Update app.py

app.py

@@ -254,10 +254,12 @@ def keyphrase_generator(article_link, model_1, model_2, max_num_keywords, model_
   df_new=pd.DataFrame(dict([ (k,pd.Series(v)) for k,v in sorted_dict.items() ]))
   
   df_final = df_new.fillna(' ')
+  fp = df_final.to_excel(r'Title_Abstract.xlsx', index = False)
 
 #############################################    Ends here    ####################################################
   
-  return df_final
+  #return df_final
+  return fp
   
 
 igen_pubmed = gr.Interface(keyphrase_generator, 
@@ -305,7 +307,8 @@ igen_pubmed = gr.Interface(keyphrase_generator,
                                         type="value", 
                                         default='pritamdeka/S-Biomed-Roberta-snli-multinli-stsb', 
                                         label="Select any SBERT model for abstracts from the list below")], 
-             outputs=gr.outputs.Dataframe(type="auto", label="Retrieved Results from PubMed",max_cols=2, overflow_row_behaviour="paginate"), 
+             #outputs=gr.outputs.Dataframe(type="auto", label="Retrieved Results from PubMed",max_cols=2, overflow_row_behaviour="paginate"), 
+             outputs=gr.outputs.File(label="Output File"),
              theme="peach", layout="horizontal",
              title="PubMed Abstract Retriever", description="Retrieves relevant PubMed abstracts for an online article which can be used as further references. Please note that it may take sometime for the models to load. Examples are provided below for demo purposes. Choose any one example to see the results. The models can be changed to see different results. ",
              examples=[