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| import requests | |
| import io | |
| from bs4 import BeautifulSoup | |
| from gtts import gTTS | |
| from deep_translator import GoogleTranslator | |
| from transformers import pipeline | |
| from keybert import KeyBERT | |
| # News Extraction | |
| def extract_news(topic): | |
| """ | |
| Extracts news articles related to the given topic from the Economic Times website. | |
| Args: | |
| topic (str): The topic for which news articles need to be extracted. | |
| Returns: | |
| list[dict]: A list of dictionaries containing news titles and summaries. | |
| """ | |
| url = f"https://economictimes.indiatimes.com/topic/{topic}" | |
| headers = {"User-Agent": "Mozilla/5.0"} | |
| try: | |
| response = requests.get(url, headers=headers) | |
| response.raise_for_status() # Raise an HTTPError for bad responses (4xx and 5xx) | |
| except requests.RequestException as e: | |
| print(f"Error fetching news: {e}") | |
| return [] | |
| soup = BeautifulSoup(response.text, "html.parser") | |
| articles = [] | |
| article_blocks = soup.find_all("div", class_="clr flt topicstry story_list") | |
| for article in article_blocks: | |
| title_tag = article.find("a", class_="wrapLines l2") | |
| summary_tag = article.find("p", class_="wrapLines l3") | |
| title = title_tag.text.strip() if title_tag else "Title not found" | |
| summary = summary_tag.text.strip() if summary_tag else "Summary not found" | |
| articles.append({"title": title, "summary": summary}) | |
| return articles | |
| # Sentiment Analysis Pipeline | |
| sentiment_pipeline = pipeline( | |
| "sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english" | |
| ) | |
| def analyze_sentiment(text): | |
| """ | |
| Analyzes the sentiment of the given text using a pre-trained model. | |
| Args: | |
| text (str): The input text to analyze. | |
| Returns: | |
| str: Sentiment label ('POSITIVE' or 'NEGATIVE'). | |
| """ | |
| result = sentiment_pipeline(text)[0] # Process text through the model | |
| return result["label"] # Extract and return sentiment label | |
| # Keyword Extraction using KeyBERT | |
| kw_model = KeyBERT("distilbert-base-nli-mean-tokens") | |
| def extract_keywords_keybert(text): | |
| """ | |
| Extracts keywords from the given text using KeyBERT. | |
| Args: | |
| text (str): The input text for keyword extraction. | |
| Returns: | |
| list[str]: A list of extracted keywords (title-cased). | |
| """ | |
| keywords = kw_model.extract_keywords(text, keyphrase_ngram_range=(1, 2), top_n=3) | |
| return [kw[0].title() for kw in keywords] | |
| # Hindi Speech Generation | |
| def generate_hindi_speech(text): | |
| """ | |
| Converts the given text into Hindi speech. | |
| Args: | |
| text (str): The input text to be translated and converted to speech. | |
| Returns: | |
| io.BytesIO: A buffer containing the generated speech audio. | |
| """ | |
| # Translate text to Hindi | |
| hindi_text = GoogleTranslator(source="auto", target="hi").translate(text) | |
| # Convert translated text to speech | |
| tts = gTTS(hindi_text, lang="hi") | |
| # Store the generated speech in memory | |
| audio_buffer = io.BytesIO() | |
| tts.write_to_fp(audio_buffer) | |
| audio_buffer.seek(0) # Reset buffer position for playback | |
| return audio_buffer | |