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app.py

@@ -16,7 +16,7 @@ model.eval()
 
 
 @spaces.GPU(duration=120)
-def api_autotag(image, max_tags):
+def api_autotag(image, max_tags, num_beams=3):
     if image is None:
         return {"error": "no image provided"}
     image = image.convert("RGB")
@@ -24,7 +24,7 @@ def api_autotag(image, max_tags):
     model.to(device)
     inputs = processor(text="<OD>", images=image, return_tensors="pt").to(device)
     with torch.no_grad():
-        gen = model.generate(**inputs, max_new_tokens=1024, num_beams=3)
+        gen = model.generate(**inputs, max_new_tokens=1024, num_beams=int(num_beams))
     text = processor.batch_decode(gen, skip_special_tokens=False)[0]
     parsed = processor.post_process_generation(text, task="<OD>", image_size=image.size)
     labels = parsed.get("<OD>", {}).get("labels", [])
@@ -39,7 +39,8 @@ with gr.Blocks(title="SAM3 AutoTag") as demo:
         inp = gr.Image(type="pil", label="Image")
         out = gr.JSON(label="Tags")
     mt = gr.Slider(1, 50, value=20, step=1, label="Max tags")
-    gr.Button("Tag").click(api_autotag, [inp, mt], out, api_name="api_autotag")
+    nb = gr.Slider(1, 8, value=3, step=1, label="Beams (higher = more precise)")
+    gr.Button("Tag").click(api_autotag, [inp, mt, nb], out, api_name="api_autotag")
 
 if __name__ == "__main__":
     demo.queue().launch(show_error=True)