app.py

import tweepy as tw
import streamlit as st
import pandas as pd
from transformers import pipeline

consumer_key = '9jIZw6aJxenAhwrbfiymcP1FL'
consumer_secret = 'PtwmIIHckaGIe4XyJsen1Oy9qCPbhce66UdWIlrnKVEMfLwKg5'
access_token = '975820852901097473-sv2EVjyrOR0TwNmnv8gyyHXu30XzJC7'
access_token_secret = 'K8bGe0Neqil5BcHMjJdE1vBecYFTvXa3AHvLZoLNQ8WBO'
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 get tweets de twitter en base a la información de de entrada y procesa los tweets usando transformers de HuggingFace para detectar comentarios sexsitas. 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=’Enter name’):
 search_words = st.text_input(‘Enter the name for which you want to know the sentiment’)
 number_of_tweets = st.number_input(‘Enter the number of latest tweets for which you want to know the sentiment(Maximum 50 tweets)’, 0,50,10)
 submit_button = st.form_submit_button(label=’Submit’)
 if submit_button:
 tweets =tw.Cursor(api.search_tweets,q=search_words,lang=”en”).items(number_of_tweets)
 tweet_list = [i.text for i in tweets]
 p = [i for i in classifier(tweet_list)]
 q=[p[i][‘label’] for i in range(len(p))]
 df = pd.DataFrame(list(zip(tweet_list, q)),columns =[‘Latest ‘+str(number_of_tweets)+’ Tweets’+’ on ‘+search_words, ‘sentiment’])
 st.write(df)

if __name__==’__main__’:
run()