SnackIt

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Inspiration: We noticed that many people struggle to decide what to cook based on what’s already in their kitchen. Wasting ingredients or constantly searching for recipes inspired us to create a solution that makes cooking simple, fun, and interactive.

What it does: SnackIt allows users to manually add ingredients and their quantities, then suggests recipes they can make with those items. Users can filter recipes by cuisine, dietary preference, or cooking time, save favorites, and share recipes and tips with the community.

How we built it: The app is built in Java for Android, using Supabase as the backend to manage user data, recipes, and real-time updates. We integrated recipe APIs like Spoonacular, Edamam, and TheMealDB for recipe suggestions, and designed a clean, intuitive interface to make manual ingredient input and recipe browsing easy.

Challenges we ran into: We faced challenges in handling dynamic recipe matching based on varying ingredient quantities and user input errors. Designing a scalable community feature and ensuring smooth real-time updates with Supabase also required careful planning.

Accomplishments that we're proud of: We successfully implemented a working recipe suggestion engine that adapts to user inputs and quantities, and created a reliable backend with Supabase that handles real-time data and authentication seamlessly. For now, we created dummy community page because our main facus on fatching recepies.

What we learned: We learned how to effectively map ingredients to recipes, manage a cloud backend with Supabase, handle user-generated content, and design a mobile app that balances usability with functionality. We also gained experience integrating multiple APIs for recipe data.

What's next for SnackIt: Future plans include AI-powered recipe recommendations, trending and seasonal recipe highlights, smarter ingredient suggestions based on leftovers, and expanding community features to include user ratings, comments, and contests for recipe sharing.