Update app.py

app.py

@@ -136,17 +136,6 @@ def calculate_similarity(company_name, company_ticker, title, threshold=0.4):
 
     return weighted_similarity_score
 
-def calculate_title_similarity(news_list):
-    similar_news = []
-    for i, news1 in enumerate(news_list):
-        for j, news2 in enumerate(news_list):
-            if i != j:
-                similarity_score = calculate_similarity(company_name, company_ticker, news1['title'])
-                if similarity_score > 0.9:
-                    similar_news.append(news1)
-                    break  # Exit inner loop to avoid duplicates
-    return similar_news
-
 def analyze_sentiment(title):
     print("Analyzing sentiment...")
     # Perform sentiment analysis on the input title
@@ -157,14 +146,22 @@ def analyze_sentiment(title):
     print("Sentiment analyzed successfully.")
     return labels, scores
 
+def calculate_title_similarity(news_list, company_name, company_ticker):
+    """Calculate similarity score between news titles."""
+    similar_news = []
+    for news in news_list:
+        similarity_score = calculate_similarity(company_name, company_ticker, news['title'])
+        if similarity_score > 0.9:
+            similar_news.append(news)
+    return similar_news
+
 def fetch_news(company_name, company_ticker, event_name, start_date, end_date, location, num_news, include_domains, exclude_domains):
     analyzed_news_name = fetch_and_analyze_news(company_name, company_ticker, event_name, start_date, end_date, location, num_news, include_domains, exclude_domains)
     
     above_threshold_news = [news for news in analyzed_news_name if news is not None and news['similarity_score'] >= 0.3]
     below_threshold_news = [news for news in analyzed_news_name if news is not None and news['similarity_score'] < 0.3]
 
-    # Calculate similarity for above threshold news
-    similar_news = calculate_title_similarity(above_threshold_news)
+    similar_news = calculate_title_similarity(above_threshold_news, company_name, company_ticker)
 
     above_threshold_df = pd.DataFrame(above_threshold_news)
     below_threshold_df = pd.DataFrame(below_threshold_news)