Upload utils.py

utils.py

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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