Upload folder using huggingface_hub

Dockerfile

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+FROM python:3.11-slim
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app.py .
+
+EXPOSE 7860
+
+CMD ["python", "app.py"]

README.md

@@ -1,10 +1,25 @@
 ---
-title: Sam3 Ls Backend
-emoji: 📊
-colorFrom: pink
-colorTo: blue
+title: SAM3 LS Backend
+emoji: 🎯
+colorFrom: blue
+colorTo: green
 sdk: docker
+app_port: 7860
 pinned: false
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# SAM3 Label Studio ML Backend
+
+Phase 1: hardcoded predictions for round-trip testing with Label Studio.
+
+Endpoints:
+- `GET /health` — liveness
+- `POST /setup` — LS project setup (no-op stub)
+- `POST /predict` — returns one fixed bounding box per task
+- `POST /webhook` — annotation events (logged, no-op)
+
+Configure in Label Studio: Settings → Machine Learning → Add Model → URL = `https://<username>-sam3-ls-backend.hf.space`.
+
+Expects an LS label config with:
+- `<Image name="image" value="$image"/>`
+- `<RectangleLabels name="label" toName="image">` containing a `<Label value="butterfly"/>` (or override `DEFAULT_LABEL` env var).

app.py

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+import logging
+import os
+import uuid
+
+from flask import Flask, jsonify, request
+
+logging.basicConfig(level=logging.INFO)
+log = logging.getLogger("sam3-ls-backend")
+
+app = Flask(__name__)
+
+MODEL_VERSION = os.environ.get("MODEL_VERSION", "sam3-stub-v1")
+DEFAULT_LABEL = os.environ.get("DEFAULT_LABEL", "butterfly")
+
+
+def stub_prediction(task: dict) -> dict:
+    return {
+        "model_version": MODEL_VERSION,
+        "score": 0.9,
+        "result": [
+            {
+                "id": str(uuid.uuid4())[:8],
+                "from_name": "label",
+                "to_name": "image",
+                "type": "rectanglelabels",
+                "original_width": 1920,
+                "original_height": 1080,
+                "image_rotation": 0,
+                "value": {
+                    "x": 25.0,
+                    "y": 25.0,
+                    "width": 50.0,
+                    "height": 50.0,
+                    "rotation": 0,
+                    "rectanglelabels": [DEFAULT_LABEL],
+                },
+            }
+        ],
+    }
+
+
+@app.route("/health", methods=["GET"])
+def health():
+    return jsonify({"status": "UP", "model_version": MODEL_VERSION})
+
+
+@app.route("/setup", methods=["POST"])
+def setup():
+    payload = request.get_json(silent=True) or {}
+    log.info("setup called: project=%s", payload.get("project"))
+    return jsonify({"model_version": MODEL_VERSION})
+
+
+@app.route("/predict", methods=["POST"])
+def predict():
+    payload = request.get_json(silent=True) or {}
+    tasks = payload.get("tasks", [])
+    log.info("predict called: %d task(s)", len(tasks))
+    return jsonify({"results": [stub_prediction(t) for t in tasks]})
+
+
+@app.route("/webhook", methods=["POST"])
+def webhook():
+    payload = request.get_json(silent=True) or {}
+    log.info("webhook event: %s", payload.get("action"))
+    return jsonify({"status": "ok"})
+
+
+@app.route("/", methods=["GET"])
+def root():
+    return jsonify(
+        {
+            "service": "sam3-ls-backend",
+            "model_version": MODEL_VERSION,
+            "endpoints": ["/health", "/setup", "/predict", "/webhook"],
+        }
+    )
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860)

requirements.txt

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+flask==3.0.3
+gunicorn==23.0.0