chore: import upstream snapshot with attribution

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

@@ -0,0 +1,36 @@
+import gradio as gr
+from transformers import pipeline
+import requests
+
+# __doc_code_begin__
+generator1 = pipeline("text-generation", model="gpt2")
+generator2 = pipeline("text-generation", model="distilgpt2")
+
+
+def model1(text):
+    generated_list = generator1(text, do_sample=True, min_length=20, max_length=100)
+    generated = generated_list[0]["generated_text"]
+    return generated
+
+
+def model2(text):
+    generated_list = generator2(text, do_sample=True, min_length=20, max_length=100)
+    generated = generated_list[0]["generated_text"]
+    return generated
+
+
+demo = gr.Interface(
+    lambda text: f"{model1(text)}\n------------\n{model2(text)}",
+    "textbox",
+    "textbox",
+    api_name="predict",
+)
+# __doc_code_end__
+
+# Test example code
+demo.launch(prevent_thread_lock=True)
+response = requests.post(
+    "http://127.0.0.1:7860/gradio_api/run/predict/", json={"data": ["My name is Lewis"]}
+)
+assert response.status_code == 200
+print("gradio-original.py: Response from example code is", response.json()["data"])