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import gradio as gr
from huggingface_hub import InferenceClient
import evaluate
# 创建困惑度计算工具
perplexity = evaluate.load("perplexity", module_type="metric")
# 创建推理客户端
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
# 计算困惑度
perplexity_results = perplexity.compute(model_id='gpt2', add_start_token=False, predictions=[response])
perplexity_value = perplexity_results['perplexity']
# 将困惑度结果添加到聊天回复中
yield f"Perplexity of the response: {perplexity_value}"
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)"
),
]
)
if __name__ == "__main__":
demo.launch()
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