Coded prototype on Github: <https://github.com/shawnd/foodie>
Design Prototype, MarvelApp: <https://marvelapp.com/c1h9c5>

## Inspiration

To solve students' problems around –

1. Not knowing what to cook
2. Not knowing what groceries to buy at a store
3. Not discovering new meals to eat
4. Not having the time to manually create grocery lists based on online recipes
5. Sharing meals and recipes amongst friends.

## What it does

1. Helps discover meals that can be cooked within a specified budget (eg: $20).
2. Curates recipes from online sources into a database.
3. Create a 'meal list' by adding recipes you want to cook.
4. Each meal list shows approximate prices for the entire meal.
5. Auto-generate a grocery list that compiles saved recipes and their ingredients.
6. Allows one-click sharing of recipes with friends.

## How we built it

We used a Python Bottle API that communicates with a Firebase distributed database service that returns data to our front-end Ionic interface. End result was a mobile application.

## Challenges we ran into

– Scaling down features for the application.

## Accomplishments that we're proud of

– Finishing on time
– Building an application over 24 hours!!
– Fun experience

## What's next for Foodie

Feedback, iteration, feedback, iteration, test, feedback, iteration, release!
## Inspiration

We were all very intrigued by machine learning and AI, so we decided to incorporate it into our project. We wanted to create something involving the webcam as well, so we tied it altogether with ScanAI.

## What it does

ScanAI aims to detect guns in schools and public areas to help alert authorities quickly in the event of a public shooting.

## How we built it

ScanAI is built entirely out of python. Computer vision python libraries were used including OpenCV, facial\_recognition, yolov5 and tkinter.

## Challenges we ran into

When training models, we ran into issues of a lack of ram and a lack of training data. We also were challenged by the problem of tackling multiple faces at once.

## Accomplishments that we're proud of

ScanAI is able to take imported files and detect multiple faces at once and apply facial recognition to all of them. ScanAI is highly accurate and has many features including Barack Obama facial recognition, object detection, live webcam viewing and scanning, and file uploading functionalities.

## What we learned

We all learned a lot about machine learning and its capabilities. Using these modules expanded our knowledge on AI and all its possible uses.

## What's next for ScanAI

Our next steps would be to improve our interface to improve user friendliness and platform compatibility. We would also want to incorporate our program with Raspberry Pi to increase its usage flexibility. Lastly, we would also want to work on improving the accuracy of the detection system by feeding it more images and feedback.
# imHungry

## Inspiration

As Berkeley students we are always prioritizing our work over our health! Students often don't have as much time to go buy food. Why not pick a food for other students while buying your own food and make a quick buck? Or perhaps, place an order with a student who was already planning to head your way after buying some food for themselves? This revolutionary business model enables students to participate in the food delivery business while avoiding the hassles that are associated with the typical food delivery app. Our service does not require students to do anything differently than what they already do!

## What it does

This application allows students to be able to purchase or help purchase food from/for their fellow students. The idea is that students already purchase food often before heading out to the library or another place on campus. This app allows these students to list their plans in advance and allow other students to put in their orders as well. These buyers will then meet the purchaser at wherever they are expected to meet. That way, the purchaser doesn't need to make any adjustments to their plan besides buy a few extra orders! The buyers will also have the convenience of picking up their order near campus to avoid walking. This app enables students to get involved in the food delivery business while doing nearly nothing additional!

## How we built it

We used Flask, JavaScript, HTML/CSS, and Python. Some technologies we used include Mapbox API, Google Firebase, and Google Firestore. We built this as a team at CalHacks!

## Challenges we ran into

We had some trouble getting starting with using Google Cloud for user authentication. One of our team members went to the Google Cloud sponsor stand and was able to help fix part of the documentation!

## Accomplishments that we're proud of

We're proud of our use of the Mapbox API because it enabled us to use some beautiful maps in our application! As a food delivery app, we found it quite important that we are able to displays restaurants and locations on campus (for delivery) that we support and Mapbox made that quite easy. We are also quite proud of our use of Firebase and Firestore because we were able to use these technologies to authenticate users as Berkeley students while also quickly storing and retrieving data from the cloud.

## What we learned

We learned how to work with some great APIs provided by Mapbox and Google Cloud!

## What's next for imHungry

We hope to complete our implementation of the user and deliverer interface and integrate a payments API to enable users to fully use the service! Additional future plans are to add time estimates, improve page content, improve our back-end algorithms, and to improve user authentication.