Create utils.py

utils.py

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+# Importing libraries
+from bs4 import BeautifulSoup
+import requests
+from langchain.schema import HumanMessage
+from langchain_groq import ChatGroq
+import json
+from dotenv import load_dotenv
+import os
+from transformers import pipeline
+
+# Load environment variables
+load_dotenv()
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+
+# Initialize the LLM model
+llm = ChatGroq(api_key=GROQ_API_KEY, model="llama-3.1-8b-instant")
+
+# Function to extract news titles and summaries from Economic Times
+def extract_titles_and_summaries(company_name, num_articles=10):
+    url = f"https://economictimes.indiatimes.com/topic/{company_name}/news"
+    try:
+        response = requests.get(url)
+        if response.status_code != 200:
+            print(f"Failed to fetch the webpage. Status code: {response.status_code}")
+            return []
+
+        soup = BeautifulSoup(response.content, "html.parser")
+        articles = soup.find_all('div', class_='clr flt topicstry story_list', limit=num_articles)
+        extracted_articles = []
+
+        for article in articles:
+            title_tag = article.find('h2')
+            if title_tag:
+                link_tag = title_tag.find('a')
+                title = link_tag.get_text(strip=True) if link_tag else "No Title Found"
+            else:
+                title = "No Title Found"
+
+            summary_tag = article.find('p')
+            summary = summary_tag.get_text(strip=True) if summary_tag else "No Summary Found"
+
+            extracted_articles.append({
+                "Title": title,
+                "Summary": summary
+            })
+
+        return {
+            "Company": company_name,
+            "Articles": extracted_articles
+        }
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return []
+
+# Function to perform sentiment analysis on extracted news articles
+def perform_sentiment_analysis(news_data):
+    from transformers import pipeline
+    articles = news_data.get("Articles", [])
+    pipe = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis", device=1)
+    sentiment_counts = {"Positive": 0, "Negative": 0, "Neutral": 0}
+
+    for article in articles:
+        content = f"{article['Title']} {article['Summary']}"
+        sentiment_result = pipe(content)[0]
+
+        sentiment_map = {
+            "positive": "Positive",
+            "negative": "Negative",
+            "neutral": "Neutral",
+            "very positive": "Positive",
+            "very negative": "Negative"
+        }
+
+        sentiment = sentiment_map.get(sentiment_result["label"].lower(), "Unknown")
+        score = float(sentiment_result["score"])
+
+        article["Sentiment"] = sentiment
+        article["Score"] = score
+
+        if sentiment in sentiment_counts:
+            sentiment_counts[sentiment] += 1
+
+    return news_data, sentiment_counts
+
+# Function to extract topics from articles using Hugging Face model
+def extract_topics_with_hf(news_data):
+    structured_data = {
+        "Company": news_data.get("Company", "Unknown"),
+        "Articles": []
+    }
+    topic_pipe = pipeline("text-classification", model="valurank/distilroberta-topic-classification", device=1)
+    articles = news_data.get("Articles", [])
+
+    for article in articles:
+        content = f"{article['Title']} {article['Summary']}"
+        topics_result = topic_pipe(content, top_k=3)
+        topics = [topic["label"] for topic in topics_result] if topics_result else ["Unknown"]
+
+        structured_data["Articles"].append({
+            "Title": article["Title"],
+            "Summary": article["Summary"],
+            "Sentiment": article.get("Sentiment", "Unknown"),
+            "Score": article.get("Score", 0.0),
+            "Topics": topics
+        })
+    return structured_data
+
+# Function to generate a final sentiment summary using LLM
+def generate_final_sentiment(news_data, sentiment_counts):
+    company_name = news_data["Company"]
+    total_articles = sum(sentiment_counts.values())
+    combined_summaries = " ".join([article["Summary"] for article in news_data["Articles"]])
+
+    prompt = f"""
+    Based on the analysis of {total_articles} articles about the company "{company_name}":
+    - Positive articles: {sentiment_counts['Positive']}
+    - Negative articles: {sentiment_counts['Negative']}
+    - Neutral articles: {sentiment_counts['Neutral']}
+    The following are the summarized key points from the articles: "{combined_summaries}".
+    Provide a single, concise summary that integrates the overall sentiment analysis and key news highlights while maintaining a natural flow. Explain its implications for the company's reputation, stock potential, and public perception.
+    Respond **ONLY** with a well-structured very concise and short paragraph in plain text, focusing on overall sentiment.
+    """
+
+    response = llm.invoke([HumanMessage(content=prompt)], max_tokens=200)
+    final_sentiment = response if response else "Sentiment analysis summary not available."
+    return final_sentiment.content  # returns a string
+
+# Function to extract JSON response from LLM output
+def extract_json(response):
+    try:
+        return json.loads(response)
+    except json.JSONDecodeError:
+        return {}
+
+# Function to compare articles based on common topics and sentiment variations
+def compare_articles(news_data, sentiment_counts):
+    articles = news_data.get("Articles", [])
+    all_topics = [set(article["Topics"]) for article in articles]
+    common_topics = set.intersection(*all_topics) if all_topics else set()
+
+    topics_prompt = f"""
+    Analyze the following article topics and identify **only three** key themes that are common across multiple articles, 
+    even if they are phrased differently. The topics from each article are:
+    {all_topics}
+    Respond **ONLY** with a JSON format:
+    {{"CommonTopics": ["topic1", "topic2", "topic3"]}}
+    """
+    
+    response = llm.invoke([HumanMessage(content=topics_prompt)]).content
+    contextual_common_topics = extract_json(response).get("CommonTopics", list(common_topics))[:3]  # Limit to 3 topics
+
+    total_articles = sum(sentiment_counts.values())
+
+    comparison_prompt = f"""
+    Provide a high-level summary comparing {total_articles} news articles about "{news_data['Company']}":
+    - Sentiment distribution: {sentiment_counts}
+    - Commonly discussed topics across articles: {contextual_common_topics}
+    Consider the following:
+    1. Notable contrasts between articles (e.g., major differences in topics and perspectives).
+    2. Overall implications for the company's reputation, stock potential, and public perception.
+    3. How sentiment varies across articles and its impact.
+    Respond **ONLY** with a concise and insightful summary in this JSON format:
+    {{
+        "Coverage Differences": [
+            {{"Comparison": "Brief contrast between Articles 1 & 2", "Impact": "Concise impact statement"}},
+            {{"Comparison": "Brief contrast between Articles 3 & 4", "Impact": "Concise impact statement"}}
+        ]
+    }}
+    """
+
+    response = llm.invoke([HumanMessage(content=comparison_prompt)]).content
+    coverage_differences = extract_json(response).get("Coverage Differences", [])
+
+    final_sentiment = generate_final_sentiment(news_data, sentiment_counts)
+
+    return {
+        "Company": news_data["Company"],
+        "Articles": articles,
+        "Comparative Sentiment Score": {
+            "Sentiment Distribution": sentiment_counts,
+            "Coverage Differences": coverage_differences,  
+            "Topic Overlap": {
+                "Common Topics": contextual_common_topics,
+                "Unique Topics": {
+                    f"Article {i+1}": list(topics - set(contextual_common_topics))
+                    for i, topics in enumerate(all_topics)
+                }
+            }
+        },
+        "Final Sentiment Analysis": final_sentiment
+    }