A modern, AI-powered system monitoring and optimization suite for Windows, macOS, and Linux. Features real-time performance dashboards, analytics, benchmarking, and automated optimization in a user-friendly CustomTkinter GUI.
- Key Features
- Screenshots
- Project Objectives
- System Architecture
- Technology Stack
- Performance Metrics
- Installation & Setup
- Usage Guide
- Advanced Features
- Security & Privacy
- Configuration
- Testing & Validation
- FAQ & Troubleshooting
- Team & Contact
- License
- Real-time Performance Monitoring
- Multi-metric system monitoring (CPU, Memory, Disk, Network, Temperature)
- High-frequency data collection with minimal system overhead
- Historical data analysis and trend identification
- Cross-platform compatibility (Windows, macOS, Linux)
- AI-Powered Optimization Engine
- Machine learning-driven performance analysis
- Predictive bottleneck detection and prevention
- Automated optimization recommendation system
- Adaptive learning from user behavior and system patterns
- Advanced Data Visualization
- Real-time interactive charts and graphs
- Modern, responsive GUI using CustomTkinter
- Multiple chart types for comprehensive analysis
- Export capabilities for reports and further analysis
- Comprehensive System Tools
- System benchmark testing suite
- Process manager with advanced controls
- System cleanup and maintenance tools
- Emergency optimization capabilities -Database persistence for historical data
Main Page
- graphs for CPU usage, Memory usage, Disk usage
- graphs for Network usage, Temperature (currently unavailable), System Health Score
- System Optimization Page
- Performance Analytics & Historical Data Page
- System Benchmark & Performance Testing Page
- System Information Page
- Settings
- Theory page
- Team Info page
- Help page
- Primary Goals
- Real-time Performance Monitoring - Multi-metric system monitoring with minimal overhead
- Intelligent AI-based Optimization - Machine learning-driven performance analysis
- Advanced User Interface Design - Modern, responsive GUI with real-time visualization
- Comprehensive Reporting - Detailed performance reports in multiple formats
- System Integration - Seamless integration with operating system APIs
- Secondary Goals
- Educational Value - Demonstrate advanced programming concepts
- Research Platform - Foundation for performance analysis research
- Extensibility - Modular architecture for easy feature additions
- Security - Secure handling of system information
- Scalability - Support for monitoring multiple systems
The application employs a sophisticated multi-layered architecture:
- Presentation Layer (CustomTkinter GUI)
- Model-View-Controller (MVC) pattern
- Tabbed interface system with custom widgets
- Theme management and event handling
- Business Logic Layer (Core Processing)
- Service-Oriented Architecture (SOA)
- Performance monitoring, AI analysis, and optimization services
- Alert management and data persistence
- Data Access Layer (System Integration)
- Repository pattern for system metrics
- SQLite database integration
- Configuration management
- AI Layer (Intelligence Engine)
- Pipeline architecture for data processing
- Statistical analysis and anomaly detection
- Rule-based expert systems
- Core Technologies
- Python 3.8+ - Cross-platform development language
- CustomTkinter - Modern GUI framework with theming
- Matplotlib - Real-time data visualization
- psutil - Cross-platform system monitoring
- SQLite - Embedded database for data persistence
- Supporting Libraries
- NumPy - Numerical computations
- Threading - Concurrent operations
- JSON/CSV - Data serialization and export
- Collections - Advanced data structures
- Optional Enhancements
- ReportLab - Advanced PDF generation
- Requests - HTTP client for cloud integration
- System Metrics Monitored
- CPU: Usage percentage, per-core distribution, temperature, frequency scaling
- Memory: Physical/virtual memory usage, availability, allocation patterns
- Storage: Disk space utilization, I/O operations, throughput, health indicators
- Network: Bandwidth usage, packet statistics, connection rates
- System Health: Overall health score, component indicators, stability metrics
- Application Performance
- Startup time and initialization speed
- Memory footprint during monitoring
- CPU overhead (< 2% during normal operation)
- Data collection accuracy (±1% for CPU, ±0.5% for memory)
- Real-time chart rendering (< 100ms updates)
- Minimum: Python 3.6+, 4GB RAM, 100MB storage
- Recommended: Python 3.8+, 8GB RAM, SSD storage
Installation Steps
- Clone the repository
git clone https://github.com/your-username/system-performance-analyzer.git
cd system-performance-analyzer- Install Dependencies
pip install customtkinter matplotlib psutil numpy- Optional Dependencies
pip install reportlab requests # For PDF export and cloud features- Run the Application
python final.pyQuick Start
- Launch the application - monitoring starts automatically
- Navigate through tabs to explore different features
- Check the Dashboard for real-time system metrics
- Use AI Optimizer for intelligent system recommendations
- Customize settings in the Settings tab
- Export reports for analysis and record-keeping
Main Interface Tabs
- Dashboard
- Real-time performance metrics with enhanced cards
- Interactive charts showing CPU, memory, disk, network usage
- System health score and alerts panel
- Quick action buttons for optimization and cleanup
- AI Optimizer
- Intelligent performance analysis and recommendations
- Automated optimization suggestions
- System health assessment
- Deep analysis capabilities
- Analytics
- Historical performance data analysis
- Time-range filtering (hour, day, week, month)
- Performance trends and patterns
- Statistical summaries
- Benchmark
- CPU, memory, and full system benchmarks
- Performance scoring and comparison
- Detailed benchmark results and analysis
- System Info
- Comprehensive system information display
- Hardware specifications and status
- Network interface details
- System uptime and boot information
- Settings
- Theme customization (light/dark mode)
- Performance monitoring configuration
- Alert threshold settings
- Notification preferences
- AI Analysis Engine
- Statistical Analysis: Moving averages, trend detection, correlation analysis
- Anomaly Detection: Threshold-based and pattern-based detection
- Expert System: Rule-based recommendations with inference engine
- Predictive Modeling: Time series forecasting and performance prediction
- Benchmark Testing
- CPU Benchmarks: Integer operations, floating-point, multi-threading
- Memory Benchmarks: Allocation, access patterns, copy operations
- Disk I/O Testing: Read/write performance analysis
- Overall System Scoring: Comprehensive performance evaluation
- Export & Reporting
- PDF Reports: Comprehensive performance analysis with charts
- CSV Data Export: Raw performance data for external analysis
- Historical Analysis: Trend analysis and pattern recognition
- Automated Scheduling: Configurable monitoring intervals
- Security Measures
- Read-only system monitoring (no unauthorized modifications)
- Local data storage with encryption options
- User permission validation for system operations
- Audit logging of optimization actions
- Privacy Protection
- No network transmission without explicit consent
- Anonymization options for exported data
- User control over data collection scope
- GDPR-compliant data handling
- Monitoring Settings
- Refresh rate: 1-10 seconds (configurable)
- Data retention: Up to 1 million data points
- Alert thresholds: Customizable for all metrics
- Logging: Enable/disable database persistence
- Performance Optimization
- Adaptive sampling rates based on system load
- Efficient data compression for storage
- Memory management with bounded data structures
- Multi-threaded architecture for responsiveness
- Performance Benchmarks
- Application startup: < 3 seconds
- Memory footprint: 30-50MB
- CPU overhead: < 2%
- Monitoring accuracy: ±1% (validated against system tools)
- Compatibility Testing
- Windows 10/11, macOS 10.14+, Linux (Ubuntu, CentOS, etc.)
- Python 3.6+ compatibility
- High DPI display support
- Multi-monitor configurations
- How do I start monitoring?
- Launch the app; monitoring starts automatically in the Dashboard.
- How do I change refresh rate or thresholds?
- Go to the Settings tab and adjust the sliders.
- Where is my data stored?
- All data is stored locally in performance_data.db.
- Export fails or charts not updating?
- Ensure all dependencies are installed.
- Check file permissions and disk space.
- Restart the application if needed.
- More help?
- See the Help tab or contact the team (below).
Team Architechs
- Harshit Jasuja (Team Lead & System Architect)
- Yashika Dixit (UI/UX Designer)
- Shivendra Srivastava (Performance Engineer & QA Lead)
- For support, feature requests, or collaboration:
- Email: [email protected]
- This project is developed as an academic project by the Architechs Team for SE(OS)-VI-T250. All rights reserved.
- For collaboration, feature requests, or partnership opportunities, please contact the development team.
- © Team Architechs
- Built with Python, CustomTkinter, Matplotlib, psutil, and more.
- Special thanks to all open-source contributors.