app.py

import streamlit as st
from  PIL import Image
from io import BytesIO
import tensorflow as tf
import matplotlib.pyplot as plt
import numpy as np

model = tf.keras.models.load_model('modelo.h5')

file = st.file_uploader("Por favor suba una imagen de ropa (jpg, png, jpeg)", type=['jpg','png','jpeg'])
if file is not None:
    bytes_data = file.read()
    x = np.array(Image.open(BytesIO(bytes_data)).convert('L'))
    x = x / 255.00
    x  = np.expand_dims(x, axis=-1)
    x  = tf.image.resize(x, [80, 80])
    plt.figure()
    plt.imshow(x, cmap=plt.cm.binary)
    plt.colorbar()
    plt.grid(False)
    plt.show()
    x = np.repeat(x[:, :, np.newaxis], 3, axis=2)
    x = np.squeeze(x)

    
    col1, col2, col3 = st.columns(3)

    with col1:
        st.write("")
    
    with col2:
        image = Image.open(file)
        st.markdown('<p style="text-align: center;">Image</p>',unsafe_allow_html=True)
        st.image(image,width=300)  
    
    with col3:
        st.write("")