input: Write a function to create a Deep Learning model in Python using Keras to predict stock prices. The function should take a structured dataset file (CSV format) as input and return a trained model that can be used to predict stock prices. The model should be built using LSTM layers and should include Dropout layers to prevent overfitting. The function should also split the dataset into training and testing sets and train the model accordingly.
  assert preprocess_data(np.array([[1],[2],[3],[4],[5],[6],[7],[8],[9],[10]]))==np.array([[[[1]],[[2]],[[3]]],[[[4]],[[5]],[[6]]],[[[7]],[[8]],[[9]]]])
  assert preprocess_data(np.array([[1]]))==np.array([[[[1]]]])
  assert preprocess_data(np.array([]))==np.array([])
output: 
  import numpy as np
  import pandas as pd
  from sklearn.model_selection import train_test_split
  from keras.models import Sequential
  from keras.layers import LSTM, Dense, Dropout
  
  def create_model(file_path):
      # Load the dataset
      df = pd.read_csv(file_path)
      
      # Assuming the last column is the target (stock price)
      X = df.iloc[:, :-1].values
      y = df.iloc[:, -1].values
      
      # Split the dataset 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)
      
      # Reshape input to be [samples, time steps, features]
      X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
      X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
      
      # Build the model
      model = Sequential()
      model.add(LSTM(50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
      model.add(Dropout(0.2))
      model.add(LSTM(50, return_sequences=False))
      model.add(Dropout(0.2))
      model.add(Dense(25))
      model.add(Dense(1))
      
      # Compile the model
      model.compile(optimizer='adam', loss='mean_squared_error')
      
      # Train the model
      model.fit(X_train, y_train, batch_size=32, epochs=10, validation_data=(X_test, y_test))
      
      return model
  