Update app.py

app.py

@@ -153,7 +153,7 @@ def main():
                answer = tokenizer.convert_tokens_to_string(tokenizer.convert_ids_to_tokens(input_ids[answer_start:answer_end]))
 
                all_results.loc[i] = answer, max(outputs.start_logits.numpy()[0]), 0, 0
-               #st.write(all_results.sort_values(by=['score']).tail(10))
+               result=all_results.sort_values(by=['score'], ascending=False, inplace=True).head(1)
                
                #outputs = qa_model(input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'])
                
@@ -177,19 +177,11 @@ def main():
                #######################
                #predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] 
                #answer=tokenizer.decode(predict_answer_tokens) 
-
-
-               if (len(all_results.loc[i, 'answer'])>0):
                
-                   #answer_start_scores= tf.math.reduce_sum(answer_start_scores)
-                   #answer_end_scores= tf.math.reduce_sum(answer_end_scores)
-                   #score = answer_start_scores*answer_end_scores
-                   #st.write(f'Aqui {answer_start_scores}' )
-                   #st.write(answer_end_scores)
-
+               if (result['answer'])>0):    
                    cantidad_respuestas = cantidad_respuestas + 1
-                   df_answer.loc[i, "answer"] = all_results.loc[i, 'answer']
-                   df_answer.loc[i, "score"]= all_results.loc[i, 'score']
+                   df_answer.loc[i, "answer"] = result.loc['answer']
+                   df_answer.loc[i, "score"]= result.loc['score']
                    lista_noticias_respuestas.append(df_answer.loc[i].to_frame().T)
                 
                # Barra de progreso