import gradio as gr
from keras.models import model_from_json
from tensorflow.keras.preprocessing import image
import numpy as np


def predict_age_gender(image):
    
    json_file_2 = open('model_gender_2.json', 'r')
    loaded_file_json_2 = json_file_2.read()
    json_file_2.close()

    model_2 = model_from_json(loaded_file_json_2)
    model_2.load_weights('model_gender_2.h5')
    
    
    json_file = open('xception.json', 'r')
    loaded_file_json = json_file.read()
    json_file.close()

    model = model_from_json(loaded_file_json)
    model.load_weights('xception_modelv2.h5')
    
    

    images = np.resize(image, new_shape=(1, 128, 128, 3))
    img_pixels = np.expand_dims(images, axis=0)
    img_pixels = img_pixels.astype('float64')
    img_pixels = img_pixels.reshape((1, 128, 128, 3))
    img_pixels /= 255

    class_names_gender = ['Fmale', 'Male']
    gender_predict = model_2.predict(img_pixels)
    predict = model.predict(img_pixels)
    age_result = predict[1][0].round()
    

    return 'Female' if gender_predict<0.5 else 'Male', age_result[0]

    
    
iface = gr.Interface(predict_age_gender, gr.Image(), [gr.Label(label='Gender'), gr.Text(label='Age')])
iface.launch()
#this is the end