import re from pathlib import Path from pprint import pprint from bs4 import BeautifulSoup, Comment, NavigableString, Tag from tiktoken import get_encoding as tiktoken_get_encoding from utils.logger import logger from markdownify import markdownify # from trafilatura import extract as extract_text_from_html # from inscriptis import get_text as extract_text_from_html # from html_text import extract_text as extract_text_from_html # from readabilipy import simple_json_from_html_string as extract_text_from_html class WebpageContentExtractor: def __init__(self): self.tokenizer = tiktoken_get_encoding("cl100k_base") def count_tokens(self, text): tokens = self.tokenizer.encode(text) token_count = len(tokens) return token_count def filter_html_str(self, html_str): soup = BeautifulSoup(html_str, "html.parser") ignore_tags = ["script", "style", "button"] ignore_classes = [ "sidebar", "footer", "related", "comment", "topbar", "menu", "offcanvas", "navbar", ] ignore_classes_pattern = f'{"|".join(ignore_classes)}' removed_element_counts = 0 for element in soup.find_all(): class_str = "" id_str = "" try: class_attr = element.get("class", []) if class_attr: class_str = " ".join(list(class_attr)) if id_str: class_str = f"{class_str} {id_str}" except: pass try: id_str = element.get("id", "") except: pass if ( (not element.text.strip()) or (element.name in ignore_tags) or (re.search(ignore_classes_pattern, class_str, flags=re.IGNORECASE)) or (re.search(ignore_classes_pattern, id_str, flags=re.IGNORECASE)) ): # try: # logger.note(f"Removing:\n{element}") # except: # logger.note(f"Removing unknown element") element.decompose() removed_element_counts += 1 logger.note( f"Elements Removed/Remained: {removed_element_counts}/{len(soup.find_all())}" ) html_str = str(soup) return html_str def extract(self, html_path): logger.note(f"Extracing content from:{html_path}") with open(html_path, "r", encoding="utf-8") as f: html_str = f.read() html_str = self.filter_html_str(html_str) # self.main_content = extract_text_from_html(html_str) # # when using `readabilipy` # self.main_content = extract_text_from_html(html_str)["plain_content"] # self.main_content = "\n".join( # item["text"] for item in extract_text_from_html(html_str)["plain_text"] # ) # self.main_content = markdownify(extract_text_from_html(html_str)["content"]) # self.main_content = markdownify(extract_text_from_html(html_str)) self.main_content = markdownify(html_str, strip="a") self.main_content = re.sub(r"\n{3,}", "\n\n", self.main_content) # logger.line(self.main_content) # pprint(self.main_content) token_count = self.count_tokens(self.main_content) logger.note(f"Token Count: {token_count}") return self.main_content if __name__ == "__main__": html_path = ( Path(__file__).parents[1] / "files" / "urls" # / "stackoverflow.com_questions_295135_turn-a-string-into-a-valid-filename.html" / "www.liaoxuefeng.com_wiki_1016959663602400_1017495723838528.html" # / "docs.python.org_zh-cn_3_tutorial_interpreter.html" ) extractor = WebpageContentExtractor() main_content = extractor.extract(html_path)