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""" |
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--> this code takes use of URLFetcher.py and fetches the text data from each of the pages |
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--> saves it in a .txt file |
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--> voila!! |
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""" |
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|
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import os |
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import json |
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os.chdir('D:/Machine Learning/SLM-Project/') |
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|
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query_file = 'Data Collection/webscrapper/search_queries.json' |
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out_file = f'Data/webscrapped data/britannica_output.txt' |
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max_limit = 10 |
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with open(query_file, 'r') as file: |
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search_queries = json.load(file) |
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from tqdm import tqdm |
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from URLFetcher import BritannicaUrls |
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scrape = BritannicaUrls(search_queries=search_queries, max_limit=10) |
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with tqdm(total=len(search_queries) * max_limit, desc="Generating URL snippets: ") as pbar: |
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url_snippets = scrape.generate_urls(progress_bar=pbar) |
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print('fetched snippets successfully!') |
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print(f"total snippets: {len(url_snippets)}") |
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import requests |
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from bs4 import BeautifulSoup |
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import re |
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def text_extractor(url_snippet): |
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target_url = f"https://britannica.com{url_snippet}" |
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r = requests.get(target_url, headers=scrape.headers) |
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if r.status_code == 200: |
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soup = BeautifulSoup(r.content, 'html.parser') |
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paragraphs = soup.find_all('p') |
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page = '\n'.join([p.get_text() for p in paragraphs if "Our editors will review what you’ve submitted and determine whether to revise the article." not in p.get_text()]) |
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page = re.sub('&\w+;','',page) |
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return page |
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|
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if __name__ == '__main__': |
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with tqdm(total=len(url_snippets), desc="Scrapping in progress: ") as pbar: |
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for snippets in url_snippets: |
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page = text_extractor(snippets) |
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with open(out_file, 'a', encoding='utf-8') as file: |
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file.write(page) |
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pbar.update(1) |
