text: import pandas as pd
  import streamlit as st
  
  from aristote.feature_extraction.sentence_encoder import MUSE
  from aristote.feature_extraction.tfidf import Tfidf
  
  
  class Featuring(object):
  
      def __init__(self, model_name):
          self.model_name = model_name
          self.tfidf = Tfidf()
          self.embedding = MUSE()
  
      def main(self):
          text = st.text_area("Enter the text to normalize here:")
          if text:
              if self.model_name == "MultilangEmbedding":
                  data = self.embedding.predict_one(x=text)
                  data = [float(x) for x in data[0]]
                  st.write(pd.DataFrame([data]))
              else:
                  bigrams = st.checkbox('Use bigrams?')
                  self.tfidf = Tfidf(bigrams=bigrams)
                  self.tfidf.fit_model(documents=[text])
                  data = self.tfidf.transform(document=[text])
                  cols = self.tfidf.get_features_name
                  df = pd.DataFrame(data)
                  df.columns = cols
                  st.write(df)
  