Wayback Machine's Robots.txt Change Visualizer

wayback-machine-s-robots-txt-change-visualizer.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMfEtDhqMwNItHeBvh0PTVK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/natzir/9d02e8e298f45d44dd78ec46675a970b/wayback-machine-s-robots-txt-change-visualizer.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Wayback Machine's Robots.txt Visualizer\n",
        "---\n",
        "\n",
        "**Author:** Natzir. Strategic, International & Technical SEO\n",
        "<br>**X/Twitter:** [@natzir9](https://twitter.com/natzir9)\n",
        "\n",
        "---\n",
        "\n",
        "This tool automates the process by fetching unique historical versions of a robots.txt file from the Wayback Machine, identifying the changes over time, and visualizing them in a GIF format. It also checks for:\n",
        "\n",
        "*   File size > 500KB\n",
        "*   UTF-8 BOM Character\n",
        "*   Disallow: /\n",
        "\n",
        "Check also my\n",
        "[Google's Robots.txt Bulk Parser Tool](https://colab.research.google.com/drive/1EBCeZKO-c0-2cc5F4urmqoGvfoItYikV)\n",
        "\n",
        "\n",
        "Before you can use this notebook, make sure to **create a copy** of it in your Google Drive (File > Save a copy in Drive)\n"
      ],
      "metadata": {
        "id": "Gy5Ex5KzzUAz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hfLpAjPHq_eI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "197305a5-6d5c-463d-f0e0-33c7147fd6dd",
        "cellView": "form"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloaded and saved: 20070821062449_robots.txt\n",
            "Downloaded and saved: 20071024021140_robots.txt\n",
            "Downloaded and saved: 20080401121837_robots.txt\n",
            "Downloaded and saved: 20080402135025_robots.txt\n",
            "Downloaded and saved: 20080410181819_robots.txt\n",
            "Downloaded and saved: 20080528180239_robots.txt\n",
            "Downloaded and saved: 20080529072746_robots.txt\n",
            "Downloaded and saved: 20090619205312_robots.txt\n",
            "Downloaded and saved: 20100105060901_robots.txt\n",
            "Downloaded and saved: 20110426175053_robots.txt\n",
            "Downloaded and saved: 20110608135236_robots.txt\n",
            "Downloaded and saved: 20140114193027_robots.txt\n",
            "Downloaded and saved: 20141105033858_robots.txt\n",
            "Downloaded and saved: 20210628181436_robots.txt\n",
            "Downloaded and saved: 20220131225535_robots.txt\n",
            "Downloaded and saved: 20220314013216_robots.txt\n",
            "Downloaded and saved: 20220315032255_robots.txt\n",
            "Downloaded and saved: 20220320070647_robots.txt\n",
            "Downloaded and saved: 20220322043215_robots.txt\n",
            "Error downloading capture http://web.archive.org/web/20220323231046/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20220323231046/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd857d00>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20220325072230/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20220325072230/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b0850>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20220827211005/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20220827211005/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b10f0>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20220901230346/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20220901230346/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b19c0>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20220909034604/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20220909034604/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd857be0>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20220915033536/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20220915033536/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b0cd0>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20230115143356/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20230115143356/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b0280>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20230320024954/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20230320024954/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b22f0>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20230524022358/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20230524022358/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b2bc0>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20230601071813/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20230601071813/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8579d0>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20230730103400/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20230730103400/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b1f90>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20231115072316/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20231115072316/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b08b0>: Failed to establish a new connection: [Errno 111] Connection refused'))\n",
            "Error downloading capture http://web.archive.org/web/20240314094935/https://johnmu.com/robots.txt: HTTPConnectionPool(host='web.archive.org', port=80): Max retries exceeded with url: /web/20240314094935/https://johnmu.com/robots.txt (Caused by NewConnectionError('<urllib3.connection.HTTPConnection object at 0x7defdd8b1090>: Failed to establish a new connection: [Errno 111] Connection refused'))\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "#@markdown # Set the robots.txt URL and then hit the Play button\n",
        "#@markdown ---\n",
        "url_robots = \"https://johnmu.com/robots.txt\" #@param {type: \"string\"}\n",
        "\n",
        "import requests\n",
        "import os\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "import difflib\n",
        "import imageio.v2 as imageio\n",
        "import time\n",
        "\n",
        "def obtain_unique_captures(url):\n",
        "    \"\"\"\n",
        "    Obtains the URLs of unique captures for a specific URL using the Wayback Machine's CDX API.\n",
        "    \"\"\"\n",
        "    base_url = \"http://web.archive.org/cdx/search/cdx\"\n",
        "    params = {\n",
        "        'url': url,\n",
        "        'output': 'json',\n",
        "        'filter': ['statuscode:200'],\n",
        "        'fl': 'timestamp,original',\n",
        "        'collapse': 'digest'  # Collapse captures by digest to obtain unique versions\n",
        "    }\n",
        "    try:\n",
        "        response = requests.get(base_url, params=params)\n",
        "        response.raise_for_status()\n",
        "    except requests.RequestException as e:\n",
        "        print(f\"Error querying the CDX API: {e}\")\n",
        "        return []\n",
        "\n",
        "    # Process the results\n",
        "    data = response.json()\n",
        "    unique_urls = [f\"http://web.archive.org/web/{entry[0]}/{entry[1]}\" for entry in data[1:]]  # Skip headers\n",
        "    time.sleep(5)\n",
        "    return unique_urls\n",
        "\n",
        "def download_captures(unique_captures, destination_directory=\"unique_captures\"):\n",
        "    \"\"\"\n",
        "    Downloads the content of each unique capture and saves it to a file.\n",
        "    \"\"\"\n",
        "    # Ensure the destination directory exists\n",
        "    os.makedirs(destination_directory, exist_ok=True)\n",
        "\n",
        "    for url in unique_captures:\n",
        "        try:\n",
        "            # Extract the timestamp from the URL to use as the filename\n",
        "            timestamp = url.split(\"/\")[4]\n",
        "            filename = f\"{timestamp}_robots.txt\"\n",
        "            full_path = os.path.join(destination_directory, filename)\n",
        "\n",
        "            # Perform the request to download the content\n",
        "            response = requests.get(url)\n",
        "            response.raise_for_status()\n",
        "\n",
        "            # Write the content to the file\n",
        "            with open(full_path, 'w', encoding='utf-8') as file:\n",
        "                file.write(response.text)\n",
        "\n",
        "            print(f\"Downloaded and saved: {filename}\")\n",
        "\n",
        "        except requests.RequestException as e:\n",
        "            print(f\"Error downloading capture {url}: {e}\")\n",
        "\n",
        "        # Pause to respect the rate limit of 15 requests per minute\n",
        "        time.sleep(5)\n",
        "\n",
        "def calculate_differences(previous_text, current_text):\n",
        "    \"\"\"\n",
        "    Calculates the differences between two texts and returns the number of lines added and removed,\n",
        "    and a boolean indicating if there are significant changes.\n",
        "    \"\"\"\n",
        "    diff = list(difflib.ndiff(previous_text.splitlines(), current_text.splitlines()))\n",
        "    lines_added = sum(1 for d in diff if d.startswith('+ '))\n",
        "    lines_removed = sum(1 for d in diff if d.startswith('- '))\n",
        "\n",
        "    significant_changes = lines_added > 0 or lines_removed > 0\n",
        "\n",
        "    return lines_added, lines_removed, significant_changes\n",
        "\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "\n",
        "def text_to_image(text, date, changes, issues, image_file, width=800, height=1200):\n",
        "    \"\"\"\n",
        "    Converts text to an image, adding the date, the number of lines added and removed at the top,\n",
        "    and displays any issues found within the robots.txt file.\n",
        "    \"\"\"\n",
        "    image = Image.new('RGB', (width, height), 'white')\n",
        "    draw = ImageDraw.Draw(image)\n",
        "    font = ImageFont.load_default()\n",
        "\n",
        "    # Define colors for the text\n",
        "    color_added = 'green'\n",
        "    color_removed = 'red'\n",
        "    color_issue = 'black'\n",
        "\n",
        "    # Draw the header text for the date\n",
        "    draw.text((10, 10), f\"Date: {date}\", fill=color_issue, font=font)\n",
        "\n",
        "    # Draw the 'Lines added' text with green color\n",
        "    draw.text((10, 30), f\"Lines added: {changes[0]}\", fill=color_added, font=font)\n",
        "\n",
        "    # Draw the 'Lines removed' text with red color\n",
        "    draw.text((10, 50), f\"Lines removed: {changes[1]}\", fill=color_removed, font=font)\n",
        "\n",
        "    # Initialize y_position for issues text, start below the changes text\n",
        "    y_position = 70\n",
        "\n",
        "    # If there are issues, display them\n",
        "    for issue in issues:\n",
        "        draw.text((10, y_position), issue, fill=color_issue, font=font)\n",
        "        y_position += 20  # Increment the y_position for the next issue\n",
        "\n",
        "    # Draw a dotted line to separate the header from the content\n",
        "    y_position += 10  # Adjust space for dotted line below the issues\n",
        "    for x_position in range(0, width, 4):\n",
        "        draw.line([(x_position, y_position), (x_position + 2, y_position)], fill='black', width=1)\n",
        "\n",
        "    # Add the content of robots.txt below the dotted line\n",
        "    draw.multiline_text((10, y_position + 10), text, fill='black', font=font, spacing=5)\n",
        "\n",
        "    image.save(image_file)\n",
        "\n",
        "def check_robots_txt(file_path):\n",
        "    \"\"\"\n",
        "    Performs various checks on the robots.txt file to detect issues that might lead to deindexing.\n",
        "    \"\"\"\n",
        "    issues = []\n",
        "    with open(file_path, 'r', encoding='utf-8-sig') as file:\n",
        "        content = file.read()\n",
        "\n",
        "    # Check for UTF-8 BOM\n",
        "    if content.startswith(u'\\ufeff'):\n",
        "        issues.append(\"UTF-8 BOM detected at the start of the file.\")\n",
        "\n",
        "    # Check file size\n",
        "    if os.path.getsize(file_path) > 500 * 1024:  # Size in kilobytes\n",
        "        issues.append(\"The robots.txt file exceeds the 500KB size limit.\")\n",
        "\n",
        "    # Check for cause deindexing\n",
        "    if \"Disallow: /\" in content:\n",
        "        issues.append(\"The robots.txt file blocks the entire site.\")\n",
        "\n",
        "    return issues\n",
        "\n",
        "\n",
        "# Main flow\n",
        "unique_captures = obtain_unique_captures(url_robots)\n",
        "\n",
        "# Download the unique captures\n",
        "download_captures(unique_captures, \"unique_captures\")\n",
        "\n",
        "# Prepare directories and previous text\n",
        "directory = \"unique_captures\"\n",
        "images_directory = \"robots_images\"\n",
        "os.makedirs(images_directory, exist_ok=True)\n",
        "\n",
        "previous_text = \"\"\n",
        "images_for_gif = []\n",
        "previous_text = None\n",
        "\n",
        "for text_file in sorted(os.listdir(directory)):\n",
        "    full_path = os.path.join(directory, text_file)\n",
        "    with open(full_path, 'r', encoding='utf-8') as f:\n",
        "        current_text = f.read()\n",
        "\n",
        "    # Extract the date from the file name\n",
        "    date = text_file.split('_')[0]\n",
        "    formatted_date = f\"{date[:4]}-{date[4:6]}-{date[6:8]}\"\n",
        "\n",
        "    # Only proceed if there are changes\n",
        "    if previous_text is not None:\n",
        "        lines_added, lines_removed, significant_changes = calculate_differences(previous_text, current_text)\n",
        "\n",
        "        if significant_changes:\n",
        "            issues = check_robots_txt(full_path)\n",
        "            image_name = text_file.replace('.txt', '.png')\n",
        "            image_path = os.path.join(images_directory, image_name)\n",
        "            text_to_image(current_text, formatted_date, (lines_added, lines_removed), issues, image_path, width=800, height=1200)\n",
        "            images_for_gif.append(image_path)\n",
        "    else:\n",
        "        issues = check_robots_txt(full_path)\n",
        "\n",
        "    previous_text = current_text\n",
        "\n",
        "# Generate the GIF after processing all files\n",
        "if images_for_gif:\n",
        "    images = [imageio.imread(image) for image in images_for_gif]\n",
        "    imageio.mimsave('historical_robots.gif', images, fps=0.5)\n",
        "else:\n",
        "    print(\"No significant changes found across the captures.\")\n",
        "\n",
        "# Generate the GIF after processing all files\n",
        "images = [imageio.imread(image) for image in images_for_gif]\n",
        "imageio.mimsave('historical_robots.gif', images, fps=0.5)\n",
        "\n",
        "from IPython.display import display, Image\n",
        "\n",
        "gif_path = 'historical_robots.gif'\n",
        "\n",
        "display(Image(filename=gif_path))\n",
        "\n",
        "# @markdown ---\n",
        "# @markdown #### Please note that the process may take a few minutes to complete, depending on the number of captures (up to 15 captures per minute to respect the Wayback Machine API limits).\n",
        "# @markdown #### Once the execution is finished, a GIF showcasing the historical changes to the `robots.txt` file will be automatically displayed.\n",
        "# @markdown #### If you want to see all the screenshots, you will find them on the left in Files > unique_captures"
      ]
    }
  ]
}