Inspiration
What it does## Inspiration
Valentine’s Day is supposed to be “happy,” but a lot of people actually feel extra pressure, loneliness, or emotional swings around it. We wanted to build something that feels gentle, quick, and non-judgmental—a tool that helps you notice patterns in your emotional health and take small steps before stress quietly builds up.
What we built
LoveCare is a lightweight emotional health check-in app. In under a minute, users log:
- mood, stress, energy, sleep
The app turns these signals into a simple weekly snapshot and a clear, supportive summary, including:
- trends (e.g., mood downtrend, sleep deficit)
- volatility (how much things swing day-to-day)
- a risk label (Green / Yellow / Red) with reasons and micro-actions
> Disclaimer: LoveCare provides wellness insights only — not medical diagnosis.
How it works (high level)
We designed a simple scoring system that balances interpretability and usefulness.
- Baseline levels: average mood/stress/energy and sleep compared to a target (e.g., 8 hours).
- Trends: we check whether mood/energy is steadily dropping over the week.
- Volatility: we measure how unstable the daily signals are (large swings can indicate strain even if the average looks “fine”).
- Risk score → label: we map the combined score to Green / Yellow / Red and generate human-readable reasons.
This keeps the logic transparent so users can understand why a label was assigned.
Tech stack
- Frontend: React (TypeScript)
- Backend: FastAPI (Python)
- Storage: lightweight database (SQLite) for users and check-ins
- API: endpoints for login, bulk check-ins, and report generation
Challenges
- Designing “helpful but safe” insights: we avoided medical claims, focused on supportive language, and always included a disclaimer.
- Scoring calibration: we iterated on weights so that one bad day doesn’t overreact, but consistent sleep deficit/high stress does surface.
- Clean UX under hackathon time: keeping the flow fast (check-in in < 60 seconds) while still producing meaningful output.
What we learned
- Simple, interpretable metrics can feel more trustworthy than a black-box model for wellness use cases.
- Good product language matters: tone + framing can change whether users feel supported or judged.
- Building end-to-end (frontend + backend + scoring) taught us how to ship a cohesive experience quickly.
Next steps
- Personalization (user-set sleep targets, different baselines)
- Optional reminders and streaks (without guilt)
- Better reflection analysis (theme extraction, mood triggers)
- More accessibility features and privacy controls
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