Basic HTML Scraping: The First Steps

Web scraping might seem intimidating at first, but like any skill, it's best learned through hands-on practice. In these first three chapters, we'll explore the fundamentals of extracting data from static HTML pages.

Chapter 1: The HTML Scaper Chad

Our journey begins with the simplest possible scenario - extracting text from a basic HTML page. This chapter introduces you to the core concepts of:

Making HTTP requests to fetch web pages
Loading HTML content into a parser
Basic DOM selection using CSS selectors

While the example may seem trivial (it's just a "hello world" after all!), it establishes the foundation for everything that follows.

<!-- Example HTML structure -->
<div class="content">
  <p>Some text we want to extract</p>
</div>

Here's a taste of what we're working with:

// Basic concepts (not the solution!)
import * as cheerio from 'cheerio';

// Loading HTML content
const $ = cheerio.load(htmlContent);

// Using CSS selectors
const text = $('p').text();  // Selects all <p> tags
const specific = $('.content p').text();  // More specific selection

Chapter 2: Structured Data

Things get more interesting as we dive into a mock e-commerce product page. Here, we're faced with multiple elements with similar structures:

<!-- Example product structure (simplified) -->
<div class="product">
  <h2>Product Name</h2>
  <span class="price">$99.99</span>
  <div class="specs">
    <ul>
      <li>Size: M</li>
      <li>Color: Blue</li>
    </ul>
  </div>
</div>

When dealing with structured data like this, you'll want to think about:

// Conceptual approach (not the solution!)
$('.product').each((index, element) => {
  // For each product, we might want to:
  // 1. Extract the basic info
  const name = $(element).find('h2').text();

  // 2. Parse nested data
  const specs = $(element).find('.specs li');

  // 3. Structure the output
  const data = {
    name,
    specs: specs.map(/* ... */),
  };
});

Pro tip: Before writing any code, take time to analyze the HTML structure. Look for patterns in how the data is organized - are there consistent class names? How are parent and child elements related?

Chapter 3: AI-Assisted Scraping

Now things get interesting! While the previous challenges taught us traditional scraping techniques, Chapter 3 introduces a modern approach: AI-assisted web scraping. We're faced with a nightmare scenario - inconsistent HTML structures, obfuscated class names, and multiple framework patterns all mixed together.

Let's look at what makes this challenge special:

<!-- Traditional product structure -->
<div data-testid="product-container-1" class="_3xj_item">
  <h2 data-qa="name">Red Sneakers</h2>
  <span data-price-current="5999">$59.99</span>
</div>

<!-- React-style component -->
<div class="ProductCard-root-1a2b3c">
  <div class="ProductCard-title-4d5e6f">Pink Walking Shoes</div>
  <div class="ProductCard-pricing-7g8h9i">$84.99</div>
</div>

<!-- Vue-style template -->
<div data-v-abcdef class="product">
  <h2 data-v-abcdef>Navy Boat Shoes</h2>
  <span data-v-abcdef>$79.99</span>
</div>

Traditional scraping approaches would struggle here because:

Class names are randomized or framework-specific
Data structures vary between products
Different frameworks use different patterns
Semantic meaning is lost in the markup

This is where AI comes to the rescue. Instead of writing brittle selectors, we can describe what we want in natural language and let AI handle the pattern matching. The key concepts in this chapter include:

Prompt engineering for web scraping
Using AI to understand semantic meaning
Handling inconsistent data structures
Dealing with framework-specific markup
Maintaining data consistency across different patterns

While AI isn't magic, it excels at tasks requiring pattern recognition and adaptation. This makes it particularly valuable for scraping modern web applications where consistent markup patterns can't be guaranteed.

A Note on AI Usage

Remember that AI assistance doesn't mean completely automated solutions. The best results come from combining:

Clear problem definition
Well-structured prompts
Data validation
Human oversight

Your challenge will be to craft prompts that help AI understand both the structure and the intent of what you're trying to extract.

Ready to combine traditional web scraping knowledge with modern AI capabilities? Let's figure out how AI can help tackle even the messiest HTML!

Hints

Experiment with different CSS selectors:

// Different ways to select elements
$('.class')           // By class
$('#id')             // By ID
$('div > p')         // Direct children
$('div p')           // All descendants
$('[data-type="x"]') // By attribute

Try modifying the output format
Think about error handling and edge cases
Consider how your solution might scale with larger datasets

All the code you need to get started is in the project repository. Clone it, set up your environment, and start scraping!

git clone https://github.com/jonaylor89/housefly.git
cd hosuefly

Looking for hints? The source HTML for each challenge is available in the apps/chapter{n}/ directory. And the working solved examples are also available in _solved/chapter{n}/. Study the structure, plan your approach, and remember - every expert was once a beginner.

Remember to handle your requests responsibly:

// Basic error handling example
async function fetchPage(url: string) {
  try {
    const response = await fetch(url);
    if (!response.ok) {
      throw new Error(`HTTP error! status: ${response.status}`);
    }
    return await response.text();
  } catch (error) {
    console.error('Failed to fetch page:', error);
    throw error;
  }
}

Happy scraping!