Update app.py

app.py

@@ -24,28 +24,20 @@ def article_selection(sentiment):
         top3 = predictions[0:3]
         top3_result = top3[['Headline_string','Url']]
         top3_result.rename(columns = {'Headline_str':'Headlines', 'Url':'URL'})
-       # predictions_df_url0 = predictions['Url'].iloc[0]
-       # predictions_df_head0 = predictions['Headline_string'].iloc[0]
-       # predictions_df_url1 = predictions['Url'].iloc[1]
-       # predictions_df_head1 = predictions['Headline_string'].iloc[1]
-       # predictions_df_url2 = predictions['Url'].iloc[2]
-       # predictions_df_head2 = predictions['Headline_string'].iloc[2]
-       # predictions_df = [[predictions_df_head0, predictions_df_url0], [predictions_df_head1, predictions_df_url1], [predictions_df_head2, predictions_df_url2]]
-       # negative_df = pd.DataFrame(negative_df, columns=['Headline','URL'])
         return top3_result
     
     elif sentiment == "Negative":
         predictions = negative_preds
-        predictions_df_url0 = predictions['Url'].iloc[0]
-        predictions_df_url1 = predictions['Url'].iloc[1]
-        predictions_df_url2 = predictions['Url'].iloc[2]
-        return predictions_df_url0, predictions_df_url1, predictions_df_url2
+        top3 = predictions[0:3]
+        top3_result = top3[['Headline_string','Url']]
+        top3_result.rename(columns = {'Headline_str':'Headlines', 'Url':'URL'})
+        return top3_result
     else:
         predictions = negative_preds
-        predictions_df_url0 = predictions['Url'].iloc[0]
-        predictions_df_url1 = predictions['Url'].iloc[1]
-        predictions_df_url2 = predictions['Url'].iloc[2]
-        return predictions_df_url0, predictions_df_url1, predictions_df_url2
+        top3 = predictions[0:3]
+        top3_result = top3[['Headline_string','Url']]
+        top3_result.rename(columns = {'Headline_str':'Headlines', 'Url':'URL'})
+        return top3_result
 
 def manual_label():
     # Selecting random row from batch data