text: from Face_Recog.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:
  	from keras.models import Model, Sequential
  	from keras.layers import Convolution2D, Flatten, Activation
  elif tf_version == 2:
  	from tensorflow.keras.models import Model, Sequential
  	from tensorflow.keras.layers import Convolution2D, Flatten, Activation
  
  #url = 'https://drive.google.com/uc?id=1wUXRVlbsni2FN9-jkS_f4UTUrm1bRLyk'
  
  def loadModel(url = 'https://github.com/serengil/deepface_models/releases/download/v1.0/gender_model_weights.h5'):
  
  	model = VGGFace.baseModel()
  
  	#--------------------------
  
  	classes = 2
  	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)
  
  	#--------------------------
  
  	gender_model = Model(inputs=model.input, outputs=base_model_output)
  
  	#--------------------------
  
  	#load weights
  
  	home = str(Path.home())
  
  	if os.path.isfile(home+'/.deepface/weights/gender_model_weights.h5') != True:
  		print("gender_model_weights.h5 will be downloaded...")
  
  		output = home+'/.deepface/weights/gender_model_weights.h5'
  		gdown.download(url, output, quiet=False)
  
  	gender_model.load_weights(home+'/.deepface/weights/gender_model_weights.h5')
  
  	return gender_model
  
  	#--------------------------
  