Hey everyone! Today, I’m excited to share with you a comprehensive guide on how to scrape any website completely for free using DeepSeek, Groq, and Crawl4AI. Web scraping has become one of the most in-demand skills in the tech industry, and mastering it can open up numerous opportunities for you. So, let’s dive in and build an AI web scraper together step-by-step, capturing leads and saving them for future follow-ups.

Why Web Scraping?

Web scraping is an essential skill for many businesses, especially those looking to gather data from various online sources. Whether it's for lead generation, market research, or competitive analysis, the ability to scrape data efficiently can give you a significant advantage. In this tutorial, I’ll guide you through the process of creating a web scraper that can extract valuable information from websites.

Tools You'll Need

To get started, we will be using three powerful tools to build our scraper:

  • Crawl4AI: An open-source library designed for easy web scraping. It not only scrapes content but can also tag that content and pass it to a language model (LLM) for further processing.
  • DeepSeek: A fast and cost-effective AI model that is capable of processing the scraped data. It is known for its efficiency, being about 20 times cheaper to run than many competitors.
  • Groq: This platform provides specialized AI chips for running models like DeepSeek quickly and efficiently. It offers a generous free tier, allowing you to run the models without any cost.
Overview of tools: Crawl4AI, DeepSeek, and Groq

Setting Up the Scenario

Let’s consider a practical example to understand the scraping process better. Imagine we are working with a wedding photographer who has just moved to a new town and is looking to connect with local wedding venues. Our goal is to build a web scraper that will extract relevant information from wedding venue websites, allowing the photographer to reach out to potential clients.

The information we want to gather includes:

  • Name of the venue
  • Location
  • Price details
  • A brief description of the venue

With this data, the photographer can have informed conversations when reaching out to the venues. Let’s get into the coding part and see how we can achieve this!

Getting Started with Coding

To set up our web scraping project, we need to create an environment that has all the necessary dependencies. Here’s how you can do it:

  1. Create a new environment using Conda.
  2. Activate your environment.
  3. Install the necessary dependencies, primarily Crawl4AI.
  4. Don’t forget to add your Groq API key to the environment file.
Setting up the environment for web scraping

Understanding the Crawler Structure

Before we dive into scraping, let’s understand the core structure of our crawler. Here are the fundamental components:

  • Browser Configuration: This determines what browser will be used for scraping. You can choose Chrome, set the window size, and specify whether you want to run it in headless mode.
  • Crawler Run Configuration: This specifies what actions the crawler should perform, such as which elements to extract and how to handle page loading.
Crawler structure overview

Building the Crawler

Now, let’s get into the exciting part—building the crawler. We want to set up a function that will scrape through the venue pages. Here’s a high-level overview of what we need to do:

  1. Set up the browser configuration to open a Chrome window.
  2. Define the LLM strategy to extract the wedding venue information.
  3. Implement a loop that continues scraping until no more pages are left.
Building the crawler function

Implementing the Scraping Logic

In our scraping function, we will check each page for results. If no results are found, we will stop the scraping process. Otherwise, we will proceed to extract the necessary information using CSS selectors to target specific elements on the page.

Here’s how this works:

  • Set the base URL and the current page number.
  • Scrape the page and check for the presence of a "no results found" message.
  • If results are found, extract the venue information using the configured CSS selectors.
Implementing the scraping logic

Running the Scraper

Once we have everything set up, it’s time to run our scraper. You’ll open your terminal, ensure you’re in the correct Conda environment, and execute the command python main.py. This will launch the browser and begin the scraping process, logging results in real-time.

Running the scraper

Saving the Data

After scraping all the pages, the final step is saving the collected data to a CSV file. This file will contain all the venue information that we extracted. You can easily share this with the photographer or upload it to Google Sheets for further analysis.

Importing Data into Google Sheets

To import the scraped data into Google Sheets, simply follow these steps:

  1. Open Google Sheets and create a new sheet.
  2. Click on the import button and upload your CSV file.
  3. Google Sheets will automatically convert the data into a table format for easy viewing and filtering.
Importing data into Google Sheets

Conclusion

Congratulations! You’ve successfully built an AI web scraper using DeepSeek, Groq, and Crawl4AI. This tool can be adapted for various purposes, whether you’re scraping for leads, product information, or market research. I can’t wait to see what you create with this knowledge!

Remember, all the source code from this tutorial is available for free in the description below. If you’re looking for support or want to connect with other AI developers, consider joining my free Skool community. Thanks for tuning in, and see you in the next tutorial!

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