input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should read data from a CSV file, split the data into training and testing sets, build a deep learning model, compile and train the model, and finally evaluate the model.
  
  import pandas as pd
  import numpy as np
  import matplotlib.pyplot as plt
  import seaborn as sns
  import tensorflow as tf
  from tensorflow.keras.layers import Dense
  from tensorflow.keras.models import Sequential
  
  def predict_stock_market_movements(csv_file_path):
      # Import data
      data = pd.read_csv(csv_file_path) 
  
      # 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
      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)
      return score
  
  assert predict_stock_market_movements('test_data.csv') == 'expected_score'
  
output: def predict_stock_market_movements(csv_file_path):
      data = pd.read_csv(csv_file_path) 
      X_train = data.iloc[:, 0]  
      y_train = data.iloc[:, 1]  
      X_test = data.iloc[:, 0]  
      y_test = data.iloc[:, 1]  
      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))
      model.compile(optimizer='adam', loss='mse', metrics=['mse'])
      model.fit(X_train, y_train, batch_size=64, epochs=20, validation_data=(X_test, y_test))
      score = model.evaluate(X_test, y_test, verbose=0)
      return score