Uplift

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  Full view with more housing and food bank options

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  Pop-up when you select a pin

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Inspiration

California is affected by a lot of income inequality. This leads to many issues for those who are disadvantaged by income inequality. This includes the housing crisis, food price inflation, and educational gaps. Being a resident of California, I wanted to create an app to combat these issues.

What it does

The user first takes an Onboarding Survey where they enter their name an age, as well as any solutions they are looking for. This could be affordable housing, shelters, food resources, scholarships, etc. Based on this info, a map will be generated that details different places they can go to find these resources. There's also separate housing and food tabs for general info on housing shelters and food banks. The education tab has its own survey that will take in info on certain scholarship criteria. This info is then sent to a ML model which will return scholarships the user can apply to. There are also suggestions for free courses and mentor matching.

How we built it

I used SwiftUI to create the basic app interface. I used other frameworks, like MapKit, for the more complex parts of the app. I used public datasets to find the locations of housing and food resources, and I used an API to integrate them into my app. I used CreateML to make the machine learning model, and I used CoreML to integrate it.

Challenges we ran into

I ran into a lot of challenges with implementing the dataset. I first tried to use a CSV file, but there were a lot of problems when parsing it. I also tried to use an API, but there must have been an issue with the API endpoint as the coordinates were constantly converted to 0.0 when I tried to access it from my app. I ended up having to hardcode in multiple housing shelters and food banks myself, which took up a large chunk of my time. It also took incredibly long to train the model, as the dataset (generated with AI), was very large. This created even more strain on the small amount of time I had to make my project.

Accomplishments that we're proud of

I'm proud of the app I was able to make in the end, especially under the time crunch, as I think it is something that would be very useful for those disadvantaged by income inequality.

What we learned

I learned a lot about the more complex parts of MapKit and how to use them in my app. I also learned more about integrating APIs and parsing CSV files, even if I couldn't use those in my app in the end. I also learned how to deal with larger, more complex ML models.

What's next for Uplift

I want to create a more user-friendly interface. I also want to use a more up-to-date and reliable API for my app. I still have the code for my API integrated version on my GitHub, so I can use that at any time. I also want to use more ML models throughout the app to create more robust capabilities for the app.

Built With

• api
• coreml
• createml
• swiftui
• xcode