"""
Preparing model:
 - Install bazel ( check tensorflow's github for more info )
    Ubuntu 14.04:
        - Requirements:
            sudo add-apt-repository ppa:webupd8team/java
            sudo apt-get update
            sudo apt-get install oracle-java8-installer
        - Download bazel, ( https://github.com/bazelbuild/bazel/releases )
          tested on: https://github.com/bazelbuild/bazel/releases/download/0.2.0/bazel-0.2.0-jdk7-installer-linux-x86_64.sh
        - chmod +x PATH_TO_INSTALL.SH
        - ./PATH_TO_INSTALL.SH --user
        - Place bazel onto path ( exact path to store shown in the output)
- For retraining, prepare folder structure as
    - root_folder_name
        - class 1
            - file1
            - file2
        - class 2
            - file1
            - file2
- Clone tensorflow
- Go to root of tensorflow
- bazel build tensorflow/examples/image_retraining:retrain
- bazel-bin/tensorflow/examples/image_retraining/retrain --image_dir /path/to/root_folder_name  --output_graph /path/output_graph.pb --output_labels /path/output_labels.txt --bottleneck_dir /path/bottleneck
** Training done. **
For testing through bazel,
    bazel build tensorflow/examples/label_image:label_image && \
    bazel-bin/tensorflow/examples/label_image/label_image \
    --graph=/path/output_graph.pb --labels=/path/output_labels.txt \
    --output_layer=final_result \
    --image=/path/to/test/image
For testing through python, change and run this code.
"""

import numpy as np
import tensorflow as tf
import sys
import requests
import time
import mimetypes
from PIL import Image
import urllib2 as urllib2

imagePath = ''
modelFullPath = 'graph.pb'
labelsFullPath = 'labels.txt'


class HeadRequest(urllib2.Request):
    def get_method(self):
        return 'HEAD'

def isImageUrl(url):
    response= urllib2.urlopen(HeadRequest(url))
    maintype= response.headers['Content-Type'].split(';')[0].lower()
    if maintype not in ('image/png', 'image/jpeg', 'image/gif'):
        return False
    else:
        return True


def create_graph():
    """Creates a graph from saved GraphDef file and returns a saver."""
    # Creates graph from saved graph_def.pb.
    with tf.gfile.FastGFile(modelFullPath, 'rb') as f:
        graph_def = tf.GraphDef()
        graph_def.ParseFromString(f.read())
        _ = tf.import_graph_def(graph_def, name='')


def run_inference_on_image():
    answer = None

    if not tf.gfile.Exists(imagePath):
        tf.logging.fatal('File does not exist %s', imagePath)
        return answer

    image_data = tf.gfile.FastGFile(imagePath, 'rb').read()

    # Creates graph from saved GraphDef.
    create_graph()

    with tf.Session() as sess:

        softmax_tensor = sess.graph.get_tensor_by_name('final_result:0')
        predictions = sess.run(softmax_tensor,
                               {'DecodeJpeg/contents:0': image_data})
        predictions = np.squeeze(predictions)

        top_k = predictions.argsort()[-5:][::-1]  # Getting top 5 predictions
        f = open(labelsFullPath, 'rb')
        lines = f.readlines()
        labels = [str(w).replace("\n", "") for w in lines]
        for node_id in top_k:
            human_string = labels[node_id]
            score = predictions[node_id]
            print('%s (score = %.5f)' % (human_string, score))

        answer = labels[top_k[0]]
        return answer

def download_from_url(url):
    if(not isImageUrl(url)):
        return False
    else:
        t0 = time.clock()
        headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'}
        response = requests.get(url, headers=headers, stream=True)
        content_type = response.headers['content-type']
        extension = mimetypes.guess_extension(content_type)
        global imagePath
        imagePath = str(t0) + extension
        with open(imagePath, 'wb') as handle:

            if not response.ok:
                print(response)

            for block in response.iter_content(1024):
                if not block:
                    break

                handle.write(block)
        img = Image.open(imagePath)
        imagePath = imagePath.replace(extension, '.jpg')
        img.save(imagePath)
        return True


if __name__ == '__main__':
    if len(sys.argv) == 2:
        url = str(sys.argv[1])
        if(download_from_url(url)):
            print(run_inference_on_image())
        else:
            print("invalid (score = 1.0)")

