app.py

import gradio as gr
from transformers import AutoTokenizer
from optimum.intel import OVModelForCausalLM
import warnings

warnings.filterwarnings("ignore", category=DeprecationWarning, message="__array__ implementation doesn't accept a copy keyword")

# 模型與標記器載入
model_id = "hsuwill000/DeepSeek-R1-Distill-Qwen-1.5B-openvino"
print("Loading model...")
model = OVModelForCausalLM.from_pretrained(model_id, device_map="auto")
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)

# 對話歷史記錄
history = []

# 回應函數
def respond(prompt):
    global history  # 使用全域變數存 history
    
    # 轉換 history 為 messages 格式
    messages = [{"role": "system", "content": "Answer the question in English only."}]
    
    # 加入歷史對話
    for user_text, assistant_text in history:
        messages.append({"role": "user", "content": user_text})
        messages.append({"role": "assistant", "content": assistant_text})

    # 加入當前輸入
    messages.append({"role": "user", "content": prompt})
    
    # 轉換為 tokenizer 需要的格式
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )

    # 進行模型推理
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=4096,
        temperature=0.7,
        top_p=0.9,
        do_sample=True
    )
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
    response = response.replace("<think>", "**THINK**").replace("</think>", "**THINK**").strip()

    # 更新 history
    history.append((prompt, response))

    return response

# 清除歷史記錄
def clear_history():
    global history
    history = []
    return "History cleared!"

# Gradio 介面
with gr.Blocks() as demo:
    gr.Markdown("# DeepSeek-R1-Distill-Qwen-1.5B-openvino")

    with gr.Tabs():
        with gr.TabItem("聊天"):
            chat_if = gr.Interface(
                fn=respond,
                inputs=gr.Textbox(label="Prompt", placeholder="請輸入訊息..."),
                outputs=gr.Textbox(label="Response", interactive=False),
                api_name="hchat",
                title="DeepSeek-R1-Distill-Qwen-1.5B-openvino(with history)",
                description="回傳輸入內容的測試 API",
            )

    with gr.Row():
        clear_button = gr.Button("🧹 Clear History")
    
    # 點擊按鈕清除 history
    clear_button.click(fn=clear_history, inputs=[], outputs=[])

if __name__ == "__main__":
    print("Launching Gradio app...")
    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)