Update app.py

app.py

@@ -68,7 +68,62 @@ def run():
                 error=True
             elif ( termino == True and usuario == True):
                 st.text('Error se han seleccionado los dos check')
-                error=True 
-      
+                error=True
+                
+            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)
+                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)
+                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)))
+                # Put model in evaluation mode
+                model.eval()
+                # Tracking variables 
+                predictions = []
+                # Predict 
+                for batch in prediction_dataloader1:
+                    batch = tuple(t.to(device) for t in batch)
+                    # Unpack the inputs from our dataloader
+                    b_input_ids1, b_input_mask1 = batch
+                    # Telling the model not to compute or store gradients, saving memory and   # speeding up prediction
+                    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 Sexista', 'Sexista')
+                
+                
+                st.table(df.reset_index(drop=True).head(20).style.applymap(color_survived, subset=['Sexista']))
 
 run()