Web Scraping with Perl & ChatGPT

Web scraping is the process of extracting data from websites. This can be useful for gathering large amounts of data for analysis. Perl is a popular language for web scraping due to its many scraping modules and simple syntax. ChatGPT is an AI assistant that can be helpful for generating code and explanations for web scraping tasks. This article will provide an overview of web scraping in Perl and how ChatGPT can assist.

Installing Perl and Dependencies

To use Perl for web scraping, you'll need a Perl environment installed on your system. The easiest way is to install Strawberry Perl which includes Perl and common modules. You'll also need to install Perl libraries like WWW::Mechanize for scraping and HTML::TreeBuilder for parsing HTML.

# Install WWW::Mechanize
cpan WWW::Mechanize

# Install HTML::TreeBuilder
cpan HTML::TreeBuilder

Introduction to Web Scraping

Web scraping involves programmatically fetching data from websites. This is done by sending HTTP requests to the target site and parsing the HTML, XML or JSON response. Popular Perl modules for web scraping include:

The general workflow for a basic web scraper is:

This can be extended to scrape various data types, handle pagination, scrape JavaScript generated content, avoid detection etc.

ChatGPT for Web Scraping Help

ChatGPT is an AI assistant created by OpenAI to be helpful, harmless, and honest. It can generate natural language explanations and code for a variety of topics. For web scraping, some ways ChatGPT can help are:

Generating Explanations

If you are stuck on a web scraping task, ChatGPT can provide explanations of web scraping concepts or specifics for your use case. Some examples:

Writing Code Snippets

You can provide a description of what you want your code to do and have ChatGPT generate starter code snippets for you. For example:

Be sure to validate any code ChatGPT provides before using it.

Improving Your Prompts

If ChatGPT is not providing helpful responses, you can ask it to suggest ways to improve your prompt to get better results.

Asking Follow-up Questions

Engage in a back and forth conversation with ChatGPT to get explanations for any follow-up questions you have.

Explaining Errors

Share any errors you are getting and ask ChatGPT to explain the issue and how to fix it.

Web Scraping Example Using ChatGPT

Let's go through an example of web scraping a Wikipedia page with some help from ChatGPT along the way.

Goal

The goal is to get the chronology of the universe located in this Wikipedia page https://en.wikipedia.org/wiki/Chronology_of_the_universe

There is a nice table in the page that gives you the timeline of events and various epochs...

![Table Image]

Step 1: The first task is to download the page. Lets ask ChatGPT for help here with this prompt...

give me a script to download this page in perl... <https://en.wikipedia.org/wiki/Chronology_of_the_universe>

# ChatGPT should give you this code
use WWW::Mechanize;

my $url = '<https://en.wikipedia.org/wiki/Chronology_of_the_universe>';
my $mech = WWW::Mechanize->new();

$mech->get($url);
my $content = $mech->content;

open my $fh, '>', 'chronology.html' or die "Cannot open file: $!";
print $fh $content;
close $fh;

print "Page downloaded successfully";

Step 2: Inspect the HTML and look for hints that we can pass to chatGPT. In this case, you can see that the whole table has a class named wikitable.

Step 3: Help out ChatGPT with this info. Here is the prompt:

I want the script to extract a table with the class wikitable - The contains the headings. Extract each row and columns into a csv file

# ChatGPT should return the following code

use strict;
use warnings;

use WWW::Mechanize;
use HTML::TreeBuilder::XPath;

my $url = '<https://en.wikipedia.org/wiki/Chronology_of_the_universe>';

my $mech = WWW::Mechanize->new();
$mech->get($url);

my $tree = HTML::TreeBuilder::XPath->new;
$tree->parse($mech->content);

# Find table with class wikitable
my @tables = $tree->findnodes('//table[contains(@class, "wikitable")]');

foreach my $table (@tables) {

  # Extract headers
  my @headers;
  foreach my $th ($table->findnodes('./tr/th')) {
    push @headers, $th->text_content;
  }

  open my $fh, '>', 'wikitable.csv' or die $!;
  print $fh join(',', @headers) . "\\n";

  # Extract rows
  foreach my $tr ($table->findnodes('./tr')) {
    my @row;
    foreach my $td ($tr->findnodes('./td')) {
      push @row, $td->text_content;
    }
    print $fh join(',', @row) . "\\n";
  }

  close $fh;
}

print "Extracted table to wikitable.csv";

And when you run it, it creates a CSV file which contains the data extracted from the table.

This demonstrates how ChatGPT can rapidly generate web scraping code snippets in Perl once you provide some hints about the page structure and data you want to extract. The same conversational prompting works for Python, Perl or any other language.

Conclusion

Some key points:

With Perl and some help from ChatGPT, you can quickly build scrapers to extract information from websites.

ChatGPT heralds an exciting new era in intelligent automation!

However, this approach also has some limitations:

A more robust solution is using a dedicated web scraping API like Proxies API

With Proxies API, you get:

With features like automatic IP rotation, user-agent rotation and CAPTCHA solving, Proxies API makes robust web scraping easy via a simple API:

curl "<https://api.proxiesapi.com/?key=API_KEY&url=targetsite.com>"

Get started now with 1000 free API calls to supercharge your web scraping!