import pathlib
import platform

plt = platform.system()
if plt == 'Linux': 
    pathlib.WindowsPath = pathlib.PosixPath

from huggingface_hub import from_pretrained_fastai
import gradio as gr
from fastai.vision.all import *
import numpy as np



# repo_id = "YOUR_USERNAME/YOUR_LEARNER_NAME"
repo_id = "KaraSpdrnr/prueba-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(np.array(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"), outputs=gr.Label(num_top_classes=3)
             ,examples=['normal.jpeg','pneumonia.jpeg']
            ).launch(share=False)