update

app.py

@@ -12,17 +12,24 @@ HF_TOKEN = os.environ["newsapi"]
 newsapi = NewsApiClient(api_key=HF_TOKEN)
 
 classifier = pipeline(model="cardiffnlp/twitter-roberta-base-sentiment") 
+today = str(date.today())
+all_articles = newsapi.get_everything(sources='the-times-of-india',
+                                    domains='timesofindia.indiatimes.com',
+                                    from_param=today,
+                                    to=today,
+                                    language='en',
+                                    sort_by='relevancy',)
 sentiment = ['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']]
 
 #Driver 
 def inference(newssource): #, date):
-  today = str(date.today())
-  all_articles = newsapi.get_everything(sources='the-times-of-india',
-                                      domains='timesofindia.indiatimes.com',
-                                      from_param=today,
-                                      to=today,
-                                      language='en',
-                                      sort_by='relevancy',)
+  #today = str(date.today())
+  #all_articles = newsapi.get_everything(sources='the-times-of-india',
+  #                                    domains='timesofindia.indiatimes.com',
+  #                                    from_param=today,
+  #                                    to=today,
+  #                                    language='en',
+  #                                    sort_by='relevancy',)
   dictnews = { 'description' : [entry['description'] for entry in all_articles['articles']],
             'content' : [entry['content'] for entry in all_articles['articles']],
             'url' : [entry['url'] for entry in all_articles['articles']],