Inspiration

The gap between fuel cost discussions (which every Indian family has) and carbon emissions (which nobody discusses). We realized: if people could SEE their environmental impact in ₹ they save/spend, they'd actually care and act.

What it does

Verifies vehicle data with Trust Score (0-100%) Calculates carbon footprint + trees needed Shows fuel costs in ₹ (annual/monthly/weekly/per km) Simulates 5 "what-if" scenarios Provides Gemini AI insights in English/Hindi Visualizes with interactive 3D charts

How we built it

Frontend: HTML/CSS/JS + Plotly.js + Glass morphism UI Backend: Python Flask REST API AI: Google Gemini 2.0 Flash (new genai package) Charts: Plotly for interactive visualizations Languages: Full bilingual support (English/Hindi)

Challenges we ran into

Gemini API Deprecation: Had to migrate from google.generativeai to new google.genai package mid-development Bilingual Implementation: Making Hindi translations fit UI without breaking design Real-time Chart Updates: Ensuring Plotly renders correctly with dynamic data Indian Number Format: Implementing lakhs/crores formatting properly

Accomplishments that we're proud of:

First digital twin with Gemini AI integration at VIT Full bilingual support with real-time switching Trust Score algorithm that actually catches anomalies 5 scenario simulations with accurate ₹ savings Glass morphism UI that looks professional Working with 2026 Indian fuel prices (₹105/litre)

What we learned

Google's new genai package is faster than the old one Indians respond 10x better to ₹ savings than CO2 numbers Real-time language switching requires careful state management Plotly + Flask is a powerful combo for hackathons Always test with real user data (not just ideal scenarios)

What's next for EcoTwin AI

Mobile App: React Native version for on-the-go access Fleet Management: Dashboard for companies with multiple vehicles Government Integration: API for RTO to verify claims

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