app.py

# app.py
import spaces
import gradio as gr
from gradio import update
from functools import lru_cache
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

# 可選模型列表
MODEL_LIST = [
    "ckiplab/gpt2-tiny-chinese",
    "ckiplab/gpt2-base-chinese",
    "liswei/Taiwan-ELM-270M-Instruct",
    "liswei/Taiwan-ELM-1_1B",
    "google/gemma-3-1b-pt",
    "benchang1110/Qwen2.5-Taiwan-1.5B-Instruct",
    "benchang1110/Taiwan-tinyllama-v1.0-base",
]

@lru_cache(maxsize=None)
def get_pipeline(model_name):
    tok = AutoTokenizer.from_pretrained(model_name)
    mdl = AutoModelForCausalLM.from_pretrained(model_name, weights_only=False)
    mdl.to("cuda")
    return pipeline("text-generation", model=mdl, tokenizer=tok, device=0)

@spaces.GPU
def suggest_next(text, model_name, k, m):
    """
    使用 Beam Search 產生 M 條最可能的下段建議，並一次更新可選項清單。
    """
    gen_pipe = get_pipeline(model_name)
    outs = gen_pipe(
        text,
        max_new_tokens=k,
        num_beams=m,
        num_return_sequences=m,
        do_sample=False,
        early_stopping=True
    )
    suggestions = [out["generated_text"][len(text):] for out in outs]
    # 保證 choices 與 value 為空字串而非 None
    return update(choices=suggestions, value="")

def append_suggestion(current, choice):
    # 防止 None 導致的拼接錯誤
    if not choice:
        return current
    return current + choice

with gr.Blocks() as demo:
    gr.Markdown(
        "## 🇹🇼 台灣中文下段預測  \n"
        "結合小型語言模型與 ZeroGPU，即時 IME 風格建議條。"
    )

    # 建議清單置頂
    suggestions = gr.Radio(
        [], label="建議清單", interactive=True, type="value", elem_id="suggestions-bar"
    )

    # 輸入區與生成按鈕並排
    with gr.Row():
        input_text = gr.TextArea(
            label="輸入文字", lines=4, placeholder="請在此輸入起始片段…"
        )
        gpu_button = gr.Button("使用 GPU 生成建議")

    # 參數設定區
    with gr.Row():
        model_selector = gr.Dropdown(
            MODEL_LIST, value=MODEL_LIST[0], label="選擇模型"
        )
        k_slider = gr.Slider(
            minimum=1, maximum=50, step=1, value=5, label="K（最大新生成詞元）"
        )
        m_slider = gr.Slider(
            minimum=1, maximum=10, step=1, value=5, label="M（建議數量 / Beam 數）"
        )

    # 事件綁定
    gpu_button.click(
        fn=suggest_next,
        inputs=[input_text, model_selector, k_slider, m_slider],
        outputs=suggestions,
    )
    suggestions.change(
        fn=append_suggestion,
        inputs=[input_text, suggestions],
        outputs=input_text,
    )

    demo.launch()