input: Write a function to create a Deep Learning Program in Python using Keras to predict stock prices. The function should define a Sequential model, add LSTM and Dropout layers, compile the model, and fit it using training and testing data from a CSV file. If the model is successfully trained, it should return the trained model. If the data file is not found or the input data is invalid, the function should return an appropriate error message.
  assert create_stock_price_model() is not None
  assert isinstance(create_stock_price_model(), Sequential)
  assert create_stock_price_model('invalid_file.csv') is "Error: Data file not found or invalid input data."
output: def create_stock_price_model(file_path='stock_data.csv'):
      try:
          import pandas as pd
          from keras.models import Sequential
          from keras.layers import LSTM, Dropout, Dense
          from sklearn.model_selection import train_test_split
          from sklearn.preprocessing import MinMaxScaler
  
          data = pd.read_csv(file_path)
          if data.empty:
              return "Error: Data file not found or invalid input data."
          
          scaler = MinMaxScaler(feature_range=(0, 1))
          scaled_data = scaler.fit_transform(data[['Close']])
          
          X = []
          y = []
          for i in range(60, len(scaled_data)):
              X.append(scaled_data[i-60:i, 0])
              y.append(scaled_data[i, 0])
          X, y = np.array(X), np.array(y)
          X = np.reshape(X, (X.shape[0], X.shape[1], 1))
          
          X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
          
          model = Sequential()
          model.add(LSTM(units=50, return_sequences=True, input_shape=(X_train.shape[1], 1)))
          model.add(Dropout(0.2))
          model.add(LSTM(units=50, return_sequences=False))
          model.add(Dropout(0.2))
          model.add(Dense(units=25))
          model.add(Dense(units=1))
          
          model.compile(optimizer='adam', loss='mean_squared_error')
          model.fit(X_train, y_train, batch_size=32, epochs=10)
          
          return model
      except FileNotFoundError:
          return "Error: Data file not found or invalid input data."
      except Exception as e:
          return "Error: Data file not found or invalid input data."