Please fix any bugs and errors found in this python code and return the cleaned code: import pickle
from PyQt5 import QtWidgets
from tensorflow import keras
from keras.layers import Input, Dense
from keras.preprocessing.sequence import pad_sequences
from keras.callbacks import EarlyStopping, ModelCheckpoint

def define_model(model_name, sequence_length):
    model_class = {
        "bert": keras.applications.bert.Bert,
        "gpt-3": keras.applications.gpt_3.GPT3,
    }[model_name]

    pretrained_model = model_class(input_shape=(sequence_length,))

    inputs = Input(shape=(sequence_length,))
    features = pretrained_model(inputs)
    outputs = Dense(1, activation="sigmoid")(features)

    model = keras.Model(inputs=inputs, outputs=outputs)
    optimizer = keras.optimizers.Adam()
    loss_function = keras.losses.BinaryCrossentropy()
    model.compile(optimizer=optimizer, loss=loss_function)

    tokenizer = keras.preprocessing.text.Tokenizer(num_words=10000)

    return model, tokenizer

def load_data(data_path, tokenizer, is_csv=False):
    with open(data_path, "r") as f:
        data = f.read().splitlines()

    data = tokenizer.texts_to_sequences(data)
    data = pad_sequences(data, maxlen=128, padding="post", truncating="post")

    if is_csv:
        with open(data_path, "r") as f:
            lines = csv.reader(f)
            labels = [line[1] for line in lines]
        labels = keras.utils.to_categorical(labels, num_classes=2)
    else:
        labels = None

    return data, labels

def train_model(model, data, epochs, batch_size=32):
    data, labels = data

    early_stopping = EarlyStopping(monitor="val_loss", patience=3)
    model_checkpoint = ModelCheckpoint(filepath="model.hdf5", save_best_only=True, monitor="val_loss")

    model.fit(
        data,
        labels,
        epochs=epochs,
        batch_size=batch_size,
        validation_split=0.2,
        callbacks=[early_stopping, model_checkpoint],
    )

    model.save_weights("model_weights.h5")

    with open("model_tokenizer.pkl", "wb") as f:
        pickle.dump(tokenizer, f)

    print("Model training completed.")

class MainWindow(QtWidgets.QWidget):
    def __init__(self):
        super().__init__()

        self.layout = QtWidgets.QVBoxLayout()
        self.model_label = QtWidgets.QLabel("Model:")
        self.model_combo = QtWidgets.QComboBox()
        self.sequence_length_label = QtWidgets.QLabel("Sequence Length:")
        self.sequence_length_spinbox = QtWidgets.QSpinBox()
        self.data_label = QtWidgets.QLabel("Data File:")
        self.data_path_edit = QtWidgets.QLineEdit()
        self.data_browse_button = QtWidgets.QPushButton("Browse")
        self.is_csv_checkbox = QtWidgets.QCheckBox("Is CSV?")
        self.train_button = QtWidgets.QPushButton("Train")
        self.log_text = QtWidgets.QTextEdit()

        self.layout.addWidget(self.model_label)
        self.layout.addWidget(self.model_combo)
        self.layout.addWidget(self.sequence_length_label)
        self.layout.addWidget(self.sequence_length_spinbox)
        self.layout.addWidget(self.data_label)
        self.layout.addWidget(self.data_path_edit)
        self.layout.addWidget(self.data_browse_button)
        self.layout.addWidget(self.is_csv_checkbox)
        self.layout.addWidget(self.train_button)
        self.layout.addWidget(self.log_text)

        self.data_browse_button.clicked.connect(self.select_data_file)
        self.train_button.clicked.connect(self.train_model)

        self.model_combo.addItems(["Model A", "Model B", "Model C"])
        self.sequence_length_spinbox.setValue(128)
        self.is_csv_checkbox.setChecked(False)

        self.log_text.setReadOnly(True)

        self.setLayout(self.layout)

    def select_data_file(self):
        file_path, _ = QtWidgets.QFileDialog.getOpenFileName(self, "Select Data File")
        self.data_path_edit.setText(file_path)

    def train_model(self):
        model_name = self.model_combo.currentText()
        sequence_length = self.sequence_length_spinbox.value()
        data_path = self.data_path_edit.text()
        is_csv = self.is_csv_checkbox.isChecked()

        model, tokenizer = define_model(model_name, sequence_length)
        data, labels = load_data(data_path, tokenizer, is_csv)
        train_model(model, data=(data, labels), epochs=10)

        self.log_text.append("Model training completed.")