Update app.py

app.py

@@ -97,7 +97,7 @@ def run():
    submit_button = col.form_submit_button(label='Analizar')
    error=False
    if submit_button:
-            date_since = "2020-09-14"
+            # Condición para el caso de que esten dos check seleccionados
             if ( termino == False and usuario == False):
                 st.text('Error no se ha seleccionado ningun check')
                 error=True
@@ -108,22 +108,15 @@ def run():
             if (error == False):
                 if (termino):
                     new_search = search_words + " -filter:retweets"
-                    tweets =tw.Cursor(api.search_tweets,q=new_search,lang="es",since=date_since).items(number_of_tweets)
+                    tweets =tw.Cursor(api.search_tweets,q=new_search,lang="es").items(number_of_tweets)
                 elif (usuario):
                     tweets = api.user_timeline(screen_name = search_words,count=number_of_tweets)
                 
                 tweet_list = [i.text for i in tweets]
-                #tweet_list = [strip_undesired_chars(i.text) for i in tweets]
                 text= pd.DataFrame(tweet_list)
-                #text[0] = text[0].apply(preprocess)
-                #text[0] = text[0].apply(preprocess_tweet)
+                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)
+                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)
@@ -135,16 +128,16 @@ def run():
                 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)))
-                # Put model in evaluation mode
+                # Pone el modelo en modo evaluación
                 model.eval()
-                # Tracking variables 
+                # Variables de Seguimiento 
                 predictions = []
                 # Predict 
                 for batch in prediction_dataloader1:
                     batch = tuple(t.to(device) for t in batch)
-                    # Unpack the inputs from our dataloader
+                    # Descomprimir las entradas de nuestro cargador de datos
                     b_input_ids1, b_input_mask1 = batch
-                    # Telling the model not to compute or store gradients, saving memory and   # speeding up prediction
+                    # 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)