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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
repo_id = "kevanme/Practica1"
learner = from_pretrained_fastai(repo_id)
labels = learner.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 = learner.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.Image(type="pil", image_mode="RGBA"), outputs=gr.Label(num_top_classes=3),examples=['0000118560.jpg','1000827761.jpg']).launch(share=False)
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