import streamlit as st
from PIL import Image               
from tensorflow.keras.models import load_model
from keras.preprocessing.image import load_img
import numpy as np


st.set_page_config(
    page_title="gender and age prediction",
    page_icon="✨",
    layout="centered",
    initial_sidebar_state="expanded",
)

main_image = Image.open('main_banner.png')


#load weights
model = load_model('Gender_and_Age.keras')
gender_dict = {0:'مرد', 1:'زن'}

st.image(main_image,use_container_width='auto')
st.markdown("<center><strong><h2>پیش بینی جنسیت و سن از روی عکس</h2></strong></center>", unsafe_allow_html=True)
st.markdown("<center><a href='https://omidsakaki.ir/'><h4>omidsakaki.ir</h4></a></center>", unsafe_allow_html=True)

uploaded_file = st.file_uploader("Upload Image", type=["png","jpg","bmp","jpeg"])

if uploaded_file is not None:
    image = Image.open(uploaded_file).convert('RGB')
    st.image(image, caption='input image 📷',use_container_width='auto')

    image.save('input_image.jpg')
        
    img = load_img('input_image.jpg', color_mode='grayscale')
    img = img.resize((128, 128), Image.Resampling.LANCZOS)
    img = np.array(img)
    img = img.reshape(1, 128, 128, 1)
    img = img/255.0
    pred = model.predict(img)
    pred_gender = gender_dict[round(pred[0][0][0])]
    pred_age = round(pred[1][0][0])
            
    st.markdown("------")
    st.write("Predicted Gender:", pred_gender)
    st.write("Predicted Age:", pred_age)

st.markdown("<br><hr><center>Made with ❤️ by <a href='https://omidsakaki.ir/'><strong>omid sakaki ghazvini</strong></a></center><hr>", unsafe_allow_html=True)