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# Use a pipeline as a high-level helper | |
from transformers import pipeline | |
pipe = pipeline("image-classification", model="julien-c/hotdog-not-hotdog") | |
def predict(input_img): | |
predictions=pipeline(input_img) | |
return input_img, {p['lbale']: p["score"] for p in predictions} | |
import gradio as gr | |
gradio_app = gr.Interface( | |
predict, | |
inputs=gr.Image(label="Select hot dog candidate", sources=['upload', 'webcam'], type="pil"), | |
outputs=[gr.Image(label="Processed Image"), gr.Label(label="Result", num_top_classes=2)], | |
title="Hot Dog? Or Not?" | |
) | |
if _name== "main_": | |
gradio_app.launch() |