import os, sys
import os.path
import tensorflow as tf
import json

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'

# change this as you see fit
image_path = open(os.environ['inputMessage']).read()
print("Path: " + image_path)
data = {}

if os.path.isfile(image_path):
    # 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("retrained_labels.txt")]

    # Unpersists graph from file
    with tf.gfile.FastGFile("retrained_graph.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')
        
        predictions = sess.run(softmax_tensor, \
                {'DecodeJpeg/contents:0': image_data})
        
        # Sort to show labels of first prediction in order of confidence
        top_k = predictions[0].argsort()[-len(predictions[0]):][::-1]

    words = image_path.split('_');
    dev = words[0].split('\\');

    data['deviceID'] = dev[2]
    data['state'] = words[1];

    for node_id in top_k:
        human_string = label_lines[node_id]
        score = predictions[0][node_id]
        data[human_string] = "%.5f" % score

os.environ['outputQueueItem'] = json.dumps(data)
