#from basemodels import VGGFace
from deepface.basemodels import VGGFace

import os
from pathlib import Path
import gdown
import numpy as np
from keras.models import Model, Sequential
from keras.layers import Convolution2D, Flatten, Activation
import zipfile

def loadModel():
	
	model = VGGFace.baseModel()
	
	#--------------------------
	
	classes = 6
	base_model_output = Sequential()
	base_model_output = Convolution2D(classes, (1, 1), name='predictions')(model.layers[-4].output)
	base_model_output = Flatten()(base_model_output)
	base_model_output = Activation('softmax')(base_model_output)
	
	#--------------------------

	race_model = Model(inputs=model.input, outputs=base_model_output)
	
	#--------------------------
	
	#load weights
	
	# home = str(Path.home())
	
	# if os.path.isfile(home+'/.deepface/weights/race_model_single_batch.h5') != True:
	# 	print("race_model_single_batch.h5 will be downloaded...")
		
	# 	#zip
	# 	url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
	# 	output = home+'/.deepface/weights/race_model_single_batch.zip'
	# 	gdown.download(url, output, quiet=False)
		
	# 	#unzip race_model_single_batch.zip
	# 	with zipfile.ZipFile(output, 'r') as zip_ref:
	# 		zip_ref.extractall(home+'/.deepface/weights/')
	
	# race_model.load_weights(home+'/.deepface/weights/race_model_single_batch.h5')

	home = str(Path(__file__).parent.absolute())
	#print(home)
	if os.path.isfile(home+'/weights/race_model_single_batch.h5') != True:
		print("race_model_single_batch.h5 will be downloaded...")
		
		#zip
		url = 'https://drive.google.com/uc?id=1nz-WDhghGQBC4biwShQ9kYjvQMpO6smj'
		output = home+'/weights/race_model_single_batch.zip'
		gdown.download(url, output, quiet=False)
		
		#unzip race_model_single_batch.zip
		with zipfile.ZipFile(output, 'r') as zip_ref:
			zip_ref.extractall(home+'/weights/')
	
	race_model.load_weights(home+'/weights/race_model_single_batch.h5')
	
	return race_model
	
	#--------------------------
