Update app.py

app.py

@@ -155,37 +155,7 @@ def main():
                all_results.loc[i] = answer, max(outputs.start_logits.numpy()[0]), 0, 0
 
                st.write(all_results)
-               #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'])
-               
-               #answer_start_index = int(tf.math.argmax(outputs.start_logits, axis=-1)[0])
-               #answer_end_index = int(tf.math.argmax(outputs.end_logits, axis=-1)[0])
-               #answer_start_scores = tf.nn.softmax(outputs.start_logits)
-               #answer_end_scores = tf.nn.softmax(outputs.end_logits)
-               #######################
-               #start_probabilities = tf.nn.softmax(outputs.start_logits, axis=-1)[0]
-               #end_probabilities = tf.nn.softmax(outputs.end_logits, axis=-1)[0]
-               #scores = start_probabilities[:, None] * end_probabilities[None, :]
-               #scores = tf.linalg.band_part(scores, 0, -1)
-               #scores = tf.reshape(scores, [-1])
-               #st.write(scores)
-               #max_index = np.argmax(scores)
-               #st.write(max_index)
-               #start_index = max_index // scores.shape[1]
-               #end_index = max_index % scores.shape[1]
-               #st.write(start_index)
-               #st.write(scores[start_index:end_index])
-               #######################
-               #predict_answer_tokens = inputs.input_ids[0, answer_start_index : answer_end_index + 1] 
-               #answer=tokenizer.decode(predict_answer_tokens) 
-               
-               #if (len(result['answer'])>0):    
-                #   cantidad_respuestas = cantidad_respuestas + 1
-                 #  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
                if (usar_barra_progreso==1):
                    porcentaje_progreso = round((i/total_respuestas)*100)
@@ -194,6 +164,14 @@ def main():
            
            my_bar.empty()
            usar_barra_progreso = 0
+
+           all_results=all_results.sort_values(by=['score'], ascending=False).head(10)
+           for row in all_results: 
+               if (len(row['answer'])>0):    
+                   cantidad_respuestas = cantidad_respuestas + 1
+                   df_answer.loc[i, "answer"] = row.loc['answer']
+                   df_answer.loc[i, "score"]= row.loc['score']
+                   lista_noticias_respuestas.append(df_answer.loc[i].to_frame().T)
             
            df_noticias_respuestas=pd.concat(lista_noticias_respuestas)
            batch_size = 5