Revert "add suggestion cleanning"
Browse filesThis reverts commit 46d41f7df74088d1f934fafcfde4f2c059d80948.
app.py
CHANGED
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@@ -1,9 +1,10 @@
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import spaces
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import gradio as gr
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from gradio import update
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from functools import lru_cache
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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from opencc import OpenCC #
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# 初始化簡體到繁體轉換器
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cc = OpenCC('s2t')
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@@ -25,19 +26,30 @@ MODEL_LIST = [
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]
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def merge_common_prefixes(suggestions, min_len=2):
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prefixes = []
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to_remove = set()
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for i in range(len(suggestions)):
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for j in range(i+1, len(suggestions)):
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s1, s2 = suggestions[i], suggestions[j]
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common = ''.join(c1 for c1, c2 in zip(s1, s2) if c1 == c2)
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if len(common) >= min_len:
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prefixes.append(common)
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to_remove.update([s1, s2])
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unique_prefixes = []
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for p in prefixes:
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if p not in unique_prefixes:
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unique_prefixes.append(p)
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remainder = [s for s in suggestions if s not in to_remove]
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return unique_prefixes + remainder
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@@ -52,7 +64,14 @@ def get_pipeline(model_name):
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@spaces.GPU
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def suggest_next(text, model_name, k, m, num_beam_groups, diversity_penalty):
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gen_pipe = get_pipeline(model_name)
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gen_kwargs = {
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"max_new_tokens": k,
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"num_beams": m,
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@@ -62,54 +81,110 @@ def suggest_next(text, model_name, k, m, num_beam_groups, diversity_penalty):
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}
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if diversity_penalty and diversity_penalty > 0:
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gen_kwargs["num_beam_groups"] = num_beam_groups
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gen_kwargs["diversity_penalty"] = diversity_penalty
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outs = gen_pipe(text, **gen_kwargs)
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#
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-
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if
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converted = cc.convert(snippet).strip()
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raw_suggestions.append(converted)
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# 去重
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unique_suggestions = []
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-
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-
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if key not in seen:
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seen.add(key)
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unique_suggestions.append(key)
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# 合併共同前綴
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-
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-
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# 最終去重並移除空項 (基於 strip 後內容)
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final_suggestions = []
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seen_final = set()
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for s in merged_prefixes:
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key = s.strip()
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if key and key not in seen_final:
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seen_final.add(key)
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final_suggestions.append(key)
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return update(choices=final_suggestions, value=None)
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def append_suggestion(text, choice):
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return text + choice
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-
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-
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#suggestions-bar .candidate-list {
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display: flex;
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-
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-
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}
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#
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-
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with gr.Column():
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suggestions = gr.Radio(
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[], label="", interactive=True, type="value",
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@@ -120,11 +195,14 @@ with gr.Blocks(css="""
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lines=1, max_lines=20, elem_id="input-box"
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)
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with gr.Row():
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auto_predict = gr.Checkbox(
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value=True, label="自動預測(內容變更時觸發)", elem_id="auto-predict"
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)
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predict_button = gr.Button(
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with gr.Accordion("進階設定", open=False):
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model_selector = gr.Dropdown(
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@@ -137,14 +215,15 @@ with gr.Blocks(css="""
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minimum=1, maximum=30, step=1, value=30, label="M(建議數/Beam 數)"
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)
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group_slider = gr.Slider(
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minimum=1, maximum=30, step=1, value=
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label="Beam 群組數 (num_beam_groups)"
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)
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diversity_penalty_slider = gr.Slider(
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minimum=0.0, maximum=2.0, step=0.1, value=0
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label="多樣性懲罰 (diversity_penalty)"
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)
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predict_button.click(
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fn=suggest_next,
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inputs=[
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@@ -179,4 +258,4 @@ with gr.Blocks(css="""
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outputs=input_text,
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)
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demo.launch()
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# app.py
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import spaces
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import gradio as gr
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from gradio import update
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from functools import lru_cache
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
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+
from opencc import OpenCC # 用於簡體轉繁體
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# 初始化簡體到繁體轉換器
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cc = OpenCC('s2t')
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]
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def merge_common_prefixes(suggestions, min_len=2):
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"""
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合併具有共同前綴的建議:
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- 找出所有長度 ≥ min_len 的共同前綴
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- 將這些前綴作為新建議,移除原有被合併的項目
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"""
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prefixes = []
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to_remove = set()
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for i in range(len(suggestions)):
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for j in range(i+1, len(suggestions)):
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s1, s2 = suggestions[i], suggestions[j]
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# 計算字元級共同前綴
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common = ''.join(c1 for c1, c2 in zip(s1, s2) if c1 == c2)
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if len(common) >= min_len:
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prefixes.append(common)
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to_remove.update([s1, s2])
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# 去重前綴
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unique_prefixes = []
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for p in prefixes:
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if p not in unique_prefixes:
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unique_prefixes.append(p)
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# 剩下沒有被合併的建議
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remainder = [s for s in suggestions if s not in to_remove]
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return unique_prefixes + remainder
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@spaces.GPU
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def suggest_next(text, model_name, k, m, num_beam_groups, diversity_penalty):
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"""
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使用 Diverse Beam Search 產生 m 條候選:
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- num_beams = m
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- num_beam_groups, diversity_penalty 可調整多樣性
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之後轉繁體、去重、合併共同前綴後回傳。
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"""
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gen_pipe = get_pipeline(model_name)
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# 構造 generate 參數字典,僅在 penalty>0 時加入 diversity 相關
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gen_kwargs = {
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"max_new_tokens": k,
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"num_beams": m,
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}
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if diversity_penalty and diversity_penalty > 0:
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gen_kwargs["num_beam_groups"] = num_beam_groups
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gen_kwargs["diversity_penalty"] = float(diversity_penalty)
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outs = gen_pipe(text, **gen_kwargs)
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# 提取純下文、過濾空字串、繁體化
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suggestions = [
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cc.convert(out["generated_text"][len(text):].strip())
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for out in outs
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if out["generated_text"][len(text):].strip()
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]
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# 去重
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unique_suggestions = []
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for s in suggestions:
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if s not in unique_suggestions:
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unique_suggestions.append(s)
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# 合併共同前綴
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final_suggestions = merge_common_prefixes(unique_suggestions, min_len=2)
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return update(choices=final_suggestions, value=None)
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def append_suggestion(current, choice):
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if choice is None:
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return current
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# 直接插入選中的候選文字
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return current + choice
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# 自訂 CSS:模擬經典中文輸入法候選欄樣式,並優化手機響應與自動高度
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custom_css = """
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#suggestions-bar {
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width: 100%;
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margin-bottom: 8px;
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}
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#suggestions-bar .candidate-list {
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display: flex;
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gap: 8px;
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background: #fff;
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border: 1px solid #999;
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border-radius: 4px;
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padding: 6px;
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overflow-x: auto;
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white-space: nowrap;
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}
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#suggestions-bar .candidate-list label {
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cursor: pointer;
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padding: 6px 10px;
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font-size: 16px;
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}
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#suggestions-bar .candidate-list label:hover {
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background: #f5f5f5;
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}
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#suggestions-bar .candidate-list input[type=radio]:checked + label {
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background: #e6f7ff;
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border: 1px solid #1890ff;
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}
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#input-box textarea {
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width: 100%;
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font-size: 16px;
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padding: 6px;
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box-sizing: border-box;
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overflow: hidden;
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resize: none;
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}
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#predict-button {
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margin-top: 8px;
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width: 100%;
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}
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/* 手機響應式 */
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@media only screen and (max-width: 600px) {
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#suggestions-bar .candidate-list label {
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padding: 8px;
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font-size: 18px;
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}
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#predict-button {
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font-size: 18px;
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}
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}
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"""
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# 自動增高腳本
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auto_height_js = """
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<script>
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window.addEventListener('load', () => {
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const textarea = document.querySelector('#input-box textarea');
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if (!textarea) return;
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textarea.style.height = 'auto';
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textarea.addEventListener('input', function() {
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this.style.height = 'auto';
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this.style.height = this.scrollHeight + 'px';
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});
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});
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</script>
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"""
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with gr.Blocks(css=custom_css) as demo:
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gr.HTML(auto_height_js)
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gr.Markdown(
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"## 🇹🇼 繁體中文 IME 加速器 \
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"
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"結合小型語言模型與 ZeroGPU,提供即時輸入法風格候選欄。"
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)
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with gr.Column():
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suggestions = gr.Radio(
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[], label="", interactive=True, type="value",
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lines=1, max_lines=20, elem_id="input-box"
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)
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# 永遠顯示預測按鈕
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with gr.Row():
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auto_predict = gr.Checkbox(
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value=True, label="自動預測(內容變更時觸發)", elem_id="auto-predict"
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)
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predict_button = gr.Button(
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"預測", elem_id="predict-button"
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)
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with gr.Accordion("進階設定", open=False):
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model_selector = gr.Dropdown(
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minimum=1, maximum=30, step=1, value=30, label="M(建議數/Beam 數)"
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)
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group_slider = gr.Slider(
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minimum=1, maximum=30, step=1, value=30,
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label="Beam 群組數 (num_beam_groups)"
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)
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diversity_penalty_slider = gr.Slider(
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minimum=0.0, maximum=2.0, step=0.1, value=1.0,
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label="多樣性懲罰 (diversity_penalty)"
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)
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# 綁定事件
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predict_button.click(
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fn=suggest_next,
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inputs=[
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outputs=input_text,
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)
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demo.launch()
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