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import os
import gradio as gr
from openai import OpenAI
title = "ERNIE 4.5 Turbo: BAIDU's LLM"
description = """
- Official Website: <https://yiyan.baidu.com/> (UI in Chinese)
- API services: [Qianfan Large Model Platform](https://cloud.baidu.com/product-s/qianfan_home) (cloud platform providing LLM services, UI in Chinese)
- [ERNIE 4.5 Turbo Demo](https://huggingface.co/spaces/PaddlePaddle/ernie_4.5_turbo_demo) | [ERNIE X1 Turbo Demo](https://huggingface.co/spaces/PaddlePaddle/ernie_x1_turbo_demo)
"""
qianfan_api_key = os.getenv("QIANFAN_TOKEN")
qianfan_model = "ernie-4.5-turbo-32k"
client = OpenAI(base_url="https://qianfan.baidubce.com/v2", api_key=qianfan_api_key)
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
messages.append({"role": "user", "content": message})
response = client.chat.completions.create(
model=qianfan_model,
messages=messages,
max_completion_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
)
output_message = ""
for chunk in response:
token = chunk.choices[0].delta.content
output_message += token
yield output_message
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="", label="System message"),
gr.Slider(minimum=2, maximum=12288, value=2048, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.7,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
title=title,
description=description,
type='messages',
concurrency_limit=50
)
if __name__ == "__main__":
demo.launch()
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