MeatAndGreet

Inspiration

Hotpot is a communal dining experience cherished by many, but the planning and execution often involve guesswork and coordination challenges. We wanted to create a platform that enhances the hotpot experience by simplifying logistics, ensuring perfect cooking times, and personalizing the meal based on everyone's preferences. After all, I am the one hosting and I was kinda lazy to coordinate :p

What it does

MeatAndGreet is a cross platform react native app designed to streamline hotpot planning and elevate the dining experience. Key features include:

• Session Planning: Users can create or join a hotpot session, allowing seamless coordination of ingredients and preferences.
• AI-Driven Suggestions: Using chatGPT and real-time data from Fairprice, the app recommends ingredient pairings that cater to everyone's preferences and ensure the best hotpot combinations to keep everyone happy.
• Built-in Timers: Each ingredient comes with a cooking timer, ensuring meats and vegetables are cooked just right.

How we built it

• Frontend: We used React Native to create a user-friendly mobile interface that works across devices.
• AI Integration: Leveraged openAI for AI-based ingredient recommendations. Data is fetched from Fairprice's API to provide real-time pricing and availability.
• Database: Used Firebase to store user preferences, hotpot session details, and ingredient timers.
• Timers: Implemented with JavaScript and integrated directly into the app, with a smooth user interface to display real-time progress.

Challenges we ran into

• Data Integration: Pulling real-time data from Fairprice and ensuring accuracy in AI suggestions was tricky due to API limitations and inconsistencies.
• Timer Precision: Calibrating ingredient timers to work for a variety of ingredients and preferences required extensive testing and fine-tuning.
• User Coordination: Designing a seamless user experience for multiple people to join and manage a single session presented challenges in UI/UX and backend synchronization.
• AI Complexity: Developing a recommendation engine that accounts for individual and group preferences, ingredient pairings, and availability was a complex but rewarding task.

Accomplishments that we're proud of

• Successfully integrated real-time data from Fairprice to provide intelligent and relevant suggestions.
• Developed an intuitive, multi-user session management system that simplifies the logistics of a communal meal.
• Created a built-in timer system that ensures perfect cooking for a wide variety of hotpot ingredients.
• Designed an engaging and accessible interface that makes hotpot planning fun and collaborative.

What we learned

• Collaborative Design: Building features for group use requires careful consideration of synchronization and usability.
• AI Personalization: Balancing personalization with real-time data input can create a powerful and engaging experience for users.
• APIs and Real-Time Data: Working with third-party APIs taught us the importance of error handling and data validation.
• Time Management: Building a complex project with multiple features within a limited timeframe pushed us to prioritize and iterate quickly.

What's next for MeatAndGreet

• Expanded Ingredient Database: Incorporate data from other supermarkets and local grocers for broader coverage.
• Dietary Preferences: Add advanced filters for dietary restrictions like vegan, gluten-free, or halal/kosher options.
• Gamification: Introduce badges and rewards for frequent users or creative hotpot combinations/who is the biggest eater :p

Additional Features

• Social Features: Enable users to share their hotpot creations or invite friends via social media.
• Custom Timers: Allow users to input their own cooking preferences for ingredients.
• Recipe Sharing: Provide a space for the community to share their unique hotpot recipes and ideas.

Built With

• openai
• react-native