Update app.py

app.py

@@ -212,6 +212,12 @@ def inference(input_batch,isurl,use_archive,limit_companies=10):
     df['sent_lbl'] = [d['label'] for d in sentiment ]
     df['sent_score'] = [d['score'] for d in sentiment ]
     print("[i] Pandas output shape:",df.shape)
+    
+    #[[], [('Nvidia', 'Information Technology')], [('Twitter', 'Communication Services'), ('Apple', 'Information Technology')], [], [], [], [], [], []]
+    for idx in range(len(df.index)):
+      if ner_labels[idx]: #not empty
+        for ner in ner_labels[idx]:
+          df = df.append(df.loc[[idx]].assign(company=ner[0], sector=ner[1]), 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