app.py

import torch
import gradio as gr
from transformers import pipeline

device = 0 if torch.cuda.is_available() else "cpu"

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

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

gr.Interface(
    predict,
    inputs=gr.Image(label="Upload hot dog candidate", type="filepath"),
    outputs=gr.Label(num_top_classes=2),
    title="Hot Dog / No Hot Dog",
    flagging_mode="manual"
).launch()

# share=True for public share link 72hr access (similar to ngrok)