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()