input: from typing import Tuple
  
  def create_stock_predictor(data_path: str, batch_size: int, epochs: int) -> Tuple[float, tf.keras.models.Sequential]:
      """
      Create an AI-based system for predicting stock market movements using a deep learning model.
      The function will:
      1. Import necessary libraries.
      2. Read data from a CSV file.
      3. Split the data into training and testing sets.
      4. Build and compile a deep learning model.
      5. Train the model using the training data.
      6. Evaluate the model using the testing data and return the loss score and the model.
  
      >>> model, score = create_stock_predictor('data.csv', 64, 20)
      >>> round(score, 2)
      0.06
      """
output:     
      import pandas as pd
      import tensorflow as tf
      from sklearn.model_selection import train_test_split
      from sklearn.preprocessing import StandardScaler
  
      # Read data from CSV file
      df = pd.read_csv(data_path)
  
      # Assuming the last column is the target variable
      X = df.iloc[:, :-1].values
      y = df.iloc[:, -1].values
  
      # Split the data into training and testing sets
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
  
      # Standardize the data
      scaler = StandardScaler()
      X_train = scaler.fit_transform(X_train)
      X_test = scaler.transform(X_test)
  
      # Build the deep learning model
      model = tf.keras.models.Sequential([
          tf.keras.layers.Dense(64, activation='relu', input_shape=(X_train.shape[1],)),
          tf.keras.layers.Dense(32, activation='relu'),
          tf.keras.layers.Dense(1, activation='sigmoid')
      ])
  
      # Compile the model
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
  
      # Train the model
      model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=0)
  
      # Evaluate the model
      loss, _ = model.evaluate(X_test, y_test, verbose=0)
  
      return loss, model
  