Create app.py

app.py

@@ -0,0 +1,19 @@
+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()