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 if "" in response: think_split = response.split("", 1) thinking = think_split[0].replace("", "").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()