Where's My Pet

User profile
Sign up page
Creating a post for a lost/found pet
Pet missing section of the home page
AWS architecture

Inspiration

Every year, thousands of pets go missing in our cities, leaving behind anxious hearts and endless waits. The inspiration for Where’s my Pet comes from a deep understanding of the emotional bond between pets and their owners and experiencing the heartbreak when pets go missing. Traditional methods of searching for lost pets such as posting flyers and searching neighbourhoods are often time consuming, cost inefficient and yield uncertain results. As a result, we recognised a need for a more efficient and effective solution to reunite lost pets with their owners which we sought to create through Where’s My Pet.

What it does

Where’s My Pet is an app where users can create accounts to post photos of their missing pets as well as found pets as well as adding location and time information. A similarity AI model is used to analyse and compare the images of lost and found pets through the recognition of facial features and markings, and if a possible match is found, the owner of the missing pet is notified. In addition to this, users can also scroll through posts of both lost and found pets and get in touch with each other.

How we built it

Backend

AWS services such as SageMaker, Lambda, S3 bucket, Amazon SNS and API gateway to deploy our machine learning model for pet recognition and notify users of possible matches and work with APIs
JavaScript and Node.js for server-side logic

Frontend

React and TailwindCSS for the user interface

Challenges we ran into

We faced challenges in implementing AWS services since none of us had any experience with it before which resulted in problems with permissions and setting up. We initially wanted to use AWS OpenSearch to make our similarity matching process faster, however, we faced challenges in trying to integrate it with our work.

Accomplishments that we’re proud of

Regardless of the challenges we faced, our team is proud of how we all came together to support this idea, learned new skills particularly in AWS services and worked hard to make it work in the 48 hours we had.

What we learned

We discovered firsthand that AWS comes with a steep learning curve. In hindsight, we could have either narrowed down our scope further or tackled a less complex problem altogether. Despite this, we are pleased that we gained valuable insights into AWS technologies throughout the process. In addition, we learned a lot about time management and how to support each other through effective task allocation, as well as design UI/UX and how sharing our ideas with each other and mentors allowed us to get more feedback and polish our ideas better.

What’s next

Moving forward, our plan is to integrate OpenSearch to enhance our system’s matching capabilities and also refine the user interface for a better experience. We would also like to create an in app chat feature so that users can message each other within the app which would solve the problem of spam calls from unknown numbers and allow for easier communication and also upscale the app by increasing support for global usage.