💡 Smart Community Hub (CivicNexus) – Live AI Dashboard

📋 Table of Contents

• Project Overview
• Agents Overview
• How to Run Locally
• Deployment Note for Streamlit Cloud
• Running Locally vs Cloud Deployment
• Manual Simulation Instructions

A real-time, modular edge-AI dashboard built to simulate and monitor smart community infrastructure in underserved areas.

This project showcases live data agents for:

• 💧 Water leak detection
• 🔋 Energy usage forecasting
• 📶 Wi-Fi load balancing
• 📘 Digital literacy behavior tracking

Built using Python + Streamlit, this dashboard helps visualize AI-powered insights in supporting UN Sustainable Development Goals:

• Goal 4: Quality Education — through adaptive digital literacy agents
• Goal 6: Clean Water and Sanitation — via water leak detection and usage optimization
• Goal 7: Affordable and Clean Energy — with predictive modeling and local demand balancing
• Goal 9: Industry, Innovation, and Infrastructure — through smart, deployable systems
• Goal 10: Reduced Inequality — by expanding equitable access to intelligent infrastructure
• Goal 11: Sustainable Cities and Communities — improving resilience in service delivery
• Goal 17: Partnerships to Achieve the Goal—enabling cooperation with municipalities, tech partners, and communities

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🧠 Agents Overview

Agent	Description
Leak Detection	Flags abnormal water usage in real time
Energy Forecast	Predicts energy spikes using rolling averages
Wi-Fi Load Balancer	Tracks bandwidth across 3 access points
Digital Literacy	Evaluates user internet habits and flags low educational use
System Log	(To be upgraded) Centralized system insight

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🚀 How to Run

1. Clone the repo

git clone https://github.com/MissK143/SmartCommunityHub.git
cd SmartCommunityHub

# Install dependencies
pip install -r requirements.txt

# Run simulators in separate terminals:
python leak_stream_simulator.py
python energy_stream_simulator.py
python wifi_stream_simulator.py
python digital_literacy_stream_simulator.py

# Run the dashboard
streamlit run smart_hub_dashboard_local.py

📚 About This Project Setup

This project was originally designed with modular architecture, separating:

• Real-time simulators (data generators)
• Dashboard (data visualization)

This is the professional way to build scalable edge-AI systems.

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☁️ Special Note for Deployment

Because platforms like Streamlit Cloud allow only a single script to run, a special deployment version of the dashboard was created:

• Simulators are integrated directly into the dashboard
• No external terminal processes required

✅ For local development, simulators run separately to preserve modular architecture.
✅ If deploying, use the integrated version for smooth cloud hosting.

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🖥️ Running Locally vs Cloud Deployment

File Name	Purpose
smart_hub_dashboard_local.py	Local development with multiple simulators running manually
smart_hub_dashboard_live.py	Streamlit Cloud version with integrated manual simulation button

When deploying to Streamlit Cloud, use smart_hub_dashboard_live.py. When working locally with separate simulators, use smart_hub_dashboard_local.py.

🔄 Manual Control Instructions

This Smart Hub dashboard uses manual simulation for real-time data.

• To simulate new water, energy, Wi-Fi, and literacy data, click the "Simulate New Data" button in the sidebar.
• This prevents data loss and allows better control over real-time behavior.
• The dashboard does not auto-refresh by itself — user triggers updates manually.

This design improves performance and stability, especially when deployed on Streamlit Cloud.