chore: import upstream snapshot with attribution

.github/ui-preview/README.md

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+# UI Preview
+
+Deploy a live preview of the MLflow UI as a [Databricks App](https://docs.databricks.com/aws/en/dev-tools/databricks-apps/) when a PR modifies the frontend (`mlflow/server/js/`).
+
+## How it works
+
+1. Add the `ui-preview` label to a PR with UI changes
+2. The [UI Preview workflow](../workflows/ui-preview.yml) builds the frontend and deploys it to a Databricks App
+3. A comment with the preview URL is posted on the PR
+4. The app is automatically deleted when the PR is closed
+
+## Access
+
+Preview apps are only accessible to core maintainers with workspace access.
+
+## API access
+
+To query or add data to a preview app, set the following environment variables:
+
+```bash
+export DATABRICKS_HOST="https://..."
+export DATABRICKS_CLIENT_ID="..."
+export DATABRICKS_CLIENT_SECRET="..."
+export APP_URL="..."
+```
+
+Then, obtain an access token:
+
+```bash
+export TOKEN=$(curl -s -X POST "$DATABRICKS_HOST/oidc/v1/token" \
+  -d "grant_type=client_credentials&client_id=$DATABRICKS_CLIENT_ID&client_secret=$DATABRICKS_CLIENT_SECRET&scope=all-apis" \
+  | jq -r '.access_token')
+```
+
+Once the token is obtained, run the following command to verify it works:
+
+```bash
+curl -s "$APP_URL/api/2.0/mlflow/experiments/search" \
+  -X POST -H "Authorization: Bearer $TOKEN" -H "Content-Type: application/json" \
+  -d '{"max_results": 10}' | jq .
+```
+
+You can also use the MLflow Python client:
+
+```bash
+export MLFLOW_TRACKING_URI="$APP_URL"
+export MLFLOW_TRACKING_TOKEN="$TOKEN"
+```
+
+```python
+import mlflow
+
+mlflow.search_experiments(max_results=10)
+```
+
+See [Connect to Databricks Apps](https://docs.databricks.com/aws/en/dev-tools/databricks-apps/connect-local) for more details on authentication.

.github/ui-preview/app.py

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+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()

.github/ui-preview/app.yaml

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+command:
+  - "python"
+  - "app.py"
+env:
+  - name: MLFLOW_SERVER_CORS_ALLOWED_ORIGINS
+    value: "__APP_URL__"
+  - name: MLFLOW_SERVER_ALLOWED_HOSTS
+    value: "*"
+  - name: MLFLOW_CRYPTO_KEK_PASSPHRASE
+    value: "__KEK_PASSPHRASE__"