app.py

"""
邮件解析 & AI 分类查看器 - 增强版
- 上传/拖拽 .msg 文件（支持多文件）
- AI 自动提取：公司名、邮件类型、正文内容
- 新增：邮件正文翻译（并排对照展示，无复制按钮）
- 新增：AI 深度解析（智能分析邮件内容）
- 公司名/类型/正文支持一键复制
- 部署：Hugging Face Spaces (Gradio SDK)
"""
import asyncio
import json
import logging
import os
import sys
from pathlib import Path
from typing import Optional

import gradio as gr

# 兼容补丁：Gradio 4.44.0 的 gradio_client/utils.py 在 JSON schema 中
# additionalProperties=True（bool）时会崩溃：`if "const" in schema` 不适用于 bool。
# Hugging Face Spaces 强制安装 gradio[oauth]==4.44.0，无法升级，故运行时打补丁。
try:
    import gradio_client.utils as _client_utils

    # 补丁 1：get_type 遇到 bool schema 时返回 Any，避免
    # `if "const" in schema` 对 bool 执行 `in` 操作而崩溃。
    _orig_get_type = _client_utils.get_type

    def _patched_get_type(schema):
        if isinstance(schema, bool):
            return "Any"
        return _orig_get_type(schema)

    _client_utils.get_type = _patched_get_type

    # 补丁 2：_json_schema_to_python_type 递归遇到 bool schema 时直接返回 "Any"，
    # 避免走到末尾抛出 APIInfoParseError: Cannot parse schema True
    _orig_json_schema_to_python_type = _client_utils._json_schema_to_python_type

    def _patched_json_schema_to_python_type(schema, defs):
        if isinstance(schema, bool):
            return "Any"
        return _orig_json_schema_to_python_type(schema, defs)

    _client_utils._json_schema_to_python_type = _patched_json_schema_to_python_type
except Exception:
    pass

# 补丁 3：禁用 Jinja2 模板缓存，避免 cache key 类型错误
# Jinja2 3.1.5+ 修改了缓存 key 的生成方式，与 Gradio 4.44.0 的 Starlette
# 模板渲染不兼容，导致 TypeError: unhashable type: 'dict'。
# 禁用缓存（cache=None）可彻底规避此问题，对性能影响极小。
try:
    from starlette.templating import Jinja2Templates as _J2T

    _orig_j2t_init = _J2T.__init__

    def _patched_j2t_init(self, *args, **kwargs):
        _orig_j2t_init(self, *args, **kwargs)
        self.env.cache = None  # 禁用模板缓存

    _J2T.__init__ = _patched_j2t_init
except Exception:
    pass

sys.path.insert(0, str(Path(__file__).parent))

from src import config as cfg
from src.email_parser import parse_msg_file, ParsedEmail
from src.ai_classifier import classify_email_sync, ClassificationResult
from src.translator import translate_email_sync, TranslationResult

logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
    datefmt="%H:%M:%S",
)
logger = logging.getLogger(__name__)

# ────────────────────────────────────────────
# CSS & JS：事件委托复制（无内联 onclick）
# ────────────────────────────────────────────

CUSTOM_CSS = """
.gradio-container { font-family: 'Inter', -apple-system, BlinkMacSystemFont, sans-serif; }

.header-banner {
    background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
    border-radius: 16px; padding: 28px 32px; margin-bottom: 8px; color: white;
}
.header-banner h1 { margin: 0 0 6px 0; font-size: 1.7rem; font-weight: 700; }
.header-banner p  { margin: 0; opacity: 0.85; font-size: 0.95rem; }

.config-panel { border: 1px solid #e2e8f0 !important; border-radius: 12px !important; }

.email-card {
    background: #fff; border: 1px solid #e2e8f0; border-radius: 14px;
    padding: 20px 24px; margin-bottom: 14px;
    box-shadow: 0 1px 4px rgba(0,0,0,0.06); transition: box-shadow 0.2s;
}
.email-card:hover { box-shadow: 0 4px 16px rgba(0,0,0,0.10); }
.email-card-header { display: flex; align-items: center; gap: 10px; margin-bottom: 14px; flex-wrap: wrap; }
.email-filename { font-size: 0.78rem; color: #94a3b8; margin-left: auto; font-family: monospace; }

.category-badge {
    display: inline-flex; align-items: center; gap: 5px;
    padding: 4px 12px; border-radius: 20px; font-size: 0.82rem; font-weight: 600;
    color: white; white-space: nowrap;
}

.field-row {
    display: flex; align-items: flex-start; gap: 10px;
    margin-bottom: 10px; padding: 10px 14px;
    background: #f8fafc; border-radius: 8px; border: 1px solid #f1f5f9;
}
.field-label {
    font-size: 0.72rem; font-weight: 700; color: #64748b;
    text-transform: uppercase; letter-spacing: 0.05em;
    min-width: 64px; padding-top: 2px; white-space: nowrap;
}
.field-value {
    flex: 1; font-size: 0.88rem; color: #1e293b;
    line-height: 1.5; word-break: break-word;
    max-height: 120px; overflow-y: auto;
}
.field-value.company { font-weight: 700; font-size: 1rem; color: #0f172a; }

.copy-btn {
    background: #f1f5f9; border: 1px solid #e2e8f0; border-radius: 6px;
    padding: 4px 10px; font-size: 0.75rem; color: #475569;
    cursor: pointer; white-space: nowrap; transition: all 0.15s; flex-shrink: 0; margin-top: 1px;
}
.copy-btn:hover { background: #e2e8f0; color: #0f172a; }
.copy-btn.copied { background: #dcfce7; border-color: #86efac; color: #166534; }

.summary-text { font-size: 0.82rem; color: #64748b; font-style: italic; padding: 6px 14px; border-left: 3px solid #e2e8f0; margin-bottom: 4px; }

.meta-row { display: flex; gap: 16px; font-size: 0.78rem; color: #94a3b8; flex-wrap: wrap; margin-top: 6px; }
.meta-row span { display: flex; align-items: center; gap: 4px; }

/* 翻译对照区域 */
.translation-section {
    margin-top: 14px; border: 1px solid #e2e8f0; border-radius: 10px;
    overflow: hidden; background: #f8fafc;
}
.translation-header {
    background: #f1f5f9; padding: 8px 14px; font-size: 0.78rem; font-weight: 600;
    color: #475569; border-bottom: 1px solid #e2e8f0; display: flex; align-items: center; gap: 8px;
}
.translation-columns { display: flex; min-height: 80px; }
.translation-original, .translation-result {
    flex: 1; padding: 12px 14px; font-size: 0.85rem; line-height: 1.6;
    color: #334155; white-space: pre-wrap; max-height: 300px; overflow-y: auto;
}
.translation-original { border-right: 1px solid #e2e8f0; background: #fff; }
.translation-result { background: #fefce8; }
.translation-lang-label {
    font-size: 0.7rem; color: #94a3b8; font-weight: 600;
    text-transform: uppercase; letter-spacing: 0.05em; margin-bottom: 6px;
}

/* AI 解析区域 */
.ai-analysis-section {
    margin-top: 14px; border: 1px solid #c4b5fd; border-radius: 10px;
    overflow: hidden; background: #faf5ff;
}
.ai-analysis-header {
    background: #ede9fe; padding: 8px 14px; font-size: 0.78rem; font-weight: 600;
    color: #6d28d9; border-bottom: 1px solid #c4b5fd; display: flex; align-items: center; gap: 8px;
}
.ai-analysis-content { padding: 14px; font-size: 0.85rem; line-height: 1.7; color: #334155; }
.ai-analysis-content h4 { margin: 12px 0 6px 0; font-size: 0.82rem; font-weight: 700; color: #6d28d9; }
.ai-analysis-content ul { margin: 6px 0; padding-left: 20px; }
.ai-analysis-content li { margin-bottom: 4px; }

.status-done  { color: #16a34a; font-weight: 600; }
.status-error { color: #dc2626; font-weight: 600; }

.progress-bar-wrap { background: #f1f5f9; border-radius: 8px; height: 8px; overflow: hidden; margin: 8px 0; }
.progress-bar-fill { background: linear-gradient(90deg, #667eea, #764ba2); height: 100%; transition: width 0.3s ease; border-radius: 8px; }

.empty-state { text-align: center; padding: 48px 24px; color: #94a3b8; }
.empty-state .icon { font-size: 3rem; margin-bottom: 12px; }

.stats-bar {
    display: flex; gap: 20px; padding: 12px 20px; background: #f8fafc;
    border-radius: 10px; border: 1px solid #e2e8f0; margin-bottom: 16px; flex-wrap: wrap;
}
.stat-item { display: flex; flex-direction: column; align-items: center; }
.stat-num  { font-size: 1.4rem; font-weight: 700; color: #0f172a; }
.stat-lbl  { font-size: 0.7rem; color: #94a3b8; text-transform: uppercase; letter-spacing: 0.05em; }

.test-result-ok  { color: #16a34a; font-weight: 600; padding: 6px 0; }
.test-result-err { color: #dc2626; font-weight: 600; padding: 6px 0; }
"""

# JavaScript：事件委托，从 data-copy-text 属性读取复制内容
COPY_JS = r"""
document.addEventListener('click', function(e) {
    var btn = e.target.closest('.copy-btn');
    if (!btn) return;
    var text = btn.getAttribute('data-copy-text') || '';
    if (!text) return;
    navigator.clipboard.writeText(text).then(function() {
        var orig = btn.innerText;
        btn.innerText = '\u2705 \u5df2\u590d\u5236';
        btn.classList.add('copied');
        setTimeout(function() { btn.innerText = orig; btn.classList.remove('copied'); }, 2000);
    }).catch(function() {
        var ta = document.createElement('textarea');
        ta.value = text; ta.style.position = 'fixed'; ta.style.opacity = '0';
        document.body.appendChild(ta); ta.focus(); ta.select();
        document.execCommand('copy'); document.body.removeChild(ta);
        var orig = btn.innerText;
        btn.innerText = '\u2705 \u5df2\u590d\u5236';
        btn.classList.add('copied');
        setTimeout(function() { btn.innerText = orig; btn.classList.remove('copied'); }, 2000);
    });
});
"""

# ─────────────────────────────────────────────
# 工具函数
# ─────────────────────────────────────────────

CATEGORY_COLORS = cfg.CATEGORY_COLORS
CATEGORY_EMOJI  = cfg.CATEGORY_EMOJI


def _esc_html(s: str) -> str:
    """HTML 转义（用于 data 属性值）"""
    return s.replace("&", "&amp;").replace('"', "&quot;").replace("<", "&lt;").replace(">", "&gt;")


def _badge_html(category: str) -> str:
    color = CATEGORY_COLORS.get(category, "#94a3b8")
    emoji = CATEGORY_EMOJI.get(category, "[邮件]")
    return f'<span class="category-badge" style="background:{color}">{emoji} {category}</span>'


def _copy_btn_html(text: str, btn_id: str, label: str = "[复制]") -> str:
    """生成复制按钮（使用 data-copy-text 属性，无内联 JS）"""
    safe = _esc_html(text)
    return f'<button id="{btn_id}" class="copy-btn" data-copy-text="{safe}">{label}</button>'


def _render_translation_section(idx: int, translation) -> str:
    """渲染翻译对照区域（并排），无复制按钮"""
    if not translation or not getattr(translation, "translated_text", ""):
        return ""
    src_lang  = getattr(translation, "source_language", "Unknown")
    translated = getattr(translation, "translated_text", "")
    is_ok    = getattr(translation, "is_already_target", False)
    orig     = getattr(translation, "original_text", "（无正文）")
    if is_ok:
        note = '<span style="color:#16a34a;font-size:0.75rem;">（原文已是目标语言）</span>'
    else:
        note = f'<span style="color:#94a3b8;font-size:0.75rem;">检测语言：{src_lang}</span>'
    return (
        '<div class="translation-section">'
        '  <div class="translation-header">'
        f'    [翻译] 邮件翻译对照 {note}'
        '  </div>'
        '  <div class="translation-columns">'
        '    <div class="translation-original">'
        f'      <div class="translation-lang-label">原文（{src_lang}）</div>'
        f'      <div>{orig}</div>'
        '    </div>'
        '    <div class="translation-result">'
        '      <div class="translation-lang-label">译文</div>'
        f'      <div>{translated}</div>'
        '    </div>'
        '  </div>'
        '</div>'
    )


def _render_ai_analysis_section(idx: int, analysis: str) -> str:
    """渲染 AI 深度解析区域"""
    if not analysis:
        return ""
    import re
    html = analysis
    html = re.sub(r"\*\*(.+?)\*\*", r"<strong>\1</strong>", html)
    html = re.sub(r"\*(.+?)\*", r"<em>\1</em>", html)
    html = html.replace("\n", "<br>")
    return (
        '<div class="ai-analysis-section">'
        '  <div class="ai-analysis-header">'
        '    [AI] AI 深度解析结果'
        '  </div>'
        '  <div class="ai-analysis-content">'
        f'    {html}'
        '  </div>'
        '</div>'
    )


def _render_email_card(
    idx: int,
    parsed,
    result,
    translation=None,
    ai_analysis=None,
) -> str:
    """渲染单封邮件 HTML 卡片"""
    if result is None:
        status = '<span class="status-error">[warn] AI 分类跳过（未配置 API Key）</span>'
    elif getattr(result, "error", ""):
        status = f'<span class="status-error">[x] {getattr(result, "error", "")[:80]}</span>'
    else:
        status = '<span class="status-done">[ok] 分类完成</span>'

    company  = getattr(result, "company_name", "") if result and not getattr(result, "error", "") else "—"
    category = getattr(result, "category", "") if result and not getattr(result, "error", "") else "—"
    summary  = getattr(result, "summary", "") if result and not getattr(result, "error", "") else ""

    body = getattr(parsed, "body_text", "")
    if body and len(body) > 800:
        body_disp = body[:800] + "...（截断，完整内容见翻译对照）"
    elif not body:
        body_disp = "（正文为空）"
    else:
        body_disp = body

    cid_c = f"cp-co-{idx}"
    cid_b = f"cp-bo-{idx}"
    cid_t = f"cp-ca-{idx}"

    badge = _badge_html(category) if category != "—" else '<span style="color:#94a3b8">—</span>'

    return (
        '<div class="email-card">'
        f'  <div class="email-card-header">{badge} {status} <span class="email-filename">{getattr(parsed, "filename", "")}</span></div>'
        '  <div class="field-row">'
        '    <span class="field-label">公司名</span>'
        f'    <span class="field-value company">{company}</span>'
        f'    {_copy_btn_html(company, cid_c)}'
        '  </div>'
        '  <div class="field-row">'
        '    <span class="field-label">类型</span>'
        f'    <span class="field-value">{badge}</span>'
        f'    {_copy_btn_html(category, cid_t)}'
        '  </div>'
        '  <div class="field-row" style="align-items:flex-start;">'
        '    <span class="field-label">正文</span>'
        f'    <span class="field-value" style="white-space:pre-wrap;max-height:160px;overflow-y:auto;">{body_disp}</span>'
        f'    {_copy_btn_html(body, cid_b)}'
        '  </div>'
        f'  {("<div class=\"summary-text\">[tip] " + summary + "</div>") if summary else ""}'
        '  <div class="meta-row">'
        f'    <span>[sender] {getattr(parsed, "sender", "") or "—"}</span>'
        f'    <span>[date] {getattr(parsed, "date_str", "") or "—"}</span>'
        f'    <span>[subject] {(getattr(parsed, "subject", "") or "")[:50]}</span>'
        '  </div>'
        f'  {_render_translation_section(idx, translation)}'
        f'  {_render_ai_analysis_section(idx, ai_analysis)}'
        '</div>'
    )


def _render_results_html(
    parsed_list: list,
    results: list,
    translations: list = None,
    analyses: list = None,
) -> str:
    if not parsed_list:
        return '<div class="empty-state"><div class="icon">[doc]</div><p>上传 .msg 文件后，解析结果将在此展示</p></div>'

    translations = translations or [None] * len(parsed_list)
    analyses     = analyses or [None] * len(parsed_list)

    total = len(parsed_list)
    done  = sum(1 for r in results if r and not getattr(r, "error", ""))
    err   = total - done
    cats  = {}
    for r in results:
        if r and getattr(r, "category", ""):
            c = getattr(r, "category", "")
            cats[c] = cats.get(c, 0) + 1

    cat_html = ""
    for k, v in sorted(cats.items(), key=lambda x: -x[1]):
        c = CATEGORY_COLORS.get(k, "#64748b")
        cat_html += f'<div class="stat-item"><span class="stat-num" style="color:{c}">{v}</span><span class="stat-lbl">{k}</span></div>'

    stats = (
        '<div class="stats-bar">'
        f'<div class="stat-item"><span class="stat-num">{total}</span><span class="stat-lbl">总封数</span></div>'
        f'<div class="stat-item"><span class="stat-num" style="color:#16a34a">{done}</span><span class="stat-lbl">已分类</span></div>'
        f'<div class="stat-item"><span class="stat-num" style="color:#dc2626">{err}</span><span class="stat-lbl">失败</span></div>'
        f'{cat_html}'
        '</div>'
    )

    cards = ""
    for i, p in enumerate(parsed_list):
        cards += _render_email_card(i, p, results[i], translations[i], analyses[i])

    return f'<script>{COPY_JS}</script>{stats}{cards}'


# ─────────────────────────────────────────────
# 主处理函数
# ─────────────────────────────────────────────

def process_files(files, provider: str, api_key: str, model: str,
                 progress=gr.Progress(track_tqdm=False)):
    """解析 .msg + AI 分类"""
    if not files:
        return _render_results_html([], []), [], [], [], []

    cfg.apply_runtime_config(provider.lower(), api_key.strip(), model.strip())

    paths = []
    for f in files:
        if hasattr(f, "name"):
            paths.append(f.name)
        elif isinstance(f, str):
            paths.append(f)

    total = len(paths)
    parsed_list = []
    results = []
    has_key = bool(cfg.get_llm_api_key())

    progress(0, desc=f"[解析] 正在解析 {total} 封邮件…")
    for i, fp in enumerate(paths):
        progress((i + 0.5) / (total * 2), desc=f"[解析] 解析 {i+1}/{total}")
        parsed_list.append(parse_msg_file(fp))

    for i, p in enumerate(parsed_list):
        progress(0.5 + (i + 0.5) / (total * 2), desc=f"[AI] 分类 {i+1}/{total}")
        if not has_key:
            results.append(None)
        elif not getattr(p, "is_valid", True):
            results.append(ClassificationResult(error=f"解析失败:{getattr(p, 'parse_error', '')}"))
        else:
            results.append(classify_email_sync(p))

    progress(1.0, desc=f"[ok] 处理完成，共 {total} 封")
    return _render_results_html(parsed_list, results), parsed_list, results, [], []


def translate_all(parsed_list, results, translations, target_lang: str,
                  progress=gr.Progress(track_tqdm=False)):
    """为所有已解析邮件翻译正文"""
    if not parsed_list:
        return _render_results_html([], []), [], []

    new_t = list(translations) if translations else [None] * len(parsed_list)
    total = len(parsed_list)

    for i, p in enumerate(parsed_list):
        progress(i / total, desc=f"[翻译] 翻译 {i+1}/{total}…")
        body = getattr(p, "body_text", "")
        if not body or not body.strip():
            new_t[i] = None
            continue
        tr = translate_email_sync(body, target_lang)
        tr.original_text = body[:2000] + ("…" if len(body) > 2000 else "")
        new_t[i] = tr

    progress(1.0, desc="[ok] 翻译完成")
    return _render_results_html(parsed_list, results, new_t), new_t


def analyze_all(parsed_list, results, analyses,
                progress=gr.Progress(track_tqdm=False)):
    """为所有已分类邮件做 AI 深度解析"""
    if not parsed_list:
        return _render_results_html([], []), [], []

    import json as _json
    new_a = list(analyses) if analyses else [None] * len(parsed_list)
    total = len(parsed_list)

    for i, p in enumerate(parsed_list):
        progress(i / total, desc=f"[AI] 解析 {i+1}/{total}…")
        body = getattr(p, "body_text", "")
        if not body or not cfg.get_llm_api_key():
            new_a[i] = None
            continue

        company  = getattr(results[i], "company_name", "") if i < len(results) and results[i] else ""
        category = getattr(results[i], "category", "") if i < len(results) and results[i] else ""

        system_prompt = """You are a senior recruitment strategist and email analyst.
Analyze the recruitment-related email deeply and provide actionable insights.

You MUST respond with valid JSON only:
{
  "intent_analysis": "<what the company is really saying>",
  "suggested_action": "<what to do next>",
  "tone_assessment": "<professional|friendly|cold|encouraging|standard>",
  "key_phrases": ["<phrase1>", "<phrase2>"],
  "follow_up_template": "<draft follow-up if appropriate, else empty>"
}"""

        user_prompt = (
            f"Company: {company}\n"
            f"Category: {category}\n"
            f"From: {getattr(p, 'sender', '')}\n"
            f"Subject: {getattr(p, 'subject', '')}\n"
            f"\nBody:\n{body[:3000]}\n"
            f"\nRespond in JSON format as specified."
        )

        try:
            from openai import AsyncOpenAI
            import asyncio as _a
            api_key  = cfg.get_llm_api_key()
            model    = cfg.get_llm_model()
            base_url = cfg.get_llm_base_url()
            ck = {"api_key": api_key}
            if base_url:
                ck["base_url"] = base_url
            client = AsyncOpenAI(**ck)

            async def _call():
                resp = await client.chat.completions.create(
                    model=model,
                    messages=[
                        {"role": "system", "content": system_prompt},
                        {"role": "user",   "content": user_prompt},
                    ],
                    temperature=0.2,
                    max_tokens=1024,
                    response_format={"type": "json_object"},
                )
                return resp.choices[0].message.content or ""

            loop = _a.new_event_loop()
            content = loop.run_until_complete(_call())
            loop.close()

            data = _json.loads(content)
            parts = []
            if data.get("intent_analysis"):
                parts.append(f'<h4>[意图] 意图分析</h4><p>{data["intent_analysis"]}</p>')
            if data.get("suggested_action"):
                parts.append(f'<h4>[行动] 建议行动</h4><p>{data["suggested_action"]}</p>')
            if data.get("tone_assessment"):
                _tm = {"professional": "专业正式", "friendly": "友好亲切", "cold": "冷淡敷衍", "encouraging": "鼓励积极", "standard": "标准常规"}
                _t = _tm.get(data["tone_assessment"], data["tone_assessment"])
                parts.append(f'<h4>[语气] 语气评估</h4><p>{_t}</p>')
            if data.get("key_phrases"):
                _ph = "".join(f"<li>{x}</li>" for x in data["key_phrases"])
                parts.append(f"<h4>[关键词] 关键短语</h4><ul>{_ph}</ul>")
            if data.get("follow_up_template"):
                parts.append(
                    f'<h4>[模板] 跟进邮件模板</h4>'
                    f'<pre style="background:#f1f5f9;padding:10px;border-radius:6px;'
                    f'font-size:0.82rem;white-space:pre-wrap;">{data["follow_up_template"]}</pre>'
                )
            new_a[i] = "\n".join(parts) if parts else "（无内容）"

        except Exception as e:
            logger.error(f"AI 解析失败: {e}")
            new_a[i] = f"[warn] 解析失败：{str(e)[:100]}"

    progress(1.0, desc="[ok] AI 解析完成")
    return _render_results_html(parsed_list, results, None, new_a), new_a


# ─────────────────────────────────────────────
# 连接测试
# ─────────────────────────────────────────────

def test_llm_connection(provider: str, api_key: str, model: str) -> str:
    if not api_key.strip():
        return "[x] 请先填写 API Key"
    cfg.apply_runtime_config(provider.lower(), api_key.strip(), model.strip())
    try:
        import openai
        burl = cfg.get_llm_base_url()
        ck = {"api_key": api_key.strip()}
        if burl:
            ck["base_url"] = burl
        client = openai.OpenAI(**ck)
        resp = client.chat.completions.create(
            model=cfg.get_llm_model(),
            messages=[{"role": "user", "content": "Reply with just: OK"}],
            max_tokens=10,
            temperature=0,
        )
        reply = resp.choices[0].message.content.strip()
        return f'<span class="test-result-ok">[ok] 连接成功！模型回复：{reply}</span>'
    except Exception as e:
        return f'<span class="test-result-err">[x] 连接失败：{str(e)[:120]}</span>'


# ─────────────────────────────────────────────
# Gradio 界面构建
# ─────────────────────────────────────────────

def build_app() -> gr.Blocks:
    curr = cfg.get_current_config()

    with gr.Blocks(
        title="邮件分析查看器",
        css=CUSTOM_CSS,
        theme=gr.themes.Soft(
            primary_hue=gr.themes.colors.violet,
            neutral_hue=gr.themes.colors.slate,
        ),
    ) as demo:

        parsed_st   = gr.State([])
        results_st  = gr.State([])
        trans_st    = gr.State([])
        analyses_st = gr.State([])

        gr.HTML(
            '<div class="header-banner">'
            '<h1>[邮件] 邮件解析 & AI 智能分析查看器</h1>'
            '<p>上传 .msg 邮件文件，AI 自动提取 / 分类 / 翻译 / 深度解析</p>'
            '</div>'
        )

        with gr.Accordion("⚙️ API 配置（展开设置 LLM 密钥）",
                        open=not bool(curr["api_key"]),
                        elem_classes="config-panel"):
            with gr.Row():
                prov_dd = gr.Dropdown(
                    choices=["deepseek", "openai"],
                    value=curr["provider"],
                    label="LLM 提供商",
                    scale=1,
                )
                key_box = gr.Textbox(
                    value=curr["api_key"],
                    label="API Key",
                    type="password",
                    scale=3,
                )
                mdl_box = gr.Textbox(
                    value=curr["model"],
                    label="模型名称",
                    scale=2,
                )
            with gr.Row():
                tst_btn   = gr.Button("🔌 测试连接", variant="secondary", size="sm")
                tst_res = gr.HTML(label="")

            tst_btn.click(test_llm_connection, [prov_dd, key_box, mdl_box], tst_res)

        gr.HTML("<br>")

        with gr.Row():
            with gr.Column(scale=1):
                upl = gr.File(
                    label="[上传] 拖拽或点击上传 .msg 文件（支持多文件）",
                    file_types=[".msg"],
                    file_count="multiple",
                    height=180,
                )
                go_btn = gr.Button(
                    "[开始] 开始解析并分类",
                    variant="primary",
                    size="lg",
                )

                with gr.Row():
                    tgt_lang = gr.Dropdown(
                        choices=["Chinese", "English", "Japanese", "German", "French"],
                        value="Chinese",
                        label="[翻译] 翻译目标语言",
                        scale=2,
                    )
                    tr_btn = gr.Button(
                        "[翻译] 翻译全部邮件正文",
                        variant="secondary",
                        size="sm",
                    )

                an_btn = gr.Button(
                    "[AI] AI 深度解析全部邮件",
                    variant="secondary",
                    size="sm",
                )

                gr.HTML(
                    '<div style="font-size:0.8rem;color:#94a3b8;margin-top:8px;line-height:1.6;">'
                    "[功能] 公司名 / 类型 / 正文 支持一键复制 · "
                    "翻译并排对照 · AI 深度解析（意图 / 行动 / 语气 / 模板）"
                    '</div>'
                )

        gr.HTML("<br>")

        res_html = gr.HTML(
            value=(
                '<div class="empty-state">'
                '<div class="icon">[doc]</div>'
                '<p>上传 .msg 文件并点击「开始解析」后，结果将在此展示</p>'
                '</div>'
            ),
            label="",
        )

        # 按钮回调
        go_btn.click(
            process_files,
            [upl, prov_dd, key_box, mdl_box],
            [res_html, parsed_st, results_st, trans_st, analyses_st],
        )
        tr_btn.click(
            translate_all,
            [parsed_st, results_st, trans_st, tgt_lang],
            [res_html, trans_st],
        )
        an_btn.click(
            analyze_all,
            [parsed_st, results_st, analyses_st],
            [res_html, analyses_st],
        )

        gr.HTML(
            '<div style="text-align:center;padding:24px 0 8px;'
            'color:#cbd5e1;font-size:0.78rem;">'
            '基于 extract-msg · AI 功能基于 OpenAI / DeepSeek'
            '</div>'
        )

    return demo


# ─────────────────────────────────────────────
# 启动
# ─────────────────────────────────────────────

if __name__ == "__main__":
    app = build_app()
    app.launch(
        server_name="0.0.0.0",
        server_port=int(os.getenv("PORT", 7860)),
        show_api=False,
        share=False,          # HF Spaces：不需要 public tunnel
        debug=False,          # 生产环境关闭 debug
    )