Update app.py

app.py

@@ -153,1264 +153,33 @@ SAVE_LOCK = threading.Lock()
 
 CUSTOM_CSS = """
 .gradio-container {
-    max-width: 1400px !important;
-    margin-left: auto !important;
-    margin-right: auto !important;
-    background:
-        radial-gradient(circle at 8% 12%, rgba(102, 124, 255, 0.16) 0%, rgba(102, 124, 255, 0) 28%),
-        radial-gradient(circle at 92% 18%, rgba(255, 107, 157, 0.11) 0%, rgba(255, 107, 157, 0) 30%),
-        linear-gradient(180deg, #0b1220 0%, #0d1526 52%, #0c1323 100%);
-}
-.title-text h1 {
-    text-align: center;
-    background: linear-gradient(135deg, #7c5cff 0%, #ff6b9d 100%);
-    -webkit-background-clip: text;
-    -webkit-text-fill-color: transparent;
-    font-size: 2rem;
-    font-weight: 760;
-    letter-spacing: 0.2px;
-    margin: 0 0 8px 0;
-}
-.title-sub {
-    text-align: center;
-    color: #c4d0f1;
-    margin-bottom: 14px;
-}
-#hero {
-    border: 1px solid #33456f;
-    border-radius: 16px;
-    padding: 20px 22px;
-    background: linear-gradient(130deg, #121f3f 0%, #1d2f5e 58%, #2b2a63 100%);
-    box-shadow: 0 14px 34px rgba(8, 10, 30, 0.42);
-    margin-bottom: 14px;
-}
-.hero-badge {
-    display: inline-block;
-    margin-top: 10px;
-    padding: 5px 12px;
-    border-radius: 999px;
-    border: 1px solid rgba(205, 220, 255, 0.42);
-    background: rgba(11, 22, 46, 0.52);
-    color: #d9e7ff;
-    font-size: 12px;
-    font-weight: 600;
-}
-.metric-grid {
-    display: grid;
-    grid-template-columns: repeat(3, minmax(110px, 1fr));
-    gap: 12px;
-    margin: 10px 0 16px 0;
-}
-.metric-card {
-    border: 1px solid #2f4166;
-    border-radius: 12px;
-    padding: 12px 14px;
-    background: linear-gradient(180deg, #111d33 0%, #111a2d 100%);
-}
-.metric-label {
-    color: #9fb0da;
-    font-size: 12px;
-    margin-bottom: 4px;
-}
-.metric-value {
-    color: #e8efff;
-    font-size: 16px;
-    font-weight: 700;
-}
-.panel {
-    border: 1px solid #2b3e64 !important;
-    border-radius: 14px !important;
-    padding: 14px !important;
-    background: linear-gradient(180deg, #111b31 0%, #10192d 100%) !important;
-    box-shadow: 0 8px 22px rgba(5, 10, 24, 0.28);
-}
-.section-title {
-    margin: 0 0 10px 0;
-    font-size: 1.04rem;
-    font-weight: 650;
-    color: #e2eaff;
-}
-.hint {
-    color: #9badd6;
-    font-size: 0.9rem;
-}
-.toolbar-btn {
-    min-height: 42px !important;
-    border-radius: 10px !important;
-}
-.gr-button-primary {
-    background: linear-gradient(180deg, #5f85ff 0%, #4a70f0 100%) !important;
-    border: 1px solid #90a8ff !important;
-    box-shadow: 0 8px 22px rgba(61, 103, 234, 0.35) !important;
-}
-.status-box {
-    border: 1px dashed #4a6398;
-    border-radius: 10px;
-    padding: 9px 11px;
-    background: #0c1529;
-}
-.soft-input textarea,
-.soft-input input,
-.soft-input .wrap {
-    border-radius: 10px !important;
-}
-.gr-accordion {
-    border-radius: 12px !important;
-    border-color: #2e426a !important;
-}
-"""
-
-
-def _safe_read_text(path: Path) -> str:
-    if not path.exists():
-        return ""
-    return path.read_text(encoding="utf-8-sig").strip()
-
-
-def load_dataset_index() -> List[Dict[str, Any]]:
-    """扫描输入目录，构建可评测样本列表（每个方法-故事仅保留1个视频）。"""
-    stories = {p.stem: _safe_read_text(p) for p in sorted(STORY_DIR.glob("*.txt"))}
-    samples: List[Dict[str, Any]] = []
-
-    if not VIDEO_DIR.exists():
-        return samples
-
-    for method_dir in sorted([d for d in VIDEO_DIR.iterdir() if d.is_dir()]):
-        method = method_dir.name
-        for story_dir in sorted([d for d in method_dir.iterdir() if d.is_dir()]):
-            story_name = story_dir.name
-            # 每个方法-故事只评一次：如果有多个视频，默认取排序后第一个
-            video_candidates = sorted(story_dir.glob("*.mp4"))
-            if not video_candidates:
-                continue
-            video_path = video_candidates[0]
-            sample_id = f"{method}__{story_name}__{video_path.stem}"
-            samples.append(
-                {
-                    "sample_id": sample_id,
-                    "method": method,
-                    "story_name": story_name,
-                    "video_name": video_path.name,
-                    "video_path": str(video_path.resolve()),
-                    "story_text": stories.get(story_name, ""),
-                }
-            )
-    return samples
-
-
-def load_evaluated_method_story_pairs() -> set:
-    """从结果目录读取已评估的 (method, story_name) 组合。"""
-    evaluated = set()
-    raw_root = OUTPUT_DIR / "raw_results"
-    if not raw_root.exists():
-        return evaluated
-
-    for fp in raw_root.rglob("*.json"):
-        try:
-            with open(fp, "r", encoding="utf-8-sig") as f:
-                data = json.load(f)
-        except Exception:
-            continue
-        sample = data.get("sample", {})
-        method = sample.get("method")
-        story_name = sample.get("story_name")
-        if method and story_name:
-            evaluated.add((method, story_name))
-    return evaluated
-
-
-def build_pending_samples() -> List[Dict[str, Any]]:
-    """构建待评估样本池，并分配匿名ID。"""
-    all_samples = load_dataset_index()
-    evaluated_pairs = load_evaluated_method_story_pairs()
-    pending = [
-        s for s in all_samples
-        if (s["method"], s["story_name"]) not in evaluated_pairs
-    ]
-    for i, sample in enumerate(pending, start=1):
-        sample["anon_id"] = f"id_{i:03d}"
-    return pending
-
-
-def build_data_diagnostics(samples: List[Dict[str, Any]]) -> str:
-    return (
-        f"**SPACE_MODE**: `{SPACE_MODE}`  \n"
-        f"**DATA_REPO_ID**: `{DATA_REPO_ID}`  \n"
-        f"**RESULTS_REPO_ID**: `{RESULTS_REPO_ID}`  \n"
-        f"**ROOT_DIR**: `{ROOT_DIR}`  \n"
-        f"**INPUT_DIR exists**: `{INPUT_DIR.exists()}`  \n"
-        f"**STORY_DIR exists**: `{STORY_DIR.exists()}`  \n"
-        f"**VIDEO_DIR exists**: `{VIDEO_DIR.exists()}`  \n"
-        f"**Pending samples**: `{len(samples)}`"
-    )
-
-
-def compute_derived(scores: Dict[str, float]) -> Dict[str, float]:
-    """计算 CL / CRh / AVG。"""
-    cl = (
-        (scores["SF"] + scores["NC"] + scores["VQ"] + scores["CC"] + scores["PLC"]) / 5.0
-        + 0.5 * ((scores["CT"] + scores["AVR"]) / 2.0)
-    )
-    crh = (
-        (scores["V_AQ"] + scores["NP"] + scores["VAC"] + scores["CD"] + scores["OQ"]) / 5.0
-        + 0.5 * ((scores["CT"] + scores["AVR"]) / 2.0)
-    )
-    avg = sum(scores[k] for k in BASE_METRIC_KEYS) / len(BASE_METRIC_KEYS)
-    return {"CL": cl, "CRh": crh, "AVG": avg}
-
-
-def save_single_result(sample: Dict[str, Any], evaluator_id: str, scores: Dict[str, int], reasons: Dict[str, str], summary: str) -> Path:
-    """保存单个问卷结果。"""
-    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-    result_dir = OUTPUT_DIR / "raw_results" / sample["method"] / sample["story_name"]
-    result_dir.mkdir(parents=True, exist_ok=True)
-    out_path = result_dir / f"{sample['video_name'].replace('.mp4', '')}_{evaluator_id}_{ts}.json"
-
-    score_float = {k: float(v) for k, v in scores.items()}
-    derived = compute_derived(score_float)
-
-    payload = {
-        "timestamp": datetime.now().isoformat(),
-        "evaluator_id": evaluator_id,
-        "sample": sample,
-        "scores": scores,
-        "reasons": reasons,
-        "summary": summary,
-        "derived": derived,
-    }
-    with open(out_path, "w", encoding="utf-8") as f:
-        json.dump(payload, f, ensure_ascii=False, indent=2)
-    return out_path
-
-
-def recompute_method_aggregates() -> Path:
-    """
-    统计每个方法各维度均分，并输出 method_aggregates.json。
-    同时给出 CL/CRh/AVG 的方法均值。
-    """
-    raw_root = OUTPUT_DIR / "raw_results"
-    method_scores: Dict[str, Dict[str, List[float]]] = defaultdict(lambda: defaultdict(list))
-    method_count: Dict[str, int] = defaultdict(int)
-
-    if raw_root.exists():
-        for fp in raw_root.rglob("*.json"):
-            with open(fp, "r", encoding="utf-8-sig") as f:
-                data = json.load(f)
-            method = data.get("sample", {}).get("method", "UNKNOWN")
-            scores = data.get("scores", {})
-            if not all(k in scores for k in BASE_METRIC_KEYS):
-                continue
-            method_count[method] += 1
-            for k in BASE_METRIC_KEYS:
-                method_scores[method][k].append(float(scores[k]))
-
-            # 衍生指标也参与方法均值统计
-            derived = compute_derived({k: float(scores[k]) for k in BASE_METRIC_KEYS})
-            for d_key, d_val in derived.items():
-                method_scores[method][d_key].append(float(d_val))
-
-    agg = {
-        "updated_at": datetime.now().isoformat(),
-        "metric_keys": BASE_METRIC_KEYS,
-        "derived_keys": ["CL", "CRh", "AVG"],
-        "methods": {},
-    }
-    for method in sorted(method_scores.keys()):
-        metric_avg = {}
-        for key, vals in method_scores[method].items():
-            metric_avg[key] = round(sum(vals) / len(vals), 4) if vals else None
-        agg["methods"][method] = {
-            "num_submissions": method_count[method],
-            "avg_scores": metric_avg,
-        }
-
-    out_path = OUTPUT_DIR / "method_aggregates.json"
-    with open(out_path, "w", encoding="utf-8") as f:
-        json.dump(agg, f, ensure_ascii=False, indent=2)
-    return out_path
-
-
-def build_sample_brief(sample: Dict[str, Any], index: int, total: int) -> str:
-    story = sample.get("story_text") or "(未找到对应 story 文本，请检查 clip_movie_story 下是否有同名 txt)"
-    return (
-        f"### 当前匿名样本 {index + 1}/{total}\n"
-        f"- **Sample ID**: `{sample['anon_id']}`\n\n"
-        f"### Story Description\n{story}"
-    )
-
-
-def create_app():
-    samples = build_pending_samples()
-    sample_map = {s["anon_id"]: s for s in samples}
-
-    with gr.Blocks(
-        title="VideoEval Movie-Level Evaluation",
-        css=CUSTOM_CSS,
-        theme=gr.themes.Soft(primary_hue="purple", secondary_hue="pink", neutral_hue="slate"),
-    ) as app:
-        gr.Markdown("# VideoEval · Movie-Level Evaluation", elem_classes=["title-text"])
-        gr.Markdown("统一电影级评测问卷，支持方法级均分统计（含 CL / CRh / AVG）", elem_classes=["title-sub"])
-
-        gr.HTML(
-            f"""
-            <div id="hero">
-                <div class="hero-badge">HF Space Ready · Gradio Blocks · Clean Review Flow</div>
-                <div class="metric-grid">
-                    <div class="metric-card">
-                        <div class="metric-label">Pending Samples</div>
-                        <div class="metric-value">{len(samples)}</div>
-                    </div>
-                    <div class="metric-card">
-                        <div class="metric-label">Evaluation Scope</div>
-                        <div class="metric-value">Movie-Level</div>
-                    </div>
-                    <div class="metric-card">
-                        <div class="metric-label">Scoring Standard</div>
-                        <div class="metric-value">12 Metrics · 1~5</div>
-                    </div>
-                </div>
-            </div>
-            """
-        )
-        gr.Markdown(
-            f"<span class='hint'>输入目录：`{INPUT_DIR}` ｜ 输出目录：`{OUTPUT_DIR}`</span>",
-        )
-        with gr.Accordion("系统诊断信息（展开查看）", open=False):
-            gr.Markdown(build_data_diagnostics(samples))
-
-        current_idx = gr.State(0)
-        evaluator_state = gr.State("anonymous")
-
-        with gr.Row():
-            with gr.Column(scale=4, elem_classes=["panel"]):
-                gr.Markdown("<div class='section-title'>1) 评测员与样本</div>")
-                with gr.Row():
-                    evaluator_input = gr.Textbox(label="Evaluator ID", value="anonymous", elem_classes=["soft-input"])
-                    sample_dropdown = gr.Dropdown(
-                        label="选择评测样本（匿名）",
-                        choices=[s["anon_id"] for s in samples],
-                        value=samples[0]["anon_id"] if samples else None,
-                        interactive=True,
-                        elem_classes=["soft-input"],
-                    )
-                with gr.Row():
-                    prev_btn = gr.Button("← Previous", elem_classes=["toolbar-btn"])
-                    next_btn = gr.Button("Next →", elem_classes=["toolbar-btn"])
-                    submit_btn = gr.Button("提交当前评分并统计", variant="primary", elem_classes=["toolbar-btn"])
-                status = gr.Markdown("<div class='status-box'>等待提交</div>")
-            with gr.Column(scale=8, elem_classes=["panel"]):
-                gr.Markdown("<div class='section-title'>2) 视频与剧情</div>")
-                movie_video = gr.Video(label="Movie Video", value=samples[0]["video_path"] if samples else None, height=460)
-                sample_info = gr.Markdown("无可用样本" if not samples else build_sample_brief(samples[0], 0, len(samples)))
-
-        gr.Markdown("## 3) Movie-Level 指标评分（1-5）")
-        gr.Markdown("<span class='hint'>先给分，再填写可选理由。未打分无法提交。</span>")
-
-        score_widgets: Dict[str, gr.Radio] = {}
-        reason_widgets: Dict[str, gr.Textbox] = {}
-        metric_groups = {
-            "I. 叙事与剧本 (NS)": ["SF", "NC"],
-            "II. 视听与技术 (AT)": ["VQ", "CC", "PLC", "V_AQ"],
-            "III. 美学与表现力 (AE)": ["CT", "AVR"],
-            "IV. 节奏与流动性 (RF)": ["NP", "VAC"],
-            "V. 情感与参与度 (EE)": ["CD"],
-            "VI. 整体体验 (OE)": ["OQ"],
-        }
-        criteria_map = {k: (name, desc) for k, name, desc in MOVIE_CRITERIA}
-
-        for section_title, keys in metric_groups.items():
-            with gr.Accordion(section_title, open=True):
-                for key in keys:
-                    name, desc = criteria_map[key]
-                    with gr.Group(elem_classes=["panel"]):
-                        gr.Markdown(f"**{key} · {name}**")
-                        gr.Markdown(f"<span class='hint'>{desc}</span>")
-                        with gr.Row():
-                            score_widgets[key] = gr.Radio(choices=[1, 2, 3, 4, 5], label=f"{key} Score", scale=1, elem_classes=["soft-input"])
-                            reason_widgets[key] = gr.Textbox(label=f"{key} Reason（可选）", lines=2, placeholder="补充评分依据", scale=2, elem_classes=["soft-input"])
-
-        final_summary = gr.Textbox(label="Final Summary（可选）", lines=4, placeholder="总结该视频的主要优缺点", elem_classes=["soft-input"])
-
-        def _sync_sample_from_dropdown(anon_id: str) -> Tuple[str, str, int]:
-            if not anon_id or anon_id not in sample_map:
-                return None, "未找到样本", 0
-            idx = next(i for i, s in enumerate(samples) if s["anon_id"] == anon_id)
-            sample = samples[idx]
-            return sample["video_path"], build_sample_brief(sample, idx, len(samples)), idx
-
-        def _go_prev(idx: int) -> Tuple[str, str, str, int]:
-            if not samples:
-                return None, "无可用样本", None, 0
-            idx = max(0, idx - 1)
-            sample = samples[idx]
-            return sample["video_path"], build_sample_brief(sample, idx, len(samples)), sample["anon_id"], idx
-
-        def _go_next(idx: int) -> Tuple[str, str, str, int]:
-            if not samples:
-                return None, "无可用样本", None, 0
-            idx = min(len(samples) - 1, idx + 1)
-            sample = samples[idx]
-            return sample["video_path"], build_sample_brief(sample, idx, len(samples)), sample["anon_id"], idx
-
-        def _submit(evaluator_id: str, anon_id: str, summary: str, *score_reason_vals):
-            if not samples:
-                return "❌ 没有可提交样本。"
-            if not anon_id or anon_id not in sample_map:
-                return "❌ 请先选择样本。"
-            sample = sample_map[anon_id]
-            evaluator_id = (evaluator_id or "anonymous").strip() or "anonymous"
-
-            # 防重复：方法-故事只允许评估一次
-            evaluated_pairs = load_evaluated_method_story_pairs()
-            if (sample["method"], sample["story_name"]) in evaluated_pairs:
-                return "⚠️ 该方法-故事已经被评估过一次，请选择其他匿名样本。"
-
-            scores: Dict[str, int] = {}
-            reasons: Dict[str, str] = {}
-            for i, key in enumerate(BASE_METRIC_KEYS):
-                score = score_reason_vals[i * 2]
-                reason = score_reason_vals[i * 2 + 1]
-                if score is None:
-                    return f"❌ 请为 `{key}` 打分。"
-                scores[key] = int(score)
-                reasons[key] = (reason or "").strip()
-
-            with SAVE_LOCK:
-                single_path = save_single_result(sample, evaluator_id, scores, reasons, summary or "")
-                agg_path = recompute_method_aggregates()
-
-            return f"✅ 已保存: `{single_path}`\n\n✅ 已更新方法统计: `{agg_path}`"
-
-        sample_dropdown.change(
-            _sync_sample_from_dropdown,
-            inputs=[sample_dropdown],
-            outputs=[movie_video, sample_info, current_idx],
-        )
-        prev_btn.click(_go_prev, inputs=[current_idx], outputs=[movie_video, sample_info, sample_dropdown, current_idx])
-        next_btn.click(_go_next, inputs=[current_idx], outputs=[movie_video, sample_info, sample_dropdown, current_idx])
-
-        submit_inputs = [evaluator_input, sample_dropdown, final_summary]
-        for key in BASE_METRIC_KEYS:
-            submit_inputs.append(score_widgets[key])
-            submit_inputs.append(reason_widgets[key])
-        submit_btn.click(_submit, inputs=submit_inputs, outputs=[status])
-
-        app.load(lambda x: x, inputs=[evaluator_input], outputs=[evaluator_state])
-
-    return app
-
-
-demo = create_app()
-
-if __name__ == "__main__":
-    allowed_paths = [str(INPUT_DIR.resolve())] if INPUT_DIR.exists() else None
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_error=True,
-        allowed_paths=allowed_paths,
-    )
-"""
-VideoEval Movie-Level 问卷应用（Hugging Face Spaces）
-仅保留 Movie-Level 评测，并支持方法级别统计输出。
-"""
-
-import json
-import os
-import threading
-from collections import defaultdict
-from datetime import datetime
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-import gradio as gr
-from huggingface_hub import CommitScheduler, snapshot_download
-
-# 路径配置（按用户要求）
-# Spaces 推荐优先读取当前 Space 仓库内文件（app.py 同级）
-APP_DIR = Path(__file__).resolve().parent
-LOCAL_INPUT_DIR = APP_DIR / "user_study_input"
-LOCAL_OUTPUT_DIR = APP_DIR / "user_study_results"
-DATA_INPUT_DIR = Path("/data/user_study_input")
-DATA_OUTPUT_DIR = Path("/data/user_study_results")
-DATA_REPO_ID = os.environ.get("DATA_REPO_ID", "MemDirector/user_study_input")
-RESULTS_REPO_ID = os.environ.get("RESULTS_REPO_ID", "MemDirector/user_study_results")
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-SPACE_MODE = os.environ.get("SPACE_MODE", "repo_first")  # repo_first / data_first / hub_only
-
-ROOT_DIR = APP_DIR
-INPUT_DIR = LOCAL_INPUT_DIR
-OUTPUT_DIR = LOCAL_OUTPUT_DIR
-STORY_DIR = INPUT_DIR / "clip_movie_story"
-VIDEO_DIR = INPUT_DIR / "video"
-
-Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True)
-scheduler: Optional[CommitScheduler] = None
-
-
-def _set_paths(input_dir: Path, output_dir: Path) -> None:
-    global INPUT_DIR, OUTPUT_DIR, STORY_DIR, VIDEO_DIR, ROOT_DIR
-    INPUT_DIR = input_dir
-    OUTPUT_DIR = output_dir
-    STORY_DIR = INPUT_DIR / "clip_movie_story"
-    VIDEO_DIR = INPUT_DIR / "video"
-    ROOT_DIR = INPUT_DIR.parent
-    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-
-
-def _try_use_local_repo_layout() -> bool:
-    # Space 仓库内自带 user_study_input 时，直接读取（最符合“已放上去直接跑”）
-    if LOCAL_INPUT_DIR.exists():
-        _set_paths(LOCAL_INPUT_DIR, LOCAL_OUTPUT_DIR)
-        return True
-    return False
-
-
-def _try_use_data_volume_layout() -> bool:
-    # 如果使用 /data 持久卷，则可放在 /data/user_study_input
-    if DATA_INPUT_DIR.exists():
-        _set_paths(DATA_INPUT_DIR, DATA_OUTPUT_DIR)
-        return True
-    return False
-
-
-def _try_download_from_hub() -> bool:
-    # 最后兜底：从 dataset repo 下载
-    if not DATA_REPO_ID:
-        return False
-    hub_root = APP_DIR / ".hf_space_cache"
-    try:
-        snapshot_download(
-            repo_id=DATA_REPO_ID,
-            repo_type="dataset",
-            local_dir=str(hub_root),
-            token=HF_TOKEN,
-            allow_patterns=[
-                "clip_movie_story/**",
-                "video/**",
-                "user_study_input/**",
-                "user_study_results/**",
-            ],
-        )
-    except Exception as e:
-        print(f"[INIT] snapshot_download failed: {e}")
-        return False
-
-    # 兼容两种 dataset 结构：
-    # A) 仓库根目录直接是 clip_movie_story/ 与 video/
-    # B) 仓库里有 user_study_input/ 子目录
-    if (hub_root / "clip_movie_story").exists() and (hub_root / "video").exists():
-        hub_input = hub_root
-    elif (hub_root / "user_study_input").exists():
-        hub_input = hub_root / "user_study_input"
-    else:
-        return False
-
-    hub_output = hub_root / "user_study_results"
-    _set_paths(hub_input, hub_output)
-    return True
-
-
-def init_space_storage() -> None:
-    """
-    Hugging Face Spaces 规范：
-    - 从 dataset repo 拉取 user_study_input 与 user_study_results 到本地 ROOT_DIR
-    - 使用 CommitScheduler 持续回写 user_study_results
-    """
-    global scheduler
-
-    if SPACE_MODE == "hub_only":
-        ok = _try_download_from_hub()
-    elif SPACE_MODE == "data_first":
-        ok = _try_use_data_volume_layout() or _try_use_local_repo_layout() or _try_download_from_hub()
-    else:
-        ok = _try_use_local_repo_layout() or _try_use_data_volume_layout() or _try_download_from_hub()
-    print(f"[INIT] storage init mode={SPACE_MODE}, success={ok}, input={INPUT_DIR}, output={OUTPUT_DIR}")
-
-    if RESULTS_REPO_ID:
-        try:
-            scheduler = CommitScheduler(
-                repo_id=RESULTS_REPO_ID,
-                repo_type="dataset",
-                folder_path=str(OUTPUT_DIR),
-                path_in_repo="user_study_results",
-                every=3,
-                token=HF_TOKEN,
-            )
-            print(f"[INIT] CommitScheduler enabled: {RESULTS_REPO_ID}")
-        except Exception as e:
-            print(f"[INIT] CommitScheduler init failed: {e}")
-
-
-init_space_storage()
-
-# Movie-Level 指标定义
-MOVIE_CRITERIA: List[Tuple[str, str, str]] = [
-    ("SF", "Script Faithfulness (剧本忠实度)", "生成的视觉内容与原始剧本描述的吻合程度。"),
-    ("NC", "Narrative Coherence (叙事连贯性)", "镜头间情节发展的逻辑性，确保故事表达清晰、不破碎。"),
-    ("VQ", "Visual Quality (视觉质量)", "画面的清晰度、噪点控制、光影效果等基础图像质量。"),
-    ("CC", "Character Consistency (角色一致性)", "同一角色在不同镜头、不同角度下的外貌、服装及特征的稳定性。"),
-    ("PLC", "Physical Law Compliance (物理规律符合度)", "运动、重力、碰撞等是否符合现实物理逻辑，是否存在严重 AI 幻觉。"),
-    ("V_AQ", "Voice/Audio Quality (语音/音频质量)", "配音、背景音乐和音效的清晰度、自然度及技术品质。"),
-    ("CT", "Cinematic Techniques (电影技巧)", "镜头运动、景深控制及构图的专业性。"),
-    ("AVR", "Audio-Visual Richness (视听丰富度)", "画面细节精细度以及音频层次（音效、氛围音）的丰富程度。"),
-    ("NP", "Narrative Pacing (叙事节奏)", "镜头剪辑长短切换是否契合故事情节张力需求。"),
-    ("VAC", "Video-Audio Coordination (视听协调性)", "画面动作与音效、音乐卡点的同步率。"),
-    ("CD", "Compelling Degree (引人入胜程度)", "吸引注意力并引发情感共鸣或沉浸感的能力。"),
-    ("OQ", "Overall Quality (整体质量)", "对生成视频作为“电影作品”的综合观感评分。"),
-]
-
-BASE_METRIC_KEYS = [k for k, _, _ in MOVIE_CRITERIA]
-SAVE_LOCK = threading.Lock()
-
-CUSTOM_CSS = """
-.gradio-container {
-    max-width: 1300px !important;
-}
-#hero {
-    border: 1px solid #2b2f3a;
-    border-radius: 16px;
-    padding: 20px 22px;
-    background: linear-gradient(135deg, #121826 0%, #1d2540 55%, #2a1f45 100%);
-    margin-bottom: 14px;
-}
-#hero h1 {
-    margin: 0 0 8px 0;
-    font-size: 1.9rem;
-}
-#hero p {
-    margin: 0;
-    color: #d0d8f0;
-}
-.panel {
-    border: 1px solid #2e3445 !important;
-    border-radius: 14px !important;
-    padding: 14px !important;
-    background: #111522 !important;
-}
-.hint {
-    color: #9aa7c9;
-    font-size: 0.9rem;
-}
-"""
-
-
-def _safe_read_text(path: Path) -> str:
-    if not path.exists():
-        return ""
-    return path.read_text(encoding="utf-8-sig").strip()
-
-
-def load_dataset_index() -> List[Dict[str, Any]]:
-    """扫描输入目录，构建可评测样本列表（每个方法-故事仅保留1个视频）。"""
-    stories = {p.stem: _safe_read_text(p) for p in sorted(STORY_DIR.glob("*.txt"))}
-    samples: List[Dict[str, Any]] = []
-
-    if not VIDEO_DIR.exists():
-        return samples
-
-    for method_dir in sorted([d for d in VIDEO_DIR.iterdir() if d.is_dir()]):
-        method = method_dir.name
-        for story_dir in sorted([d for d in method_dir.iterdir() if d.is_dir()]):
-            story_name = story_dir.name
-            # 每个方法-故事只评一次：如果有多个视频，默认取排序后第一个
-            video_candidates = sorted(story_dir.glob("*.mp4"))
-            if not video_candidates:
-                continue
-            video_path = video_candidates[0]
-            sample_id = f"{method}__{story_name}__{video_path.stem}"
-            samples.append(
-                {
-                    "sample_id": sample_id,
-                    "method": method,
-                    "story_name": story_name,
-                    "video_name": video_path.name,
-                    "video_path": str(video_path.resolve()),
-                    "story_text": stories.get(story_name, ""),
-                }
-            )
-    return samples
-
-
-def load_evaluated_method_story_pairs() -> set:
-    """从结果目录读取已评估的 (method, story_name) 组合。"""
-    evaluated = set()
-    raw_root = OUTPUT_DIR / "raw_results"
-    if not raw_root.exists():
-        return evaluated
-
-    for fp in raw_root.rglob("*.json"):
-        try:
-            with open(fp, "r", encoding="utf-8-sig") as f:
-                data = json.load(f)
-        except Exception:
-            continue
-        sample = data.get("sample", {})
-        method = sample.get("method")
-        story_name = sample.get("story_name")
-        if method and story_name:
-            evaluated.add((method, story_name))
-    return evaluated
-
-
-def build_pending_samples() -> List[Dict[str, Any]]:
-    """构建待评估样本池，并分配匿名ID。"""
-    all_samples = load_dataset_index()
-    evaluated_pairs = load_evaluated_method_story_pairs()
-    pending = [
-        s for s in all_samples
-        if (s["method"], s["story_name"]) not in evaluated_pairs
-    ]
-    for i, sample in enumerate(pending, start=1):
-        sample["anon_id"] = f"id_{i:03d}"
-    return pending
-
-
-def build_data_diagnostics(samples: List[Dict[str, Any]]) -> str:
-    return (
-        f"**SPACE_MODE**: `{SPACE_MODE}`  \n"
-        f"**DATA_REPO_ID**: `{DATA_REPO_ID}`  \n"
-        f"**RESULTS_REPO_ID**: `{RESULTS_REPO_ID}`  \n"
-        f"**ROOT_DIR**: `{ROOT_DIR}`  \n"
-        f"**INPUT_DIR exists**: `{INPUT_DIR.exists()}`  \n"
-        f"**STORY_DIR exists**: `{STORY_DIR.exists()}`  \n"
-        f"**VIDEO_DIR exists**: `{VIDEO_DIR.exists()}`  \n"
-        f"**Pending samples**: `{len(samples)}`"
-    )
-
-
-def compute_derived(scores: Dict[str, float]) -> Dict[str, float]:
-    """计算 CL / CRh / AVG。"""
-    cl = (
-        (scores["SF"] + scores["NC"] + scores["VQ"] + scores["CC"] + scores["PLC"]) / 5.0
-        + 0.5 * ((scores["CT"] + scores["AVR"]) / 2.0)
-    )
-    crh = (
-        (scores["V_AQ"] + scores["NP"] + scores["VAC"] + scores["CD"] + scores["OQ"]) / 5.0
-        + 0.5 * ((scores["CT"] + scores["AVR"]) / 2.0)
-    )
-    avg = sum(scores[k] for k in BASE_METRIC_KEYS) / len(BASE_METRIC_KEYS)
-    return {"CL": cl, "CRh": crh, "AVG": avg}
-
-
-def save_single_result(sample: Dict[str, Any], evaluator_id: str, scores: Dict[str, int], reasons: Dict[str, str], summary: str) -> Path:
-    """保存单个问卷结果。"""
-    ts = datetime.now().strftime("%Y%m%d_%H%M%S")
-    result_dir = OUTPUT_DIR / "raw_results" / sample["method"] / sample["story_name"]
-    result_dir.mkdir(parents=True, exist_ok=True)
-    out_path = result_dir / f"{sample['video_name'].replace('.mp4', '')}_{evaluator_id}_{ts}.json"
-
-    score_float = {k: float(v) for k, v in scores.items()}
-    derived = compute_derived(score_float)
-
-    payload = {
-        "timestamp": datetime.now().isoformat(),
-        "evaluator_id": evaluator_id,
-        "sample": sample,
-        "scores": scores,
-        "reasons": reasons,
-        "summary": summary,
-        "derived": derived,
-    }
-    with open(out_path, "w", encoding="utf-8") as f:
-        json.dump(payload, f, ensure_ascii=False, indent=2)
-    return out_path
-
-
-def recompute_method_aggregates() -> Path:
-    """
-    统计每个方法各维度均分，并输出 method_aggregates.json。
-    同时给出 CL/CRh/AVG 的方法均值。
-    """
-    raw_root = OUTPUT_DIR / "raw_results"
-    method_scores: Dict[str, Dict[str, List[float]]] = defaultdict(lambda: defaultdict(list))
-    method_count: Dict[str, int] = defaultdict(int)
-
-    if raw_root.exists():
-        for fp in raw_root.rglob("*.json"):
-            with open(fp, "r", encoding="utf-8-sig") as f:
-                data = json.load(f)
-            method = data.get("sample", {}).get("method", "UNKNOWN")
-            scores = data.get("scores", {})
-            if not all(k in scores for k in BASE_METRIC_KEYS):
-                continue
-            method_count[method] += 1
-            for k in BASE_METRIC_KEYS:
-                method_scores[method][k].append(float(scores[k]))
-
-            # 衍生指标也参与方法均值统计
-            derived = compute_derived({k: float(scores[k]) for k in BASE_METRIC_KEYS})
-            for d_key, d_val in derived.items():
-                method_scores[method][d_key].append(float(d_val))
-
-    agg = {
-        "updated_at": datetime.now().isoformat(),
-        "metric_keys": BASE_METRIC_KEYS,
-        "derived_keys": ["CL", "CRh", "AVG"],
-        "methods": {},
-    }
-    for method in sorted(method_scores.keys()):
-        metric_avg = {}
-        for key, vals in method_scores[method].items():
-            metric_avg[key] = round(sum(vals) / len(vals), 4) if vals else None
-        agg["methods"][method] = {
-            "num_submissions": method_count[method],
-            "avg_scores": metric_avg,
-        }
-
-    out_path = OUTPUT_DIR / "method_aggregates.json"
-    with open(out_path, "w", encoding="utf-8") as f:
-        json.dump(agg, f, ensure_ascii=False, indent=2)
-    return out_path
-
-
-def build_sample_brief(sample: Dict[str, Any], index: int, total: int) -> str:
-    story = sample.get("story_text") or "(未找到对应 story 文本，请检查 clip_movie_story 下是否有同名 txt)"
-    return (
-        f"### 当前匿名样本 {index + 1}/{total}\n"
-        f"- **Sample ID**: `{sample['anon_id']}`\n\n"
-        f"### Story Description\n{story}"
-    )
-
-
-def create_app():
-    samples = build_pending_samples()
-    sample_map = {s["anon_id"]: s for s in samples}
-
-    with gr.Blocks(
-        title="VideoEval Movie-Level Evaluation",
-        css=CUSTOM_CSS,
-        theme=gr.themes.Soft(primary_hue="violet", secondary_hue="blue", neutral_hue="slate"),
-    ) as app:
-        gr.HTML(
-            """
-            <div id="hero">
-                <h1>VideoEval · Movie-Level Evaluation</h1>
-                <p>统一电影级评测问卷，支持方法级均分统计（含 CL / CRh / AVG）</p>
-            </div>
-            """
-        )
-        gr.Markdown(
-            f"<span class='hint'>输入目录：`{INPUT_DIR}` ｜ 输出目录：`{OUTPUT_DIR}`</span>",
-        )
-        gr.Markdown(build_data_diagnostics(samples))
-
-        current_idx = gr.State(0)
-        evaluator_state = gr.State("anonymous")
-
-        with gr.Row():
-            with gr.Column(scale=5, elem_classes=["panel"]):
-                gr.Markdown("### 1) 评测员与样本")
-                with gr.Row():
-                    evaluator_input = gr.Textbox(label="Evaluator ID", value="anonymous")
-                    sample_dropdown = gr.Dropdown(
-                        label="选择评测样本（匿名）",
-                        choices=[s["anon_id"] for s in samples],
-                        value=samples[0]["anon_id"] if samples else None,
-                        interactive=True,
-                    )
-                with gr.Row():
-                    prev_btn = gr.Button("← Previous")
-                    next_btn = gr.Button("Next →")
-                    submit_btn = gr.Button("提交当前评分并统计", variant="primary")
-                status = gr.Markdown("")
-            with gr.Column(scale=7, elem_classes=["panel"]):
-                gr.Markdown("### 2) 视频与剧情")
-                movie_video = gr.Video(label="Movie Video", value=samples[0]["video_path"] if samples else None, height=460)
-                sample_info = gr.Markdown("无可用样本" if not samples else build_sample_brief(samples[0], 0, len(samples)))
-
-        gr.Markdown("## 3) Movie-Level 指标评分（1-5）")
-        gr.Markdown("<span class='hint'>先给分，再填写可选理由。未打分无法提交。</span>")
-
-        score_widgets: Dict[str, gr.Radio] = {}
-        reason_widgets: Dict[str, gr.Textbox] = {}
-        metric_groups = {
-            "I. 叙事与剧本 (NS)": ["SF", "NC"],
-            "II. 视听与技术 (AT)": ["VQ", "CC", "PLC", "V_AQ"],
-            "III. 美学与表现力 (AE)": ["CT", "AVR"],
-            "IV. 节奏与流动性 (RF)": ["NP", "VAC"],
-            "V. 情感与参与度 (EE)": ["CD"],
-            "VI. 整体体验 (OE)": ["OQ"],
-        }
-        criteria_map = {k: (name, desc) for k, name, desc in MOVIE_CRITERIA}
-
-        for section_title, keys in metric_groups.items():
-            with gr.Accordion(section_title, open=True):
-                for key in keys:
-                    name, desc = criteria_map[key]
-                    with gr.Group(elem_classes=["panel"]):
-                        gr.Markdown(f"**{key} · {name}**")
-                        gr.Markdown(f"<span class='hint'>{desc}</span>")
-                        with gr.Row():
-                            score_widgets[key] = gr.Radio(choices=[1, 2, 3, 4, 5], label=f"{key} Score", scale=1)
-                            reason_widgets[key] = gr.Textbox(label=f"{key} Reason（可选）", lines=2, placeholder="补充评分依据", scale=2)
-
-        final_summary = gr.Textbox(label="Final Summary（可选）", lines=4, placeholder="总结该视频的主要优缺点")
-
-        def _sync_sample_from_dropdown(anon_id: str) -> Tuple[str, str, int]:
-            if not anon_id or anon_id not in sample_map:
-                return None, "未找到样本", 0
-            idx = next(i for i, s in enumerate(samples) if s["anon_id"] == anon_id)
-            sample = samples[idx]
-            return sample["video_path"], build_sample_brief(sample, idx, len(samples)), idx
-
-        def _go_prev(idx: int) -> Tuple[str, str, str, int]:
-            if not samples:
-                return None, "无可用样本", None, 0
-            idx = max(0, idx - 1)
-            sample = samples[idx]
-            return sample["video_path"], build_sample_brief(sample, idx, len(samples)), sample["anon_id"], idx
-
-        def _go_next(idx: int) -> Tuple[str, str, str, int]:
-            if not samples:
-                return None, "无可用样本", None, 0
-            idx = min(len(samples) - 1, idx + 1)
-            sample = samples[idx]
-            return sample["video_path"], build_sample_brief(sample, idx, len(samples)), sample["anon_id"], idx
-
-        def _submit(evaluator_id: str, anon_id: str, summary: str, *score_reason_vals):
-            if not samples:
-                return "❌ 没有可提交样本。"
-            if not anon_id or anon_id not in sample_map:
-                return "❌ 请先选择样本。"
-            sample = sample_map[anon_id]
-            evaluator_id = (evaluator_id or "anonymous").strip() or "anonymous"
-
-            # 防重复：方法-故事只允许评估一次
-            evaluated_pairs = load_evaluated_method_story_pairs()
-            if (sample["method"], sample["story_name"]) in evaluated_pairs:
-                return "⚠️ 该方法-故事已经被评估过一次，请选择其他匿名样本。"
-
-            scores: Dict[str, int] = {}
-            reasons: Dict[str, str] = {}
-            for i, key in enumerate(BASE_METRIC_KEYS):
-                score = score_reason_vals[i * 2]
-                reason = score_reason_vals[i * 2 + 1]
-                if score is None:
-                    return f"❌ 请为 `{key}` 打分。"
-                scores[key] = int(score)
-                reasons[key] = (reason or "").strip()
-
-            with SAVE_LOCK:
-                single_path = save_single_result(sample, evaluator_id, scores, reasons, summary or "")
-                agg_path = recompute_method_aggregates()
-
-            return f"✅ 已保存: `{single_path}`\n\n✅ 已更新方法统计: `{agg_path}`"
-
-        sample_dropdown.change(
-            _sync_sample_from_dropdown,
-            inputs=[sample_dropdown],
-            outputs=[movie_video, sample_info, current_idx],
-        )
-        prev_btn.click(_go_prev, inputs=[current_idx], outputs=[movie_video, sample_info, sample_dropdown, current_idx])
-        next_btn.click(_go_next, inputs=[current_idx], outputs=[movie_video, sample_info, sample_dropdown, current_idx])
-
-        submit_inputs = [evaluator_input, sample_dropdown, final_summary]
-        for key in BASE_METRIC_KEYS:
-            submit_inputs.append(score_widgets[key])
-            submit_inputs.append(reason_widgets[key])
-        submit_btn.click(_submit, inputs=submit_inputs, outputs=[status])
-
-        app.load(lambda x: x, inputs=[evaluator_input], outputs=[evaluator_state])
-
-    return app
-
-
-demo = create_app()
-
-if __name__ == "__main__":
-    allowed_paths = [str(INPUT_DIR.resolve())] if INPUT_DIR.exists() else None
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=False,
-        show_error=True,
-        allowed_paths=allowed_paths,
-    )
-"""
-VideoEval Movie-Level 问卷应用（Hugging Face Spaces）
-仅保留 Movie-Level 评测，并支持方法级别统计输出。
-"""
-
-import json
-import os
-import threading
-from collections import defaultdict
-from datetime import datetime
-from pathlib import Path
-from typing import Any, Dict, List, Optional, Tuple
-
-import gradio as gr
-from huggingface_hub import CommitScheduler, snapshot_download
-
-# 路径配置（按用户要求）
-# Spaces 推荐优先读取当前 Space 仓库内文件（app.py 同级）
-APP_DIR = Path(__file__).resolve().parent
-LOCAL_INPUT_DIR = APP_DIR / "user_study_input"
-LOCAL_OUTPUT_DIR = APP_DIR / "user_study_results"
-DATA_INPUT_DIR = Path("/data/user_study_input")
-DATA_OUTPUT_DIR = Path("/data/user_study_results")
-DATA_REPO_ID = os.environ.get("DATA_REPO_ID", "MemDirector/user_study_input")
-RESULTS_REPO_ID = os.environ.get("RESULTS_REPO_ID", "MemDirector/user_study_results")
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-SPACE_MODE = os.environ.get("SPACE_MODE", "repo_first")  # repo_first / data_first / hub_only
-
-ROOT_DIR = APP_DIR
-INPUT_DIR = LOCAL_INPUT_DIR
-OUTPUT_DIR = LOCAL_OUTPUT_DIR
-STORY_DIR = INPUT_DIR / "clip_movie_story"
-VIDEO_DIR = INPUT_DIR / "video"
-
-Path(OUTPUT_DIR).mkdir(parents=True, exist_ok=True)
-scheduler: Optional[CommitScheduler] = None
-
-
-def _set_paths(input_dir: Path, output_dir: Path) -> None:
-    global INPUT_DIR, OUTPUT_DIR, STORY_DIR, VIDEO_DIR, ROOT_DIR
-    INPUT_DIR = input_dir
-    OUTPUT_DIR = output_dir
-    STORY_DIR = INPUT_DIR / "clip_movie_story"
-    VIDEO_DIR = INPUT_DIR / "video"
-    ROOT_DIR = INPUT_DIR.parent
-    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
-
-
-def _try_use_local_repo_layout() -> bool:
-    # Space 仓库内自带 user_study_input 时，直接读取（最符合“已放上去直接跑”）
-    if LOCAL_INPUT_DIR.exists():
-        _set_paths(LOCAL_INPUT_DIR, LOCAL_OUTPUT_DIR)
-        return True
-    return False
-
-
-def _try_use_data_volume_layout() -> bool:
-    # 如果使用 /data 持久卷，则可放在 /data/user_study_input
-    if DATA_INPUT_DIR.exists():
-        _set_paths(DATA_INPUT_DIR, DATA_OUTPUT_DIR)
-        return True
-    return False
-
-
-def _try_download_from_hub() -> bool:
-    # 最后兜底：从 dataset repo 下载
-    if not DATA_REPO_ID:
-        return False
-    hub_root = APP_DIR / ".hf_space_cache"
-    try:
-        snapshot_download(
-            repo_id=DATA_REPO_ID,
-            repo_type="dataset",
-            local_dir=str(hub_root),
-            token=HF_TOKEN,
-            allow_patterns=[
-                "clip_movie_story/**",
-                "video/**",
-                "user_study_input/**",
-                "user_study_results/**",
-            ],
-        )
-    except Exception as e:
-        print(f"[INIT] snapshot_download failed: {e}")
-        return False
-
-    # 兼容两种 dataset 结构：
-    # A) 仓库根目录直接是 clip_movie_story/ 与 video/
-    # B) 仓库里有 user_study_input/ 子目录
-    if (hub_root / "clip_movie_story").exists() and (hub_root / "video").exists():
-        hub_input = hub_root
-    elif (hub_root / "user_study_input").exists():
-        hub_input = hub_root / "user_study_input"
-    else:
-        return False
-
-    hub_output = hub_root / "user_study_results"
-    _set_paths(hub_input, hub_output)
-    return True
-
-
-def init_space_storage() -> None:
-    """
-    Hugging Face Spaces 规范：
-    - 从 dataset repo 拉取 user_study_input 与 user_study_results 到本地 ROOT_DIR
-    - 使用 CommitScheduler 持续回写 user_study_results
-    """
-    global scheduler
-
-    if SPACE_MODE == "hub_only":
-        ok = _try_download_from_hub()
-    elif SPACE_MODE == "data_first":
-        ok = _try_use_data_volume_layout() or _try_use_local_repo_layout() or _try_download_from_hub()
-    else:
-        ok = _try_use_local_repo_layout() or _try_use_data_volume_layout() or _try_download_from_hub()
-    print(f"[INIT] storage init mode={SPACE_MODE}, success={ok}, input={INPUT_DIR}, output={OUTPUT_DIR}")
-
-    if RESULTS_REPO_ID:
-        try:
-            scheduler = CommitScheduler(
-                repo_id=RESULTS_REPO_ID,
-                repo_type="dataset",
-                folder_path=str(OUTPUT_DIR),
-                path_in_repo="user_study_results",
-                every=3,
-                token=HF_TOKEN,
-            )
-            print(f"[INIT] CommitScheduler enabled: {RESULTS_REPO_ID}")
-        except Exception as e:
-            print(f"[INIT] CommitScheduler init failed: {e}")
-
-
-init_space_storage()
-
-# Movie-Level 指标定义
-MOVIE_CRITERIA: List[Tuple[str, str, str]] = [
-    ("SF", "Script Faithfulness (剧本忠实度)", "生成的视觉内容与原始剧本描述的吻合程度。"),
-    ("NC", "Narrative Coherence (叙事连贯性)", "镜头间情节发展的逻辑性，确保故事表达清晰、不破碎。"),
-    ("VQ", "Visual Quality (视觉质量)", "画面的清晰度、噪点控制、光影效果等基础图像质量。"),
-    ("CC", "Character Consistency (角色一致性)", "同一角色在不同镜头、不同角度下的外貌、服装及特征的稳定性。"),
-    ("PLC", "Physical Law Compliance (物理规律符合度)", "运动、重力、碰撞等是否符合现实物理逻辑，是否存在严重 AI 幻觉。"),
-    ("V_AQ", "Voice/Audio Quality (语音/音频质量)", "配音、背景音乐和音效的清晰度、自然度及技术品质。"),
-    ("CT", "Cinematic Techniques (电影技巧)", "镜头运动、景深控制及构图的专业性。"),
-    ("AVR", "Audio-Visual Richness (视听丰富度)", "画面细节精细度以及音频层次（音效、氛围音）的丰富程度。"),
-    ("NP", "Narrative Pacing (叙事节奏)", "镜头剪辑长短切换是否契合故事情节张力需求。"),
-    ("VAC", "Video-Audio Coordination (视听协调性)", "画面动作与音效、音乐卡点的同步率。"),
-    ("CD", "Compelling Degree (引人入胜程度)", "吸引注意力并引发情感共鸣或沉浸感的能力。"),
-    ("OQ", "Overall Quality (整体质量)", "对生成视频作为“电影作品”的综合观感评分。"),
-]
-
-BASE_METRIC_KEYS = [k for k, _, _ in MOVIE_CRITERIA]
-SAVE_LOCK = threading.Lock()
-
-CUSTOM_CSS = """
-.gradio-container {
-    max-width: 1360px !important;
-    background:
-        radial-gradient(circle at 8% 12%, rgba(103, 136, 255, 0.12) 0%, rgba(103, 136, 255, 0) 32%),
-        radial-gradient(circle at 92% 18%, rgba(255, 145, 200, 0.1) 0%, rgba(255, 145, 200, 0) 34%),
-        linear-gradient(180deg, #fcfdff 0%, #f9fbff 52%, #ffffff 100%);
-    padding-bottom: 18px;
-    color: #2e4168;
-}
-body,
-html {
-    background: #f6f9ff !important;
-    color-scheme: light;
-}
-.gradio-container .block,
-.gradio-container .form {
-    background: transparent !important;
-}
-.gradio-container .prose,
-.gradio-container .gr-box,
-.gradio-container .gr-panel,
-.gradio-container .gr-group {
-    background: #ffffff !important;
+    max-width: 1300px !important;
 }
 #hero {
-    border: 1px solid #d6dfff;
+    border: 1px solid #2b2f3a;
     border-radius: 16px;
-    padding: 22px 24px;
-    background: linear-gradient(130deg, #f5f8ff 0%, #f7faff 55%, #fcf7ff 100%);
-    box-shadow: 0 6px 16px rgba(102, 118, 175, 0.1);
-    margin-bottom: 12px;
+    padding: 20px 22px;
+    background: linear-gradient(135deg, #121826 0%, #1d2540 55%, #2a1f45 100%);
+    margin-bottom: 14px;
 }
 #hero h1 {
-    margin: 0;
-    font-size: 1.75rem;
-    letter-spacing: 0.3px;
-    color: #23325b;
+    margin: 0 0 8px 0;
+    font-size: 1.9rem;
 }
 #hero p {
-    margin: 8px 0 0 0;
-    color: #4d5f8d;
-}
-#hero .hero-badge {
-    display: inline-flex;
-    gap: 8px;
-    align-items: center;
-    margin-top: 14px;
-    padding: 6px 12px;
-    border-radius: 999px;
-    border: 1px solid #c9d7ff;
-    background: #ffffffcc;
-    color: #49609d;
-    font-size: 12px;
-}
-.topbar {
-    margin: 12px 0 16px 0;
-    display: grid;
-    grid-template-columns: repeat(3, minmax(120px, 1fr));
-    gap: 12px;
-}
-.metric-card {
-    border: 1px solid #d7e1ff;
-    border-radius: 12px;
-    padding: 12px 14px;
-    background: linear-gradient(180deg, #ffffff 0%, #fbfcff 100%);
-    box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.85);
-}
-.metric-label {
-    font-size: 12px;
-    color: #667aa8;
-    margin-bottom: 4px;
-}
-.metric-value {
-    color: #2d3f68;
-    font-size: 16px;
-    font-weight: 650;
+    margin: 0;
+    color: #d0d8f0;
 }
 .panel {
-    border: 1px solid #dbe4ff !important;
+    border: 1px solid #2e3445 !important;
     border-radius: 14px !important;
     padding: 14px !important;
-    background: #ffffff !important;
-    box-shadow: 0 3px 12px rgba(101, 118, 172, 0.08);
-}
-.section-title {
-    margin: 0 0 10px 0;
-    font-size: 1.03rem;
-    font-weight: 620;
-    color: #2a3c63;
+    background: #111522 !important;
 }
 .hint {
-    color: #6f82ad;
+    color: #9aa7c9;
     font-size: 0.9rem;
 }
-.toolbar-btn {
-    min-height: 42px !important;
-    border-radius: 10px !important;
-}
-.gr-button-primary {
-    background: linear-gradient(180deg, #7496ff 0%, #5b82ff 100%) !important;
-    border: 1px solid #8aa7ff !important;
-    box-shadow: 0 6px 18px rgba(93, 125, 223, 0.28) !important;
-}
-.status-box {
-    border: 1px dashed #9fb2e3;
-    border-radius: 10px;
-    padding: 9px 11px;
-    background: #f8faff;
-    color: #3f527f;
-}
-.soft-input textarea,
-.soft-input input,
-.soft-input .wrap {
-    border-radius: 10px !important;
-    background: #ffffff !important;
-    border-color: #d8e2ff !important;
-    color: #2e4168 !important;
-}
-.gr-markdown,
-.gradio-container p,
-.gradio-container span,
-.gradio-container label {
-    color: #2e4168;
-}
-.gr-accordion {
-    border-radius: 12px !important;
-    border-color: #d5def7 !important;
-    background: #ffffff !important;
-}
 """
 
 
@@ -1599,59 +368,42 @@ def create_app():
     with gr.Blocks(
         title="VideoEval Movie-Level Evaluation",
         css=CUSTOM_CSS,
-        theme=gr.themes.Soft(primary_hue="blue", secondary_hue="indigo", neutral_hue="gray"),
+        theme=gr.themes.Soft(primary_hue="violet", secondary_hue="blue", neutral_hue="slate"),
     ) as app:
         gr.HTML(
-            f"""
+            """
             <div id="hero">
                 <h1>VideoEval · Movie-Level Evaluation</h1>
                 <p>统一电影级评测问卷，支持方法级均分统计（含 CL / CRh / AVG）</p>
-                <div class="hero-badge">HF Space Ready · Gradio Blocks · Clean Review Flow</div>
-            </div>
-            <div class="topbar">
-                <div class="metric-card">
-                    <div class="metric-label">Pending Samples</div>
-                    <div class="metric-value">{len(samples)}</div>
-                </div>
-                <div class="metric-card">
-                    <div class="metric-label">Evaluation Scope</div>
-                    <div class="metric-value">Movie-Level</div>
-                </div>
-                <div class="metric-card">
-                    <div class="metric-label">Scoring Standard</div>
-                    <div class="metric-value">12 Metrics · 1~5</div>
-                </div>
             </div>
             """
         )
         gr.Markdown(
             f"<span class='hint'>输入目录：`{INPUT_DIR}` ｜ 输出目录：`{OUTPUT_DIR}`</span>",
         )
-        with gr.Accordion("系统诊断信息（展开查看）", open=False):
-            gr.Markdown(build_data_diagnostics(samples))
+        gr.Markdown(build_data_diagnostics(samples))
 
         current_idx = gr.State(0)
         evaluator_state = gr.State("anonymous")
 
         with gr.Row():
-            with gr.Column(scale=4, elem_classes=["panel"]):
-                gr.Markdown("<div class='section-title'>1) 评测员与样本</div>")
+            with gr.Column(scale=5, elem_classes=["panel"]):
+                gr.Markdown("### 1) 评测员与样本")
                 with gr.Row():
-                    evaluator_input = gr.Textbox(label="Evaluator ID", value="anonymous", elem_classes=["soft-input"])
+                    evaluator_input = gr.Textbox(label="Evaluator ID", value="anonymous")
                     sample_dropdown = gr.Dropdown(
                         label="选择评测样本（匿名）",
                         choices=[s["anon_id"] for s in samples],
                         value=samples[0]["anon_id"] if samples else None,
                         interactive=True,
-                        elem_classes=["soft-input"],
                     )
                 with gr.Row():
-                    prev_btn = gr.Button("← Previous", elem_classes=["toolbar-btn"])
-                    next_btn = gr.Button("Next →", elem_classes=["toolbar-btn"])
-                    submit_btn = gr.Button("提交当前评分并统计", variant="primary", elem_classes=["toolbar-btn"])
-                status = gr.Markdown("<div class='status-box'>等待提交</div>")
-            with gr.Column(scale=8, elem_classes=["panel"]):
-                gr.Markdown("<div class='section-title'>2) 视频与剧情</div>")
+                    prev_btn = gr.Button("← Previous")
+                    next_btn = gr.Button("Next →")
+                    submit_btn = gr.Button("提交当前评分并统计", variant="primary")
+                status = gr.Markdown("")
+            with gr.Column(scale=7, elem_classes=["panel"]):
+                gr.Markdown("### 2) 视频与剧情")
                 movie_video = gr.Video(label="Movie Video", value=samples[0]["video_path"] if samples else None, height=460)
                 sample_info = gr.Markdown("无可用样本" if not samples else build_sample_brief(samples[0], 0, len(samples)))
 
@@ -1678,10 +430,10 @@ def create_app():
                         gr.Markdown(f"**{key} · {name}**")
                         gr.Markdown(f"<span class='hint'>{desc}</span>")
                         with gr.Row():
-                            score_widgets[key] = gr.Radio(choices=[1, 2, 3, 4, 5], label=f"{key} Score", scale=1, elem_classes=["soft-input"])
-                            reason_widgets[key] = gr.Textbox(label=f"{key} Reason（可选）", lines=2, placeholder="补充评分依据", scale=2, elem_classes=["soft-input"])
+                            score_widgets[key] = gr.Radio(choices=[1, 2, 3, 4, 5], label=f"{key} Score", scale=1)
+                            reason_widgets[key] = gr.Textbox(label=f"{key} Reason（可选）", lines=2, placeholder="补充评分依据", scale=2)
 
-        final_summary = gr.Textbox(label="Final Summary（可选）", lines=4, placeholder="总结该视频的主要优缺点", elem_classes=["soft-input"])
+        final_summary = gr.Textbox(label="Final Summary（可选）", lines=4, placeholder="总结该视频的主要优缺点")
 
         def _sync_sample_from_dropdown(anon_id: str) -> Tuple[str, str, int]:
             if not anon_id or anon_id not in sample_map: