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# Selecting columns with data type as 'object'
columns = dataset.columns[dataset.dtypes.eq('object')]

# Convert to numeric values
dataset[columns] = dataset[columns].apply(pd.to_numeric, errors='coerce')

# Viewing the details
dataset.info()

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# Dropping the TBG column as it contains extremely high number of null values
dataset.drop('TBG', axis = 1, inplace=True)

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# Replacing ? into NaN values 
dataset.replace(to_replace='?', inplace=True, value=np.NaN)

# Count the number of null values
dataset.isnull().sum()

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# Displaying the categories in different columns
print("Unique categories in the column 'pregnant'", dataset['pregnant'].unique())
print("Count of categories in the column 'pregnant' \n", dataset["pregnant"].value_counts())

print("\nUnique categories in the column 'T3 measured'", dataset['T3_measured'].unique())
print("Count of categories in the column 'T3 measured' \n", dataset["T3_measured"].value_counts())

print("\nUnique categories in the column 'Gender'", dataset['Gender'].unique())
print("Count of categories in the column 'Gender' \n", dataset["Gender"].value_counts())

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# mapping the values into binary
dataset["target"] = dataset["target"].map({"negative":0,"hypothyroid":1})

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# Renaming the first column as target
dataset = dataset.rename(columns = {dataset.columns[0]:"target"})

# Check the count of data in target
dataset["target"].value_counts()

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show_images = 5
# Plot the images from last epoch
data_images = helper.get_batch(glob(os.path.join("./generated_images/epoch_" + str(num_epochs-1) +"/", '*.jpg'))[:show_images], 64, 64, 'RGB')
plt.imshow(helper.images_square_grid(data_images, 'RGB'))

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# Size of latent (noise) vector to generator
z_dim = 100 

# Learning ratess
learning_rate_D =  .00005 
learning_rate_G = 2e-4 

# Batch size
batch_size = 4
# Number of epochs

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def train_gan_model(epoch, batch_size, z_dim, learning_rate_D, learning_rate_G, beta1, get_batches, data_shape, data_image_mode, alpha):
    """
    Train the GAN model.
    
    Arguments:
    ----------
    :param epoch: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate

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def generator_output(sess, n_images, input_z, output_channel_dim, image_mode, image_path):
    """
    Save output from the generator.
    
    Arguments:
    ----------
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor (noise vector)
    :param output_channel_dim: The number of channels in the output image