# app.py
import gradio as gr
from transformers import pipeline

clf = pipeline("image-classification", model="nateraw/food")  # example model

def predict(img):
    preds = clf(img)  # list of {label, score}
    # return top 10 in format expected by gr.Label
    return {p["label"]: float(p["score"]) for p in preds[:10]}

demo = gr.Interface(fn=predict,
                    inputs=gr.Image(type="pil"),
                    outputs=gr.Label(num_top_classes=3),
                    title="🍽️ Food detector")
if __name__ == "__main__":
    demo.launch()