import gradio as gr    
from huggingface_hub import from_pretrained_keras
import numpy as np
import logging
from PIL import Image



 
def fun(a):
    Im=Image.fromarray(a).resize((48,48))
    
    reloaded_model = from_pretrained_keras('jmparejaz/Facial_Age-gender-eth_Recognition')
    reloaded_model_eth = from_pretrained_keras('jmparejaz/Facial_eth_recognition')
    #img=load_img(a, grayscale=True)
    a=np.asarray(Im)
    a=a.reshape(1, 48, 48, 1)
    a=a/255
    #reshape((-1,48,48,1))
    pred=reloaded_model.predict(a)
    pred_eth=reloaded_model_eth.predict(a)
    dict_gender={0:'Male',1:'Female'}
    dict_eth={0:"White", 1:"Black", 2:"Asian", 3:"Indian", 4:"Hispanic"}
    
    a = dict_gender[np.round(pred[0][0][0])]
    b = np.round(pred[1][0][0])
    c = dict_eth[np.argmax(pred_eth)]
    
    return a,b,c


gr.Interface(fn=fun, inputs=gr.inputs.Image(image_mode='L',type='numpy',invert_colors=False), outputs=["text","text","text"]).launch()