HeatShe

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  Risk input form for district, occupation, hours, age, pregnancy, and temperature.

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  Explainable risk result with score, breakdown, and safety guidance.

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  District heat map view showing Karnataka-wide vulnerability patterns.

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  Project homepage with the hero message and live demo entry point.

Inspiration

HeatShe came from a simple but urgent question: how can we help women outdoor workers make safer decisions during extreme heat?

Regular weather apps only tell temperature. They do not explain what that heat means for a brick kiln worker, a sanitation worker, or a farm worker spending hours in the sun. I wanted to build something more human, more practical, and more action-ready.

What it does

HeatShe turns live weather, district-level heat risk, occupation, age, pregnancy status, and outdoor work duration into one clear risk score. It then explains the result in simple language and gives a next action, so the user knows whether to continue, slow down, or stop work.

How I built it

I built HeatShe as a solo project using HTML, CSS, and vanilla JavaScript for the interface, with a modular risk engine for the scoring logic. The app is deployed on Vercel and uses Open-Meteo for live weather. I kept the system lightweight so it can run well on low-end devices and remain easy to use in real-world conditions.

What I learned

This project taught me how important clarity is in AI systems. A score is not enough unless people can understand why it was generated. I learned how to combine data, explainability, and design into one experience that feels useful instead of overwhelming.

Challenges I faced

The biggest challenge was balancing accuracy with simplicity. I had to make the scoring system strong enough to feel credible, but still easy for a user to understand in seconds. Another challenge was making the design feel premium while keeping the app fast and lightweight.

Built With

• css
• html
• javascript
• open-meteo-api
• vercel