chore: import upstream snapshot with attribution

doc/source/serve/doc_code/gradio-integration.py

@@ -0,0 +1,70 @@
+import requests
+from ray import serve
+
+# __doc_import_begin__
+from ray.serve.gradio_integrations import GradioServer
+
+import gradio as gr
+
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# __doc_import_end__
+
+
+# __doc_gradio_app_begin__
+example_input = (
+    "HOUSTON -- Men have landed and walked on the moon. "
+    "Two Americans, astronauts of Apollo 11, steered their fragile "
+    "four-legged lunar module safely and smoothly to the historic landing "
+    "yesterday at 4:17:40 P.M., Eastern daylight time. Neil A. Armstrong, the "
+    "38-year-old commander, radioed to earth and the mission control room "
+    'here: "Houston, Tranquility Base here. The Eagle has landed." The '
+    "first men to reach the moon -- Armstrong and his co-pilot, Col. Edwin E. "
+    "Aldrin Jr. of the Air Force -- brought their ship to rest on a level, "
+    "rock-strewn plain near the southwestern shore of the arid Sea of "
+    "Tranquility. About six and a half hours later, Armstrong opened the "
+    "landing craft's hatch, stepped slowly down the ladder and declared as "
+    "he planted the first human footprint on the lunar crust: \"That's one "
+    'small step for man, one giant leap for mankind." His first step on the '
+    "moon came at 10:56:20 P.M., as a television camera outside the craft "
+    "transmitted his every move to an awed and excited audience of hundreds "
+    "of millions of people on earth."
+)
+
+
+def gradio_summarizer_builder():
+    tokenizer = AutoTokenizer.from_pretrained("t5-small")
+    summarizer_model = AutoModelForSeq2SeqLM.from_pretrained("t5-small")
+
+    def model(text):
+        input_ids = tokenizer(f"summarize: {text}", return_tensors="pt").input_ids
+        output_ids = summarizer_model.generate(
+            input_ids, num_beams=4, early_stopping=True, max_length=200
+        )
+        return tokenizer.decode(
+            output_ids[0], skip_special_tokens=True, clean_up_tokenization_spaces=False
+        )
+
+    return gr.Interface(
+        fn=model,
+        inputs=[gr.Textbox(value=example_input, label="Input prompt")],
+        outputs=[gr.Textbox(label="Model output")],
+        api_name="predict",
+    )
+    # __doc_gradio_app_end__
+
+
+# __doc_app_begin__
+app = GradioServer.options(ray_actor_options={"num_cpus": 4}).bind(
+    gradio_summarizer_builder
+)
+# __doc_app_end__
+
+# Test example code
+serve.run(app)
+response = requests.post(
+    "http://127.0.0.1:8000/gradio_api/run/predict/", json={"data": [example_input]}
+)
+assert response.status_code == 200
+print("gradio-integration.py: Response from example code is", response.json()["data"])
+serve.shutdown()