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from fastai.basics import *
from fastai.vision import models
from fastai.vision.all import *
from fastai.metrics import *
from fastai.data.all import *
from fastai.callback import *
from pathlib import Path
import random
import gradio as gr
# Cargamos el learner
learn = load_learner('unet.pht')
# Definimos las etiquetas de nuestro modelo
labels = learn.dls.vocab
# Definimos una función que se encarga de llevar a cabo las predicciones
def predict(img):
img = PILImage.create(img)
pred,pred_idx,probs = learn.predict(img)
return {labels[i]: float(probs[i]) for i in range(len(labels))}
# Creamos la interfaz y la lanzamos.
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=3),examples=['color_154.jpg','color_155.jpg']).launch(share=False)
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