app.py

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()