## Inspiration

**Problem Statement**
“I don’t know what food to make” is a common phrase we often say in the kitchen. We sometimes have trouble deciding on what to make for breakfast, lunch, and/or dinner, even though the refrigerator is fully stocked. As you update Friji with the items stocked in your kitchen, you are given a selection on what you can make based on the available ingredients.

**Pain Points**

* Food waste: Leftover food and ingredients go unused because they are forgotten about
* Saving money: Ingredients that go unused can contribute to a problematic expense
* Confusion: Canadians want to learn how to cook, but don’t know where to start

## What It Does

**Our Solution**

* Food Waste: Reduces leftover food and ingredients by helping the user keep track of what’s exactly inside of their fridge
* Saving money: Save money by using what you already have, buy less groceries/takeout
* Education: A database of recipes that can tell the user exactly where to start with what they currently have

## How We Built It

After discussing problems we relate to, we came up with the idea of food management and meal planning. After sketching out possible flows and the one we would develop for the presentation, we wired them and designed hi-definition screens in Figma. Once the design of the application was complete, we wanted to focus more on the back-end portion of the app.

## Challenges We Ran Into

* Visual Studio for Mac was very difficult to work with
* Xamarin was a library that none of the group members had any experience and spent time learning it during the Hackathon
* Working with APIs was a learning experience for us and took time to understand how to use it

## Accomplishments That We’re Proud Of

We’re proud of being able to use a new tool cross functionally. Normally, Figma is used by designers but getting our team to use it for dev work was a huge realization on the power the tool has and will affect our workflow in the future. For this, we were very proud that we taught ourselves how to use APIs, such one from Spoonacular and Microsoft.

## What’s Next for Friji

We hope to build out more flows and work out the kinks when it comes to the item recognition. In the future as we learn more about AI and machine learning, and instead of taking one picture of an item at a time, it would be able to identify multiple at once.

## Built With

Figma, Microsoft Cognitive Vision, C#, Xamarin, Spoonacular API, Visual Studio
## Inspiration

Created an idea

## What it does

Our app uses geofences to help business promote their restaurant out when people walk near them.

## How I built it

-Created a website interface for businesses to set up their business with a geofence.
-Used php to interact with an SQL database to save information

## Challenges I ran into

-Unable to provide work for everyone to do
-learning the radar.io documentation was very confusing

## Accomplishments that I'm proud of

Created a feasible idea

## What I learned

Having a diversity of skills comes in handy. Assigning roles in the beginning of the event is very important.

## What's next for RadarDishes

We must complete the app where users will constantly send their GPS location and receive notifications when they are in a geofence.
## Inspiration

Partially inspired by the Smart Cities track, we wanted our app to have the direct utility of ordering food, while still being fun to interact with. We aimed to combine convenience with entertainment, making the experience more enjoyable than your typical drive-through order.

## What it does

You interact using only your voice. The app automatically detects when you start and stop talking, uses AI to transcribe what you say, figures out the food items (with modifications) you want to order, and adds them to your current order. It even handles details like size and flavor preferences. The AI then generates text-to-speech audio, which is played back to confirm your order in a humorous, engaging way. There is absolutely zero set-up or management necessary, as the program will completely ignore all background noises and conversation. Even then, it will still take your order with staggering precision.

## How we built it

The frontend of the app is built with React and TypeScript, while the backend uses Flask and Python. We containerized the app using Docker and deployed it using Defang. The design of the menu is also done in Canva with a dash of Harvard colors.

## Challenges we ran into

One major challenge was getting the different parts of the app—frontend, backend, and AI—to communicate effectively. From media file conversions to AI prompt engineering, we worked through each of the problems together. We struggled particularly with maintaining smooth communication once the app was deployed. Additionally, fine-tuning the AI to accurately extract order information from voice inputs while keeping the interaction natural was a big hurdle.

## Accomplishments that we're proud of

We're proud of building a fully functioning product that successfully integrates all the features we envisioned. We also managed to deploy the app, which was a huge achievement given the complexity of the project. Completing our initial feature set within the hackathon timeframe was a key success for us. Trying to work with Python data type was difficult to manage, and we were proud to navigate around that. We are also extremely proud to meet a bunch of new people and tackle new challenges that we were not previously comfortable with.

## What we learned

We honed our skills in React, TypeScript, Flask, and Python, especially in how to make these technologies work together. We also learned how to containerize and deploy applications using Docker and Docker Compose, as well as how to use Defang for cloud deployment.

## What's next for Harvard Burger

Moving forward, we want to add a business-facing interface, where restaurant staff would be able to view and fulfill customer orders. There will also be individual kiosk devices to handle order inputs. These features would allow *Harvard Burger* to move from a demo to a fully functional app that restaurants could actually use. Lastly, we can sell the product by designing marketing strategies for fast food chains.