import tensorflow as tf
import numpy as np


def get_labels(filename):
    image_path = filename

    # Read in the image_data
    image_data = tf.gfile.FastGFile(image_path, 'rb').read()

    # Loads label file, strips off carriage return
    label_lines = [line.rstrip() for line
                   in tf.gfile.GFile("/home/eshwar/Downloads/tf_files/Apparel_Dataset/retrained_labels_apparel.txt")]

    # Unpersists graph from file
    with tf.gfile.FastGFile("/home/eshwar/Downloads/tf_files/Apparel_Dataset/retrained_graph_apparel.pb", 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')

    with tf.Session() as sess:
        # Feed the image_data as input to the graph and get first prediction
        softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
        feature_tensor = sess.graph.get_tensor_by_name('pool_3:0')
        predictions = sess.run(softmax_tensor, \
                               {'DecodeJpeg/contents:0': image_data})
        feature_set = sess.run(feature_tensor,
                               {'DecodeJpeg/contents:0': image_data})
        feature_set = np.squeeze(feature_set)

        # Sort to show labels of first prediction in order of confidence
        top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]
        parse = []
        for node_id in top_k:
            human_string = label_lines[node_id]
            score = predictions[0][node_id]
            parse.append(human_string)
            parse.append(score)
        return parse, feature_set


# get_labels("/home/eshwar/Downloads/tf_files/flower_photos/test/11487842682005-Nike-Men-Black-Printed-Round-Neck-T-Shirt-3241487842681784-1.jpg")


def get_bottleneck(image_path):
    image_data = tf.gfile.FastGFile(image_path, 'rb').read()
    with tf.Session() as sess:
        feature_tensor = sess.graph.get_tensor_by_name('pool_3:0')
        feature_set = sess.run(feature_tensor,
                               {'DecodeJpeg/contents:0': image_data})
        feature_set = np.squeeze(feature_set)
        return feature_set
