Razor Proxy

There are three types of proxies that work using three types of protocols. The weakest one is HTTP. It is long outdated and unsuitable for visiting web resources. HTTPS works through a secure protocol and is most often used for web surfing. SOCKS5 proxies are capable of working with the largest number of programs and protocols. They are also beneficial because they keep your IP address anonymous in the request header.

In e-mail, proxy servers are used for secure data exchange as well as for collecting e-mails from several e-mail addresses at once. For example, this is how Gmail works, which also allows you to receive e-mails from mail.ru and other e-mail services.

The HTMLCleaner library is typically used for cleaning and transforming HTML documents, but it does not provide a direct API for parsing HTML. Instead, it's often used in conjunction with an HTML parser to clean and format the HTML content.

Here's an example using HTMLCleaner along with the Jsoup library, which is a popular HTML parser in Java

Add the HTMLCleaner and Jsoup dependencies to your project. You can use Maven or Gradle to include them.

For Maven:

    net.sourceforge.htmlcleaner
    htmlcleaner
    2.25



    org.jsoup
    jsoup
    1.14.3

For Gradle:

implementation 'net.sourceforge.htmlcleaner:htmlcleaner:2.25'
implementation 'org.jsoup:jsoup:1.14.3'

Use HTMLCleaner and Jsoup to parse and clean HTML:

import org.htmlcleaner.CleanerProperties;
import org.htmlcleaner.HtmlCleaner;
import org.htmlcleaner.TagNode;
import org.htmlcleaner.XPatherException;
import org.jsoup.Jsoup;
import org.jsoup.nodes.Document;

public class HtmlParsingExample {
    public static void main(String[] args) {
        String htmlContent = "
Hello, world!
";

        // Parse HTML using Jsoup
        Document document = Jsoup.parse(htmlContent);

        // Clean the parsed HTML using HTMLCleaner
        TagNode tagNode = cleanHtml(document.outerHtml());

        // Perform additional operations with the cleaned HTML
        // For example, extracting text content using XPath
        try {
            Object[] result = tagNode.evaluateXPath("//body/p");
            if (result.length > 0) {
                TagNode paragraph = (TagNode) result[0];
                String textContent = paragraph.getText().toString();
                System.out.println("Text content: " + textContent);
            }
        } catch (XPatherException e) {
            e.printStackTrace();
        }
    }

    private static TagNode cleanHtml(String html) {
        HtmlCleaner cleaner = new HtmlCleaner();
        CleanerProperties properties = cleaner.getProperties();

        // Configure cleaner properties if needed
        properties.setOmitXmlDeclaration(true);

        try {
            return cleaner.clean(html);
        } catch (Exception e) {
            e.printStackTrace();
            return null;
        }
    }
}

In this example, Jsoup is used for initial HTML parsing, and HTMLCleaner is used to clean the HTML. You can perform additional operations on the cleaned HTML, such as using XPath to extract specific elements.

Web scraping to collect email addresses from web pages raises ethical and legal considerations. It's important to respect privacy and adhere to the terms of service of the websites you are scraping. Additionally, harvesting email addresses for unsolicited communication may violate anti-spam regulations.

If you have a legitimate use case, here's a basic example in Python using the requests library and regular expressions to extract email addresses. Note that this is a simplistic example and may not cover all email address variations:

import re
import requests

def extract_emails_from_text(text):
    email_pattern = r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
    return re.findall(email_pattern, text)

def scrape_emails_from_url(url):
    response = requests.get(url)
    if response.status_code == 200:
        page_content = response.text
        emails = extract_emails_from_text(page_content)
        return emails
    else:
        print(f"Failed to fetch content from {url}. Status code: {response.status_code}")
        return []

# Example usage
url_to_scrape = 'https://example.com'
emails_found = scrape_emails_from_url(url_to_scrape)

if emails_found:
    print("Email addresses found:")
    for email in emails_found:
        print(email)
else:
    print("No email addresses found.")

Keep in mind the following:

1. Ethics and Legality:

   • Ensure that your scraping activities are ethical and comply with the laws and terms of service of the websites you are scraping.

2. Robots.txt:

   • Check the website's robots.txt file to understand if scraping is allowed or restricted.

3. Consent:

   • Respect user privacy and obtain consent if necessary.

4. Anti-Spam Regulations:

   • Be aware of and comply with anti-spam regulations in your region.

5. Variability of Email Formats:

   • Email address formats can vary, and the regular expression provided is a basic example. You may need to adjust it based on the actual formats you encounter.

6. Use of APIs:

   • If available, consider using official APIs provided by websites to access data rather than scraping.

Such proxy redirects requests from clients to different servers (globally or within a single local network). It can be used for load balancing in different Internet services, for testing web applications, for secured access to local network servers (all "non-client" traffic is ignored).