Glasc

Inspiration

As students (especially for computer science student), we often struggle with skincare due to limited knowledge, tight budgets, and busy schedules. Many of us want to take better care of our skin but don't know where to start or which products would actually work for our specific needs. Furthermore, sometimes lack of research can lead to the opposite of our a clear glass skin. Glasc comes in to solve this common student problems. We created a skincare app that acts as our companion.

What it does

Our app serves a main feature that can help scan skincare-products and automatically detect it's ingredients. It will later on be analyzed by giving a matching percentage by considering your personal skin conditions and your current routine.

Other features includes:

Personalized skincare routine
Find similar kind of the same products
Give suggestions of products

How we built it

Considering users time in their phone, we decided to build it as a mobile app using React Native as it is compatible and easy to deploy between iOS and Android. On the initial meeting of the team, we made and pinpoint some of the features we need to include. And then separated them to three iterations. With Amazon Q, it helps to split those features into user stories, split it up through three iterations and giving some the necessary engineering tasks to.

Every end of iteration we evaluate and split the job for the next iteration. As this is hosted by AWS, we tried to use as many of the AWS service's provided. As in our project we hosted a PostgreSQL in Amazon RDS, as well as using AWS Textract to help with the OCR implemented in our scan feature.

Challenges we ran into

Of course in our approximately one-month development time, we did encountered several issues. Within those not-so-long development time, every iteration falls back to be completed. At least either one or two user stories is completed after every iteration. But that is why it is important to held meeting for every iteration as it helps to plan for the next iteration and complete the previously incomplete task.

As you can see, we did not successfully implemented all of sub-features, but we did managed to complete all of the necessary features of it.

Accomplishments that we're proud of

We are proud to make a subtle features like the similarity product recommendation and also skincare analyzer by considering skin goals and current personal skincare routine as well. Even though it is not as obvious to the user, but these small algorithm brings it closer to a suitable and real app.

What we learned

We were only used to here AGILE methodology in a theoretical sense, but never did experienced it. We do know that AGILE does give a upper hand in fast development making sure that it is completed in rapid time. But, since it is our first time working together as well, many adjustment is made. We face some technical debts, and technology issue where it is our first time using AWS and implementing it in mobile development.

What's next for Glasc

We do hope that the app can be further developed, complete the other sub-features that is not implemented yet. Moreover, we do would like to update the database as well since it does not cover all of the skincare yet. We would like to engage in user participation where if the product does not match, user can actively add it to our database, so that the database can vary and covers more skincare products.