Update app.py

app.py

@@ -111,6 +111,7 @@ def _inference_classifier(text):
     return sigmoid(ort_outs[0])
 
 def inference(input_batch,isurl,use_archive,limit_companies=10):
+    url_list = []    #Only used if isurl
     input_batch_content = []
 #    if file_in.name is not "":
 #        print("[i] Input is file:",file_in.name)
@@ -136,6 +137,7 @@ def inference(input_batch,isurl,use_archive,limit_companies=10):
                 url = row_in[0]
             else:
                 url = row_in
+            url_list.append(url)
             if use_archive:
                 archive = is_in_archive(url)
                 if archive['archived']:
@@ -163,6 +165,10 @@ def inference(input_batch,isurl,use_archive,limit_companies=10):
     #summary = _inference_summary_model_pipeline(input_batch_content )[0]['generated_text']
     #ner_labels = _inference_ner_spancat(input_batch_content ,summary, penalty = 0.8, limit_outputs=limit_companies)
     df = pd.DataFrame(prob_outs,columns =['E','S','G'])
+    if isurl:
+        df['URL'] = url_list
+    else:
+        df['content_id'] = range(1, len(input_batch_r)+1)
     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)