Ounce

Ounce is a health productivity app that allows you to track your progress, discover isolated exercises, and seamlessly tackle obstacles that are holding you back from your new years fitness goals.

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Inspiration and what it does

We were inspired to create an app to create a customized workout plan for individuals with a limited space and time with an addition of a curated youtube video, especially with gyms being inaccessible due to COVID-19 restrictions and lockdown. Users have the freedom to pick and choose their duration of time, the space availability and area of focus in improving their strength for certain parts of their body. Part of the experience that makes this app exciting is that it incorporates functions that psychologically can motivate individuals to workout, such as personalized workout plan, voice memos, transcribed notes, and a tally of successful workout that has been done throughout the day. We designed this app to be inviting, and simple to use so that users will only have to worry to login to the app and track their workout daily to start their habit and stick with it in the year of 2022.

How we built it

We built this project using Visual Studio Code as it is flexible to modify the design elements of the app using Flutter. Using Android Studio we were able to simulate the app upon hot restart and see how it was currently functioning. Used AssemblyAI, streamlit, and python to create the youtube video transcriber.

Challenges we ran into and accomplishments that we're proud of

The biggest challenge was the lack of app development experience between the 4 of us. We all made our earnest attempt to learn and encounter new things as we set out to ambitiously write an app in one day. By step 1, there were already roadblocks with installing source code editing software and getting the environment ready for writing the app and all its design elements. Then came writing code in Dart, which was made easier by the Flutter demo presented Saturday morning, but did not move past basic element design. We also had difficulties deciding whether to use Next, we were able to get AssemblyAI configured with transcribing YouTube videos, but unfortunately could not get it to work with uploaded MP3 files (voice memos). Mostly the time constraint for our level of experience with something outside of all our depths was very challenging, but we did our best working at all hours and through the night to pull things together and make a presentable prototype.

What we learned

Initially we built the project using Flutter and Dart with an option of exporting it to a html format or android studio. For Android Studio, we have attempted to install necessary plugins, and found that it can be quite large and daunting since we have a limited hardware space required to export the app product. We also learned how to use Figma interface to html, which is simpler although there is a learning curve in order to get to the level of proficiency we wanted with a working prototype especially with sorting the YouTube algorithm to display videos based on the user’s input. Due to restricted time, we wanted to show the prototype of our ideas about this app by using Figma.

What's next for Ounce

Next we hope to further develop our prototype and release the app for thousands of potential users to benefit from

Built With

  • assemblyais-voice-to-text-transcriber
  • figma
  • flutter
  • google-cloud-services-to-provide-the-youtube-data-api
  • python
  • streamlit
  • vscode
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