update

app.py

@@ -22,6 +22,9 @@ all_top_headlines = newsapi.get_top_headlines(country='in')
 sentiment_tophead = ['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_top_headlines['articles']]
 print(f"sentiment_tophead length is {len(sentiment_tophead)}")
 print(f"all_top_headlines length is {len(all_top_headlines['articles'])}")
+print("************** sentiment start ****************")
+print(sentiment_tophead)
+print("************** sentiment end ****************")
 
 #times of india
 all_articles_toi = newsapi.get_everything(sources='the-times-of-india',
@@ -59,16 +62,12 @@ def inference(newssource): #, date):
   print(f"sentiment length is - {len(sentiment)}")
   
   dictnews = { 'description' : description, 'content' : content, 'url' : url, 'urlToImage' : urlToImage, 'sentiment' : sentiment,}
-  #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']],
-  #          'urlToImage' : [entry['urlToImage'] for entry in all_articles['articles']],
-  #          'sentiment' : sentiment,
-  #          }
+
   df  = pd.DataFrame.from_dict(dictnews)
   print(f"dataframe shape is :,{df.shape}")
-  html_out = "<img src= " + dictnews['urlToImage'][0] + ">"
-  print(f"html_out is : {html_out}")
+  if dictnews['urlToImage'][0] is not None :
+    html_out = "<img src= " + dictnews['urlToImage'][0] + ">"
+    print(f"html_out is : {html_out}")
   return df, html_out