Sync from v0.13

examples/online_serving/gradio_openai_chatbot_webserver.py

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+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Example for starting a Gradio OpenAI Chatbot Webserver
+Start vLLM API server:
+    vllm serve meta-llama/Llama-2-7b-chat-hf
+
+Start Gradio OpenAI Chatbot Webserver:
+    python examples/online_serving/gradio_openai_chatbot_webserver.py \
+                    -m meta-llama/Llama-2-7b-chat-hf
+
+Note that `pip install --upgrade gradio` is needed to run this example.
+More details: https://github.com/gradio-app/gradio
+
+If your antivirus software blocks the download of frpc for gradio,
+you can install it manually by following these steps:
+
+1. Download this file: https://cdn-media.huggingface.co/frpc-gradio-0.3/frpc_linux_amd64
+2. Rename the downloaded file to: frpc_linux_amd64_v0.3
+3. Move the file to this location: /home/user/.cache/huggingface/gradio/frpc
+"""
+
+import argparse
+
+import gradio as gr
+from openai import OpenAI
+
+
+def predict(message, history, client, model_name, temp, stop_token_ids):
+    messages = [
+        {"role": "system", "content": "You are a great AI assistant."},
+        *history,
+        {"role": "user", "content": message},
+    ]
+
+    # Send request to OpenAI API (vLLM server)
+    stream = client.chat.completions.create(
+        model=model_name,
+        messages=messages,
+        temperature=temp,
+        stream=True,
+        extra_body={
+            "repetition_penalty": 1,
+            "stop_token_ids": [int(id.strip()) for id in stop_token_ids.split(",")]
+            if stop_token_ids
+            else [],
+        },
+    )
+
+    # Collect all chunks and concatenate them into a full message
+    full_message = ""
+    for chunk in stream:
+        full_message += chunk.choices[0].delta.content or ""
+
+    # Return the full message as a single response
+    return full_message
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Chatbot Interface with Customizable Parameters"
+    )
+    parser.add_argument(
+        "--model-url", type=str, default="http://localhost:8000/v1", help="Model URL"
+    )
+    parser.add_argument(
+        "-m", "--model", type=str, required=True, help="Model name for the chatbot"
+    )
+    parser.add_argument(
+        "--temp", type=float, default=0.8, help="Temperature for text generation"
+    )
+    parser.add_argument(
+        "--stop-token-ids", type=str, default="", help="Comma-separated stop token IDs"
+    )
+    parser.add_argument("--host", type=str, default=None)
+    parser.add_argument("--port", type=int, default=8001)
+    return parser.parse_args()
+
+
+def build_gradio_interface(client, model_name, temp, stop_token_ids):
+    def chat_predict(message, history):
+        return predict(message, history, client, model_name, temp, stop_token_ids)
+
+    return gr.ChatInterface(
+        fn=chat_predict,
+        title="Chatbot Interface",
+        description="A simple chatbot powered by vLLM",
+    )
+
+
+def main():
+    # Parse the arguments
+    args = parse_args()
+
+    # Set OpenAI's API key and API base to use vLLM's API server
+    openai_api_key = "EMPTY"
+    openai_api_base = args.model_url
+
+    # Create an OpenAI client
+    client = OpenAI(api_key=openai_api_key, base_url=openai_api_base)
+
+    # Define the Gradio chatbot interface using the predict function
+    gradio_interface = build_gradio_interface(
+        client, args.model, args.temp, args.stop_token_ids
+    )
+
+    gradio_interface.queue().launch(
+        server_name=args.host, server_port=args.port, share=True
+    )
+
+
+if __name__ == "__main__":
+    main()