text: """Prediction of Users based on Tweet embeddings."""
  import numpy as np
  from sklearn.linear_model import LogisticRegression
  
  from .models import User
  from .twitter import BASILICA
  
  
  def predict_user(user1_name, user2_name, tweet_text):
      """Determine and return which user is more likely to say a given Tweet."""
  
      user1 = User.query.filter(User.name == user1_name).one()
      user2 = User.query.filter(User.name == user2_name).one()
      user1_embeddings = np.array([tweet.embedding for tweet in user1.tweets])
      user2_embeddings = np.array([tweet.embedding for tweet in user2.tweets])
      embeddings = np.vstack([user1_embeddings, user2_embeddings])
      labels = np.concatenate([np.ones(len(user1.tweets)),
                               np.zeros(len(user2.tweets))])
      log_reg = LogisticRegression().fit(embeddings, labels)
  
      tweet_embedding = BASILICA.embed_sentence(tweet_text, model='twitter')
      return log_reg.predict(np.array(tweet_embedding).reshape(1, -1))