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import gradio as gr
from huggingface_hub import InferenceClient
from dotenv import load_dotenv
import os
# โหลดตัวแปรจาก .env
load_dotenv()
# ดึง token จาก environment variable
HF_TOKEN = os.getenv("HF_TOKEN")
# สร้าง InferenceClient ด้วย token
client = InferenceClient("iapp/chinda-qwen3-4b", token=HF_TOKEN)
# ฟังก์ชันสำหรับประมวลผลข้อความสนทนา
def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
# เตรียมข้อความตาม ChatML format
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})
response = ""
# เรียกใช้งานแบบ streaming
for msg in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = msg.choices[0].delta.content
response += token
# แยก 🧠 Thinking กับ 💬 Response ถ้ามี </think>
if "</think>" in response:
think_split = response.split("</think>", 1)
thinking = think_split[0].replace("<think>", "").strip()
content = think_split[1].strip()
yield f"🧠 Thinking: {thinking}\n\n💬 Response: {content}"
else:
yield response
# สร้าง UI ด้วย Gradio
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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