Create app.py

app.py

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+import tweepy as tw
+import streamlit as st
+import pandas as pd
+import torch
+import numpy as np
+import re
+
+from torch.utils.data import TensorDataset, DataLoader, RandomSampler, SequentialSampler
+from transformers import AutoTokenizer, AutoModelForSequenceClassification,AdamW
+tokenizer = AutoTokenizer.from_pretrained('hackathon-pln-es/twitter_sexismo-finetuned-exist2021-metwo')
+model = AutoModelForSequenceClassification.from_pretrained("hackathon-pln-es/twitter_sexismo-finetuned-exist2021-metwo")
+
+import torch
+if torch.cuda.is_available():  
+    device = torch.device("cuda")
+    print('I will use the GPU:', torch.cuda.get_device_name(0))
+    
+else:
+    print('No GPU available, using the CPU instead.')
+    device = torch.device("cpu")
+
+consumer_key = st.secrets["consumer_key"]
+consumer_secret = st.secrets["consumer_secret"]
+access_token = st.secrets["access_token"]
+access_token_secret = st.secrets["access_token_secret"]
+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(),
+                                     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()