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import requests
from bs4 import BeautifulSoup
from transformers import pipeline
from newspaper import Article
from gtts import gTTS
import os
# Function to fetch and parse news article
def fetch_article(url):
article = Article(url)
article.download()
article.parse()
return article.text
# Function to summarize text using Hugging Face model
def summarize_text(text):
summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
summary = summarizer(text, max_length=150, min_length=50, do_sample=False)
return summary[0]['summary_text']
# Function to perform sentiment analysis on a text
def sentiment_analysis(text):
sentiment_analyzer = pipeline("sentiment-analysis")
sentiment = sentiment_analyzer(text)[0]
return sentiment['label']
# Function to fetch news articles based on company name
def fetch_news(company_name, num_articles=10):
search_url = f"https://news.google.com/search?q={company_name}&hl=en-US&gl=US&ceid=US%3Aen"
response = requests.get(search_url)
soup = BeautifulSoup(response.content, 'html.parser')
articles = []
for item in soup.find_all('article')[:num_articles]:
title = item.find('h3').text
link = item.find('a')['href']
full_url = "https://news.google.com" + link[1:] if link.startswith('.') else link
text = fetch_article(full_url)
summary = summarize_text(text)
sentiment = sentiment_analysis(text)
articles.append({
'Title': title,
'Summary': summary,
'Sentiment': sentiment,
'Link': full_url
})
return articles
# Function to generate TTS in Hindi
def generate_tts_hindi(text, output_file='output.mp3'):
tts = gTTS(text, lang='hi')
tts.save(output_file)
return output_file
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