🌿 EcoPulse AI: Real-Time Environmental Intelligence

EcoPulse AI is a next-generation environmental health awareness platform. It combines high-performance streaming analytics with Generative AI to provide "living" city intelligence—interpreting complex sensor data into actionable human safety protocols.

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📑 Table of Contents

• Project Overview
• System Architecture
• Key Features
• Folder Structure Explanation
• Modular Design Principles
• Tech Stack
• Getting Started
• Example Usage
• Future Improvements
• Development & Quality
• License

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🌎 Project Overview

Traditional environmental monitoring systems provide static, historical data. EcoPulse AI bridges the gap by treating environmental data as a continuous stream. It doesn't just tell you the AQI was bad 2 hours ago; it uses Pathway to detect momentum peaks as they happen and OpenAI GPT-4o to issue immediate, intelligent safety mandates.

The system is designed for city administrators, health officials, and citizens who need real-time, scientifically-backed environmental guidance.

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🏗️ System Architecture

Our architecture is designed for low-latency event processing and scientific reasoning.

graph TD
    subgraph "Data Generation Layer"
        P[Kafka Producer Simulator] -->|JSON Telemetry| K[Apache Kafka]
    end

    subgraph "Processing Layer (Pathway)"
        K -->|Poll Stream| PW[Pathway CEP Engine]
        PW -->|Feature Extraction| AN[Analytics Module]
        AN -->|Volatility/Momentum| DB[(Live State)]
    end

    subgraph "Intelligence Layer"
        DB -->|Context| COP[Climate Copilot RAG]
        COP -->|Reasoning| API[Flask Web Service]
    end

    subgraph "Consumer Layer"
        API -->|Dashboards| UI1[City Admin View]
        API -->|PDF Reports| UI2[Gov Reporting]
    end

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🚀 Key Features

• ⚡ Real-Time Stream Processing: Sub-second analysis of AQI, PM2.5, CO2, and Meteorological factors using Pathway.
• 🧠 Climate Copilot (RAG Integration): A context-aware AI safety officer that interprets live data to provide human-readable advice.
• 📉 Root Cause Attribution: Scientifically calculates the percentage impact of Traffic vs. Industry on current pollution levels.
• 🏦 Gov-Grade Reporting: Export professional "Mayor Briefings" or "Full Environmental Audits" in PDF format instantly.
• 🌡️ Adaptive Alerts: Dynamic thresholds that adjust based on peak hours and historical volatility.
• 🔮 Urban Simulations: "What-if" scenarios to predict AQI based on hypothetical traffic reductions or industrial restrictions.

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📂 Folder Structure Explanation

The repository follows an enterprise-grade modular structure:

• ecopulse_ai/: Root package.
  • api/: The Presentation Layer. Contains Flask routes, user models, and the orchestration factory (app.py).
  • analytics/: The Core logic. Standalone scientific modules for calculating health scores, alerts, and time-series predictions.
  • kafka/: Infrastructure. Contains the producer script that simulates a multi-sensor city-wide mesh.
  • streaming/: The Data Backbone. Contains the Pathway pipeline logic and the "Windows Shim" for local development compatibility.
  • rag/: AI Intelligence. Prompt definitions and LLM orchestration for the Climate Copilot.
  • reports/: Documentation Layer. Logic for PDF generation and muni-report styling.
  • templates/ & static/: Frontend assets for the Flask web application.
  • tests/: Quality Assurance. Organized into unit and integration suites.

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🧩 Modular Design Principles

EcoPulse AI adheres to strict Senior Engineering standards:

1. Separation of Concerns: Analytics logic is decoupled from transport layers (Kafka/Pathway).
2. Stateless API: The Web layer acts as a pure proxy to the streaming state, ensuring horizontal scalability.
3. Defensive Programming: All telemetry inputs are validated and cast safely before computation.
4. Logging > Printing: Industry-standard logging is used across all modules for a professional audit trail.

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🛠️ Tech Stack

Layer	Tools
Streaming Engine	Pathway (Complex Event Processing)
Data Broker	Apache Kafka (High-throughput Messaging)
Logic Server	Flask 3.x, Python 3.10+
AI Reasoning	OpenAI GPT-4o, Custom RAG Orchestration
Frontend	Vanilla CSS (Glassmorphism), Chart.js 4.0, Leaflet.js
Reporting	fpdf2 (High-precision document generation)

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🚀 Getting Started

1. Prerequisites

• Python 3.10+
• A running Kafka broker (Local or Cloud-based)
• OpenAI API Key (for Copilot and Planner features)

2. Installation

# Clone the repository
git clone https://github.com/DhanushN2005/EcoPluse-AI.git
cd EcoPluse-AI

# Create and activate virtual environment
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

3. Configuration

Create a .env file in the root directory:

OPENAI_API_KEY=sk-xxxx...
KAFKA_BOOTSTRAP_SERVERS=localhost:9092
DEBUG=True

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🖥️ Example Usage

Launching the System

Launch the unified orchestrator to start all micro-services simultaneously:

python main.py

Typical Workflow

1. Monitor: Observe the live AQI gauges on the dashboard.
2. Simulate: Use the "What-if" slider to see how a 50% reduction in traffic would affect city-wide health scores.
3. Query: Ask the Copilot: "Should schools in the Industrial North district stay open today?"
4. Report: Push the "Generate Audit" button to export a signed PDF briefing for legislative review.

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🔮 Future Improvements

• 📡 IoT Edge Support: Direct MQTT ingestion for real-world sensor integration.
• 📱 Mobile Companion: React Native App for citizen-level push notifications.
• 🌦️ Weather Integration: Correlation models between humidity/pressure and pollutant entrapment.
• 🏛️ Blockchain Archiving: Immutable storage of environmental incident logs for policy accountability.

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🧪 Development & Quality

We maintain a strict Elite Quality Standard:

• Linting: Strict PEP8 adherence via Flake8.
• Format: Automated Black formatting.
• Type Safety: 100% coverage with Python Type Hints.
• Testing: Run pytest tests/ to ensure numerical accuracy of analytics.

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⚖️ License

Distributed under the MIT License. See LICENSE for details.

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Built with ❤️ by Dhanush N
Empowering Cities with Real-Time Environmental Vision.

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This repository has undergone a Final Quality Review and Senior Engineering Refactor (Feb 2026).