Update app.py

app.py

@@ -301,10 +301,12 @@ def inference(input_batch,isurl,use_archive,limit_companies=10):
     print("[i] Pandas output shape:",df.shape)
     
     #[[], [('Nvidia', 'Information Technology')], [('Twitter', 'Communication Services'), ('Apple', 'Information Technology')], [], [], [], [], [], []]
+    df["company"] = np.nan
+    df["sector"] = np.nan
     for idx in range(len(df.index)):
       if ner_labels[idx]: #not empty
         for ner in ner_labels[idx]:
-          df = pd.concat(df,df.loc[[idx]].assign(company=ner[0], sector=ner[1]), axis=0, join='outer', ignore_index=True)
+          df = pd.concat([df,df.loc[[idx]].assign(company=ner[0], sector=ner[1])], axis=0, join='outer', ignore_index=True)
 
     return df #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