Update app.py

app.py

@@ -65,62 +65,67 @@ def run():
         termino=st.checkbox('Término')
         usuario=st.checkbox('Usuario')
         submit_button = st.form_submit_button(label='Analizar')
+        error=False
         if submit_button:
             date_since = "2020-09-14"
-            if (termino):
+            if ( termino == False and usuario == False):
+                st.text('Error no se ha seleccionado ningun check')
+                error=True
+            elif (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)
-                     
-            #new_search = search_words + " -filter:retweets" 
-            #tweets = tweepy.Cursor(api.search,q=new_search,lang="es",since=date_since).items(number_of_tweets)
-            #tweets =tw.Cursor(api.search_tweets,q=search_words).items(number_of_tweets)
-            #tweets =tw.Cursor(api.search_tweets,q=new_search,lang="es",since=date_since).items(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)
-            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 =['Latest'+str(number_of_tweets)+'Tweets'+' on '+search_words, 'Sexista'])
-            df['Sexista']= np.where(df['Sexista']== 0, 'No Sexista', 'Sexista')
-            st.table(df)
+            elif ( termino == True and usuario == True):
+                st.text('Error se han seleccionado los dos check')
+                error=True
+            
+           
+            if (error == False):
+                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)
+                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 =['Latest'+str(number_of_tweets)+'Tweets'+' on '+search_words, 'Sexista'])
+                df['Sexista']= np.where(df['Sexista']== 0, 'No Sexista', 'Sexista')
+                st.table(df)
             #st.write(df)
 run()