input: Write a function to create an AI-based system in Python for predicting stock market movements. The function should import necessary libraries, load data from a CSV file, split the data into training and testing datasets, build a deep learning model using TensorFlow and Keras, compile and train the model, and finally evaluate the model. If the given CSV file is empty or missing, the function should return an appropriate error message.
  assert predict_stock_market_movements('data.csv')==None
  assert predict_stock_market_movements('missing_file.csv')=="CSV file not found or is empty."
  assert predict_stock_market_movements('empty_file.csv')=="CSV file not found or is empty."
output: def predict_stock_market_movements(csv_file):
      import pandas as pd
      import numpy as np
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
      from tensorflow.keras.models import Sequential
      from tensorflow.keras.layers import Dense
  
      try:
          df = pd.read_csv(csv_file)
          if df.empty:
              return "CSV file not found or is empty."
      except FileNotFoundError:
          return "CSV file not found or is empty."
  
      X = df.iloc[:, :-1].values
      y = df.iloc[:, -1].values
  
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      model = Sequential()
      model.add(Dense(64, input_dim=X_train.shape[1], activation='relu'))
      model.add(Dense(32, activation='relu'))
      model.add(Dense(1, activation='linear'))
  
      model.compile(optimizer='adam', loss='mean_squared_error')
      model.fit(X_train, y_train, epochs=50, batch_size=32, verbose=0)
  
      loss = model.evaluate(X_test, y_test, verbose=0)
      return None