import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
import tensorflow as tf
import numpy as np
import cv2
import matplotlib.pyplot  as plt
#from PIL import Image
#import time
tf.compat.v1.enable_eager_execution()

#config = tf.ConfigProto()
#config.intra_op_parallelism_threads = 1
#config.inter_op_parallelism_threads = 1

#tf.keras.backend.set_session(tf.Session(config=config))

IMG_SIZE = 250
fashion_filepath = 'model/Fashion_niche_resnet50_model_wo_opt.hdf5'
beauty_health_filepath = 'model/Beauty_Health_niche_resnet50_model_wo_opt.hdf5'
food_beverage_filepath = 'model/Food_Beverage_niche_resnet50_model_wo_opt.hdf5'
lifestyle_travel_filepath = 'model/Lifestyle_Travel_niche_resnet50_model_wo_opt.hdf5'
technology_filepath = 'model/Technology_niche_resnet50_model_wo_opt.hdf5'


Fashion_classifier = tf.keras.models.load_model(fashion_filepath, custom_objects=None, compile=False)
Beauty_Health_classifier =  tf.keras.models.load_model(beauty_health_filepath, custom_objects=None, compile=False)
Food_Beverage_classifier = tf.keras.models.load_model(food_beverage_filepath, custom_objects=None, compile=False)
Lifestyle_Travel_classifier = tf.keras.models.load_model(lifestyle_travel_filepath, custom_objects=None, compile=False)
Technology_classifier = tf.keras.models.load_model(technology_filepath, custom_objects=None, compile=False)


def preprocess_image(image):

    old_size = image.shape[:2] # old_size is in (height, width) format

    ratio = float(IMG_SIZE)/max(old_size)
    new_size = tuple([int(x*ratio) for x in old_size])

    # new_size should be in (width, height) format

    image = cv2.resize(image, dsize = (new_size[1], new_size[0]), interpolation=cv2.INTER_NEAREST)

    delta_w = IMG_SIZE - new_size[1]
    delta_h = IMG_SIZE - new_size[0]
    top, bottom = delta_h//2, delta_h-(delta_h//2)
    left, right = delta_w//2, delta_w-(delta_w//2)

    color = [0, 0, 0]
    image = cv2.copyMakeBorder(image, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)
    #image = cv2.resize(image, dsize=(IMG_SIZE, IMG_SIZE ), interpolation=cv2.INTER_NEAREST)
    image = (image/255.0) # normalize to [0,1] range
    return image


def output(Status, fashion_score, beauty_health_score, food_beverage_score, lifestyle_travel_score, technology_score, images_flag):
    return {'Status': Status, 'Niche_score': {'Fashion': fashion_score, 'Beauty & Health': beauty_health_score, 'Food & Beverage': food_beverage_score,
            'Lifestyle & Travel': lifestyle_travel_score, 'Technology': technology_score}, 'Pic_flag' : images_flag}


def calculate_score(folderpath):
    user_im = []
    success_image = []
    image_files = os.listdir(folderpath)
    for image_path in image_files:
        try:
            image = plt.imread('{}/{}'.format(folderpath, image_path), format='jpeg')
            #image = file.read()
            #image = Image.open(io.BytesIO(image))
            #image = plt.imread(file, format='jpeg')
            user_im.append(preprocess_image(image))
            success_image.append(image_path)
        except:
            pass

    if len(user_im) >  0:
        tensor_image = np.asanyarray(user_im)
    else:
        return output('Failed', 0, 0, 0, 0, 0, 'Empty')

    fashion_proba_image = Fashion_classifier.predict(tensor_image)
    beauty_health_proba_image = Beauty_Health_classifier.predict(tensor_image)
    food_beverage_proba_image = Food_Beverage_classifier.predict(tensor_image)
    lifestyle_travel_proba_image = Lifestyle_Travel_classifier.predict(tensor_image)
    technology_proba_image = Technology_classifier.predict(tensor_image)

    flag_fashion = np.argmax(fashion_proba_image, axis = 1)
    flag_beauty_health = np.argmax(beauty_health_proba_image, axis = 1)
    flag_food_beverage = np.argmax(food_beverage_proba_image, axis = 1)
    flag_lifestyle_travel = np.argmax(lifestyle_travel_proba_image, axis = 1)
    flag_technology = np.argmax(technology_proba_image, axis = 1)

    images_flag = {}

    for i in range(len(success_image)):
        value = []
        if flag_fashion[i]==1:
            value.append('Fashion')
        if flag_beauty_health[i]==1:
            value.append('Beauty and Health')
        if flag_food_beverage[i] == 1:
            value.append('Food and Beverage')
        if flag_lifestyle_travel[i] == 1:
            value.append('Lifestyle and Travel')
        if flag_technology[i] == 1:
            value.append('Technology')

        images_flag[image_files[i]] = value

    fashion_score = flag_fashion.sum()/len(fashion_proba_image)
    beauty_health_score = flag_beauty_health.sum()/len(beauty_health_proba_image)
    food_beverage_score =  flag_food_beverage.sum()/len(food_beverage_proba_image)
    lifestyle_travel_score =  flag_lifestyle_travel.sum()/len(lifestyle_travel_proba_image)
    technology_score =  flag_technology.sum()/len(technology_proba_image)

    return output('Success', fashion_score, beauty_health_score, food_beverage_score, lifestyle_travel_score, technology_score, images_flag)