input: from typing import Tuple
  
  def setup_ai_stock_prediction(csv_file: str) -> Tuple[float, float]:
      """Sets up an AI-based system that predicts stock market movements using a deep learning model.
      
      Parameters:
      csv_file (str): The path to the CSV file containing the stock market data.
      
      Returns:
      Tuple[float, float]: A tuple representing the loss and mean squared error (mse) of the model on the test data.
      
      Example:
      >>> setup_ai_stock_prediction('data.csv')
      (4.89, 4.89)
      """
output:     import pandas as pd
      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
  
      # Load the data
      data = pd.read_csv(csv_file)
  
      # Preprocess the data
      X = data.drop('target', axis=1)
      y = data['target']
      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)
  
      # Build the model
      model = Sequential([
          Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          Dense(64, activation='relu'),
          Dense(1)
      ])
  
      model.compile(optimizer='adam', loss='mean_squared_error')
  
      # Train the model
      model.fit(X_train, y_train, epochs=10, batch_size=32, verbose=0)
  
      # Evaluate the model
      loss, mse = model.evaluate(X_test, y_test, verbose=0)
  
      return loss, mse