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import pandas as pd
import numpy as np
from bs4 import BeautifulSoup
import requests as r
import regex as re
from dateutil import parser
import logging
import multiprocessing
def date_time_parser(dt):
"""
Computes the minutes elapsed since published time.
:param dt: date
:return: int, minutes elapsed.
"""
return int(np.round((dt.now(dt.tz) - dt).total_seconds() / 60, 0))
def text_clean(desc):
"""
Cleans the text by removing special chars.
:param desc: string containing description
:return: str, cleaned description.
"""
desc = desc.replace("<", "<")
desc = desc.replace(">", ">")
desc = re.sub("<.*?>", "", desc)
desc = desc.replace("#39;", "'")
desc = desc.replace('"', '"')
desc = desc.replace(' ', ' ')
desc = desc.replace('#32;', ' ')
return desc
def rss_parser(i):
"""
Returns a data frame of parsed news item.
:param i: single news item in RSS feed.
:return: Data frame of parsed news item.
"""
b1 = BeautifulSoup(str(i), "xml")
title = "" if b1.find("title") is None else b1.find("title").get_text()
title = text_clean(title)
url = "" if b1.find("link") is None else b1.find("link").get_text()
desc = "" if b1.find("description") is None else b1.find("description").get_text()
desc = text_clean(desc)
desc = f'{desc[:300]}...' if len(desc) >= 300 else desc
date = "Sat, 12 Aug 2000 13:39:15 +05:30" if ((b1.find("pubDate") == "") or (b1.find("pubDate") is None)) else b1.find("pubDate").get_text()
if url.find("businesstoday.in") >= 0:
date = date.replace("GMT", "+0530")
date1 = parser.parse(date)
return pd.DataFrame({"title": title,
"url": url,
"description": desc,
"parsed_date": date1}, index=[0])
def src_parse(rss):
"""
Returns the root domain name (eg. livemint.com is extracted from www.livemint.com
:param rss: RSS URL
:return: str, string containing the source name
"""
if rss.find('ndtvprofit') >= 0:
rss = 'ndtv profit'
if rss.find('ndtv') >= 0:
rss = 'ndtv.com'
if rss.find('telanganatoday') >= 0:
rss = 'telanganatoday.com'
rss = rss.replace("https://www.", "")
rss = rss.split("/")
return rss[0]
def news_agg(rss):
"""
Returns feeds from each 'rss' URL.
:param rss: RSS URL.
:return: Data frame of processed articles.
"""
try:
rss_df = pd.DataFrame()
# user_agent = "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/109.0.0.0 Safari/537.36"
headers = {
'authority': 'www.google.com',
'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7',
'accept-language': 'en-US,en;q=0.9',
'cache-control': 'max-age=0',
'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36'
}
timeout = 5
resp = r.get(rss, timeout=timeout, headers=headers)
logging.warning(f'{rss}: {resp.status_code}')
b = BeautifulSoup(resp.content, "xml")
items = b.find_all("item")
for i in items:
# rss_df = rss_df.append(rss_parser(i)).copy()
rss_df = pd.concat([rss_df, rss_parser(i)], axis=0)
rss_df.reset_index(drop=True, inplace=True)
rss_df["description"] = rss_df["description"].replace([" NULL", ''], np.nan)
#### UNCOMMENT IN CASE OF OOM ERROR IN RENDER
# rss_df.dropna(inplace=True)
####
rss_df["src"] = src_parse(rss)
rss_df["elapsed_time"] = rss_df["parsed_date"].apply(date_time_parser)
rss_df["parsed_date"] = rss_df["parsed_date"].astype("str")
# rss_df["elapsed_time_str"] = rss_df["elapsed_time"].apply(elapsed_time_str)
except Exception as e:
print(e)
pass
return rss_df
# List of RSS feeds
rss = ['https://www.economictimes.indiatimes.com/rssfeedstopstories.cms',
'https://www.thehindu.com/news/feeder/default.rss',
# 'https://telanganatoday.com/feed',
'https://www.businesstoday.in/rssfeeds/?id=225346',
'https://feeds.feedburner.com/ndtvnews-latest',
'https://www.hindustantimes.com/feeds/rss/world-news/rssfeed.xml',
'https://www.indiatoday.in/rss/1206578',
'https://www.moneycontrol.com/rss/latestnews.xml',
'https://www.livemint.com/rss/news',
'https://www.zeebiz.com/latest.xml/feed',
'https://www.timesofindia.indiatimes.com/rssfeedmostrecent.cms']
def get_news_rss(url):
# final_df = pd.DataFrame()
# for i in rss:
# # final_df = final_df.append(news_agg(i))
# final_df = pd.concat([final_df, news_agg(i)], axis=0)
final_df = news_agg(url)
final_df.reset_index(drop=True, inplace=True)
final_df.sort_values(by="elapsed_time", inplace=True)
# final_df['src_time'] = final_df['src'] + (" " * 5) + final_df["elapsed_time_str"]
# final_df.drop(columns=['date', 'parsed_date', 'src', 'elapsed_time', 'elapsed_time_str'], inplace=True)
final_df.drop(columns=['elapsed_time'], inplace=True)
#### UNCOMMENT 1ST STATEMENT AND REMOVE 2ND STATEMENT IN CASE OF OOM ERROR IN RENDER
# final_df.drop_duplicates(subset='description', inplace=True)
final_df.drop_duplicates(subset='url', inplace=True)
####
final_df = final_df.loc[(final_df["title"] != ""), :].copy()
final_df.loc[(final_df['description'].isna()) | (final_df['description']=='')| (final_df['description']==' '), 'description'] = final_df.loc[(final_df['description'].isna()) | (final_df['description']=='')| (final_df['description']==' '), 'title']
return final_df
def get_news_multi_process(urls):
'''
Get the data shape by parallely calculating lenght of each chunk and
aggregating them to get lenght of complete training dataset
'''
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
results = []
for url in urls:
f = pool.apply_async(get_news_rss, [url]) # asynchronously applying function to chunk. Each worker parallely begins to work on the job
results.append(f) # appending result to results
final_df = pd.DataFrame()
for f in results:
# print(f.get())
final_df = pd.concat([final_df, f.get(timeout=120)], axis=0) # getting output of each parallel job
final_df.reset_index(drop=True, inplace=True)
logging.warning(final_df['src'].unique())
pool.close()
pool.join()
return final_df
def get_news():
return get_news_multi_process(rss)
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