update

app.py

@@ -14,6 +14,8 @@ newsapi = NewsApiClient(api_key=HF_TOKEN)
 classifier = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment") 
 today = str(date.today())
 
+print(f"HF_TOKEN is - {HF_TOKEN}")
+
 #times of india
 all_articles = newsapi.get_everything(sources='the-times-of-india',
                                     domains='timesofindia.indiatimes.com',
@@ -22,7 +24,7 @@ all_articles = newsapi.get_everything(sources='the-times-of-india',
                                     language='en',
                                     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['articles']]
-
+print(f"sentiment_toi length is {len(sentiment_toi)}")
 #the hindu
 all_articles = newsapi.get_everything(sources='the-hindu',
                                     domains='thehindu.com',
@@ -31,6 +33,7 @@ all_articles = newsapi.get_everything(sources='the-hindu',
                                     language='en',
                                     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['articles']]
+print(f"sentiment_toi length is {len(sentiment_hindu)}")
 
 #google news
 all_articles = newsapi.get_everything(sources='google-news-in',
@@ -40,6 +43,7 @@ all_articles = newsapi.get_everything(sources='google-news-in',
                                     language='en',
                                     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['articles']]
+print(f"sentiment_google length is {len(sentiment_google)}")
 
 #Driver 
 def inference(newssource): #, date):