This is a submission for Weekend Challenge: Earth Day Edition

EcoTrack AI — Track, Visualize & Reduce Your Carbon Footprint
This is my individual work, built entirely from scratch for the #weekendchallenge.

🔗 Links
🚀 Live Demo: ecotrack-ai-317275340485.asia-south1.run.app
💻 GitHub Repo: github.com/Gowtham280103/greenprint
💡 What is EcoTrack AI?
EcoTrack AI is a full-stack web application that helps users track, visualize, and reduce their daily carbon footprint using AI-powered suggestions, interactive charts, and gamification.

You enter your daily habits — travel, electricity, food, and shopping — and the app instantly calculates your CO₂ emissions, gives you an Eco Score, and generates personalized tips to help you live greener.

✨ Features
🎯 Daily Tracker — Log travel, electricity, food & shopping habits
📊 Eco Score (0–100) — Animated SVG ring showing your green rating
🟢 Impact Level — Low / Medium / High with color coding
🌳 CO₂ Equivalents — See your footprint as trees, flights, phone charges
🤖 AI Suggestions — Powered by Google Gemini API with smart local fallback
📉 7-Day Trend Chart — Line chart with global average reference line
🥧 Category Breakdown — Doughnut & pie charts via Chart.js
🏆 Badges & Gamification — Earn badges like "Green Warrior", "EV Rider", "Cyclist"
🎮 Daily Challenges — 9 eco challenges with XP rewards
🌙 Dark Mode — Full dark/light theme toggle with persistence
📱 Fully Responsive — Works on mobile, tablet, and desktop
📋 History Log — All past entries with Eco Score column
🛠️ Tech Stack
Layer Technology
Backend Python, Flask, Flask-CORS
Frontend HTML5, CSS3, Vanilla JavaScript
Charts Chart.js 4
AI Google Gemini API
Storage JSON file-based (no database needed)
Deployment Google Cloud Run + Docker
Font Inter (Google Fonts)
🏗️ Architecture
greenprint/
├── Dockerfile
├── .dockerignore
└── ecotrack/
├── backend/
│ ├── app.py # Flask API + static file serving
│ ├── calculator.py # Emission logic + AI suggestions + Eco Score
│ ├── storage.py # JSON persistence
│ └── requirements.txt
└── frontend/
├── index.html # 4-page SPA (Tracker, Dashboard, History, Challenges)
├── style.css # 700-line modern dashboard CSS with dark mode
└── app.js # 600-line frontend logic + Gemini integration
🚀 How to Run Locally

Clone

git clone https://github.com/Gowtham280103/greenprint.git
cd greenprint

Install dependencies

pip install -r ecotrack/backend/requirements.txt

Start server

python ecotrack/backend/app.py
Then open http://localhost:5000

☁️ Deployment
Deployed on Google Cloud Run using Docker. The container auto-scales to zero when idle (free tier friendly).

gcloud run deploy ecotrack-ai \
--source . \
--region asia-south1 \
--allow-unauthenticated
🧠 How the AI Works
The app uses rule-based logic with real emission factors from EPA & IPCC to calculate CO₂, then calls the Google Gemini API to generate personalized, context-aware suggestions like:

"Your 20 km petrol car trip contributes 3.84 kg CO₂. Switching to public transport 2 days/week saves ~1.1 kg CO₂/day — that's 286 kg/year!"

If no Gemini API key is set, a smart local fallback generates equally personalized insights.

📸 Screenshots