diff --git a/app.py b/app.py
index 8cc182a..14672e8 100644
--- a/app.py
+++ b/app.py
@@ -1,378 +1,386 @@
-"""
-Trading Studio — 自动化交易复盘视频配音系统
-Gradio Web 中控：音色锁定 → Whisper 识别 → Gemma4 润色 → ChatTTS 合成
-"""
-
-from __future__ import annotations
-
-import logging
-import shutil
-import sys
-import uuid
-from pathlib import Path
-
-import gradio as gr
-
-from config import (
-    GIT_REPO_URL,
-    HOST,
-    MODEL_NAME,
-    OLLAMA_URL,
-    PORT,
-    SPEAKER_EMB_PATH,
-    UPLOAD_DIR,
-)
-from llm_service import check_ollama_health, polish_text
-from tts_service import generate_voice, save_fixed_speaker, speaker_is_ready
-from whisper_service import transcribe_audio
-
-# ---------------------------------------------------------------------------
-# 日志
-# ---------------------------------------------------------------------------
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
-    handlers=[
-        logging.StreamHandler(sys.stdout),
-        logging.FileHandler("trading_studio.log", encoding="utf-8"),
-    ],
-)
-logger = logging.getLogger("trading_studio")
-
-# ---------------------------------------------------------------------------
-# 全局 UI 状态（Gradio State）
-# ---------------------------------------------------------------------------
-# raw_transcript / polished_script 在流水线中传递
-
-
-def _save_upload(upload_file) -> str | None:
-    """将 Gradio 上传文件复制到本地 uploads 目录，返回持久化路径。"""
-    if upload_file is None:
-        return None
-
-    src = Path(upload_file)
-    if not src.exists():
-        return None
-
-    dest = UPLOAD_DIR / f"{uuid.uuid4().hex}_{src.name}"
-    shutil.copy2(src, dest)
-    return str(dest)
-
-
-# ---------------------------------------------------------------------------
-# 模块 1：音色锁定
-# ---------------------------------------------------------------------------
-def ui_lock_speaker(audio_file, sample_transcript: str) -> str:
-    """【音色锁定】从参考人声提取并保存 Speaker Embedding。"""
-    path = _save_upload(audio_file)
-    if not path:
-        return "请上传 10-30 秒干净参考人声（wav/mp3 均可）。"
-
-    ok, msg = save_fixed_speaker(path, sample_transcript or "")
-    return msg if ok else f"❌ {msg}"
-
-
-def ui_speaker_status() -> str:
-    """刷新音色状态。"""
-    ok, msg = speaker_is_ready()
-    return f"✅ {msg}" if ok else f"⚠️ {msg}"
-
-
-# ---------------------------------------------------------------------------
-# 模块 2：音频极速识别
-# ---------------------------------------------------------------------------
-def ui_transcribe(audio_file) -> tuple[str, str]:
-    """【Whisper 识别】返回 (转写文本, 状态日志)。"""
-    path = _save_upload(audio_file)
-    if not path:
-        return "", "请上传待识别的碎碎念录音。"
-
-    ok, result = transcribe_audio(path)
-    if ok:
-        return result, f"✅ 识别完成，共 {len(result)} 字。"
-    return "", f"❌ {result}"
-
-
-# ---------------------------------------------------------------------------
-# 模块 3：Gemma4 纪律审判
-# ---------------------------------------------------------------------------
-def ui_polish(raw_text: str) -> tuple[str, str]:
-    """【LLM 润色】对转写稿进行严厉自我反思式润色。"""
-    if not raw_text or not raw_text.strip():
-        return "", "请先完成语音识别或手动粘贴转写文本。"
-
-    ok, result = polish_text(raw_text)
-    if ok:
-        return result, f"✅ Gemma4 润色完成，共 {len(result)} 字。"
-    return "", f"❌ {result}"
-
-
-def ui_check_ollama() -> str:
-    """检测远程 Ollama 节点状态。"""
-    ok, msg = check_ollama_health()
-    return f"✅ {msg}" if ok else f"❌ {msg}"
-
-
-# ---------------------------------------------------------------------------
-# 模块 4：ChatTTS 音频合成
-# ---------------------------------------------------------------------------
-def ui_synthesize(polished_text: str) -> tuple[str | None, str]:
-    """【TTS 合成】生成最终 wav 配音文件。"""
-    if not polished_text or not polished_text.strip():
-        return None, "请先完成 Gemma4 润色。"
-
-    ok, msg, wav_path = generate_voice(polished_text)
-    if ok and wav_path:
-        return wav_path, f"✅ {msg}"
-    return None, f"❌ {msg}"
-
-
-# ---------------------------------------------------------------------------
-# 一键流水线
-# ---------------------------------------------------------------------------
-def ui_full_pipeline(
-    audio_file,
-    skip_polish: bool,
-    manual_raw: str,
-) -> tuple[str, str, str | None, str]:
-    """
-    串联执行：识别 → 润色（可跳过）→ 合成。
-    返回 (raw, polished, wav_path, log)
-    """
-    logs: list[str] = []
-
-    # Step 1: 识别
-    if manual_raw and manual_raw.strip():
-        raw = manual_raw.strip()
-        logs.append(f"使用手动输入转写稿（{len(raw)} 字）。")
-    else:
-        path = _save_upload(audio_file)
-        if not path:
-            return "", "", None, "❌ 请上传录音或手动填写转写文本。"
-        ok, result = transcribe_audio(path)
-        if not ok:
-            return "", "", None, f"❌ 识别失败: {result}"
-        raw = result
-        logs.append(f"✅ Whisper 识别完成（{len(raw)} 字）。")
-
-    # Step 2: 润色
-    if skip_polish:
-        polished = raw
-        logs.append("已跳过 LLM 润色，直接使用原文。")
-    else:
-        ok, result = polish_text(raw)
-        if not ok:
-            return raw, "", None, f"❌ 润色失败: {result}\n" + "\n".join(logs)
-        polished = result
-        logs.append(f"✅ Gemma4 润色完成（{len(polished)} 字）。")
-
-    # Step 3: 合成
-    ok, msg, wav_path = generate_voice(polished)
-    if not ok:
-        return raw, polished, None, f"❌ 合成失败: {msg}\n" + "\n".join(logs)
-
-    logs.append(f"✅ {msg}")
-    return raw, polished, wav_path, "\n".join(logs)
-
-
-# ---------------------------------------------------------------------------
-# Gradio 界面
-# ---------------------------------------------------------------------------
-CUSTOM_CSS = """
-/* 硬核暗黑科技风 */
-.gradio-container {
-    background: linear-gradient(160deg, #0a0a0f 0%, #12121a 40%, #0d0d12 100%) !important;
-    color: #c8c8d0 !important;
-}
-.dark-panel {
-    border: 1px solid #2a2a35;
-    border-radius: 8px;
-    padding: 16px;
-    background: rgba(18, 18, 26, 0.85);
-    margin-bottom: 12px;
-}
-h1, h2, h3 { color: #e8e8f0 !important; letter-spacing: 0.05em; }
-.status-bar {
-    font-family: 'Consolas', 'Monaco', monospace;
-    font-size: 0.85em;
-    color: #7a7a90;
-}
-footer { visibility: hidden; }
-"""
-
-
-def build_app() -> gr.Blocks:
-    """构建 Gradio 主界面。"""
-    theme = gr.themes.Monochrome(
-        primary_hue="slate",
-        secondary_hue="gray",
-        neutral_hue="slate",
-        font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"],
-        font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "Consolas", "monospace"],
-    ).set(
-        body_background_fill="#0a0a0f",
-        body_background_fill_dark="#0a0a0f",
-        block_background_fill="#12121a",
-        block_background_fill_dark="#12121a",
-        block_border_color="#2a2a35",
-        block_label_text_color="#9090a0",
-        input_background_fill="#1a1a24",
-        button_primary_background_fill="#3a3a50",
-        button_primary_background_fill_hover="#4a4a60",
-    )
-
-    with gr.Blocks(
-        title="Trading Studio | 交易复盘配音中控",
-        theme=theme,
-        css=CUSTOM_CSS,
-    ) as demo:
-        gr.Markdown(
-            f"""
-# ⚡ Trading Studio
-**本地量化交易复盘 → B 站配音生产流水线**
-
-`Whisper(CUDA)` → `Gemma4 @ {OLLAMA_URL}` → `ChatTTS(CUDA)`
-
-> 仓库: [{GIT_REPO_URL}]({GIT_REPO_URL})
-            """,
-            elem_classes=["dark-panel"],
-        )
-
-        with gr.Row():
-            ollama_status = gr.Textbox(
-                label="Ollama 节点",
-                value=f"模型: {MODEL_NAME}",
-                interactive=False,
-                scale=3,
-                elem_classes=["status-bar"],
-            )
-            speaker_status = gr.Textbox(
-                label="音色状态",
-                value="检测中...",
-                interactive=False,
-                scale=2,
-                elem_classes=["status-bar"],
-            )
-            refresh_btn = gr.Button("🔄 刷新状态", scale=1)
-
-        refresh_btn.click(
-            fn=lambda: (ui_check_ollama(), ui_speaker_status()),
-            outputs=[ollama_status, speaker_status],
-        )
-
-        with gr.Tabs():
-            # ---- Tab 1: 音色锁定 ----
-            with gr.Tab("🎙️ 音色锁定"):
-                gr.Markdown(
-                    "上传 **10-30 秒** 干净人声样本，系统将提取 Speaker Embedding "
-                    f"并保存至 `{SPEAKER_EMB_PATH.name}`，后续合成 100% 还原音色。"
-                )
-                with gr.Row():
-                    spk_audio = gr.Audio(
-                        label="参考人声（碎碎念盲录样本）",
-                        type="filepath",
-                        sources=["upload", "microphone"],
-                    )
-                    spk_transcript = gr.Textbox(
-                        label="参考音频精确转写（可选，提升还原度）",
-                        placeholder="尽量与参考音频内容完全一致...",
-                        lines=6,
-                    )
-                lock_btn = gr.Button("🔒 锁定音色", variant="primary")
-                lock_log = gr.Textbox(label="锁定结果", lines=4, interactive=False)
-                lock_btn.click(ui_lock_speaker, [spk_audio, spk_transcript], lock_log)
-
-            # ---- Tab 2: 分步操作 ----
-            with gr.Tab("🔧 分步流水线"):
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Step 1 · 音频极速识别")
-                        rec_audio = gr.Audio(
-                            label="交易复盘碎碎念录音",
-                            type="filepath",
-                            sources=["upload", "microphone"],
-                        )
-                        transcribe_btn = gr.Button("⚡ Faster-Whisper 识别", variant="primary")
-                        transcribe_log = gr.Textbox(label="识别日志", lines=2, interactive=False)
-
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Step 2 · Gemma4 纪律审判")
-                        raw_text = gr.Textbox(
-                            label="转写原文（可编辑）",
-                            lines=10,
-                            placeholder="识别结果将显示在此，也可手动粘贴...",
-                        )
-                        polish_btn = gr.Button("⚖️ 远程 Gemma4 严厉润色", variant="primary")
-                        polish_log = gr.Textbox(label="润色日志", lines=2, interactive=False)
-
-                    with gr.Column(scale=1):
-                        gr.Markdown("### Step 3 · ChatTTS 配音合成")
-                        polished_text = gr.Textbox(
-                            label="润色配音稿（可编辑）",
-                            lines=10,
-                            placeholder="润色结果将显示在此...",
-                        )
-                        synth_btn = gr.Button("🔊 合成配音 WAV", variant="primary")
-                        synth_log = gr.Textbox(label="合成日志", lines=2, interactive=False)
-                        output_audio = gr.Audio(label="成品配音", type="filepath")
-
-                transcribe_btn.click(ui_transcribe, rec_audio, [raw_text, transcribe_log])
-                polish_btn.click(ui_polish, raw_text, [polished_text, polish_log])
-                synth_btn.click(ui_synthesize, polished_text, [output_audio, synth_log])
-
-            # ---- Tab 3: 一键生产 ----
-            with gr.Tab("🚀 一键生产"):
-                gr.Markdown(
-                    "上传碎碎念录音，系统自动完成 **识别 → 润色 → 合成** 全流程。"
-                )
-                with gr.Row():
-                    pipe_audio = gr.Audio(
-                        label="复盘录音",
-                        type="filepath",
-                        sources=["upload", "microphone"],
-                    )
-                    pipe_manual = gr.Textbox(
-                        label="或手动输入转写（跳过识别）",
-                        lines=4,
-                        placeholder="若已有转写文本，可直接粘贴，留空则走 Whisper 识别",
-                    )
-                skip_polish_cb = gr.Checkbox(
-                    label="跳过 Gemma4 润色（仅测试 TTS）",
-                    value=False,
-                )
-                pipeline_btn = gr.Button("▶ 启动全流程", variant="primary", size="lg")
-                pipeline_log = gr.Textbox(label="流水线日志", lines=6, interactive=False)
-                with gr.Row():
-                    pipe_raw = gr.Textbox(label="转写原文", lines=6)
-                    pipe_polished = gr.Textbox(label="润色稿", lines=6)
-                pipe_output = gr.Audio(label="成品配音", type="filepath")
-
-                pipeline_btn.click(
-                    ui_full_pipeline,
-                    [pipe_audio, skip_polish_cb, pipe_manual],
-                    [pipe_raw, pipe_polished, pipe_output, pipeline_log],
-                )
-
-        demo.load(
-            fn=lambda: (ui_check_ollama(), ui_speaker_status()),
-            outputs=[ollama_status, speaker_status],
-        )
-
-    return demo
-
-
-def main() -> None:
-    """主入口：启动 Gradio 服务。"""
-    logger.info("Trading Studio 启动中... HOST=%s PORT=%s", HOST, PORT)
-    app = build_app()
-    app.launch(
-        server_name=HOST,
-        server_port=PORT,
-        share=False,
-        show_error=True,
-        allowed_paths=[str(Path(__file__).resolve().parent / "outputs")],
-    )
-
-
-if __name__ == "__main__":
-    main()
+"""
+Trading Studio — 自动化交易复盘视频配音系统
+Gradio Web 中控：音色锁定 → Whisper 识别 → Gemma4 润色 → ChatTTS 合成
+"""
+
+from __future__ import annotations
+
+import logging
+import shutil
+import sys
+import uuid
+from pathlib import Path
+
+import gradio as gr
+
+from config import (
+    GIT_REPO_URL,
+    HOST,
+    MODEL_NAME,
+    OLLAMA_URL,
+    PORT,
+    SPEAKER_EMB_PATH,
+    UPLOAD_DIR,
+)
+from llm_service import check_ollama_health, polish_text
+from tts_service import generate_voice, save_fixed_speaker, speaker_is_ready
+from whisper_service import transcribe_audio
+
+# ---------------------------------------------------------------------------
+# 日志
+# ---------------------------------------------------------------------------
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s [%(levelname)s] %(name)s: %(message)s",
+    handlers=[
+        logging.StreamHandler(sys.stdout),
+        logging.FileHandler("trading_studio.log", encoding="utf-8"),
+    ],
+)
+logger = logging.getLogger("trading_studio")
+
+# ---------------------------------------------------------------------------
+# 全局 UI 状态（Gradio State）
+# ---------------------------------------------------------------------------
+# raw_transcript / polished_script 在流水线中传递
+
+
+def _save_upload(upload_file) -> str | None:
+    """将 Gradio 上传文件复制到本地 uploads 目录，返回持久化路径。"""
+    if upload_file is None:
+        return None
+
+    src = Path(upload_file)
+    if not src.exists():
+        return None
+
+    dest = UPLOAD_DIR / f"{uuid.uuid4().hex}_{src.name}"
+    shutil.copy2(src, dest)
+    return str(dest)
+
+
+# ---------------------------------------------------------------------------
+# 模块 1：音色锁定
+# ---------------------------------------------------------------------------
+def ui_lock_speaker(audio_file, sample_transcript: str) -> tuple[str, str]:
+    """【音色锁定】从参考人声提取并保存 Speaker Embedding。"""
+    path = _save_upload(audio_file)
+    if not path:
+        return "请上传 10-30 秒干净参考人声（wav/mp3 均可）。", ui_speaker_status()
+
+    ok, msg = save_fixed_speaker(path, sample_transcript or "")
+    result = msg if ok else f"❌ {msg}"
+    return result, ui_speaker_status()
+
+
+def ui_speaker_status() -> str:
+    """刷新音色状态。"""
+    ok, msg = speaker_is_ready()
+    return f"✅ {msg}" if ok else f"⚠️ {msg}"
+
+
+# ---------------------------------------------------------------------------
+# 模块 2：音频极速识别
+# ---------------------------------------------------------------------------
+def ui_transcribe(audio_file) -> tuple[str, str]:
+    """【Whisper 识别】返回 (转写文本, 状态日志)。"""
+    path = _save_upload(audio_file)
+    if not path:
+        return "", "请上传待识别的碎碎念录音。"
+
+    ok, result = transcribe_audio(path)
+    if ok:
+        return result, f"✅ 识别完成，共 {len(result)} 字。"
+    return "", f"❌ {result}"
+
+
+# ---------------------------------------------------------------------------
+# 模块 3：Gemma4 纪律审判
+# ---------------------------------------------------------------------------
+def ui_polish(raw_text: str) -> tuple[str, str]:
+    """【LLM 润色】对转写稿进行严厉自我反思式润色。"""
+    if not raw_text or not raw_text.strip():
+        return "", "请先完成语音识别或手动粘贴转写文本。"
+
+    ok, result = polish_text(raw_text)
+    if ok:
+        return result, f"✅ Gemma4 润色完成，共 {len(result)} 字。"
+    return "", f"❌ {result}"
+
+
+def ui_check_ollama() -> str:
+    """检测远程 Ollama 节点状态。"""
+    ok, msg = check_ollama_health()
+    return f"✅ {msg}" if ok else f"❌ {msg}"
+
+
+# ---------------------------------------------------------------------------
+# 模块 4：ChatTTS 音频合成
+# ---------------------------------------------------------------------------
+def ui_synthesize(polished_text: str) -> tuple[str | None, str]:
+    """【TTS 合成】生成最终 wav 配音文件。"""
+    if not polished_text or not polished_text.strip():
+        return None, "请先完成 Gemma4 润色。"
+
+    ok, msg, wav_path = generate_voice(polished_text)
+    if ok and wav_path:
+        return wav_path, f"✅ {msg}"
+    return None, f"❌ {msg}"
+
+
+# ---------------------------------------------------------------------------
+# 一键流水线
+# ---------------------------------------------------------------------------
+def ui_full_pipeline(
+    audio_file,
+    skip_polish: bool,
+    manual_raw: str,
+) -> tuple[str, str, str | None, str]:
+    """
+    串联执行：识别 → 润色（可跳过）→ 合成。
+    返回 (raw, polished, wav_path, log)
+    """
+    logs: list[str] = []
+
+    # Step 1: 识别
+    if manual_raw and manual_raw.strip():
+        raw = manual_raw.strip()
+        logs.append(f"使用手动输入转写稿（{len(raw)} 字）。")
+    else:
+        path = _save_upload(audio_file)
+        if not path:
+            return "", "", None, "❌ 请上传录音或手动填写转写文本。"
+        ok, result = transcribe_audio(path)
+        if not ok:
+            return "", "", None, f"❌ 识别失败: {result}"
+        raw = result
+        logs.append(f"✅ Whisper 识别完成（{len(raw)} 字）。")
+
+    # Step 2: 润色
+    if skip_polish:
+        polished = raw
+        logs.append("已跳过 LLM 润色，直接使用原文。")
+    else:
+        ok, result = polish_text(raw)
+        if not ok:
+            return raw, "", None, f"❌ 润色失败: {result}\n" + "\n".join(logs)
+        polished = result
+        logs.append(f"✅ Gemma4 润色完成（{len(polished)} 字）。")
+
+    # Step 3: 合成
+    ok, msg, wav_path = generate_voice(polished)
+    if not ok:
+        return raw, polished, None, f"❌ 合成失败: {msg}\n" + "\n".join(logs)
+
+    logs.append(f"✅ {msg}")
+    return raw, polished, wav_path, "\n".join(logs)
+
+
+# ---------------------------------------------------------------------------
+# Gradio 界面
+# ---------------------------------------------------------------------------
+CUSTOM_CSS = """
+/* 硬核暗黑科技风 */
+.gradio-container {
+    background: linear-gradient(160deg, #0a0a0f 0%, #12121a 40%, #0d0d12 100%) !important;
+    color: #c8c8d0 !important;
+}
+.dark-panel {
+    border: 1px solid #2a2a35;
+    border-radius: 8px;
+    padding: 16px;
+    background: rgba(18, 18, 26, 0.85);
+    margin-bottom: 12px;
+}
+h1, h2, h3 { color: #e8e8f0 !important; letter-spacing: 0.05em; }
+.status-bar {
+    font-family: 'Consolas', 'Monaco', monospace;
+    font-size: 0.85em;
+    color: #7a7a90;
+}
+footer { visibility: hidden; }
+"""
+
+
+def build_theme() -> gr.themes.Base:
+    """构建暗黑科技风 Monochrome 主题（Gradio 6.0 需在 launch() 传入）。"""
+    return gr.themes.Monochrome(
+        primary_hue="slate",
+        secondary_hue="gray",
+        neutral_hue="slate",
+        font=[gr.themes.GoogleFont("Inter"), "system-ui", "sans-serif"],
+        font_mono=[gr.themes.GoogleFont("JetBrains Mono"), "Consolas", "monospace"],
+    ).set(
+        body_background_fill="#0a0a0f",
+        body_background_fill_dark="#0a0a0f",
+        block_background_fill="#12121a",
+        block_background_fill_dark="#12121a",
+        block_border_color="#2a2a35",
+        block_label_text_color="#9090a0",
+        input_background_fill="#1a1a24",
+        button_primary_background_fill="#3a3a50",
+        button_primary_background_fill_hover="#4a4a60",
+    )
+
+
+def build_app() -> gr.Blocks:
+    """构建 Gradio 主界面。"""
+    with gr.Blocks(
+        title="Trading Studio | 交易复盘配音中控",
+    ) as demo:
+        gr.Markdown(
+            f"""
+# ⚡ Trading Studio
+**本地量化交易复盘 → B 站配音生产流水线**
+
+`Whisper(CUDA)` → `Gemma4 @ {OLLAMA_URL}` → `ChatTTS(CUDA)`
+
+> 仓库: [{GIT_REPO_URL}]({GIT_REPO_URL})
+            """,
+            elem_classes=["dark-panel"],
+        )
+
+        with gr.Row():
+            ollama_status = gr.Textbox(
+                label="Ollama 节点",
+                value="检测中...",
+                interactive=False,
+                scale=3,
+                elem_classes=["status-bar"],
+            )
+            speaker_status = gr.Textbox(
+                label="音色状态",
+                value="检测中...",
+                interactive=False,
+                scale=2,
+                elem_classes=["status-bar"],
+            )
+            refresh_btn = gr.Button("🔄 刷新状态", scale=1)
+
+        refresh_btn.click(
+            fn=lambda: (ui_check_ollama(), ui_speaker_status()),
+            outputs=[ollama_status, speaker_status],
+        )
+
+        with gr.Tabs():
+            # ---- Tab 1: 音色锁定 ----
+            with gr.Tab("🎙️ 音色锁定"):
+                gr.Markdown(
+                    "上传 **10-30 秒** 干净人声样本，系统将提取 Speaker Embedding "
+                    f"并保存至 `{SPEAKER_EMB_PATH.name}`，后续合成 100% 还原音色。"
+                )
+                with gr.Row():
+                    spk_audio = gr.Audio(
+                        label="参考人声（碎碎念盲录样本）",
+                        type="filepath",
+                        sources=["upload", "microphone"],
+                    )
+                    spk_transcript = gr.Textbox(
+                        label="参考音频精确转写（可选，提升还原度）",
+                        placeholder="尽量与参考音频内容完全一致...",
+                        lines=6,
+                    )
+                lock_btn = gr.Button("🔒 锁定音色", variant="primary")
+                lock_log = gr.Textbox(label="锁定结果", lines=4, interactive=False)
+                lock_btn.click(
+                    ui_lock_speaker,
+                    [spk_audio, spk_transcript],
+                    [lock_log, speaker_status],
+                )
+
+            # ---- Tab 2: 分步操作 ----
+            with gr.Tab("🔧 分步流水线"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        gr.Markdown("### Step 1 · 音频极速识别")
+                        rec_audio = gr.Audio(
+                            label="交易复盘碎碎念录音",
+                            type="filepath",
+                            sources=["upload", "microphone"],
+                        )
+                        transcribe_btn = gr.Button("⚡ Faster-Whisper 识别", variant="primary")
+                        transcribe_log = gr.Textbox(label="识别日志", lines=2, interactive=False)
+
+                    with gr.Column(scale=1):
+                        gr.Markdown("### Step 2 · Gemma4 纪律审判")
+                        raw_text = gr.Textbox(
+                            label="转写原文（可编辑）",
+                            lines=10,
+                            placeholder="识别结果将显示在此，也可手动粘贴...",
+                        )
+                        polish_btn = gr.Button("⚖️ 远程 Gemma4 严厉润色", variant="primary")
+                        polish_log = gr.Textbox(label="润色日志", lines=2, interactive=False)
+
+                    with gr.Column(scale=1):
+                        gr.Markdown("### Step 3 · ChatTTS 配音合成")
+                        polished_text = gr.Textbox(
+                            label="润色配音稿（可编辑）",
+                            lines=10,
+                            placeholder="润色结果将显示在此...",
+                        )
+                        synth_btn = gr.Button("🔊 合成配音 WAV", variant="primary")
+                        synth_log = gr.Textbox(label="合成日志", lines=2, interactive=False)
+                        output_audio = gr.Audio(label="成品配音", type="filepath")
+
+                transcribe_btn.click(ui_transcribe, rec_audio, [raw_text, transcribe_log])
+                polish_btn.click(ui_polish, raw_text, [polished_text, polish_log])
+                synth_btn.click(ui_synthesize, polished_text, [output_audio, synth_log])
+
+            # ---- Tab 3: 一键生产 ----
+            with gr.Tab("🚀 一键生产"):
+                gr.Markdown(
+                    "上传碎碎念录音，系统自动完成 **识别 → 润色 → 合成** 全流程。"
+                )
+                with gr.Row():
+                    pipe_audio = gr.Audio(
+                        label="复盘录音",
+                        type="filepath",
+                        sources=["upload", "microphone"],
+                    )
+                    pipe_manual = gr.Textbox(
+                        label="或手动输入转写（跳过识别）",
+                        lines=4,
+                        placeholder="若已有转写文本，可直接粘贴，留空则走 Whisper 识别",
+                    )
+                skip_polish_cb = gr.Checkbox(
+                    label="跳过 Gemma4 润色（仅测试 TTS）",
+                    value=False,
+                )
+                pipeline_btn = gr.Button("▶ 启动全流程", variant="primary", size="lg")
+                pipeline_log = gr.Textbox(label="流水线日志", lines=6, interactive=False)
+                with gr.Row():
+                    pipe_raw = gr.Textbox(label="转写原文", lines=6)
+                    pipe_polished = gr.Textbox(label="润色稿", lines=6)
+                pipe_output = gr.Audio(label="成品配音", type="filepath")
+
+                pipeline_btn.click(
+                    ui_full_pipeline,
+                    [pipe_audio, skip_polish_cb, pipe_manual],
+                    [pipe_raw, pipe_polished, pipe_output, pipeline_log],
+                )
+
+        demo.load(
+            fn=lambda: (ui_check_ollama(), ui_speaker_status()),
+            outputs=[ollama_status, speaker_status],
+        )
+
+    return demo
+
+
+def main() -> None:
+    """主入口：启动 Gradio 服务。"""
+    logger.info("Trading Studio 启动中... HOST=%s PORT=%s", HOST, PORT)
+    app = build_app()
+    app.launch(
+        server_name=HOST,
+        server_port=PORT,
+        share=False,
+        show_error=True,
+        theme=build_theme(),
+        css=CUSTOM_CSS,
+        allowed_paths=[str(Path(__file__).resolve().parent / "outputs")],
+    )
+
+
+if __name__ == "__main__":
+    main()