The idea for Fourfit originated from the need to help individuals discover and enhance their fashion sense by offering personalized clothing suggestions based on their uploaded images. We aimed to create an engaging and user-friendly platform that utilizes AI technology to analyze outfits and provide tailored recommendations, helping users feel more confident in their fashion choices.

Fourfit allows users to upload images of their outfits and receive instant feedback and fashion suggestions. The AI analyzes the uploaded image, identifying clothing items and their styles, and offers complementary items or fashion tips to enhance the overall look. By providing a personalized experience, Fourfit helps users make informed fashion choices, elevating their style effortlessly.

We built Fourfit using Python for the backend and JavaScript with React for the frontend. The application integrates AWS services for image storage and processing. Using TensorFlow, we developed machine learning models that analyze clothing items in the uploaded images and generate fashion suggestions. We also utilized various APIs to enhance user experience and ensure quick response times for outfit evaluations.

One significant challenge was ensuring the accuracy of clothing item recognition and suggestions. Fine-tuning the machine learning models to deliver precise recommendations required extensive testing and iteration. Additionally, managing the image processing and storage efficiently was crucial to maintain performance.

We are proud of the seamless integration of AI technology in providing personalized fashion recommendations. Creating a responsive and visually appealing user interface was another achievement, along with building a robust backend that efficiently processes user-uploaded images. We’re excited about the potential impact Fourfit can have on users' confidence and style.

Through this project, we learned about integrating machine learning with web applications, improving our skills in Python and React, and understanding the nuances of user experience design in the fashion tech industry.

We plan to enhance the current dataset with more clothing variety using vector search on several brands' datasets/collections, providing features such as adding trackers for drops, sales, and stock, and expanding clothing recommendations with features like tag exploration, and saving/sharing digital outfits. Enhanced user interaction tracking will also provide better personalized suggestions.