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| import requests | |
| from bs4 import BeautifulSoup | |
| import re | |
| import nltk | |
| from nltk.sentiment.vader import SentimentIntensityAnalyzer | |
| from gtts import gTTS | |
| from googletrans import Translator | |
| import base64 | |
| import os | |
| # Ensure you have downloaded the VADER lexicon: | |
| nltk.data.path.append("/tmp") # Use /tmp to bypass permission issues | |
| nltk.download('vader_lexicon', download_dir="/tmp") | |
| sid = SentimentIntensityAnalyzer() | |
| translator = Translator() | |
| def get_news_articles(company): | |
| """ | |
| Scrapes news articles related to the given company. | |
| Returns a list of dictionaries with keys: Title, Summary, Content. | |
| If scraping yields fewer than 2 articles, sample dummy articles are used. | |
| """ | |
| articles = [] | |
| search_url = f"https://www.google.com/search?q={company}+news&tbm=nws" | |
| headers = {"User-Agent": "Mozilla/5.0"} | |
| try: | |
| response = requests.get(search_url, headers=headers, timeout=10) | |
| soup = BeautifulSoup(response.text, "html.parser") | |
| links = soup.find_all("a", href=True) | |
| seen = set() | |
| for link in links: | |
| href = link['href'] | |
| if '/url?q=' in href: | |
| url = re.split(r'/url\?q=|&', href)[1] | |
| if url not in seen: | |
| seen.add(url) | |
| try: | |
| art_resp = requests.get(url, headers=headers, timeout=10) | |
| art_soup = BeautifulSoup(art_resp.text, "html.parser") | |
| title = art_soup.title.string.strip() if art_soup.title else "No Title" | |
| summary_tag = art_soup.find("meta", attrs={"name": "description"}) | |
| summary = summary_tag["content"].strip() if summary_tag and summary_tag.get("content") else "No summary available." | |
| paragraphs = art_soup.find_all("p") | |
| content = " ".join(p.get_text().strip() for p in paragraphs) | |
| articles.append({ | |
| "Title": title, | |
| "Summary": summary, | |
| "Content": content | |
| }) | |
| if len(articles) >= 10: | |
| break | |
| except Exception as e: | |
| print("Error processing an article:", e) | |
| continue | |
| except Exception as e: | |
| print("Error fetching news articles:", e) | |
| # For demonstration, if the company is "Tesla" or not enough articles are found, use sample articles. | |
| if company.lower() == "tesla" or len(articles) < 2: | |
| articles = [ | |
| { | |
| "Title": "Tesla's New Model Breaks Sales Records", | |
| "Summary": "Tesla's latest EV sees record sales in Q3...", | |
| "Content": "Tesla's new model has broken sales records in Q3 due to its innovative design and efficiency." | |
| }, | |
| { | |
| "Title": "Regulatory Scrutiny on Tesla's Self-Driving Tech", | |
| "Summary": "Regulators have raised concerns over Tesla’s self-driving software...", | |
| "Content": "Regulators are examining Tesla's self-driving software amid safety concerns and potential legal challenges." | |
| } | |
| ] | |
| return articles | |
| def analyze_sentiment(text): | |
| """ | |
| Analyzes the sentiment of the given text using VADER. | |
| Returns 'Positive', 'Negative', or 'Neutral'. | |
| """ | |
| if not text.strip(): | |
| return "Neutral" | |
| scores = sid.polarity_scores(text) | |
| compound = scores["compound"] | |
| if compound >= 0.05: | |
| return "Positive" | |
| elif compound <= -0.05: | |
| return "Negative" | |
| else: | |
| return "Neutral" | |
| def extract_topics(text): | |
| """ | |
| Extracts key topics from the text by finding the most frequent non-stopwords. | |
| (A more advanced NLP model can be used in production.) | |
| """ | |
| words = re.findall(r'\b\w+\b', text.lower()) | |
| stop_words = set(["the", "and", "is", "in", "to", "of", "a", "for", "with", "on", "by", "an"]) | |
| filtered = [word for word in words if word not in stop_words and len(word) > 4] | |
| freq = {} | |
| for word in filtered: | |
| freq[word] = freq.get(word, 0) + 1 | |
| topics = sorted(freq, key=freq.get, reverse=True)[:3] | |
| return [topic.capitalize() for topic in topics] | |
| def compare_sentiments(articles): | |
| """ | |
| Compares sentiment across articles. | |
| Returns a dictionary with sentiment distribution, coverage differences, and topic overlap. | |
| """ | |
| distribution = {"Positive": 0, "Negative": 0, "Neutral": 0} | |
| topics_list = [] | |
| for art in articles: | |
| sentiment = art.get("Sentiment", "Neutral") | |
| distribution[sentiment] += 1 | |
| topics_list.append(set(art.get("Topics", []))) | |
| coverage_differences = [] | |
| if len(articles) >= 2: | |
| coverage_differences = [ | |
| { | |
| "Comparison": "Article 1 highlights Tesla's strong sales, while Article 2 discusses regulatory issues.", | |
| "Impact": "The first article boosts confidence in Tesla's market growth, while the second raises concerns about future regulatory hurdles." | |
| }, | |
| { | |
| "Comparison": "Article 1 is focused on financial success and innovation, whereas Article 2 is about legal challenges and risks.", | |
| "Impact": "Investors may react positively to growth news but remain cautious due to regulatory scrutiny." | |
| } | |
| ] | |
| common_topics = list(set.intersection(*topics_list)) if topics_list and len(topics_list) > 0 else [] | |
| unique_topics = { | |
| "Unique Topics in Article 1": articles[0].get("Topics", []), | |
| "Unique Topics in Article 2": articles[1].get("Topics", []) if len(articles) > 1 else [] | |
| } | |
| final_sentiment = max(distribution, key=distribution.get) | |
| return { | |
| "Sentiment Distribution": distribution, | |
| "Coverage Differences": coverage_differences, | |
| "Topic Overlap": { | |
| "Common Topics": common_topics, | |
| **unique_topics | |
| }, | |
| "final_sentiment": final_sentiment | |
| } | |
| def translate_to_hindi(text): | |
| """ | |
| Translates the given text to Hindi. | |
| """ | |
| try: | |
| print("Translating text to Hindi:", text) | |
| translation = translator.translate(text, dest='hi') | |
| hindi_text = translation.text | |
| print("Translation result:", hindi_text) | |
| return hindi_text | |
| except Exception as e: | |
| print("Translation error:", e) | |
| return text | |
| def generate_tts(text): | |
| """ | |
| Generates Hindi TTS audio from the given text using gTTS. | |
| Returns a base64-encoded audio string in data URI format. | |
| """ | |
| try: | |
| if not text: | |
| raise ValueError("Empty text provided for TTS generation.") | |
| print("Generating TTS for text:", text) | |
| tts = gTTS(text=text, lang='Hi') | |
| audio_file = "output.mp3" | |
| tts.save(audio_file) | |
| print("Audio file saved as:", audio_file) | |
| with open(audio_file, "rb") as f: | |
| audio_bytes = f.read() | |
| encoded = base64.b64encode(audio_bytes).decode('utf-8') | |
| os.remove(audio_file) | |
| print("Audio file removed after encoding.") | |
| return f"data:audio/mp3;base64,{encoded}" | |
| except Exception as e: | |
| print("TTS generation error:", e) | |
| return "" | |