Create app.py

app.py

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+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()