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)