Update app.py

app.py

@@ -27,61 +27,71 @@ auth = tw.OAuthHandler(consumer_key, consumer_secret)
 auth.set_access_token(access_token, access_token_secret)
 api = tw.API(auth, wait_on_rate_limit=True)
 
-
-
-
 st.title('Analisis de comentarios sexistas en Twitter con Tweepy and HuggingFace Transformers')
 st.markdown('Esta app utiliza tweepy para descargar tweets de twitter en base a la información de entrada y procesa los tweets usando transformers de HuggingFace para detectar comentarios sexistas. El resultado y los tweets correspondientes se almacenan en un dataframe para mostrarlo que es lo que se ve como resultado')
 
-def run():
-    with st.form(key='Introduzca nombre'):
-        search_words = st.text_input('Introduzca el termino para analizar')
-        number_of_tweets = st.number_input('Introduzca número de twweets a analizar. Máximo 50', 0,50,10)
-        submit_button = st.form_submit_button(label='Submit')
-        if submit_button:
-            tweets =tw.Cursor(api.search_tweets,q=search_words).items(number_of_tweets)
-            tweet_list = [i.text for i in tweets]
-            text= pd.DataFrame(tweet_list)
-            text1=text[0].values
-            indices1=tokenizer.batch_encode_plus(text1.tolist(),
+def principal(tweets):
+    tweet_list = [i.text for i in tweets]
+    text= pd.DataFrame(tweet_list)
+    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)
+     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)
+
+def run():
+    with st.form(key='Introduzca nombre'):
+        search_words = st.text_input('Introduzca el termino para analizar o Usuario a analizar')
+        number_of_tweets = st.number_input('Introduzca número de twweets a analizar. Máximo 50', 0,50,10)
+        submit_button = st.form_submit_button(label='Término')
+        submit_button1 = st.form_submit_button(label='Usuario')
+        
+        if submit_button:
+            date_since = "2020-09-14"
+            new_search = search_words + " -filter:retweets" 
+            tweets = tweepy.Cursor(api.search,q=new_search,lang="es",since=date_since).items(number_of_tweets)
+            principal(tweets)
+            #tweets =tw.Cursor(api.search_tweets,q=search_words).items(number_of_tweets)
+        if submit_button1:
+            tweets = api.user_timeline(screen_name = screen_name,count=500)
+            principal(tweets)
+        
+            
             #st.write(df)
 run()