title	EcoTrack AI
emoji	🌿
colorFrom	green
colorTo	green
sdk	docker
pinned	false

🌿 EcoTrack AI : Carbon Intelligence Platform

AI-powered carbon footprint calculator with ML predictions, Groq AI insights, anomaly detection, AR product scanner, carbon offset marketplace, and community leaderboard. Hack2Skill Challenge 3 submission.

🔗 Live Demo: https://eco-track-ai-three.vercel.app
⚙️ Backend API: https://aaricacoding-ecotrack-ai.hf.space

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Challenge Requirements Coverage

✅ Carbon Footprint Calculation: Transport, Home Energy, Diet, and Shopping modules.

✅ Carbon Reduction Recommendations: Ranked tips sorted by CO₂ impact.

✅ Machine Learning Forecasting: 6-month predictive trends using Ridge Regression.

✅ AI-Powered Insights: Groq LLaMA footprint analysis and EcoBot coach.

✅ Community Features: Global anonymous ranking and footprint distribution.

✅ Sustainability Awareness: AR product scanner and offset marketplace

✅ Secure Authentication: Robust JWT lifecycle and password hashing.

✅ Accessibility Support: Semantic HTML5 and ARIA compliant UI.

✅ Responsive Design: Mobile-first dashboard and metrics tracking.

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What It Does

Feature	Detail
4-Step Carbon Wizard	Transport · Home Energy · Diet · Shopping — under 2 minutes
ML Trend Forecast	Polynomial Ridge Regression — 6-month carbon trajectory
Anomaly Detection	Isolation Forest detects unusual emission spikes in history
AI Insights	Groq LLaMA generates personalised narrative footprint analysis
EcoBot AI Coach	Multi-turn AI carbon coach — knows your exact footprint data
AR Carbon Scanner	Point camera at product barcode — instant CO₂ footprint overlay
Offset Marketplace	4 verified Indian carbon offset projects with real pricing
Community Leaderboard	Anonymous global ranking with footprint distribution chart
Smart Dashboard	Donut chart · comparison bars vs global/India benchmarks
Ranked Tips	Personalised actions sorted by CO₂ impact — highest savings first
Secure Accounts	JWT + bcrypt authentication with IP rate limiting

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Architecture

ecotrack-ai/
├── backend/
│   ├── app/
│   │   ├── core/        # config · security · database
│   │   ├── models/      # Pydantic schemas · SQLAlchemy ORM
│   │   ├── routers/     # auth · carbon · predictions · tips · insights
│   │   ├── services/    # carbon_calculator · ml_predictor · ai_insights · anomaly_detector
│   │   └── main.py      # FastAPI app · middleware · security headers
│   ├── tests/
│   │   ├── test_all.py          # Unit tests
│   │   └── test_integration.py  # Integration & API tests
│   ├── requirements.txt
│   └── .env.example
└── frontend/
    └── index.html       # Single-file SPA · 9 tabs · Chart.js · ARIA compliant

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Quick Start

cd backend
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
cp .env.example .env
uvicorn app.main:app --reload --port 8000

pytest backend/tests/ -v --tb=short

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API Endpoints

Method	Endpoint	Description
POST	/api/auth/register	Register new user
POST	/api/auth/login	Login and receive JWT
GET	/api/auth/me	Get current user profile
POST	/api/carbon/calculate	Calculate footprint (authenticated)
POST	/api/carbon/calculate/anonymous	Calculate footprint (no auth)
GET	/api/carbon/history	Get user calculation history
POST	/api/predictions/forecast	6-month ML forecast
POST	/api/tips/personalized	Get ranked reduction tips
POST	/api/insights/analyze	Groq LLaMA footprint analysis
POST	/api/insights/anomaly	Isolation Forest anomaly detection
POST	/api/insights/chat	EcoBot multi-turn conversation
GET	/api/community/stats	Global footprint distribution
GET	/api/community/rank/{user_id}	Get user community rank
GET	/health	Health check
GET	/	Root endpoint

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Security

Robust security controls implemented across the API surface:

• JWT Authentication: Strict token lifecycle management.
• Authorization Checks: Ownership validation on private resources (e.g., IDOR prevention).
• bcrypt Password Hashing: Salted and hashed credentials via passlib.
• Input Validation: Strict Pydantic v2 schemas and payload sanitization.
• Rate Limiting: IP-based throttles via slowapi on auth, AI, and ML endpoints.
• Content Security Policy (CSP): default-src 'none' API policy.
• HSTS Headers: Enforced Strict-Transport-Security.
• SQL Injection Protection: Pure SQLAlchemy ORM bindings.
• Environment Secret Management: Startup .env validation block.
• Global Exception Handling: Stack trace redaction and opaque 500 JSON responses.

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Testing : 68 Passing

The backend is backed by a robust, 100% passing test suite designed for stability and evaluation compliance.

• Unit Tests: Validating calculators, parsers, and utilities.
• Integration Tests: End-to-end database interactions and auth flows.
• Security Tests: Verifying bcrypt hashing, JWT tampering, and bounds rejection.
• ML Tests: Validating deterministic anomaly detection and positive regression forecasts.
• API Tests: Endpoint contract validation and error handling.

Category	Coverage / Focus
TestTransportCalculation	Zero input · petrol vs EV · flights
TestHomeCalculation	Solar · per-capita scaling
TestDietCalculation	Vegan vs omnivore · waste multiplier
TestRatingAndPercentile	All 5 tiers · monotonicity · bounds
TestFullFootprint	Total integrity · valid rating
TestMLPrediction	Determinism · output length · non-negative
TestInputValidation	Negative values · invalid enums
TestSecurity	bcrypt · JWT roundtrip · tampered token
TestAnomalyDetection	Spike detection · z-scores · determinism
TestInsightSchemas	AI request response validation
TestIntegration	Full request lifecycle, database state

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Accessibility

The frontend SPA implements inclusive design principles:

• Semantic HTML5: Proper heading hierarchy and landmark elements implemented.
• ARIA Labels: Interactive elements tagged for screen reader context.
• Keyboard Navigation: Tab-index support for forms and dashboard tabs.
• Responsive Design: Mobile-first media queries for seamless mobile/desktop usage.
• Screen Reader Support: (Standard browser support; advanced custom roles omitted for lightweight design).
• High Contrast Compatibility: (Pending explicit high-contrast toggle; native OS inversion supported).

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Performance Optimizations

• SQL query limits: Capped row fetches (e.g., limit(1000)) preventing memory exhaustion.
• Cached configuration via @lru_cache: Single-parse environment variable loading.
• Optimized SQLAlchemy queries: Minimized N+1 problems via targeted aggregations.
• Rate-limited AI endpoints: Prevents API quota exhaustion via Groq.
• Efficient ML inference: Lightweight scikit-learn models executed efficiently.
• Lightweight frontend architecture: Single-file Vanilla JS SPA minimizes asset payload.

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ML Models

Polynomial Ridge Regression (Trend Forecasting)

• PolynomialFeatures(degree=2) + Ridge(alpha=1.0, random_state=42)
• Captures realistic reduction curve — rapid early gains then plateau
• Uses real DB history when available, simulated otherwise

Isolation Forest (Anomaly Detection)

• IsolationForest(n_estimators=100, contamination=0.15, random_state=42)
• Trains on user personal history — not global thresholds
• Returns per-category z-scores and human-readable explanation

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Emission Factors

Category	Factor	Source
Petrol car	0.192 kg CO₂e/km	EPA 2023
EV India grid	0.053 kg CO₂e/km	CEA 2023
Electricity India	0.716 kg CO₂e/kWh	CEA 2023
Short-haul flight	255 kg CO₂e/trip	ICAO
Long-haul flight	1050 kg CO₂e/trip	ICAO
Vegan diet	1500 kg CO₂e/yr	Poore & Nemecek 2018
Global average	4000 kg CO₂e/yr	IPCC AR6
India average	1800 kg CO₂e/yr	World Bank

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Tech Stack

Layer	Technology
Backend	FastAPI · Python 3.11 · Uvicorn
Database	SQLAlchemy · SQLite
ML	scikit-learn · numpy
AI	Groq LLaMA 3.1 8B
Auth	python-jose · passlib bcrypt
Frontend	Vanilla JS · Chart.js · BarcodeDetector API
Deployment	HuggingFace Spaces · Netlify
Testing	pytest · 62 tests

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License

MIT License : Built for Hack2Skill Challenge 3