input: 
  def predict_stock_market(data_file: str) -> None:
      """
      Create an AI-based system in Python for predicting stock market movements.
      The function reads a CSV file containing stock data, prepares the data, 
      builds a deep learning model with TensorFlow, trains the model, and 
      finally evaluates the model's performance.
      
      >>> predict_stock_market('data.csv')
      The function processes the data, builds and trains a model, and outputs the evaluation score.
      """
      #import libraries
      import pandas as pd
      import numpy as np
      import matplotlib.pyplot as plt
      import seaborn as sns
  
      #import data
      data = pd.read_csv(data_file) 
  
      #split data into train and test data
      X_train=data.iloc[:, 0] #Choosing the independent variable 
      y_train=data.iloc[:, 1]  #Choosing the dependent variable
      X_test=data.iloc[:, 0] #Choosing the independent variable 
      y_test=data.iloc[:, 1]  #Choosing the dependent variable
  
      #Build a deep learning model
      #import necessary libraries
      import tensorflow as tf
      from tensorflow.keras.layers import Dense
      from tensorflow.keras.models import Sequential
  
      #create a model and add layers
      model = Sequential()
      model.add(Dense(64, input_shape=(1,), activation='relu'))
      model.add(Dense(128, activation='relu'))
      model.add(Dense(64, activation='relu'))
      model.add(Dense(1))
  
      #compile the model
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
  
      #train the model
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
  
      #Evaluate the model
      score = model.evaluate(X_test, y_test, verbose=0)
  
output:     print(f"The function processes the data, builds and trains a model, and outputs the evaluation score: {score[1]}")