NUS HacknRoll 2025
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
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.
Frontend: We used React Native to create a user-friendly mobile interface that works across devices. AI Integration: Leveraged ** 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.
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.
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.
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.
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
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.
- Clone the repository.
- Create a new file in the root directory, called "config.js".
- Insert your OpenAI API Key as follows:
""" // for environment variables // not pushed to git
const config = { OPENAI_API_KEY: 'YOUR-API-KEY-HERE', };
export default config; """
- run npm install
- run npm start
openai react-native