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import gradio as gr
from huggingface_hub import InferenceClient
# Use the Primus-Merged model from Hugging Face
client = InferenceClient("trendmicro-ailab/Llama-Primus-Merged")
def respond(
message: str,
history: list[tuple[str, str]],
system_message: str,
max_tokens: int,
temperature: float,
top_p: float,
):
# Build chat messages payload
messages = [{"role": "system", "content": system_message}]
for user_msg, bot_msg in history:
if user_msg:
messages.append({"role": "user", "content": user_msg})
if bot_msg:
messages.append({"role": "assistant", "content": bot_msg})
messages.append({"role": "user", "content": message})
# Streamed response
response = ""
for chunk in client.chat_completion(
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
):
delta = chunk.choices[0].delta.content or ""
response += delta
yield response
demo = gr.ChatInterface(
fn=respond,
additional_inputs=[
gr.Textbox(value="You are a helpful security assistant.", 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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