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')

labels = ['T-shirt/top',
 'Trouser',
 'Pullover',
 'Dress',
 'Coat',
 'Sandal',
 'Shirt',
 'Sneaker',
 'Bag',
 'Ankle boot']

file = st.file_uploader("Por favor suba una imagen de una prenda 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])
    x = np.repeat(x[:, :, np.newaxis], 3, axis=2)
    x = np.squeeze(x)
    x  = np.expand_dims(x, axis=0)
    prediction = model.predict(x)
    index = np.argmax(prediction[0])
    label = labels[index]
    text = f"<p style='text-align: center;'>{label}</p>"
    col1, col2, col3 = st.columns(3)
    with col1:
        st.write("")
    
    with col2:
        image = Image.open(file)
        st.markdown(text,unsafe_allow_html=True)
        st.image(image,width=300)  
    
    with col3:
        st.write("")