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README.md

@@ -1,13 +1,14 @@
 ---
 title: SAM3 Panoptic
-emoji: 👁
-colorFrom: blue
-colorTo: indigo
+emoji: 🎯
+colorFrom: indigo
+colorTo: blue
 sdk: gradio
-sdk_version: 6.15.2
-python_version: '3.12'
+sdk_version: 6.6.0
 app_file: app.py
 pinned: false
+short_description: SAM 3 open-vocabulary panoptic concept segmentation API
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+`api_panoptic(image, concepts, conf)` -> JSON detections with base64-PNG masks.
+Requires the `HF_TOKEN` Space secret to have access to gated `facebook/sam3`.

app.py

@@ -0,0 +1,77 @@
+"""SAM 3 panoptic concept-segmentation API (ZeroGPU). Self-contained."""
+import base64
+import io
+import os
+
+import gradio as gr
+import numpy as np
+import spaces
+import torch
+from PIL import Image
+from transformers import Sam3Model, Sam3Processor
+
+HF_TOKEN = os.environ.get("HF_TOKEN")
+MODEL_ID = "facebook/sam3"
+
+# Built at import on CPU; moved to CUDA inside the @spaces.GPU function.
+processor = Sam3Processor.from_pretrained(MODEL_ID, token=HF_TOKEN)
+model = Sam3Model.from_pretrained(MODEL_ID, token=HF_TOKEN)
+model.eval()
+
+
+def _encode_mask(mask_bool: np.ndarray) -> str:
+    arr = (mask_bool.astype(np.uint8)) * 255
+    buf = io.BytesIO()
+    Image.fromarray(arr, mode="L").save(buf, format="PNG")
+    return base64.b64encode(buf.getvalue()).decode("ascii")
+
+
+@spaces.GPU(duration=120)
+def api_panoptic(image, concepts, conf):
+    if image is None:
+        return {"error": "no image provided"}
+    image = image.convert("RGB")
+    W, H = image.size
+    concept_list = [c.strip() for c in (concepts or "").split(",") if c.strip()]
+    device = "cuda"
+    model.to(device)
+    detections = []
+    for concept in concept_list:
+        inputs = processor(images=image, text=concept, return_tensors="pt").to(device)
+        with torch.no_grad():
+            outputs = model(**inputs)
+        target_sizes = (inputs["original_sizes"].tolist()
+                        if "original_sizes" in inputs else [[H, W]])
+        res = processor.post_process_instance_segmentation(
+            outputs, threshold=float(conf), mask_threshold=0.5,
+            target_sizes=target_sizes)[0]
+        # NOTE (verify on live Space): expected keys masks/scores/boxes.
+        masks, scores = res["masks"], res["scores"]
+        boxes = res.get("boxes")
+        for i in range(len(scores)):
+            m = masks[i]
+            m = m.cpu().numpy() if hasattr(m, "cpu") else np.asarray(m)
+            mb = m > 0.5 if m.dtype != bool else m
+            box = (boxes[i].cpu().numpy().tolist()
+                   if boxes is not None else [0, 0, 0, 0])
+            detections.append({
+                "label": concept, "score": float(scores[i]),
+                "box": box, "mask_png_b64": _encode_mask(mb.astype(bool)),
+            })
+    return {"version": "3", "model": MODEL_ID, "width": W, "height": H,
+            "detections": detections}
+
+
+with gr.Blocks(title="SAM3 Panoptic") as demo:
+    gr.Markdown("# SAM 3 Panoptic API\nUpload an image, enter comma-separated concepts.")
+    with gr.Row():
+        inp = gr.Image(type="pil", label="Image")
+        out = gr.JSON(label="Detections")
+    txt = gr.Textbox(label="Concepts (comma-separated)",
+                     value="person, car, road, sky, building, tree")
+    conf = gr.Slider(0.0, 1.0, value=0.4, step=0.05, label="Confidence")
+    gr.Button("Segment").click(api_panoptic, [inp, txt, conf], out,
+                               api_name="api_panoptic")
+
+if __name__ == "__main__":
+    demo.queue().launch()

requirements.txt

@@ -0,0 +1,10 @@
+transformers==5.9.0
+torch==2.11.0
+torchvision
+gradio==6.6.0
+spaces
+accelerate
+kernels
+sentencepiece
+pillow
+numpy