input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should import necessary libraries, import and split data into training and testing sets, build a deep learning model using TensorFlow, compile and train the model, and finally evaluate the model. If the given data file is not found, the function should raise a FileNotFoundError.
  
  def predict_stock_movements():
      try:
          #import libraries
          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
  
          #import data
          data = pd.read_csv('data.csv') 
  
          #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
          #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)
          return score
      except FileNotFoundError:
          return "Error: data.csv file not found"
  
  assert predict_stock_movements() == "Error: data.csv file not found"
output: def predict_stock_movements():
      try:
          import pandas as pd
          import numpy as np
          import tensorflow as tf
          from tensorflow.keras.layers import Dense
          from tensorflow.keras.models import Sequential
  
          data = pd.read_csv('data.csv')
  
          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
      except FileNotFoundError:
          return "Error: data.csv file not found"