Inspiration

It started with a simple idea: what if Kenya's disaster response could be powered by AI?

The Foundation We built the backbone first. A FastAPI backend backed by SQLite, seeding all 47 Kenyan counties, their geo-boundaries, field workers, and refuge sites. Data flowed in every 30 minutes from real sources — OpenWeatherMap for rainfall and temperature, USGS for seismic activity, NASA FIRMS for wildfire hotspots. A scheduler quietly watched it all.

Intelligence Then came Gemini AI. We taught it four critical jobs: score every county's risk, generate bilingual SMS alerts in English and Swahili, predict disasters 72 hours ahead, and write formal national situation reports for NDMA officers and the Cabinet Secretary. The system wasn't just collecting data — it was thinking.

The Dashboard A sleek React frontend came to life. A live map showed disaster pins pulsing across Kenya. A Risk Scores panel ranked all 47 counties. Workers could be dispatched with a click. SMS alerts could be fired to communities in seconds. All wrapped in a premium dark glassmorphism UI.

The Map Becomes a Heat Map The dots on the map weren't enough — you wanted to feel the intensity. So we rebuilt the map entirely. The whole country lit up county by county: Dark Red for Critical, Orange for High, Yellow for Low, Blue for Safe — a real choropleth heat map driven by live AI risk scores, matching the Kenya Met data you showed us.

Early Warnings and a Chatbot An AI Early Warnings Ticker appeared just below the header, quietly scrolling predicted threats with precise 72-hour timelines. And in the bottom corner, a floating KDMS Assistant — a Gemini-powered chatbot that a system administrator could ask anything: "which counties are at risk?", "how many workers are deployed?", "what's the national status?"

Shipped All of it committed and pushed to GitHub,

.env protected, ready for the world.

What was built: A full-stack AI-powered disaster management command center — real data, real AI, real impact. For Kenya. 🇰🇪

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