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

repo_id = "sadie27/satellite"

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):
    if isinstance(img, dict):  # Gradio newer format
        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))}

# Creamos la interfaz y la lanzamos. 
gr.Interface(fn=predict,inputs=gr.Image(type="pil"),outputs=gr.Label(num_top_classes=4),
    title="Satellite Image Classifier",
    description="Clasifica imágenes de satélite en: cloudy, desert, green_area o water",
    examples=['Forest_1830.jpg', 'SeaLake_1022.jpg']
).launch(share=False)