app.py

import gradio as gr
from transformers import pipeline

pipeline =pipeline("image-classification",model="p1atdev/siglip-tagger-test-3",trust_remote_code=True)

def predict(input_img):
 predictions = pipeline(input_img , threshold=0.5, #optional parameter defaults to 0
 return_scores = False #optional parameter defaults to False)
 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="Hot Dog? Or Not?",
)

if __name__ == "__main__":
 gradio_app.launch()