🌿 Aura — India's Localized Carbon Footprint Platform

Aura is a full-stack, AI-powered carbon footprint tracker and ecosystem feed built specifically for India. Designed with rich, cinematic dark-mode aesthetics, Aura empowers Indian households to measure, analyze, and offset their daily emissions—from auto-rickshaws and local trains to regional delicacies like Chicken Biryani versus Dal Rice.

---

🎯 Challenge Submission & Alignment

1. Chosen Vertical

• Vertical: Challenge 3 — Carbon Footprint Awareness Platform.
• Objective: Design a solution that helps individuals understand, track, and reduce their carbon footprint through simple actions and personalized insights.

2. Approach and Logic

• Natural Language Parsing: Users describe their day naturally. The platform extracts activities and computes carbon metrics.
• Cascading LLM Fallback Loop: To guarantee maximum uptime and survive free-tier rate limits, Aura utilizes a 6-stage model cascade (gemini-3.5-flash ➡️ gemini-2.5-flash ➡️ gemini-3.1-flash-lite ➡️ gemini-2.5-pro ➡️ gemini-2.0-flash ➡️ gemini-1.5-flash).
• Offline Heuristics: If the SDK loses internet connection or all Gemini API endpoints fail, a local regex-based heuristic parser extracts transport distances, food items, and energy usage.
• Efficiency Cache Layer: Implemented a server-side cache map with a 30-minute Time-to-Live (TTL) and eviction limits. Repeated requests are fetched instantly from local memory.
• Optimized Calculations: Data arrays are processed using React useMemo hooks to compute rolling weekly metrics, preventing main-thread rendering lag.

3. How the Solution Works

1. Onboarding: Users sign up securely via Firebase Auth (supporting Email/Password, Google OAuth, and GitHub OAuth).
2. Dashboard Logs: Users log their actions. The AI parses the input, visualizes the emissions breakdown, and suggests personalized localization tips (e.g. setting AC to 24°C).
3. Weekly Analytics: A weekly Recharts area chart visualizes cumulative carbon performance compared to daily targets.
4. Community Feed: Users share their carbon-saving actions. Aura's Net Savings Estimator compares the action against a high-carbon alternative (e.g., Chennai Metro vs. Uber Cab) to dynamically calculate the net kg of CO2 saved and details the audit explanation to the community.

4. Assumptions Made

• India GHG Metrics: Sourced and calibrated emission factor constants directly from the India GHG Program (e.g., Auto-rickshaw is 0.08 kg/km, Metro is 0.04 kg/km, Veg Meal is 0.8 kg, Chicken Biryani is 3.2 kg).
• Per-Capita Safety Limits: Assumed India's average daily target threshold is 5.2 kg CO2 per person based on annual averages.
• Default Baselines: Assumed a general baseline overhead of 1.2 kg CO2 if zero activities are identified during a tracking day.

---

📂 Project Structure

aura/
├── app/                            # Next.js App Router Pages & Layouts
│   ├── auth/                       # Authentication gateway (Email, Google, GitHub OAuth)
│   ├── community/                  # Real-time ecosystem savings post feed & estimator
│   ├── dashboard/                  # Main analytics console & input tracker logger
│   ├── globals.css                 # Custom HSL/OKLCH variables & tailwind directives
│   ├── layout.tsx                  # App layout wrapping Auth, Themes & Navbar/Footer
│   └── page.tsx                    # Landing Page with GSAP/Awwwards scroll animations
├── components/                     # Reusable React components
│   ├── layout/                     # Global layout structures (Navbar, Footer, Auth Provider)
│   └── ui/                         # Custom design system components
│       ├── analytics-charts.tsx    # Recharts graph memoized with useMemo hooks
│       ├── animated-counter.tsx    # GSAP numerical display counter
│       ├── floating-particles.tsx  # Canvas-based drifting molecular particles
│       ├── glass-card.tsx          # Awwwards-style glassmorphism cards
│       ├── sparkles.tsx            # Procedural particle sparkle overlays
│       └── tracing-beam.tsx        # Scroll-tracing SVG drawing vertical beam
├── lib/                            # Platform core utilities & integrations
│   ├── __tests__/                  # Vitest testing directory
│   │   └── carbon-engine.test.ts   # Unit tests covering fallbacks, diets & caching
│   ├── actions/                    # Next.js Server Actions
│   │   └── gemini-parser.ts        # AI Cascading Parser with in-memory caching
│   ├── data/                       # Carbon multipliers data layer
│   │   └── emission-factors.ts     # Localized transport, food & AC emission constants
│   ├── firebase/                   # Cloud database integration
│   │   ├── config.ts               # Modular Firebase config init
│   │   ├── auth.ts                 # Email, Google, GitHub OAuth auth helpers
│   │   └── firestore.ts            # CRUD operations with auto-sanitization for undefined fields
│   └── utils.ts                    # Classnames utility helpers
├── LICENSE                         # MIT License (Harshavardhan K copyright)
├── package.json                    # Configuration, scripts, and dependencies
├── tsconfig.json                   # Path mappings & TS specifications
└── vitest.config.ts                # Vitest configuration (jsdom environment)

---

⚙️ Local Development Setup

1. Prerequisites

Ensure you have Node.js 18+ and an active Firebase Project + Gemini API key.

2. Environment Variables Configuration

Create a .env.local file in the root directory:

# Firebase Configuration (Client-side)
NEXT_PUBLIC_FIREBASE_API_KEY=your_firebase_api_key
NEXT_PUBLIC_FIREBASE_AUTH_DOMAIN=your_project_id.firebaseapp.com
NEXT_PUBLIC_FIREBASE_PROJECT_ID=your_project_id
NEXT_PUBLIC_FIREBASE_STORAGE_BUCKET=your_project_id.appspot.com
NEXT_PUBLIC_FIREBASE_MESSAGING_SENDER_ID=your_messaging_sender_id
NEXT_PUBLIC_FIREBASE_APP_ID=your_app_id

# Gemini API Configuration (Server-side)
GEMINI_API_KEY=your_gemini_api_key

3. Installation & Run

# Install dependencies
npm install

# Start local server with Turbopack compilation
npm run dev

Open http://localhost:3000 in your web browser.

4. Running Tests

To run the automated Vitest test runner:

npm run test

5. Firestore Composite Indexes

To ensure Dashboard analytics load correctly, create the following composite indexes in your Firebase console:

1. Collection carbonEntries: userId (Ascending), createdAt (Descending)
2. Collection carbonEntries: userId (Ascending), date (Ascending)

---

🚀 Production Build & Deployment

Aura is completely optimized for production build pipelines.

# Verify build passes
npm run build

Vercel Deployment

1. Link your repository in Vercel.
2. Inject the contents of .env.local into the Environment Variables panel in Vercel settings.
3. Click Deploy. Vercel automatically builds, tests, and serves Aura globally.