Update app.py

app.py

@@ -128,46 +128,45 @@ def run():
                     
                 elif (usuario):
                     tweets = api.user_timeline(screen_name = search_words,count=number_of_tweets)
-                
-                tweet_list = [i.text for i in tweets]
-                text= pd.DataFrame(tweet_list)
-                text[0] = text[0].apply(preprocess_tweet)
-                text1=text[0].values
-                indices1=tokenizer.batch_encode_plus(text1.tolist(),max_length=128,add_special_tokens=True, return_attention_mask=True,pad_to_max_length=True,truncation=True)
-                input_ids1=indices1["input_ids"]
-                attention_masks1=indices1["attention_mask"]
-                prediction_inputs1= torch.tensor(input_ids1)
-                prediction_masks1 = torch.tensor(attention_masks1)
-                # Set the batch size.  
-                batch_size = 25
-                # Create the DataLoader.
-                prediction_data1 = TensorDataset(prediction_inputs1, prediction_masks1)
-                prediction_sampler1 = SequentialSampler(prediction_data1)
-                prediction_dataloader1 = DataLoader(prediction_data1, sampler=prediction_sampler1, batch_size=batch_size)
-                print('Predicting labels for {:,} test sentences...'.format(len(prediction_inputs1)))
-                # Pone el modelo en modo evaluación
-                model.eval()
-                # Variables de Seguimiento 
-                predictions = []
-                # Predict 
-                for batch in prediction_dataloader1:
-                    batch = tuple(t.to(device) for t in batch)
-                    # Descomprimir las entradas de nuestro cargador de datos
-                    b_input_ids1, b_input_mask1 = batch
-                    # Decirle al modelo que no calcule ni almacene gradientes, ahorrando memoria y # acelerando la predicción.
-                    with torch.no_grad():
-                        # Forward pass, calculate logit predictions
-                        outputs1 = model(b_input_ids1, token_type_ids=None,attention_mask=b_input_mask1)
-                    logits1 = outputs1[0]
-                    # Move logits and labels to CPU
-                    logits1 = logits1.detach().cpu().numpy()
-                    # Store predictions and true labels
-                    predictions.append(logits1)
-                flat_predictions = [item for sublist in predictions for item in sublist]
-                flat_predictions = np.argmax(flat_predictions, axis=1).flatten()#p = [i for i in classifier(tweet_list)]
-                df = pd.DataFrame(list(zip(tweet_list, flat_predictions)),columns =['Últimos '+ str(number_of_tweets)+' Tweets'+' de '+search_words, 'Sexista'])
-                df['Sexista']= np.where(df['Sexista']== 0, 'No Sexistas', 'Sexistas')
-                
-                st.table(df.reset_index(drop=True).head(20).style.applymap(color_survived, subset=['Sexista']))
+                    tweet_list = [i.text for i in tweets]
+                    text= pd.DataFrame(tweet_list)
+                    text[0] = text[0].apply(preprocess_tweet)
+                    text1=text[0].values
+                    indices1=tokenizer.batch_encode_plus(text1.tolist(),max_length=128,add_special_tokens=True, return_attention_mask=True,pad_to_max_length=True,truncation=True)
+                    input_ids1=indices1["input_ids"]
+                    attention_masks1=indices1["attention_mask"]
+                    prediction_inputs1= torch.tensor(input_ids1)
+                    prediction_masks1 = torch.tensor(attention_masks1)
+                    # Set the batch size.  
+                    batch_size = 25
+                    # Create the DataLoader.
+                    prediction_data1 = TensorDataset(prediction_inputs1, prediction_masks1)
+                    prediction_sampler1 = SequentialSampler(prediction_data1)
+                    prediction_dataloader1 = DataLoader(prediction_data1, sampler=prediction_sampler1, batch_size=batch_size)
+                    print('Predicting labels for {:,} test sentences...'.format(len(prediction_inputs1)))
+                    # Pone el modelo en modo evaluación
+                    model.eval()
+                    # Variables de Seguimiento 
+                    predictions = []
+                    # Predict 
+                    for batch in prediction_dataloader1:
+                        batch = tuple(t.to(device) for t in batch)
+                        # Descomprimir las entradas de nuestro cargador de datos
+                        b_input_ids1, b_input_mask1 = batch
+                        # Decirle al modelo que no calcule ni almacene gradientes, ahorrando memoria y # acelerando la predicción.
+                        with torch.no_grad():
+                            # Forward pass, calculate logit predictions
+                            outputs1 = model(b_input_ids1, token_type_ids=None,attention_mask=b_input_mask1)
+                        logits1 = outputs1[0]
+                        # Move logits and labels to CPU
+                        logits1 = logits1.detach().cpu().numpy()
+                        # Store predictions and true labels
+                        predictions.append(logits1)
+                    flat_predictions = [item for sublist in predictions for item in sublist]
+                    flat_predictions = np.argmax(flat_predictions, axis=1).flatten()#p = [i for i in classifier(tweet_list)]
+                    df = pd.DataFrame(list(zip(tweet_list, flat_predictions)),columns =['Últimos '+ str(number_of_tweets)+' Tweets'+' de '+search_words, 'Sexista'])
+                    df['Sexista']= np.where(df['Sexista']== 0, 'No Sexistas', 'Sexistas')
+                    
+                    st.table(df.reset_index(drop=True).head(20).style.applymap(color_survived, subset=['Sexista']))
 
 run()