Medium to Markdown

MediumConverter.py

import mimetypes
import os

import markdownify
import requests

mimetypes.init()


class MediumConverter(markdownify.MarkdownConverter):
    def convert_figure(self, el, text, convert_as_inline):
        img_element = el.find('img')
        caption_element = el.find('figcaption')

        if img_element:
            src = img_element.attrs.get('src', None) or el.select_one('source').attrs['srcset'].split()[0]
            caption = caption_element.get_text(strip=True) if caption_element else ''
            alt = (img_element.attrs.get('alt', None) or
                   img_element.attrs.get('title', None) or
                   caption_element.get_text(strip=True) or '')

            if src:
                return f'![{alt}]({src})\n*{caption}*' if caption else f'![{alt}]({src})'
        return ''

    def convert_soup(self, soup):
        section = soup.select_one('section')

        ignored = section.select('.speechify-ignore')
        for i in ignored:
            i.decompose()

        hr = section.select('div[role="separator"]')
        for i in hr:
            # replace with hr tag
            i.replace_with(soup.new_tag('hr'))

        if self.save:
            if not self.name:
                self.name = (
                    soup.select_one('title')
                    .get_text(strip=True)
                    .split('|')[0].strip()
                    .replace(' ', '_', -1).replace(':', '_', -1)
                    .replace('?', '', -1).replace('!', '', -1)
                    .replace('’', '', 1).replace('‘', '', -1)
                    .replace('“', '', -1).replace('”', '', -1)
                )

            if self.name:
                self.base_save_path += self.name + '/'
                os.makedirs(self.base_save_path, exist_ok=True)

        converted = super().convert_soup(section)

        if self.save:
            filename = 'output.md'
            with open(f"{self.base_save_path}{filename}", 'w') as f:
                f.write(converted)
                print(f'Document saved as {self.base_save_path}{filename}')

        return converted

    image_i = 0

    def convert_img(self, el, text, convert_as_inline):
        if self.save:
            src = el.get('src')
            if not src:
                src = el.parent.select('source')[-1].get('srcset').split()[0]

            img_resp = requests.get(src)
            ext = mimetypes.guess_extension(img_resp.headers['Content-Type'])

            filename = f"{self.image_i}{ext}"
            self.image_i += 1
            with open(f"{self.base_save_path}{filename}", 'wb') as f:
                f.write(img_resp.content)
                print(f'Image saved as {self.base_save_path}{filename}')
                el.attrs['src'] = filename
        return super().convert_img(el, text, convert_as_inline)

    def __init__(self, save=False, name=None, *args, **kwargs):
        self.save = save
        self.name = name

        if save:
            self.base_save_path = 'out/'
            os.makedirs(self.base_save_path, exist_ok=True)

        super().__init__(heading_style=markdownify.ATX, *args, **kwargs)

mediumd.ipynb

{
 "cells": [
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "# Medium to Markdown\n",
    "This notebook convert a given medium article to markdown format."
   ],
   "id": "3eb10085964fdf50"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "!pip install markdownify bs4 requests",
   "id": "initial_id",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Cookies\n",
    "To bypass the paywall or to get access to draft articles, you need to provide the cookies.\n",
    "\n",
    "The cookies available in javascript don't contain all the necessary information, so you should copy it from the request headers by inspecting the network tab in the developer tools, then serialize them using the code below:\n",
    "\n",
    "```javascript\n",
    "var cookiesHeader = 'cookie1=value1; cookie2=value2; cookie3=value3';  // paste the Cookies Header value here\n",
    "\n",
    "var cookieMap = Object.fromEntries(cookiesHeader.split(';').map(cookie => {\n",
    "  const [name, value] = cookie.split('=');\n",
    "  return [name.trim(), value.trim()];\n",
    "}));\n",
    "console.log(JSON.stringify(cookieMap));\n",
    "```\n",
    "\n",
    "After that, you can copy the output and paste it in the cell below."
   ],
   "id": "2ccfd525333f0b3d"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "cookies = {}  # optional: paste the cookies here",
   "id": "e9bbdcd918950f1c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Convert to markdown\n",
    "The code below will convert the article content to markdown."
   ],
   "id": "41b65e845c337050"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import requests\n",
    "from MediumConverter import MediumConverter\n",
    "\n",
    "url = \"https://towardsdatascience.com/building-llm-apps-a-clear-step-by-step-guide-1fe1e6ef60fd\" # for draft articles, use the preview URL\n",
    "name = 'llm-development-proccess'\n",
    "\n",
    "resp = requests.get(url, cookies=cookies, headers={'User-Agent': 'Mozilla/5.0'})\n",
    "converter = MediumConverter(save=True, name=name)\n",
    "md = converter.convert(resp.text)"
   ],
   "id": "b6e196daa9592311",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "## Display the markdown content\n",
    "The markdown content will be displayed below."
   ],
   "id": "c3520ea3ce59d6e3"
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": "print(md)  # display the Markdown code",
   "id": "e2b26309c5337aef",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from IPython import display\n",
    "\n",
    "display.display_markdown(md, raw=True)  # display the markdown content"
   ],
   "id": "cb506361c5dfcbad",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}