Pulse

Inspiration

We noticed that students and busy individuals often make poor energy and food decisions when stressed, tired, or pressed for time. We wanted a simple tool to help make real-time, personalized recommendations that boost focus and prevent energy crashes. My sister has an Oura ring that tracks the changes in her routine like sleep, when she eats, and when she exercises, but it doesn't give her advice on what to do with those stats. We wanted to expand that through our code!

What it does

Pulse takes inputs like sleep hours, stress level, available time, and goal, then calculates an energy score. It recommends the best and backup food choices and actionable steps to maintain focus, prevent burnout, and optimize energy throughout the day.

How we built it

-Tech stack: Python + Streamlit -Rule-based system for energy scoring -Interactive UI with sliders, dropdowns, and buttons -Randomized food recommendations filtered by time and goal -Functional prototype deployed via Streamlit Community Cloud

Challenges we ran into

-Handling multiple user inputs and filtering food options dynamically -Designing clear, intuitive UI for quick decisions -Fixing Python syntax errors (curly quotes vs standard quotes) to ensure the app runs

Accomplishments that we're proud of

-Built a fully functional real-time decision engine -Developed an energy scoring algorithm that adapts recommendations to user state -Deployed a live demo accessible via browser

What we learned

-How to turn simple heuristic rules into actionable insights -Streamlit’s capabilities for interactive web apps -Importance of UI/UX clarity when presenting personalized recommendations

What's next for Pulse

-Integrate real-time food/dining APIs for dynamic recommendations -Add wearable integration (sleep trackers, heart rate) -Introduce machine learning for personalized energy predictions -Expand to mobile-friendly interface and user accounts

Built With

• python
• vsc