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import socket
import subprocess
import gradio as gr
from openai import OpenAI
def get_local_ip():
# 建立一個 UDP socket，連到外部伺服器（不會真的發送資料）
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
try:
# 這裡用 Google 的公共 DNS IP 來確保路徑有效
s.connect(("8.8.8.8", 80))
ip = s.getsockname()[0]
except Exception:
ip = "127.0.0.1"
finally:
s.close()
return ip
print("本機 IP:", get_local_ip())
# ✅ 設定 base URL 連接本地 llama.cpp API
client = OpenAI(
base_url="http://0.0.0.0:8000/v1",
api_key="sk-local", # llama.cpp 不檢查內容，只要有就行
timeout=600
)
# ✅ 回應函式（改成 stream 模式）
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for user, assistant in history:
if user:
messages.append({"role": "user", "content": user})
if assistant:
messages.append({"role": "assistant", "content": assistant})
messages.append({"role": "user", "content": message})
try:
# 🔹 修改 1: 開啟 stream 模式
stream = client.chat.completions.create(
model="qwen3", # ⚠️ 替換成你 llama.cpp 載入的模型名稱
messages=messages,
max_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
stream=True,
)
output = ""
# 🔹 修改 2: 逐步處理流式回應
for chunk in stream:
delta = chunk.choices[0].delta.content or ""
output += delta
yield output # ✅ 即時回傳給 Gradio
except Exception as e:
print(f"[Error] {e}")
yield "⚠️ Llama.cpp server 沒有回應，請稍後再試。"
# ✅ Gradio 介面（修改 3: 啟用 generator）
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly assistant.", label="System message"),
gr.Slider(minimum=1, maximum=4096, value=1024, 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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