Update app.py

app.py

@@ -27,66 +27,61 @@ 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 principal(tweets,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(), 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)
-
 def run():
     with st.form(key='Introduzca nombre'):
-        search_words = st.text_input('Introduzca el termino para analizar o Usuario a analizar')
+        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='Término')
-        submit_button1 = st.form_submit_button(label='Usuario')
-        
+        submit_button = st.form_submit_button(label='Submit')
         if submit_button:
-            date_since = "2020-09-14"
-            new_search = search_words + " -filter:retweets" 
-            tweets = tw.Cursor(api.search_tweets,q=new_search,lang="es",since=date_since).items(number_of_tweets)
-            principal(tweets,number_of_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,number_of_tweets)
-        
-            
+            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(),
+                                     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()