive up our engagement more. We are looking for payment processing vendors to allow our users to operate the most advanced LLMs at a slight premium for less than a coffee a month.

## Challenges, themes, prizes we are submitting for

Sponsor Challenges: None
Themes: Artificial Intelligence & Sustainability
Prizes: Best AI Hack, Best Sustainability Hack, Best Use of MongoDB Atlas, Most Creative Use of Github, Top 3 Prize