Create app.py

app.py

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+import re
+import numpy as np
+import matplotlib.pyplot as plt
+
+import gradio as gr
+from transformers import pipeline
+
+
+# ---------- 1. 加载 Hugging Face 模型 ----------
+
+# 中 → 英 翻译
+translator = pipeline("translation", model="Helsinki-NLP/opus-mt-zh-en")
+
+# 英文礼貌度（4 类：polite / somewhat polite / neutral / impolite）
+politeness_cls = pipeline("text-classification", model="Intel/polite-guard")
+
+# 英文正式度（3 类：formal / neutral / informal）
+formality_cls = pipeline("text-classification", model="LenDigLearn/formality-classifier-mdeberta-v3-base")
+
+# 英文 hedge / uncertainty（委婉/模糊表达）
+hedge_cls = pipeline("text-classification", model="ChrisLiewJY/BERTweet-Hedge")
+
+
+# ---------- 2. 一些简单的中文 & 英文规则打分函数 ----------
+
+POLITE_WORDS_ZH = ["请", "麻烦您", "劳烦", "敬请", "拜托", "打扰了", "烦请"]
+HEDGE_WORDS_ZH = ["是否", "可能", "大概", "也许", "好像", "觉得", "有点"]
+IMPERATIVE_WORDS_ZH = ["必须", "务必", "不得", "不准", "立即", "马上", "必须要"]
+
+def score_chinese_features(text: str):
+    """非常简单的中文语气打分：返回 0~1 之间的几个指标"""
+    if not text.strip():
+        return 0.5, 0.5, 0.0  # 默认中等
+
+    length = max(len(text), 1)
+
+    polite_hits = sum(text.count(w) for w in POLITE_WORDS_ZH)
+    hedge_hits = sum(text.count(w) for w in HEDGE_WORDS_ZH)
+    imp_hits = sum(text.count(w) for w in IMPERATIVE_WORDS_ZH)
+
+    polite_score = np.clip(polite_hits / 3.0, 0, 1)   # 出现次数越多分越高
+    hedge_score = np.clip(hedge_hits / 3.0, 0, 1)
+    imp_score = np.clip(imp_hits / 2.0, 0, 1)
+
+    return float(polite_score), float(hedge_score), float(imp_score)
+
+
+def map_polite_guard_to_score(label: str):
+    """把 Intel/polite-guard 的 4 类映射到 [0,1] 礼貌度"""
+    label = label.lower()
+    if "polite" == label:
+        return 1.0
+    if "somewhat polite" in label:
+        return 0.75
+    if "neutral" in label:
+        return 0.5
+    if "impolite" in label:
+        return 0.0
+    return 0.5
+
+
+def map_formality_to_score(label: str):
+    """formal / neutral / informal → [0,1] 正式度"""
+    label = label.lower()
+    if "formal" in label:
+        return 1.0
+    if "neutral" in label:
+        return 0.5
+    if "informal" in label:
+        return 0.0
+    return 0.5
+
+
+def map_hedge_to_score(label: str):
+    """
+    BERTweet-Hedge 的 label 可能类似 "Hedge" / "No_Hedge" / 多类。
+    这里只是示意：如果包含 hedge 就算高 hedge。
+    """
+    label = label.lower()
+    if "hedge" in label and "no" not in label:
+        return 1.0
+    if "no_hedge" in label:
+        return 0.0
+    # 多类时可以更细分，这里先给中等
+    return 0.5
+
+
+IMPERATIVE_TRIGGER_EN = [
+    r"^please\b",
+    r"^kindly\b",
+    r"^do\b",
+    r"^make\b",
+    r"^send\b",
+    r"^provide\b",
+    r"\byou must\b",
+    r"\byou have to\b",
+    r"\byou are required to\b",
+]
+
+
+def score_imperative_en(text: str):
+    """用很简单的规则估计英文命令语气强度"""
+    t = text.strip().lower()
+    if not t:
+        return 0.0
+    hits = 0
+    for pat in IMPERATIVE_TRIGGER_EN:
+        if re.search(pat, t):
+            hits += 1
+    # 多个命令触发就提高分数
+    return float(np.clip(hits / 2.0, 0, 1))
+
+
+# ---------- 3. 核心：分析函数 ----------
+
+def analyze_letter(chinese_text: str):
+    if not chinese_text.strip():
+        return (
+            "",  # 英文翻译
+            {},  # 中文指标
+            {},  # 英文指标
+            "N/A",  # PD 等级
+            0.0,   # PD 分数
+            None,  # bar fig
+            None,  # radar fig
+        )
+
+    # 1) 中文语气分析（规则）
+    polite_zh, hedge_zh, imp_zh = score_chinese_features(chinese_text)
+
+    zh_stats = {
+        "politeness": polite_zh,
+        "hedging": hedge_zh,
+        "imperative": imp_zh,
+    }
+
+    # 2) 中 → 英 翻译
+    translated = translator(chinese_text, max_length=512)[0]["translation_text"]
+
+    # 3) 英文礼貌度
+    pol_out = politeness_cls(translated)[0]
+    polite_en = map_polite_guard_to_score(pol_out["label"])
+
+    # 4) 英文正式度
+    form_out = formality_cls(translated)[0]
+    formality_en = map_formality_to_score(form_out["label"])
+
+    # 5) 英文 hedge 程度
+    hedge_out = hedge_cls(translated)[0]
+    hedge_en = map_hedge_to_score(hedge_out["label"])
+
+    # 6) 英文命令式强度
+    imp_en = score_imperative_en(translated)
+
+    en_stats = {
+        "politeness": polite_en,
+        "formality": formality_en,
+        "hedging": hedge_en,
+        "imperative": imp_en,
+    }
+
+    # 7) 计算英文侧权力距离得分（0~1）
+    power_distance_score = (
+        0.35 * (1 - polite_en)
+        + 0.25 * formality_en
+        + 0.25 * (1 - hedge_en)
+        + 0.15 * imp_en
+    )
+
+    # 三分类
+    if power_distance_score < 0.33:
+        level = "Low"
+    elif power_distance_score < 0.66:
+        level = "Medium"
+    else:
+        level = "High"
+
+    # ---------- 4. 画柱状图：中文 vs 英文对比 ----------
+    features = ["politeness", "formality", "hedging", "imperative"]
+    zh_vals = [zh_stats.get(k, 0.5 if k != "imperative" else 0.0) for k in features]
+    en_vals = [en_stats.get(k, 0.0) for k in features]
+
+    x = np.arange(len(features))
+    width = 0.35
+
+    fig_bar, ax_bar = plt.subplots()
+    ax_bar.bar(x - width/2, zh_vals, width, label="Chinese (source)")
+    ax_bar.bar(x + width/2, en_vals, width, label="English (translation)")
+    ax_bar.set_ylim(0, 1)
+    ax_bar.set_xticks(x)
+    ax_bar.set_xticklabels(features)
+    ax_bar.set_ylabel("Score (0–1)")
+    ax_bar.set_title("Chinese vs English stylistic features")
+    ax_bar.legend()
+    fig_bar.tight_layout()
+
+    # ---------- 5. 画雷达图 ----------
+    fig_radar = plt.figure()
+    ax_radar = fig_radar.add_subplot(111, polar=True)
+
+    labels = features
+    angles = np.linspace(0, 2 * np.pi, len(labels), endpoint=False)
+    zh_vals_closed = zh_vals + [zh_vals[0]]
+    en_vals_closed = en_vals + [en_vals[0]]
+    angles_closed = list(angles) + [angles[0]]
+
+    ax_radar.plot(angles_closed, zh_vals_closed, marker="o", label="Chinese")
+    ax_radar.fill(angles_closed, zh_vals_closed, alpha=0.1)
+
+    ax_radar.plot(angles_closed, en_vals_closed, marker="o", linestyle="--", label="English")
+    ax_radar.fill(angles_closed, en_vals_closed, alpha=0.1)
+
+    ax_radar.set_xticks(angles)
+    ax_radar.set_xticklabels(labels)
+    ax_radar.set_yticklabels([])
+    ax_radar.set_title("Stylistic profile (radar)")
+    ax_radar.legend(loc="upper right", bbox_to_anchor=(1.3, 1.1))
+    fig_radar.tight_layout()
+
+    return translated, zh_stats, en_stats, level, round(power_distance_score, 3), fig_bar, fig_radar
+
+
+# ---------- 6. Gradio 界面 ----------
+
+with gr.Blocks(title="Power Distance Checker") as demo:
+    gr.Markdown(
+        """
+        # 📨 中译英权力距离检测（Power Distance）
+        输入一段 **中文信件**，系统会：
+        1. 自动翻译为英文  
+        2. 分析中英文两侧的礼貌度、正式度、委婉程度、命令语气  
+        3. 给出英文译文的 **权力距离等级：Low / Medium / High**  
+        4. 用柱状图 + 雷达图展示风格变化
+        """
+    )
+
+    with gr.Row():
+        input_box = gr.Textbox(label="输入中文信件", lines=6, placeholder="例如：您好，我想向您反馈近期的项目进度，如有不妥之处，还请您多多指正。")
+
+    run_btn = gr.Button("分析语气与权力距离")
+
+    with gr.Row():
+        output_en = gr.Textbox(label="英文翻译", lines=6)
+
+    with gr.Row():
+        zh_json = gr.JSON(label="中文侧语气指标（0–1）")
+        en_json = gr.JSON(label="英文侧语气指标（0–1）")
+
+    with gr.Row():
+        pd_label = gr.Label(label="Power Distance Level (English translation)")
+        pd_score = gr.Number(label="Power Distance Score (0–1)", precision=3)
+
+    with gr.Row():
+        bar_plot = gr.Plot(label="Bar Chart：Chinese vs English")
+        radar_plot = gr.Plot(label="Radar Chart：Stylistic Profile")
+
+    run_btn.click(
+        fn=analyze_letter,
+        inputs=[input_box],
+        outputs=[output_en, zh_json, en_json, pd_label, pd_score, bar_plot, radar_plot],
+    )
+
+if __name__ == "__main__":
+    demo.launch()