CorePulse ⚡

Professional AI-Powered System Monitor for Windows, macOS, and Linux

Features • Installation • Usage • Documentation • Screenshots

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🌟 Overview

CorePulse is a production-grade system monitoring application that combines real-time performance tracking with AI-powered predictive analytics. Built with Python, FastAPI, and PyQt5, it delivers a modern, responsive interface for tracking CPU, RAM, disk, and network metrics across all major operating systems.

Why CorePulse?

• 🎯 Predictive Analytics - TensorFlow-powered LSTM models forecast resource usage
• ⚡ Lightning Fast - Sub-100ms API response times with intelligent caching
• 🎨 Modern UI - Beautiful dark-themed interface with real-time graphs
• 🔒 Secure - Localhost-only API, zero external connections
• 🌍 Cross-Platform - One codebase, runs everywhere
• 📦 Standalone - Builds to native executables (no Python required)

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✨ Features

Core Monitoring

• ✅ Real-Time Metrics - CPU, RAM, Disk, Network with 1-second refresh
• ✅ Live Graphs - Smooth, animated charts (2-minute rolling window)
• ✅ Process Tracking - Top resource consumers with sorting
• ✅ Thread Count - System-wide thread monitoring
• ✅ Historical Data - SQLite-backed data logging with auto-cleanup

AI & Predictions

• 🔮 ML Forecasting - LSTM-based predictions (10, 20, 30 minutes ahead)
• 📈 Trend Visualization - Dedicated prediction dashboard with gauges
• 🎯 Anomaly Detection - Smart alerts for unusual behavior
• 🔄 Auto-Training - Models retrain every 6 hours

User Experience

• 🎨 Modern Design - Gradient cards, glassmorphism, smooth animations
• 🌙 Dark Theme - Eye-friendly interface
• ⚙️ Customizable - Configurable thresholds and settings
• 🔔 Smart Notifications - Cooldown-based alerts (no spam)
• 🚀 Hybrid Mode - Backend runs independently or with UI

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🚀 Installation

Prerequisites

• Python 3.8 or higher
• 500 MB free disk space
• 500 MB RAM minimum

Quick Start

# Clone the repository
git clone https://github.com/yourusername/CorePulse.git
cd CorePulse

# Create virtual environment
python -m venv venv

# Activate virtual environment
# On Linux/macOS:
source venv/bin/activate
# On Windows:
venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Run CorePulse
python run.py

That's it! CorePulse will open automatically.

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💻 Usage

Running the Application

Option 1: Quick Start (Both Backend + UI)

python run.py

Option 2: 24/7 Monitoring (Recommended)

# Terminal 1: Start backend (keep running)
python start_backend.py

# Terminal 2: Launch UI (anytime)
python start_ui.py

Option 3: Standalone Executable

# Build first (one-time)
./build.sh  # Linux/macOS
build.bat   # Windows

# Run executable
./dist/CorePulse  # No Python needed!

Stopping CorePulse

• UI + Backend: Use Ctrl+Q or File → Quit All
• Backend Only: python stop_backend.py

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📖 Documentation

Document	Description
USAGE.md	Detailed usage guide and hybrid mode
CROSS_PLATFORM.md	Platform-specific setup instructions
BUILD.md	Building executables for distribution

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🎯 Architecture

┌─────────────────────────────────────────────────────────┐
│                    Desktop UI (PyQt5)                   │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐ │
│  │  Dashboard   │  │ Live Graphs  │  │ Predictions  │ │
│  └──────────────┘  └──────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────┘
                         ↕ HTTP
┌─────────────────────────────────────────────────────────┐
│               Backend API (FastAPI)                     │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐ │
│  │System Monitor│  │  ML Predictor│  │ Data Logger  │ │
│  └──────────────┘  └──────────────┘  └──────────────┘ │
└─────────────────────────────────────────────────────────┘
                         ↕
┌─────────────────────────────────────────────────────────┐
│         Data Layer (SQLite + TensorFlow Models)         │
└─────────────────────────────────────────────────────────┘

Technology Stack:

• Backend: FastAPI, psutil, TensorFlow, NumPy, Pandas
• Frontend: PyQt5, Matplotlib
• Database: SQLite
• ML: TensorFlow/Keras (LSTM networks)
• Build: PyInstaller

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Dashboard

Main monitoring dashboard with live metrics

Live Graphs

Real-time CPU and RAM graphs with 2-minute history

Predictions

AI-powered usage forecasting with trend visualization

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🛠️ Development

Project Structure

CorePulse/
├── backend/              # FastAPI server
│   ├── api/             # REST endpoints
│   └── services/        # Business logic
├── desktop/             # PyQt5 UI
│   └── widgets/         # UI components
├── data/                # SQLite database (runtime)
├── models/              # ML models (runtime)
└── dist/                # Built executables

Running Tests

# Backend
python -m pytest backend/tests/

# Integration
python -m pytest tests/

Building Executables

# Linux/macOS
./build.sh

# Windows
build.bat

Output:

• Linux: dist/CorePulse
• Windows: dist/CorePulse.exe
• macOS: dist/CorePulse.app

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🤝 Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

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📊 Performance

• API Response Time: <100ms
• UI Refresh Rate: 1 second
• Memory Footprint: ~150-200 MB
• CPU Usage: <5% idle, <10% active
• Startup Time: 2-3 seconds

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🔐 Security

• ✅ Backend binds to 127.0.0.1 (localhost only)
• ✅ No external network connections
• ✅ No telemetry or data collection
• ✅ All data stored locally

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📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

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🙏 Acknowledgments

• FastAPI - Modern web framework
• PyQt5 - Powerful GUI framework
• TensorFlow - ML predictions
• psutil - System monitoring

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📬 Contact

• Issues: GitHub Issues
• Discussions: GitHub Discussions

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