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✨ Article Crawler is a package used to crawl articles with Markdown format from a specific webpage and store them locally in HTML / Markdown formats.

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Article Crawler

PyPI Latest Release PyPI Downloads

English Doc | 中文文档

✨ Introduction

Article Crawler is a package used to crawl articles with Markdown format from a specific webpage and store them locally in HTML / Markdown formats.

🚀 Quick Start

  1. Install through pip

    pip install article-crawler
  2. Usage

    Usage: python3 -m article_crawler -u [url] -t [type] -o [output_folder] -c [class_] -i [id]

    Options:
     --version show program's version number and exit
     -h, --help show this help message and exit
     -u URL, --url=URL crawled url (required)
     -t TYPE, --type=TYPE crawled article type [csdn] | [juejin] | [zhihu] | [jianshu]
     -o OUTPUT_FOLDER, --output_folder=OUTPUT_FOLDER
     output html / markdown / pdf folder (required)
     -w WEBSITE_TAG, --website_tag=WEBSITE_TAG
     position of the article content in HTML (not required if 'type' is specified)
     -c CLASS_, --class=CLASS_
     position of the article content in HTML (not required if 'type' is specified)
     -i ID, --id=ID position of the article content in HTML (not required if 'type' is specified)
    
    • type: Specific websites, currently supported are CSDN, Zhihu, Juejin, and Jianshu.

    • website_tag / class_ / id:

      e.g. <div id="article_content" class="article_content clearfix"></div>

      • In this element, website_tag, class_, id is div, article_content clearfix, article_content respectively.
      1. You don't need to specify type when you specify website_tag / class_ / id.
      2. You need to use the web console to locate the position of the article.
      3. website_tag / class_ / id is used to locate the position of the article in HTML. It is possible to only use one or two of them instead of all.

Open Source License

MIT License see https://opensource.org/license/mit/

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✨ Article Crawler is a package used to crawl articles with Markdown format from a specific webpage and store them locally in HTML / Markdown formats.

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