| import gradio as gr | |
| from transformers import pipeline | |
| pipeline = pipeline(task="image-classification", model="julien-c/hotdog-not-hotdog") | |
| def predict(input_img): | |
| predictions = pipeline(input_img) | |
| return input_img, {p["label"]: p["score"] for p in predictions} | |
| 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="Does this picture contain a Hot Dog?", | |
| description = 'This is a demo of the hotdog-not-hotdog model by julien-c.<br><br>', | |
| css=".gradio-container {background-color: blanchedalmond;}" | |
| ) | |
| if __name__ == "__main__": | |
| gradio_app.launch() | |