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

path = untar_data(URLs.PETS)

learn = load_learner('export.pkl')

files = get_image_files(path/'images')

pat = r'^(.*)_\d+.jpg'

dls = ImageDataLoaders.from_name_re(path, files, pat, item_tfms=Resize(460),
                                    batch_tfms=aug_transforms(size=224))

learn = vision_learner(dls, resnet34, metrics=error_rate)

learn.fine_tune(2, 0.0008)

labels = learn.dls.vocab
def predict(img):
    img = PILImage.create(img)
    pred,pred_idx,probs = learn.predict(img)
    return {labels[i]: float(probs[i]) for i in range(len(labels))}

demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil", image_mode="RGB", height=512, width=512, sources=["upload"]),
    outputs=gr.Label(num_top_classes=3),
    title="Image Classifier",
    description="Upload an image; the app resizes to 512×512 inside predict()."
)

if __name__ == "__main__":
    demo.launch()   # trên Spaces không dùng share=True