Snowcap

Inspiration

We got this inspiration for this project from the recent snowstorms and blizzards that have plagued New Jersey over the last month. When in such dire situations, we often don't think about our survival as a community until the very end. However, the community is most helpful: between sharing tools, helping out with shovelling, and assisting the elderly, we are most capable when we share our abilities together during times of need. This was the inspiration for our app, Snowcap.

What it does

Snowcap connects communities during times of need. We do this by allowing people within a storm to see multiple things: a power page that shows the status of power in their area, as well as likelihoods for power outages using a custom linear regression model; a communications page where anyone in a radius from the person can communicate with their neighbours; a "trades" page that looks for local professionals to help out people for money; and a "jobs" page where anyone can volunteer to help with snow-related tasks like removal for people that may not be able to. These pages all have a purpose that allows people to stay safe and working during times of need where struggle is found.

How we built it

We used FastAPI and React Native to build this app. We did this because a mobile app with a fast backend would allow for the best chance of this app being able to be deployed in emergency situations. We developed this app by implementing the frontend page by page first, and then implementing the backend resources necessary for each page. We used MongoDB as our backend database to provide a steady, fast, and reliable data source. We built the linear regression model

Challenges we ran into

Some challenges we ran into were in the planning and design of the app. Learning about some of the important characteristics that make up a usable AI was somewhat of a challenge, and it took us a couple of iterations at the whiteboard to really get an idea of what we wanted. On top of that, we had development issues. We had to deal with a lot of merge conflicts as all of us worked on different branches implementing different features that tapped into existing files. Also, since we were making a React Native app, the setup on Android and iOS made the environments, and issues, harder to reproduce and solve quicker.

Accomplishments that we're proud of

We are very proud of our robust backend, which is able to be fast in times of need. It includes all data and models necessary to run our frontend app, and is in general, a large scale API that is both efficient and able to handle concurrent requests.

What we learned

We learned about the implementation of making large-scale FastAPI backends to run apps in scenarios that matter a ton. We also learned about the implementations of linear regression models, a founding core of AI models, in order to make predictive analyses.

What's next for Snowcap

We plan on implementing electronics such as a sensor module that can help provide more real-time data about the current surroundings of an area. We would also like to expand on the other pages to make them work better and have more features that can expand the scope of our app.

Built With

• fastapi
• javascript
• linear-regression
• mongodb
• python
• react-native