class TF_visualizer(object):

    def __init__(self, dimension, vecs_file, metadata_file, output_path):

        self.dimension = dimension

        self.vecs_file = vecs_file

        self.metadata_file = metadata_file

        self.output_path = output_path

        self.vecs = []

        with open(self.vecs_file, 'r') as vecs:

        #with open(self.vecs_file, 'rb') as vecs:

            for i, line in enumerate(vecs):

                if line != '': self.vecs.append(line)

    def visualize(self):

        # adding into projector

        config = projector.ProjectorConfig()

        placeholder = np.zeros((len(self.vecs), self.dimension))

        for i, line in enumerate( self.vecs ):

            placeholder[i] = np.fromstring(line, sep=',')

        #for i,line in enumerate(self.vecs):

        #    placeholder[i] = np.fromstring(line)

        embedding_var = tf.Variable(placeholder, trainable=False, name='amazon')

        embed = config.embeddings.add()

        embed.tensor_name = embedding_var.name

        embed.metadata_path = self.metadata_file

        # define the model without training

        sess = tf.InteractiveSession()

        tf.global_variables_initializer().run()

        saver = tf.train.Saver()

        saver.save(sess, os.path.join(self.output_path, 'w2x_metadata.ckpt'))

        writer = tf.summary.FileWriter(self.output_path, sess.graph)

        projector.visualize_embeddings(writer, config)

        sess.close()

        print('Run `tensorboard --logdir={0}` to run visualize result on tensorboard'.format(self.output_path))

output = '/home/xx'

# create a new tensor board visualizer

visualizer = TF_visualizer(dimension = 768,

                           vecs_file = os.path.join(output, 'amazon_vec.tsv'),

                           #vecs_file = os.path.join(output, 'mnist_10k_784d_tensors.bytes'),

                           metadata_file = os.path.join(output, 'amazon.tsv'),

                           output_path = output)

visualizer.visualize()