kyrillosishak/codes

code (1)

Support Ticketing System

The Support Ticketing System is a command-line application that allows users to create support tickets, list open tickets, and resolve tickets. It provides a simple interface for managing customer support requests efficiently.

Features

Create a support ticket by providing the category, type, and details of the issue. List open tickets to view their category, type, and details. Resolve a ticket by either its ID or its type. Display available categories and their associated types. Data is stored in a file, allowing persistence between program executions. Prerequisites C++ compiler File access permissions Getting Started Clone the repository:

git clone https://github.com/kyrillosishak/codes.git

Compile the source code:

g++ -o support-ticketing-system code1.cpp

Run the application:

./support-ticketing-system

Usage

Choose an option from the main menu by entering the corresponding number and pressing Enter. Follow the prompts to perform the desired action. The application will display the results or appropriate messages. Press Enter to continue after viewing the output. File Structure main.cpp: The main C++ source code file containing the implementation of the Support Ticketing System. tickets.txt: A text file used to store the ticket data. The file is created automatically and updated when tickets are created or resolved. issue_types.txt: A text file used to store the available categories and types of issues. The file should be populated with category and type information before running the application.

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Graph Loop Finder

This tool is designed to find and sketch loops in a graph based on edges defined in a CSV file.

Requirements

• Python 3.x
• pandas
• networkx
• matplotlib

Installation

1. Clone the repository or download the source code.
2. Install the required Python libraries by running the following command:

pip install pandas networkx matplotlib

Usage

Prepare a CSV file with two columns: 'A' and 'B'. Each row represents an edge in the graph, where 'A' is the source node and 'B' is the target node.

Example CSV file:

A,B
A,C
B,D
C,D
D,A

Update the file_path variable in the main.py file with the path to your CSV file.

Run the tool by executing the following command:

python main.py

The tool will find and display the loops in the graph, if any, and visualize the graph using matplotlib.

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URLExtractor

URLExtractor is a Java tool that extracts URLs recursively from websites. It utilizes the Jsoup library for HTML parsing and provides multi-threaded extraction for faster processing.

How to Add Jsoup Library

To use the Jsoup library in your Java project, follow these steps:

Download the Jsoup library from the official website: https://jsoup.org/.

Extract the downloaded zip file to obtain the Jsoup JAR file.

Copy the Jsoup JAR file to your project's directory.

Open your Java project in your preferred development environment.

Add the Jsoup JAR file to your project's classpath:

For command-line compilation, include the JAR file in the classpath using the -cp flag:

javac -cp path/to/jsoup.jar YourJavaFile.java

For IDEs (e.g. IntelliJ IDEA), follow these steps:

IntelliJ IDEA: Right-click on your project and select Open Module Settings. In the Dependencies tab, click the + button and select JARs or directories. Browse and select the Jsoup JAR file, then click OK. You can now import and use the Jsoup library in your Java code:

import org.jsoup.Jsoup;
import org.jsoup.nodes.Document;
import org.jsoup.nodes.Element;
import org.jsoup.select.Elements;

Example usage:

Document doc = Jsoup.connect("https://www.example.com").get();
Elements links = doc.select("a[href]");

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Sentence Similarity Graph Construction

This notebook demonstrates how to construct a graph based on the similarity between sentences using BERT embeddings. The graph represents each sentence as a node, and an edge exists between two nodes if the similarity between their corresponding sentences is above a user-defined threshold.

Installation

• Install the required libraries by running the following command:

!pip install transformers networkx

Usage

1- Load Pre-trained BERT Model: The notebook utilizes the BERT (Bidirectional Encoder Representations from Transformers) model for sentence embeddings. The model is loaded using the BertTokenizer and BertModel classes from the transformers library.

2- Calculate Cosine Similarity: The calculate_similarity function is defined to calculate the cosine similarity between two sentences using BERT embeddings. It tokenizes the sentences, obtains the token embeddings from the BERT model, and calculates the cosine similarity based on the embeddings.

3- Construct Graph: The notebook constructs a graph using the provided list of sentences. Each sentence is represented as a node in the graph. The construct_graph function adds nodes to the graph and connects them based on the similarity above the given threshold.

4- Visualize the Graph: The resulting graph is visualized using the networkx and matplotlib libraries. Nodes are represented as circles, and edges represent the connections between sentences. The resulting graph provides an intuitive visualization of sentence similarity.