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

learn = load_learner('model.pkl')
labels = learn.dls.vocab

@spaces.GPU
def predict(img):
    # Wrap the incoming PIL image as a fastai PILImage so the model's
    # item/batch transforms (which turn it into a tensor) actually fire.
    img = PILImage.create(img)
    pred, pred_idx, probs = learn.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=3),
    title="Board Classifier",
    description="Classifies Arduino, ESP32, and Raspberry Pi boards from a photo.",
).launch()