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# app.py | |
import gradio as gr | |
import tensorflow as tf | |
from PIL import Image | |
import numpy as np | |
# Cargar el modelo .h5 | |
model = tf.keras.models.load_model('sports.h5') | |
# Definir las clases | |
classes = ['americano', 'ciclismo', 'golf', 'futbol', 'tenis', 'basket', 'natacion', 'boxeo', 'beisball', 'f1'] | |
# Funci贸n de predicci贸n | |
def predict(image): | |
image = Image.fromarray(image).resize((224, 224)) | |
image = np.array(image) / 255.0 | |
image = np.expand_dims(image, axis=0) | |
predictions = model.predict(image) | |
predicted_class = classes[np.argmax(predictions)] | |
return predicted_class | |
# Interfaz de Gradio | |
interface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type="numpy"), outputs="text") | |
# Ejecutar la interfaz | |
if __name__ == "__main__": | |
interface.launch() | |