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from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
repo_id = "nlmaldonadog/pract1-firstmodel-chest-xray"
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.
lista = [
'NORMAL2-IM-0035-0001.jpeg',
'NORMAL2-IM-0349-0001.jpeg',
'NORMAL2-IM-0374-0001.jpeg',
'person78_bacteria_386.jpeg',
'person83_bacteria_407.jpeg',
'person91_bacteria_447.jpeg'
]
gr.Interface(fn=predict, inputs=gr.inputs.Image(shape=(128, 128)), outputs=gr.outputs.Label(num_top_classes=2),examples=lista).launch(share=False)