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

repo_id = "humellad/entregable_2"

learner = from_pretrained_fastai(repo_id)
labels = learner.dls.vocab

def predict(img):
    if isinstance(img, dict):
        img = img["image"]
    img = PILImage.create(img)
    pred, pred_idx, probs = learner.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

gr.Interface(
    fn=predict, 
    inputs=gr.Image(type="pil"), 
    outputs=gr.Label(num_top_classes=5),
    examples=['Apple Braeburn.jpg','Watermelon.jpg', 'Maiz.jpg', 'Melon PS.jpg']
).launch(share=False)