Dress2Impress

Inspiration

We wanted to create an intuitive and seamless way for users to discover and save their favorite fashion items based on images they upload. Since we had no prior experience with the technologies used, this hackathon was an opportunity to learn and challenge ourselves while building something useful.

What it does

Dress2Impress allows users to upload a photo, and using Inditex's Visual Search API, it finds similar clothing items available in their stores. Users can save their favorite items to a personalized list, making it easy to revisit and compare options later.

How we built it

We developed the backend using Spring Boot to handle API requests and manage user data, while the frontend was built with React to provide a smooth and responsive user experience. The application integrates Inditex’s Visual Search API to retrieve product recommendations based on uploaded images. Challenges we ran into

Since none of us had experience with Spring Boot, setting up the backend and understanding how to properly integrate the API was challenging. We also had to quickly learn how to structure our project efficiently while ensuring smooth communication between the frontend and backend within the limited hackathon time.

Accomplishments that we're proud of

We successfully built a functional web application from scratch while learning new technologies along the way. We managed to integrate the Visual Search API and create a user-friendly interface, despite our initial lack of experience with the tech stack.

What we learned

We gained hands-on experience with Spring Boot for backend development and improved our skills in React for frontend development. Additionally, we learned how to work effectively as a team under time constraints and how to quickly adapt to new technologies.

What's next for Dress2Impress

In the future, we would like to enhance the application by improving the UI/UX, adding user authentication for a more personalized experience, and exploring additional features such as filters and recommendations based on user preferences.