## Inspiration

Throughout Quarantine and the global pandemic that we are all currently experiencing, I have begun to reflect on my own life and health in general. We may believe we have a particular ailment when, in fact, it is actually our fear getting the best of us. But how can one be sure that the symptoms they are having are as severe as they seem to be? As a result, we developed the Skin Apprehensiveness Valdiator and Educator App to help not only maintain our mental balance in regards to paranoia about our own health, but also to help front line staff solve the major pandemic that has plagued the planet.

## What it does

The home page is the most critical aspect of the app. It has a simple user interface that allows the user to choose between using an old photo or taking a new photo of any skin issues they may have. They can then pick or take a screenshot, and then, after submission, we can use the cloud to run our model and receive results from it, thanks to Google's Cloud ML Kit. We get our overall diagnosis of what illness our Model thinks it is, as well as our trust level. Following that, we have the choice of viewing more information about this disease through a wikipedia link or sending this to a specialist for confirmation.
Tensorflow and a convolutional neural network with several hidden layers were used to build the first Machine Learning Algorithm. It uses the Adam optimizer and has a relu activation layer. We've also improved the accuracy by using cross validation.

The second section of the app is the specialist section, where you can view doctors whose diagnoses you can check online. You may send them a text, email, or leave a voicemail to request a consultation and learn more about your diagnosis. This involves questions like what should my treatment be, when should I begin, where should I go, and how much will it cost, as well as some others.
The third section of the app is the practices section, which helps you to locate dermatology practices in your area that can assist you with your care. You can see a variety of information about a location, including its Google Review Average ranking, total number of reviews, phone number, address, and other relevant information. You also get a glimpse into how their office is decorated. You may also click on the location to be redirected to Google Maps directions to that location.

The tips section of the app is where you can find different links from reputable sources that will help you get advice on care, diagnosis, or skin disorders in a geological environment.

## How we built it

ReactJS, Expo, Google Cloud ML Kit, Tensorflow, practoAPI, Places API
## Inspiration

Each year, more people are diagnosed with skin cancer than all other cancer's combined. It is also estimated that one in five people will develop skin cancer by the time they reach 70 years of age. Skin cancer is also one of the easiest to cure, if detected early. With this in mind, we were inspired to develop a solution that empowers users to analyze and monitor the health of their skin.

## What it does

Möle allows users to take pictures of freckles, blemishes and moles on their skin. The application then uses a Machine Learning image processing model to compare it to more than ten thousand images of various skin conditions. The resulting analysis informs the user whether the blemish is benign or malignant, and if it resembles Melanoma, Bowens Disease, Basal Cell Carcinoma, Benign lesions, Dermatofibroma, or Melanocytic Nevi.

The application also allows users to track blemishes over time, providing updates on their health as they develop and change.

## How we built it

Möle is built for Android devices, using Kotlin/Java and a Realm database. The image processing agent was built using Azure's Custom Vision Model, trained using HAM10000, a neural network training dataset published in the Harvard Dataverse, with over ten thousand images of common skin lesions.

For more information, please visit: <https://bit.ly/2UlvD7P>

## Challenges we ran into

All team members were unfamiliar with the projects technical stack at the beginning of this competition. The learning curve, coupled with the limited development time, proved to be our biggest challenge. Sorting, tagging and uploading over 10 000 images for our computer vision model was also a challenge on its own, which required a fair amount of scripting.

## Accomplishments that we're proud of

All members of our team undertook a large part of this project, working with tech we were not familiar with, and managed to complete a project within 24 hours! On top of this, we feel that the product we created could be used to help educate and inform millions of people worldwide, who may not have access to dermatologists or skin care professionals.

## What we learned

We would struggle to fit everything we learned over the past 24 hours onto one page, but we feel the biggest lessons were that...

* To always focus on the minimum viable product when time is limited
* It's more fun to work on projects you're excited about
* Wear more sunscreen, skin care is no joke!

## What's next for Möle

Training our model with larger datasets to improve accuracy, and fine tuning the interface to create a more seamless user experience. We also intend to better integrate users age, sex, and geographical location into the analysis in order to produce more accurate results.
## 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.