update

app.py

@@ -26,6 +26,7 @@ all_articles_toi = newsapi.get_everything(sources='the-times-of-india',
                                     sort_by='relevancy',)
 sentiment_toi = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABEL_0' else 'Neutral' if classifier(entry['content'])[0]['label'] == 'LABEL_1' else 'Positive' for entry in all_articles_toi['articles']]
 print(f"sentiment_toi length is {len(sentiment_toi)}")
+print(f"all_articles_toi length is {len(all_articles_toi)}")
 
 #the hindu
 all_articles_hindu = newsapi.get_everything(sources='the-hindu',
@@ -36,6 +37,7 @@ all_articles_hindu = newsapi.get_everything(sources='the-hindu',
                                     sort_by='relevancy',)
 sentiment_hindu = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABEL_0' else 'Neutral' if classifier(entry['content'])[0]['label'] == 'LABEL_1' else 'Positive' for entry in all_articles_hindu['articles']]
 print(f"sentiment_hindu length is {len(sentiment_hindu)}")
+print(f"all_articles_hindu length is {len(all_articles_hindu)}")
 
 #google news
 all_articles_google = newsapi.get_everything(sources='google-news-in',
@@ -46,6 +48,7 @@ all_articles_google = newsapi.get_everything(sources='google-news-in',
                                     sort_by='relevancy',)
 sentiment_google = ['Negative' if classifier(entry['content'])[0]['label'] == 'LABEL_0' else 'Neutral' if classifier(entry['content'])[0]['label'] == 'LABEL_1' else 'Positive' for entry in all_articles_google['articles']]
 print(f"sentiment_google length is {len(sentiment_google)}")
+print(f"all_articles_google length is {len(all_articles_google)}")
 
 #Driver 
 def inference(newssource): #, date):