Update app.py

app.py

@@ -352,8 +352,8 @@ def inference(input_batch,isurl,use_archive,filt_companies_topic,limit_companies
               if news_sectors[idx] != ner[1]:
                   continue
           dfo = pd.concat( [dfo, df.loc[[idx]].assign(company=ner[0], sector=ner[1], symbol=ner[2])], join='outer', ignore_index=True) #axis=0
-     print("[i] Pandas output shape:",dfo.shape)
-     return dfo #ner_labels, {'E':float(prob_outs[0]),"S":float(prob_outs[1]),"G":float(prob_outs[2])},{sentiment['label']:float(sentiment['score'])},"**Summary:**\n\n" + summary
+    print("[i] Pandas output shape:",dfo.shape)
+    return dfo #ner_labels, {'E':float(prob_outs[0]),"S":float(prob_outs[1]),"G":float(prob_outs[2])},{sentiment['label']:float(sentiment['score'])},"**Summary:**\n\n" + summary
 
 title = "ESG API Demo"
 description = """This is a demonstration of the full ESG pipeline backend where given a list of URL (english, news) the news contents are extracted, using extractnet, and fed to three models: