Inspiration
Nagpur generates over 1,800 tonnes of waste every single day. Yet when we looked at the data, we found something shocking — NMC supervisors were receiving huge Excel tables full of numbers every month with absolutely no explanation of what those numbers meant or what to do about them. One zone would be performing brilliantly while the zone right next to it was struggling — and nobody knew why, because nobody had the time or tools to dig into the data.We also found that Nagpur dropped from 18th rank in 2020 to 86th in 2023 in India's national cleanliness survey. That's not a small drop — that's a collapse. And a big part of the reason is poor internal coordination and no system for learning from data.
What it does
The AI KPI Analyst is a simple web dashboard that lets anyone in NMC ask plain English questions about waste collection performance across all 10 zones of Nagpur
How we built it
We built it in three parts that work together. The first part is a Python file that acts as the brain. It reads the KPI data from a CSV file, takes the question the user typed, combines them into a smart prompt, and sends everything to the Google Gemini API. When Gemini replies, the Python file sends that answer back to the browser. The second part is the Google Gemini AI. We gave it a specific role — "you are an NMC waste management analyst" and fed it the real 2024 KPI data with every question. This way Gemini always answers based on actual numbers, not general knowledge. The third part is the dashboard a clean dark-themed web page built in HTML and JavaScript. It has a zone list on the left with live KPI scores, a chat window in the middle, quick question buttons, a zone selector dropdown, and the Monthly Report button. Everything talks to the Python backend running on the laptop.
Challenges we ran into
Accomplishments that we're proud of
We are proud that the dashboard actually works end to end a real question gets a real data-backed answer from real NMC 2024 data. That feels meaningful.
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