Realtime_Process_Dashboard_CSE316

ACADEMIC TASK II - Group Project

Group Members:
1. Shraddha Gupta - 12304195
2. Khushi Gupta - 12303449
3. Shreyansh Tripathi - 12324670

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Project Overview

The Real-Time Process Monitoring Dashboard is a Python-based application that provides a graphical interface to monitor system metrics in real-time. This tool enables users to track CPU usage, memory usage, and active processes while offering features such as visual alerts and process management. Built with Python libraries like psutil, Tkinter, and Matplotlib, the dashboard is a practical tool for system administrators and developers.

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Features

• Real-Time Monitoring: Displays live updates of CPU, memory, and process details.
• Interactive Dashboard: Includes charts, tables, and alerts for an engaging user experience.
• Process Management: Allows process filtering, sorting, and interaction (e.g., termination and priority adjustment).
• Historical Visualization: Shows CPU usage trends in a line chart.
• Proactive Alerts: Provides visual indicators for CPU usage exceeding thresholds.

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Installation

1. Clone the Repository:

   git clone https://github.com/shreyanshtripathi-01/Realtime_Process_Dashboard_CSE316.git
   

2. Navigate to the Project Directory: cd Real-Time-Process-Monitoring-Dashboard

3. Install Dependencies pip install -r requirements.txt

4. Run the Application: python dashboard.py

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Change Log

Day 1:

• Created the repository and initialized the project structure.
• Added the base README.md file.

Day 2:

• Integrated the psutil library to fetch system metrics (CPU, memory, processes).

Day 3:

• Built the data collection module for periodic polling and normalization of metrics.

Day 4:

• Designed the basic Tkinter window layout with placeholders for CPU and memory usage.

Day 5:

• Implemented the process table using ttk.Treeview in Tkinter for displaying live process data.

Day 6:

• Added dynamic updating for CPU and memory usage labels.
• Established periodic refresh logic.

Day 7:

• Introduced the CPU usage history chart using Matplotlib and integrated it into Tkinter.

Day 8:

• Optimized data polling to handle exceptions (e.g., inaccessible processes).

Day 9:

• Implemented alert functionality for CPU usage exceeding 80%.

Day 10:

• Enhanced the dashboard layout and added styles for better readability.

Day 11:

• Enabled sorting and filtering for process table columns (CPU %, Memory %).

Day 12:

• Added tooltips and hover functionality for better user interaction.

Day 13:

• Improved logging to store key events and system alerts for analysis.

Day 14:

• Conducted code refactoring and modularized functions for better maintainability.

Day 15:

• Finalized the project.
• Added installation instructions and screenshots to README.md.

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