Downloading Images from a Website with Elixir and Floki

Oct 15, 2023 · 4 min read

In this article, we will learn how to use Elixir and the HTTPoison and Floki libraries to download all the images from a Wikipedia page.

—-

Overview

The goal is to extract the names, breed groups, local names, and image URLs for all dog breeds listed on this Wikipedia page. We will store the image URLs, download the images and save them to a local folder.

Here are the key steps we will cover:

  1. Import required modules
  2. Send HTTP request to fetch the Wikipedia page
  3. Parse the page HTML using Floki
  4. Find the table with dog breed data using a CSS selector
  5. Iterate through the table rows
  6. Extract data from each column
  7. Download images and save locally
  8. Print/process extracted data

Let's go through each of these steps in detail.

Imports

We need these modules:

import HTTPoison
import Floki
  • HTTPoison - Sends HTTP requests
  • Floki - Parses HTML/XML
  • Send HTTP Request

    To download the web page:

    url = "<https://commons.wikimedia.org/wiki/List_of_dog_breeds>"
    
    response = HTTPoison.get!(url, [], hackney: [pool: :default, recv_timeout: 25000])
    

    We make a GET request to the URL.

    Parse HTML

    To parse the HTML:

    html = response.body |> Floki.parse_document!()
    

    The html var now contains the parsed document.

    Find Breed Table

    We use a CSS selector to find the table element:

    table = html |> Floki.find("table.wikitable.sortable")
    

    This selects the

    tag with the required CSS classes.

    Iterate Through Rows

    We loop through the rows like this:

    Floki.find(table, "tr") |> Enum.each(fn row ->
    
      # Extract data
    
    end)
    

    We iterate through each

    element within the table.

    Extract Column Data

    Inside the loop, we get the column data:

    [name_cell, group_cell, local_cell, img_cell] = Floki.find(row, "td, th")
    
    name = Floki.text(name_cell) |> String.trim()
    group = Floki.text(group_cell) |> String.trim()
    
    local_name = Floki.text(local_cell) |> String.trim()
    
    img = Floki.attribute(img_cell, "src")
    

    We use text/1 for text and attribute/2 for attributes.

    Download Images

    To download and save images:

    unless img == nil do
      {:ok, image} = HTTPoison.get(img, [], hackney: [pool: :default])
    
      File.write("dog_images/#{name}.jpg", image.body)
    end
    

    We reuse the HTTP client and write the image binary to a file.

    Store Extracted Data

    We store the extracted data:

    names = [name | names]
    groups = [group | groups]
    local_names = [local_name | local_names]
    images = [img | images]
    

    The lists can then be processed as needed.

    And that's it! Here is the full code:

    # Imports
    import HTTPoison
    import Floki
    
    # Lists to store data
    names = []
    groups = []
    local_names = []
    images = []
    
    # Fetch HTML
    url = "<https://commons.wikimedia.org/wiki/List_of_dog_breeds>"
    
    response = HTTPoison.get!(url, [], hackney: [pool: :default, recv_timeout: 25000])
    
    html = response.body |> Floki.parse_document!()
    
    # Find table
    table = html |> Floki.find("table.wikitable.sortable")
    
    # Iterate rows
    Floki.find(table, "tr") |> Enum.each(fn row ->
    
      [name_cell, group_cell, local_cell, img_cell] = Floki.find(row, "td, th")
    
      name = Floki.text(name_cell) |> String.trim()
      group = Floki.text(group_cell) |> String.trim()
    
      local_name = Floki.text(local_cell) |> String.trim()
    
      img = Floki.attribute(img_cell, "src")
    
      unless img == nil do
        {:ok, image} = HTTPoison.get(img, [], hackney: [pool: :default])
    
        File.write("dog_images/#{name}.jpg", image.body)
      end
    
      names = [name | names]
      groups = [group | groups]
      local_names = [local_name | local_names]
      images = [img | images]
    
    end)
    

    This provides a complete Elixir solution using HTTPoison and Floki to scrape data and images from HTML tables. The same approach can apply to many websites.

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