Fitty
Overview
Fitty is a mobile application designed to help users manage and plan their outfits efficiently. The app allows users to upload their wardrobe, shuffle items to generate new outfits, and lock specific items to avoid reshuffling. Users can save their favorite outfits for future reference. Additionally, Fitty features a social feed where users can follow others, share their outfit creations, and interact with the community by liking posts. The app aims to provide a seamless and personalized experience, focusing on user engagement and outfit planning across devices.
Technology Stack
- Frontend: Flutter and Swift
- Backend: Firebase (Firestore for database, Firebase Authentication for user management, and Firebase Storage for image handling)
- Languages: Dart and Swift
- Other Tools: Firebase Cloud Functions for server-side logic, Firebase Analytics for tracking user engagement
Challenges
One of the main challenges was creating a flexible and intuitive outfit-shuffling mechanism. The algorithm needed to ensure that locked items remained in place while generating new outfit combinations based on color compatibility. Another challenge involved maintaining seamless data synchronization across devices so users could access their wardrobes and saved outfits from any platform without interruptions. Lastly, scaling the social feed to handle user interactions such as following, sharing, and liking outfits without affecting app performance required careful consideration, especially in terms of optimizing data queries and caching.
Solutions
To address the challenge of outfit generation, the team developed an algorithm that intelligently shuffles wardrobe items based on color palettes. This ensures that the generated outfits are aesthetically coherent, providing users with practical suggestions. To tackle the issue of data synchronization, Firebase’s real-time capabilities were utilized, allowing for smooth data updates across multiple devices, ensuring users experience consistent interactions with their wardrobe and outfits. For the social feed, caching mechanisms and optimized Firestore queries were implemented to handle a growing number of users, ensuring fast load times and efficient interactions.
Future Enhancements
Looking ahead, the plan is to incorporate machine learning to offer personalized outfit recommendations based on users’ preferences, usage patterns, and seasonal trends. This would elevate the app's ability to provide unique style suggestions tailored to each user. Additionally, expanding the social features is on the roadmap, with potential enhancements including the ability for users to comment on posts, participate in fashion challenges, and create groups for sharing outfits. Another possible future enhancement is integrating e-commerce functionalities, allowing users to purchase new items directly through the app, enhancing the wardrobe management experience.