Update utils.py

utils.py

@@ -1,148 +1,150 @@
-import requests
-from bs4 import BeautifulSoup
-import time
-import nltk
-from nltk.sentiment import SentimentIntensityAnalyzer
-from nltk.tokenize import word_tokenize
-from nltk.corpus import stopwords
-from collections import Counter
-from gtts import gTTS
-import os
-import platform
-
-# Download required NLTK data files (if not already available).
-nltk.download('vader_lexicon')
-nltk.download('punkt')
-nltk.download('averaged_perceptron_tagger')
-nltk.download('stopwords')
-
-def get_bing_news_articles(company_name, num_articles=10):
-    """
-    Scrapes Bing News search results for a given company name.
-    Returns a list of articles with metadata: title, summary, URL, and source.
-    """
-    query = company_name.replace(" ", "+")
-    url = f"https://www.bing.com/news/search?q={query}&FORM=HDRSC6"
-    headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
-    response = requests.get(url, headers=headers)
-    if response.status_code != 200:
-        return []
-    soup = BeautifulSoup(response.text, "html.parser")
-    articles = []
-    news_cards = soup.find_all("div", class_="news-card")
-    for card in news_cards:
-        title_tag = card.find("a", class_="title")
-        if not title_tag:
-            continue
-        title = title_tag.get_text(strip=True)
-        article_url = title_tag.get("href")
-        snippet_tag = card.find("div", class_="snippet")
-        snippet = snippet_tag.get_text(strip=True) if snippet_tag else ""
-        source_tag = card.find("div", class_="source")
-        source = source_tag.get_text(strip=True) if source_tag else ""
-        articles.append({
-            "title": title,
-            "summary": snippet,
-            "url": article_url,
-            "source": source
-        })
-        if len(articles) >= num_articles:
-            break
-    return articles
-
-def analyze_sentiment(text):
-    """
-    Analyzes the sentiment of the given text using NLTK's VADER.
-    Returns:
-        sentiment (str): "Positive", "Negative", or "Neutral"
-        scores (dict): The full set of polarity scores.
-    """
-    sia = SentimentIntensityAnalyzer()
-    scores = sia.polarity_scores(text)
-    compound = scores["compound"]
-    if compound >= 0.05:
-        sentiment = "Positive"
-    elif compound <= -0.05:
-        sentiment = "Negative"
-    else:
-        sentiment = "Neutral"
-    return sentiment, scores
-
-def extract_topics(text):
-    """
-    Extracts topics from the input text using basic noun extraction.
-    Tokenizes the text, removes stopwords and punctuation, and returns a list of unique nouns.
-    """
-    text = text.lower()
-    tokens = word_tokenize(text)
-    stop_words = set(stopwords.words("english"))
-    filtered = [word for word in tokens if word.isalpha() and word not in stop_words]
-    tagged = nltk.pos_tag(filtered)
-    nouns = [word for word, pos in tagged if pos in ["NN", "NNS", "NNP", "NNPS"]]
-    return list(set(nouns))
-
-def comparative_analysis(articles):
-    """
-    Performs comparative analysis across articles.
-    Returns a dictionary with:
-      - Sentiment Distribution: Count of articles per sentiment.
-      - Coverage Differences: Insights based on keyword presence.
-      - Topic Overlap: Common topics and unique topics per article.
-    """
-    sentiment_distribution = {"Positive": 0, "Negative": 0, "Neutral": 0}
-    sales_keywords = {"sales", "growth", "record", "profit"}
-    regulatory_keywords = {"regulation", "regulatory", "scrutiny", "lawsuit", "legal", "compliance"}
-    sales_count = 0
-    reg_count = 0
-    all_topics = []
-    for article in articles:
-        sentiment = article.get("sentiment", "Neutral")
-        sentiment_distribution[sentiment] += 1
-        combined_text = f"{article['title']} {article['summary']}".lower()
-        if any(keyword in combined_text for keyword in sales_keywords):
-            sales_count += 1
-        if any(keyword in combined_text for keyword in regulatory_keywords):
-            reg_count += 1
-        topics = extract_topics(combined_text)
-        article["topics"] = topics
-        all_topics.extend(topics)
-    if sales_count > reg_count:
-        coverage_insight = (f"More articles ({sales_count}) emphasize sales and financial growth compared to regulatory concerns ({reg_count}).")
-    elif reg_count > sales_count:
-        coverage_insight = (f"More articles ({reg_count}) focus on regulatory or legal challenges compared to sales aspects ({sales_count}).")
-    else:
-        coverage_insight = (f"An equal number of articles emphasize sales/growth and regulatory issues ({sales_count} each).")
-    topic_counter = Counter(all_topics)
-    common_topics = [topic for topic, count in topic_counter.items() if count > 1]
-    unique_topics = {}
-    for i, article in enumerate(articles, start=1):
-        unique = [topic for topic in article.get("topics", []) if topic_counter[topic] == 1]
-        unique_topics[f"Article {i}"] = unique
-    analysis = {
-        "Sentiment Distribution": sentiment_distribution,
-        "Coverage Differences": coverage_insight,
-        "Topic Overlap": {
-            "Common Topics": common_topics,
-            "Unique Topics": unique_topics
-        }
-    }
-    return analysis
-
-def convert_text_to_hindi_tts(text, output_file="output.mp3"):
-    """
-    Converts the input text into Hindi speech using gTTS and saves it as an MP3 file.
-    """
-    tts = gTTS(text=text, lang='hi', slow=False)
-    tts.save(output_file)
-    return output_file
-
-def play_audio(file_path):
-    """
-    Plays an audio file using the system's default media player.
-    """
-    if platform.system() == "Windows":
-        os.startfile(file_path)
-    elif platform.system() == "Darwin":
-        os.system(f"open {file_path}")
-    else:
-        os.system(f"mpg123 {file_path}")
+import nltk
+nltk.download('punkt')  # Download the required resource
+
+import requests
+from bs4 import BeautifulSoup
+import time
+from nltk.sentiment import SentimentIntensityAnalyzer
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from collections import Counter
+from gtts import gTTS
+import os
+import platform
+
+# Download required NLTK data files (if not already available).
+nltk.download('vader_lexicon')
+
+nltk.download('averaged_perceptron_tagger')
+nltk.download('stopwords')
+
+def get_bing_news_articles(company_name, num_articles=10):
+    """
+    Scrapes Bing News search results for a given company name.
+    Returns a list of articles with metadata: title, summary, URL, and source.
+    """
+    query = company_name.replace(" ", "+")
+    url = f"https://www.bing.com/news/search?q={query}&FORM=HDRSC6"
+    headers = {"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64)"}
+    response = requests.get(url, headers=headers)
+    if response.status_code != 200:
+        return []
+    soup = BeautifulSoup(response.text, "html.parser")
+    articles = []
+    news_cards = soup.find_all("div", class_="news-card")
+    for card in news_cards:
+        title_tag = card.find("a", class_="title")
+        if not title_tag:
+            continue
+        title = title_tag.get_text(strip=True)
+        article_url = title_tag.get("href")
+        snippet_tag = card.find("div", class_="snippet")
+        snippet = snippet_tag.get_text(strip=True) if snippet_tag else ""
+        source_tag = card.find("div", class_="source")
+        source = source_tag.get_text(strip=True) if source_tag else ""
+        articles.append({
+            "title": title,
+            "summary": snippet,
+            "url": article_url,
+            "source": source
+        })
+        if len(articles) >= num_articles:
+            break
+    return articles
+
+def analyze_sentiment(text):
+    """
+    Analyzes the sentiment of the given text using NLTK's VADER.
+    Returns:
+        sentiment (str): "Positive", "Negative", or "Neutral"
+        scores (dict): The full set of polarity scores.
+    """
+    sia = SentimentIntensityAnalyzer()
+    scores = sia.polarity_scores(text)
+    compound = scores["compound"]
+    if compound >= 0.05:
+        sentiment = "Positive"
+    elif compound <= -0.05:
+        sentiment = "Negative"
+    else:
+        sentiment = "Neutral"
+    return sentiment, scores
+
+def extract_topics(text):
+    """
+    Extracts topics from the input text using basic noun extraction.
+    Tokenizes the text, removes stopwords and punctuation, and returns a list of unique nouns.
+    """
+    text = text.lower()
+    tokens = word_tokenize(text)
+    stop_words = set(stopwords.words("english"))
+    filtered = [word for word in tokens if word.isalpha() and word not in stop_words]
+    tagged = nltk.pos_tag(filtered)
+    nouns = [word for word, pos in tagged if pos in ["NN", "NNS", "NNP", "NNPS"]]
+    return list(set(nouns))
+
+def comparative_analysis(articles):
+    """
+    Performs comparative analysis across articles.
+    Returns a dictionary with:
+      - Sentiment Distribution: Count of articles per sentiment.
+      - Coverage Differences: Insights based on keyword presence.
+      - Topic Overlap: Common topics and unique topics per article.
+    """
+    sentiment_distribution = {"Positive": 0, "Negative": 0, "Neutral": 0}
+    sales_keywords = {"sales", "growth", "record", "profit"}
+    regulatory_keywords = {"regulation", "regulatory", "scrutiny", "lawsuit", "legal", "compliance"}
+    sales_count = 0
+    reg_count = 0
+    all_topics = []
+    for article in articles:
+        sentiment = article.get("sentiment", "Neutral")
+        sentiment_distribution[sentiment] += 1
+        combined_text = f"{article['title']} {article['summary']}".lower()
+        if any(keyword in combined_text for keyword in sales_keywords):
+            sales_count += 1
+        if any(keyword in combined_text for keyword in regulatory_keywords):
+            reg_count += 1
+        topics = extract_topics(combined_text)
+        article["topics"] = topics
+        all_topics.extend(topics)
+    if sales_count > reg_count:
+        coverage_insight = (f"More articles ({sales_count}) emphasize sales and financial growth compared to regulatory concerns ({reg_count}).")
+    elif reg_count > sales_count:
+        coverage_insight = (f"More articles ({reg_count}) focus on regulatory or legal challenges compared to sales aspects ({sales_count}).")
+    else:
+        coverage_insight = (f"An equal number of articles emphasize sales/growth and regulatory issues ({sales_count} each).")
+    topic_counter = Counter(all_topics)
+    common_topics = [topic for topic, count in topic_counter.items() if count > 1]
+    unique_topics = {}
+    for i, article in enumerate(articles, start=1):
+        unique = [topic for topic in article.get("topics", []) if topic_counter[topic] == 1]
+        unique_topics[f"Article {i}"] = unique
+    analysis = {
+        "Sentiment Distribution": sentiment_distribution,
+        "Coverage Differences": coverage_insight,
+        "Topic Overlap": {
+            "Common Topics": common_topics,
+            "Unique Topics": unique_topics
+        }
+    }
+    return analysis
+
+def convert_text_to_hindi_tts(text, output_file="output.mp3"):
+    """
+    Converts the input text into Hindi speech using gTTS and saves it as an MP3 file.
+    """
+    tts = gTTS(text=text, lang='hi', slow=False)
+    tts.save(output_file)
+    return output_file
+
+def play_audio(file_path):
+    """
+    Plays an audio file using the system's default media player.
+    """
+    if platform.system() == "Windows":
+        os.startfile(file_path)
+    elif platform.system() == "Darwin":
+        os.system(f"open {file_path}")
+    else:
+        os.system(f"mpg123 {file_path}")