| | import requests |
| | from datasets import Dataset |
| | from selectolax.lexbor import LexborHTMLParser |
| |
|
| | |
| | |
| | N_PAGES_OF_ARTICLES_RECOMMENDATIONS = 100 |
| |
|
| | base_url = "https://www.storm.mg/articles/%i" |
| | user_agent = ( |
| | |
| | "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) " |
| | "Chrome/121.0.0.0 Safari/537.36 OPR/107.0.0.0" |
| | ) |
| |
|
| | def read_article(link: str): |
| | """Read an article on www.storm.mg.""" |
| | r = requests.get(link, headers={ "User-Agent": user_agent }) |
| | r.raise_for_status() |
| |
|
| | contents = [] |
| | parser = LexborHTMLParser(r.text) |
| |
|
| | for paragraph in parser.css("p[aid]"): |
| | contents.append(paragraph.text(separator=" ", strip=True)) |
| |
|
| | return contents |
| |
|
| |
|
| | def generate_dataset(): |
| | """Generate the dataset.""" |
| | for page_id in range(N_PAGES_OF_ARTICLES_RECOMMENDATIONS): |
| | r = requests.get(base_url % (page_id + 1), headers={ |
| | "User-Agent": user_agent |
| | }) |
| | r.raise_for_status() |
| |
|
| | parser = LexborHTMLParser(r.text) |
| | articles = parser.css(".category_cards_wrapper .category_card.card_thumbs_left") |
| |
|
| | for article in articles: |
| | image = article.css_first("img").attributes['src'] |
| | title = article.css_first(".card_title").text() |
| | tag = article.css_first(".tags_wrapper a").text() |
| |
|
| | info = article.css_first("p.card_info.right") |
| | author = info.css_first(".info_author").text() |
| | timestamp = info.css_first(".info_time").text() |
| | link = article.css_first(".link_title").attributes['href'] |
| |
|
| | yield { |
| | "image": image, |
| | "title": title, |
| | "content": "\n".join(read_article(link)), |
| | "tag": tag, |
| | "author": author, |
| | "timestamp": timestamp, |
| | "link": link |
| | } |
| |
|
| | dataset = Dataset.from_generator(generate_dataset) |
| | dataset.save_to_disk( |
| | f"storm-org-articles-{20 * N_PAGES_OF_ARTICLES_RECOMMENDATIONS}" |
| | ) |
| |
|