MacroMap

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  Possible macros, with available menu

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  Setting your goals

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  Target macro screen

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  User personalization

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  Generated user menu 1

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  Generated user menu 2

Inspiration

College students are increasingly focused on weight management, muscle building, and overall health. However, while dining halls provide nutritional data through Nutrislice, they do not personalize that information to individual fitness goals.

We noticed a gap: students have access to calorie and ingredient data, but they don’t have an easy way to translate that into actionable meal decisions aligned with their personal macro targets.

MacroMap was created to bridge that gap — connecting fitness goals directly to dining hall menus using AI and optimization.

What it does

MacroMap is a personalized meal planning system built around your macro targets.

It: • Calculates your maintenance calories using the Mifflin–St Jeor equation • Adjusts calories based on fitness goals (cut, maintain, bulk) • Sets a personalized protein target • Extracts real-time dining hall menu data from Nutrislice • Filters based on dietary restrictions • Generates optimized meal plans that match calorie and protein goals

Instead of manually tracking meals, students receive intelligent recommendations instantly.

How we built it

MacroMap was built using a full-stack architecture that integrates real-time dining hall data with intelligent optimization. The frontend provides a clean and intuitive interface where users input their body metrics, activity level, and dietary preferences. On the backend, we used Python with Flask to handle calorie calculations, macro target setting, and meal optimization logic. We implemented the scientifically validated Mifflin–St Jeor equation to estimate maintenance calories and adjust targets based on fitness goals. To retrieve daily menu data, we extracted structured information from Nutrislice’s API, including calories, protein values, and dietary tags. Our protein-prioritized optimization algorithm then generates meal combinations that meet calorie targets while maximizing protein intake and respecting dietary restrictions. Finally, we integrated Gemini 2.5 Flash to provide AI-generated explanations for meal recommendations, enhancing transparency and user trust.

Challenges we ran into

One of our biggest challenges was extracting and structuring real-time menu data from Nutrislice in a consistent and usable format. Once we obtained the data, we had to dynamically handle diverse dietary restrictions such as vegetarian, vegan, and gluten-free without breaking the optimization logic. Another major challenge was balancing calorie targets while maximizing protein intake, ensuring that meal plans were both nutritionally aligned and practical. Finally, we worked to create an interface that felt simple and intuitive, while still supporting complex backend calculations and AI-driven recommendations.

Accomplishments that we're proud of

We are proud of successfully integrating real dining hall data into a live optimization system that generates personalized meal plans in real time. We implemented the scientifically validated Mifflin–St Jeor equation to ensure accurate calorie estimation and built a fully functional macro-targeting algorithm that balances calorie goals while prioritizing protein intake. In addition to the technical achievements, we designed a clean and intuitive user interface that makes complex nutritional calculations feel simple and accessible. Most importantly, we delivered a practical solution that students can use immediately to make smarter dining decisions aligned with their fitness goals.

What we learned

• How to integrate external APIs into real-time systems
• The importance of user experience in technical products
• How AI can enhance explainability in decision-making systems
• The value of collaboration across technical roles

What's next for MacroMap

Looking ahead, we plan to expand MacroMap beyond UW–Madison by integrating dining data from additional universities, making the platform scalable nationwide. We aim to develop a dedicated mobile application to make personalized meal planning even more accessible and convenient for students on the go. Future updates will also include a “liked foods” feature, allowing users to prioritize preferred dining items while still meeting macro targets. Additionally, we plan to introduce a progress tracker that monitors calorie intake, protein consistency, and goal adherence over time, helping students visualize their nutritional progress and stay accountable.

Built With

• flask
• gemini
• python
• typescript