| import streamlit as st | |
| import tensorflow as tf | |
| from tensorflow.keras.preprocessing.image import load_img, img_to_array | |
| import numpy as np | |
| def preprocesa_img(img_path): | |
| img = load_img(img_path, color_mode="grayscale", target_size=(28, 28)) | |
| img_array = img_to_array(img) | |
| img_array = 255 - img_array | |
| img_array = img_array.reshape(1, 784) / 255.0 | |
| return img_array | |
| model = tf.keras.models.load_model("identificadordigitos.h5") | |
| st.title("Clasificaci贸n de im谩genes de d铆gitos") | |
| uploaded_file = st.file_uploader("Subir una imagen de un d铆gito", type=["jpg", "png"]) | |
| if uploaded_file is not None: | |
| image_array = preprocesa_img(uploaded_file) | |
| st.write("Imagen preprocesada:", image_array) | |
| preliminar = model.predict(image_array) | |
| st.write("Predicci贸n del modelo (vector de probabilidades):", preliminar) | |
| prediccion = np.argmax(preliminar, axis=1)[0] | |
| st.success(f"Predicci贸n: {prediccion}") |