chore: import upstream snapshot with attribution

python/ray/serve/tests/test_gradio.py

@@ -0,0 +1,97 @@
+import os
+import sys
+
+import gradio as gr
+import httpx
+import pytest
+
+import ray
+from ray import serve
+from ray._common.test_utils import wait_for_condition
+from ray.serve.gradio_integrations import GradioIngress, GradioServer
+
+
+@pytest.fixture
+def serve_start_shutdown():
+    ray.init()
+    serve.start()
+    yield
+    serve.shutdown()
+    ray.shutdown()
+
+
+@pytest.mark.parametrize("use_user_defined_class", [False, True])
+def test_gradio_ingress_correctness(serve_start_shutdown, use_user_defined_class: bool):
+    """
+    Ensure a Gradio app deployed to a cluster through GradioIngress still
+    produces the correct output.
+    """
+
+    def greet(name):
+        return f"Good morning {name}!"
+
+    if use_user_defined_class:
+
+        @serve.deployment
+        class UserDefinedGradioServer(GradioIngress):
+            def __init__(self):
+                super().__init__(
+                    lambda: gr.Interface(
+                        fn=greet, inputs="text", outputs="text", api_name="predict"
+                    )
+                )
+
+        app = UserDefinedGradioServer.bind()
+    else:
+        app = GradioServer.bind(
+            lambda: gr.Interface(
+                fn=greet, inputs="text", outputs="text", api_name="predict"
+            )
+        )
+
+    serve.run(app)
+
+    test_input = "Alice"
+    response = httpx.post(
+        "http://127.0.0.1:8000/gradio_api/run/predict/", json={"data": [test_input]}
+    )
+    assert response.status_code == 200 and response.json()["data"][0] == greet(
+        test_input
+    )
+
+
+def test_gradio_ingress_scaling(serve_start_shutdown):
+    """
+    Check that a Gradio app that has been deployed to a cluster through
+    GradioIngress scales as needed, i.e. separate client requests are served by
+    different replicas.
+    """
+
+    def f(*args):
+        return os.getpid()
+
+    app = GradioServer.options(num_replicas=2).bind(
+        lambda: gr.Interface(fn=f, inputs="text", outputs="text", api_name="predict")
+    )
+    serve.run(app)
+
+    def two_pids_returned():
+        @ray.remote
+        def get_pid_from_request():
+            r = httpx.post(
+                "http://127.0.0.1:8000/gradio_api/run/predict/",
+                json={"data": ["input"]},
+            )
+            r.raise_for_status()
+            return r.json()["data"][0]
+
+        return (
+            len(set(ray.get([get_pid_from_request.remote() for _ in range(10)]))) == 2
+        )
+
+    # Verify that the requests are handled by two separate replicas.
+    wait_for_condition(two_pids_returned)
+
+
+if __name__ == "__main__":
+    sys.exit(pytest.main(["-v", "-s", __file__]))