app.py

import gradio as gr
from transformers import pipeline

pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog")



def predict(image):
  predictions = pipeline(image)
  return {p["label"]: p["score"] for p in predictions}

window.addEventListener('load', function () {
  gradioURL = window.location.href
  if (!gradioURL.endsWith('?__theme=dark')) {
    window.location.replace(gradioURL + '?__theme=dark');
  }
});

gr.Interface(
    predict,
    inputs=gr.inputs.Image(label="Upload hot dog candidate", type="filepath"),
    outputs=gr.outputs.Label(num_top_classes=2),
    title="Hot Dog? Or Not?",
    theme='dark',
).launch()