chore: import upstream snapshot with attribution

.github/ui-preview/app.py

@@ -0,0 +1,57 @@
+import logging
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import mlflow.server
+from mlflow.demo import generate_all_demos
+
+logging.basicConfig(level=logging.INFO)
+_logger = logging.getLogger(__name__)
+
+
+def setup():
+    # Extract UI build assets into the mlflow package's expected location
+    tar_path = Path(__file__).parent.resolve() / "build.tar.gz"
+    target_dir = Path(mlflow.server.__file__).parent / "js"
+    target_dir.mkdir(parents=True, exist_ok=True)
+
+    _logger.info("Extracting UI assets to %s", target_dir)
+    subprocess.check_call(["tar", "xzf", tar_path, "-C", target_dir])
+
+    # Generate demo data. Always refresh so the preview app reflects the latest
+    # demo content (e.g. new trace types) even if the SQLite database persisted
+    # from a previous deploy with stale demo data.
+    os.environ["MLFLOW_TRACKING_URI"] = "sqlite:///mlflow.db"
+    _logger.info("Generating demo data...")
+    generate_all_demos(refresh=True)
+    _logger.info("Demo data generated.")
+
+
+def main():
+    setup()
+
+    cmd = [
+        sys.executable,
+        "-m",
+        "mlflow",
+        "server",
+        "--backend-store-uri",
+        "sqlite:///mlflow.db",
+        "--default-artifact-root",
+        "./mlartifacts",
+        "--serve-artifacts",
+        "--host",
+        "0.0.0.0",
+        "--port",
+        "8000",
+        "--workers",
+        "1",
+    ]
+    _logger.info("Starting MLflow server: %s", " ".join(cmd))
+    os.execvp(cmd[0], cmd)
+
+
+if __name__ == "__main__":
+    main()