code: import re
  import pickle
  import numpy as np
  import pandas as pd
  import torch
  from string import punctuation
  from nltk.stem import WordNetLemmatizer
  from nltk.tokenize import sent_tokenize, word_tokenize 
  from sklearn.feature_extraction.text import CountVectorizer
  from flask import Flask, render_template, request, jsonify
  
  # import MLP module definition
  from models import MLP
  
  class CustomUnpickler(pickle.Unpickler):
  
      def find_class(self, module, name):
          try:
              return super().find_class(__name__, name)
          except AttributeError:
              return super().find_class(module, name)
  
  # load saved model parameters and vectorizers
  model = CustomUnpickler(open('data/multi-layer-perceptron-parameters.pkl', 'rb')).load()
  text_vectorizer = CustomUnpickler(open('data/text_vectorizer.pkl','rb')).load()
  
  
  def preprocess(df):
      """
      Preprocess user input in the same way we preprocessed the training data.
  
      1. Remove non-alphabetic characters, convert to lowercase
      2. Tokenize (word_tokenizer from nltk)
      3. Lemmatize (WordNetLemmatizer)
      4. Vectorize (CountVectorizer)
  
      Use the same CountVectorizers from training in order to extract
      the same features and have the same output dimensions.
      """
      lemmatizer = WordNetLemmatizer()
  
      text_processed = []
      for text in df.text:
          # remove punctuation and lowercase
          text = re.sub(r'[^a-zA-Z]', ' ', text) 
          text = text.lower()
          
          # tokenize and lemmatize tokens
          tokens = word_tokenize(text)
          tokens = [lemmatizer.lemmatize(x) for x in tokens]
          text_processed.append(' '.join(tokens))
  
      # vectorize
      text_matrix = text_vectorizer.transform(text_processed).toarray()
      
      # return np matrix
      return text_matrix
  
  app = Flask(__name__)
  
  @app.route('/')
  def home():
      return render_template('home.html')
  
  @app.route('/predict', methods=['POST'])
  def predict():
      text = request.json['text']
      d = {'text': [text]}
      # create dataframe from user input
      X_df = pd.DataFrame(data=d)
  
      # preprocess df and return np array
      X_np = preprocess(X_df)
  
      # convert to tensor
      X_tensor = torch.Tensor(X_np)
  
      # predict
      y_pred = model(X_tensor)
      y_pred_max = torch.max(y_pred,1)[1]
      if y_pred_max == 1:
          result = "real"
      else:
          result = "fake"
      return jsonify({"result": result})
  
  if __name__ == '__main__':
      app.run()
apis: pandas.DataFrame torch.Tensor torch.max