Update app.py

app.py

@@ -344,7 +344,7 @@ def inference(input_batch,isurl,use_archive,filt_companies_topic,limit_companies
     df["company"] = np.nan
     df["sector"] = np.nan
     df["symbol"] = np.nan
-    dfo = pd.DataFrame(columns=['E','S','G','URL','sent_lbl','sent_score','sector_pred','company','sector','symbol'])
+    dfo = pd.DataFrame(columns=['E','S','G','URL','sent_lbl','sent_score','topic','company','sector','symbol'])
     for idx in range(len(df.index)):
       if ner_labels[idx]: #not empty
         for ner in ner_labels[idx]:
@@ -373,7 +373,6 @@ API input parameters:
 examples = [[ [['https://www.bbc.com/news/uk-62732447'],
             ['https://www.bbc.com/news/science-environment-62758811'],
             ['https://www.bbc.com/news/business-62524031'],
-            ['https://www.bbc.com/news/business-62728621'],
             ["https://www.knowesg.com/investors/blackstone-and-sphera-work-together-for-portfolio-decarbonization-program-17022022"],
             ["https://www.esgtoday.com/amazon-partners-with-matt-damons-water-org-to-provide-water-access-to-100-million-people/"],
             ["https://www.esgtoday.com/walmart-allocates-over-1-billion-to-renewable-energy-sustainable-buildings-circular-economy/"],