from deepface.basemodels import VGGFace
import os
from pathlib import Path
import gdown
import numpy as np

import tensorflow as tf
tf_version = int(tf.__version__.split(".")[0])

if tf_version == 1:
	import keras
	from keras.models import Model, Sequential
	from keras.layers import Convolution2D, Flatten, Activation	
elif tf_version == 2:
	from tensorflow import keras
	from tensorflow.keras.models import Model, Sequential
	from tensorflow.keras.layers import Convolution2D, Flatten, Activation

def loadModel():
	
	model = VGGFace.baseModel()
	
	#--------------------------
	
	classes = 101
	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)
	
	#--------------------------

	age_model = Model(inputs=model.input, outputs=base_model_output)
	
	#--------------------------
	
	#load weights
	
	home = str(Path.home())
	
	if os.path.isfile(home+'/.deepface/weights/age_model_weights.h5') != True:
		print("age_model_weights.h5 will be downloaded...")
		
		url = 'https://drive.google.com/uc?id=1YCox_4kJ-BYeXq27uUbasu--yz28zUMV'
		output = home+'/.deepface/weights/age_model_weights.h5'
		gdown.download(url, output, quiet=False)
	
	age_model.load_weights(home+'/.deepface/weights/age_model_weights.h5')
	
	return age_model
	
	#--------------------------

def findApparentAge(age_predictions):
	output_indexes = np.array([i for i in range(0, 101)])
	apparent_age = np.sum(age_predictions * output_indexes)
	return apparent_age