title	EcoNav AI
emoji	🌍
colorFrom	green
colorTo	blue
sdk	docker
app_port	7860
pinned	true

🌱 EcoNav AI — Exposure Credit Platform

EcoNav AI is an intelligent routing platform and Reinforcement Learning (RL) environment built for the Meta AI Environmental Decision Intelligence Hackathon. It empowers users and agents to navigate urban networks while minimizing pollution exposure and maximizing health "Exposure Credits."

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🌍 Project Overview

EcoNav AI transforms navigation from a simple distance-minimization problem into a multi-objective environmental optimization task. By integrating real-time Air Quality Index (AQI) data with dynamic traffic simulations, the platform provides a realistic testing ground for eco-aware agents.

Key Innovations:

• Live AQI Integration: Real-time pollution data fetched from the Open-Meteo API for 50+ network nodes across India.
• Exposure Credits: A gamified health currency that penalizes high-pollution segments (Grade F) and rewards clean-air segments (Grade A).
• Dynamic Traffic Engine: Simulates congestion levels that impact both travel time and localized pollution exposure.
• OpenEnv Compliance: Fully compatible with the OpenEnv specification for RL evaluation and agent deployment.

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🛠 Tech Stack

The project is architected as a modern monorepo for seamless development and deployment.

• Backend: Python 3.10+, FastAPI, Uvicorn, Pydantic, Torch (ML scoring).
• Frontend: Vite, Vanilla JS, CSS3 (Glassmorphism), Leaflet.js (Mapping), Chart.js (Analytics).
• Infrastructure: Docker, Turbo (Build system), GitHub Actions (CI/CD).
• Quality: Ruff (Linting), Prettier (Formatting).

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📂 Repository Structure

EcoNav-AI/
├── apps/
│   ├── backend/         # FastAPI server, AI services, and RL endpoints
│   ├── frontend/        # Vite-powered dashboard and mapping interface
│   ├── simulator/       # ML training and evaluation logic
│   └── web/             # (Deprecated) Static web assets
├── packages/
│   └── env_core/        # Shared RL environment logic (OpenEnv Spec)
├── requirements/        # Modularized dependency lists
├── server/              # Production entry points for Docker/HF Spaces
├── turbo.json           # Monorepo configuration
└── inference.py         # Baseline agent evaluation script

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🚀 Getting Started

Prerequisites

• Node.js: >= 18.0.0
• Python: >= 3.10
• npm: >= 11.11.0

Quick Start (Recommended)

1. Clone the repository:

   git clone https://github.com/omdharb-bit/EcoNav-AI.git
   cd EcoNav-AI

2. Install Dependencies:

   npm install      # Installs monorepo build tools
   pip install -r requirements/backend.txt  # Installs Python backend deps

3. Run Everything: Use the Turbo-powered development command to start both the backend and frontend:

   npm run dev

   Note: If running on Windows, you may need to set PYTHONPATH="." manually if starting the backend via script.

Manual Startup

• Backend: python server/app.py (Runs on http://localhost:7860)
• Frontend: npm run dev --prefix apps/frontend (Runs on http://localhost:5173)

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🧠 RL Evaluation (OpenEnv)

The environment supports four standard evaluation tasks of increasing complexity:

1. easy_route: Delhi to Kolkata (15 steps).
2. medium_route: Delhi to Kolkata (8 steps).
3. hard_pollution_dodge: Agra to Kolkata (6 steps).
4. expert_credit_max: Maximize credits while reaching the goal (10 steps).

Baseline Evaluation:

export ENV_URL="http://127.0.0.1:7860"
python inference.py

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🧪 Development & Quality

We maintain high code quality standards through automated linting and formatting.

• Linting (Python): ruff check .
• Fix Linting: ruff check --fix .
• Formatting (JS/TS/MD): npm run format

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🏆 Project Status

Check	Status
CI/CD Pipeline	Passing ✅
OpenEnv Spec	Compliant (v1.0) 🟢
Real-time Data	Active (Open-Meteo) 🛰️
Model Version	EcoScorer v2.1 🧠

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📜 License

Distributed under the MIT License. See LICENSE for more information.