first

README.md

@@ -1,13 +1,12 @@
 ---
-title: Video Eval
+title: Videoeval Humaneval
 emoji: 👁
-colorFrom: pink
-colorTo: gray
+colorFrom: gray
+colorTo: purple
 sdk: gradio
-sdk_version: 5.49.1
+sdk_version: 5.47.2
 app_file: app.py
 pinned: false
-short_description: 'Human Evaluation: Action Consistency & Physical Plausibility'
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,1018 @@
+import os, io, csv, json, random
+from datetime import datetime
+import gradio as gr
+from huggingface_hub import HfApi, hf_hub_download
+
+import subprocess, pathlib, hashlib
+
+import secrets, time, string
+
+
+# 사용자가 '선택'을 완료한 값 (초기엔 None)
+selected_action = gr.State(None)
+selected_phys   = gr.State(None)
+
+def _recompute_save(pid_text: str, sel_a, sel_p):
+    ok = bool(pid_text and pid_text.strip()) and (sel_a is not None) and (sel_p is not None)
+    return gr.update(interactive=ok, variant=("primary" if ok else "secondary"))
+
+
+REWARD_FILE = "reward_codes.csv"   # 리워드 코드 기록용 파일 (HF dataset 안에 저장)
+def _read_codes_bytes():
+    try:
+        p = hf_hub_download(
+            repo_id=REPO_ID, filename=REWARD_FILE, repo_type="dataset",
+            token=HF_TOKEN, local_dir="/tmp", local_dir_use_symlinks=False
+        )
+        return open(p, "rb").read()
+    except Exception:
+        return None
+
+def _append_code(old_bytes, row):
+    s = io.StringIO()
+    w = csv.writer(s)
+    if not old_bytes:
+        # 새 헤더
+        w.writerow(["ts_iso", "participant_id", "reward_code", "total_done"])
+    else:
+        s.write(old_bytes.decode("utf-8", errors="ignore"))
+    w.writerow(row)
+    return s.getvalue().encode("utf-8")
+
+def _persist_reward_code(pid: str, code: str, total_done: int):
+    """리워드 코드를 HF에 reward_codes.csv로 누적 저장(append)."""
+    old = _read_codes_bytes()
+    row = [datetime.utcnow().isoformat(), pid.strip(), code, int(total_done)]
+    newb = _append_code(old, row)
+    api.upload_file(
+        path_or_fileobj=io.BytesIO(newb),
+        path_in_repo=REWARD_FILE,
+        repo_id=REPO_ID,
+        repo_type="dataset",
+        token=HF_TOKEN,
+        commit_message="append reward code"
+    )
+
+def _gen_reward_code(pid: str, length: int = 10, forbid_ambiguous: bool=True) -> str:
+    """
+    참가자에게 보여줄 랜덤 코드. 충돌 위험 매우 낮음.
+    - PID + 시각 + 보안랜덤으로 시드
+    - 대문자/숫자 조합
+    """
+    alphabet = "ABCDEFGHJKLMNPQRSTUVWXYZ23456789" if forbid_ambiguous else string.ascii_uppercase + string.digits
+    # secrets 기반 난수 + pid/time 섞어서 해시 비슷한 효과
+    rnd = secrets.token_hex(8) + pid + str(time.time_ns())
+    # 섞고 뽑기
+    rng = secrets.SystemRandom()
+    return "".join(rng.choice(alphabet) for _ in range(length))
+
+
+
+
+MUTED_CACHE_DIR = "/tmp/hf_video_cache_muted"
+pathlib.Path(MUTED_CACHE_DIR).mkdir(parents=True, exist_ok=True)
+
+def _sha1_8(s: str) -> str:
+    return hashlib.sha1(s.encode("utf-8")).hexdigest()[:8]
+
+def ensure_muted_copy(src_path: str) -> str:
+    """
+    주어진 mp4에서 오디오 트랙을 제거(-an)한 무음 복사본을 캐시에 생성.
+    이미 있으면 그 파일 경로 반환.
+    실패하면 원본 경로 반환.
+    """
+    if not src_path or not os.path.exists(src_path):
+        return src_path
+    out = os.path.join(MUTED_CACHE_DIR, _sha1_8(src_path) + ".mp4")
+    if os.path.exists(out):
+        return out
+    try:
+        subprocess.run(
+            ["ffmpeg", "-y", "-i", src_path, "-c:v", "copy", "-an", out],
+            stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL, check=True
+        )
+        return out if os.path.exists(out) else src_path
+    except Exception:
+        return src_path
+
+
+FEEDBACK_FILE = "final_feedback.csv"
+
+def _read_feedback_bytes():
+    try:
+        p = hf_hub_download(
+            repo_id=REPO_ID, filename=FEEDBACK_FILE, repo_type="dataset",
+            token=HF_TOKEN, local_dir="/tmp", local_dir_use_symlinks=False
+        )
+        return open(p, "rb").read()
+    except Exception:
+        return None
+
+def _append_feedback(old_bytes, row):
+    s = io.StringIO()
+    w = csv.writer(s)
+    if not old_bytes:
+        # 최종 코멘트 전용 CSV 헤더
+        w.writerow(["ts_iso", "participant_id", "final_comment"])
+    else:
+        s.write(old_bytes.decode("utf-8", errors="ignore"))
+    w.writerow(row)
+    return s.getvalue().encode("utf-8")
+
+def push_final_feedback(participant_id: str, comment: str):
+    """마지막에 받는 자유 코멘트를 FEEDBACK_FILE로 저장."""
+    if not participant_id or not participant_id.strip():
+        return gr.update(visible=True, value="❗ Missing participant ID.")
+    if comment is None or not str(comment).strip():
+        # 비어있으면 저장하지 않고 안내만
+        return gr.update(visible=True, value="ℹ️ No comment entered — nothing to submit.")
+
+    try:
+        old = _read_feedback_bytes()
+        row = [datetime.utcnow().isoformat(), participant_id.strip(), comment.strip()]
+        newb = _append_feedback(old, row)
+
+        if not REPO_ID:
+            return gr.update(visible=True, value="❗ RESULTS_REPO is not set.")
+        if not HF_TOKEN:
+            return gr.update(visible=True, value="❗ HF_TOKEN is missing. Set a write token for the dataset repo.")
+
+        api.upload_file(
+            path_or_fileobj=io.BytesIO(newb),
+            path_in_repo=FEEDBACK_FILE,
+            repo_id=REPO_ID,
+            repo_type="dataset",
+            token=HF_TOKEN,
+            commit_message="append final feedback"
+        )
+        return gr.update(visible=True, value="✅ Thanks! Your feedback was submitted.")
+    except Exception as e:
+        return gr.update(
+            visible=True,
+            value=f"❌ Feedback save failed: {type(e).__name__}: {e}"
+        )
+
+
+# -------------------- Config --------------------
+REPO_ID = os.getenv("RESULTS_REPO", "dghadiya/video_eval")  # 업로드한 리포와 일치
+HF_TOKEN = os.getenv("HF_TOKEN")
+RESULTS_FILE = "results.csv"
+TOTAL_PER_PARTICIPANT = 30  # 목표 평가 개수(세션 기준)
+
+
+# videos.json 예시: {"url": "...mp4", "id": "BodyWeightSquats__XXXX.mp4", "action": "BodyWeightSquats"}
+with open("videos.json", "r", encoding="utf-8") as f:
+    V = json.load(f)
+
+api = HfApi()
+
+# 교수님 지침(그대로, 굵게 처리 포함)
+# INSTRUCTION_MD = """
+# **Task:** You will watch a series of **AI-generated videos**. For each video, your job is to rate how well the person’s action in the AI-generated video matches the action specified as "**expected action**". Some things to keep in mind:
+# - The generated video should **capture** the expected action **throughout the video**.
+# - Try to **focus only** on the expected action and do **not** judge **video quality**, **attractiveness**, **background**, **camera motion**, or **objects**.
+# - You will be **paid** once **all the videos are viewed and rated**.
+# """
+INSTRUCTION_MD = """
+**Task:** You will watch a series of **AI-generated videos**.  
+For each video, your job is to rate:
+
+1. **Action Consistency** - how well the person’s action matches the action specified as the "**Expected action**".  
+2. **Physical Plausibility** - how natural and physically possible the motion looks.
+
+Some things to keep in mind:
+- Try to **focus only** on the expected action and motion quality, and do **not** judge **video quality**, **attractiveness**, **background**, **camera motion**, or **objects**.
+- For **physical plausibility**, look for **smooth and realistic** motion without impossible poses, missing limbs, or extreme stretching.
+- Action consistency and physical plausibility are **not mutually exclusive** with each other.
+- **Physically plausible motion does not imply correct depiction of action.**
+- **A video cannot portray action consistency if it has physically impossible movements.**
+- **0: indicates really poor depiction, 10: represents perfect, realistic depiction**
+- The **Save & Next** button will be enabled **only after you have clicked or adjusted both sliders at least once**.
+- You will be **paid** once **all the videos are viewed and rated**.
+"""
+
+# -------------------- Helper funcs --------------------
+def _load_eval_counts():
+    """
+    Hugging Face dataset의 results.csv를 읽어 video_id별 평가 개수(dict)를 반환.
+    없으면 0으로 초기화.
+    """
+    # 모든 id를 0으로 초기화
+    counts = {}
+    for v in V:
+        vid = _get_video_id(v)
+        counts[vid] = 0
+
+    b = _read_csv_bytes()
+    if not b:
+        return counts
+
+    s = io.StringIO(b.decode("utf-8", errors="ignore"))
+    r = csv.reader(s)
+    rows = list(r)
+    if not rows:
+        return counts
+
+    # 헤더 파악
+    header = rows[0]
+    body = rows[1:] if header and ("video_id" in header or "overall" in header) else rows
+    vid_col = None
+    if header and "video_id" in header:
+        vid_col = header.index("video_id")
+
+    for row in body:
+        try:
+            vid = row[vid_col] if vid_col is not None else row[2]  # 기본 포맷: ts, pid, video_id, overall, notes
+            if vid in counts:
+                counts[vid] += 1
+        except Exception:
+            continue
+    return counts
+
+def _get_video_id(v: dict) -> str:
+    if "id" in v and v["id"]:
+        return v["id"]
+    # id가 없으면 URL 파일명으로 대체
+    return os.path.basename(v.get("url", ""))
+
+def _read_csv_bytes():
+    try:
+        p = hf_hub_download(
+            repo_id=REPO_ID, filename=RESULTS_FILE, repo_type="dataset",
+            token=HF_TOKEN, local_dir="/tmp", local_dir_use_symlinks=False
+        )
+        return open(p, "rb").read()
+    except Exception:
+        return None
+
+
+# def _append(old_bytes, row):
+#     s = io.StringIO()
+#     w = csv.writer(s)
+#     if not old_bytes:
+#         # ✅ 새 헤더
+#         w.writerow(["ts_iso", "participant_id", "video_id", "overall", "notes"])
+#     else:
+#         s.write(old_bytes.decode("utf-8", errors="ignore"))
+#     w.writerow(row)
+#     return s.getvalue().encode("utf-8")
+
+def _append(old_bytes, row):
+    s = io.StringIO()
+    w = csv.writer(s)
+    if not old_bytes:
+        # ✅ new header with two scores
+        w.writerow(["ts_iso", "participant_id", "video_id",
+                    "action_consistency", "physical_plausibility", "notes"])
+    else:
+        s.write(old_bytes.decode("utf-8", errors="ignore"))
+    w.writerow(row)
+    return s.getvalue().encode("utf-8")
+
+
+# def push(participant_id, video_id, score, notes=""):
+#     if not participant_id or not participant_id.strip():
+#         return gr.update(visible=True, value="❗ Please enter your Participant ID before proceeding.")
+#     if not video_id or score is None:
+#         return gr.update(visible=True, value="❗ Fill out all fields.")
+
+#     try:
+#         old = _read_csv_bytes()
+#         row = [
+#             datetime.utcnow().isoformat(),
+#             participant_id.strip(),
+#             video_id,           # ✅ action 대신 video_id 저장
+#             float(score),       # overall
+#             notes or ""
+#         ]
+#         newb = _append(old, row)
+
+#         if not REPO_ID:
+#             return gr.update(visible=True, value="❗ RESULTS_REPO is not set.")
+#         if not HF_TOKEN:
+#             return gr.update(visible=True, value="❗ HF_TOKEN is missing. Set a write token for the dataset repo.")
+
+#         api.upload_file(
+#             path_or_fileobj=io.BytesIO(newb),
+#             path_in_repo=RESULTS_FILE,
+#             repo_id=REPO_ID,
+#             repo_type="dataset",
+#             token=HF_TOKEN,
+#             commit_message="append"
+#         )
+#         return gr.update(visible=True, value=f"✅ Saved successfully!")
+#     except Exception as e:
+#         return gr.update(
+#             visible=True,
+#             value=f"❌ Save failed: {type(e).__name__}: {e}\n"
+#                   f"- Check HF_TOKEN permission\n- Check REPO_ID\n- Create dataset repo if missing"
+#         )
+
+def push(participant_id, video_id, action_score, phys_score, notes=""):
+    if not participant_id or not participant_id.strip():
+        return gr.update(visible=True, value="❗ Please enter your Participant ID before proceeding.")
+    if not video_id or action_score is None or phys_score is None:
+        return gr.update(visible=True, value="❗ Fill out all fields.")
+
+    try:
+        old = _read_csv_bytes()
+        row = [
+            datetime.utcnow().isoformat(),
+            participant_id.strip(),
+            video_id,
+            float(action_score),
+            float(phys_score),
+            notes or ""
+        ]
+        newb = _append(old, row)
+
+        if not REPO_ID:
+            return gr.update(visible=True, value="❗ RESULTS_REPO is not set.")
+        if not HF_TOKEN:
+            return gr.update(visible=True, value="❗ HF_TOKEN is missing. Set a write token for the dataset repo.")
+
+        api.upload_file(
+            path_or_fileobj=io.BytesIO(newb),
+            path_in_repo=RESULTS_FILE,
+            repo_id=REPO_ID,
+            repo_type="dataset",
+            token=HF_TOKEN,
+            commit_message="append"
+        )
+        return gr.update(visible=True, value=f"✅ Saved successfully!")
+    except Exception as e:
+        return gr.update(
+            visible=True,
+            value=f"❌ Save failed: {type(e).__name__}: {e}\n"
+                  f"- Check HF_TOKEN permission\n- Check REPO_ID\n- Create dataset repo if missing"
+        )
+
+
+def _extract_action(v):
+    if "action" in v and v["action"]:
+        return v["action"]
+    raw = v.get("id", "")
+    return raw.split("__")[0].split(".")[0]
+
+def pick_one():
+    v = random.choice(V)
+    return v["url"], _extract_action(v)
+
+def _progress_html(done, total):
+    pct = int(100 * done / max(1, total))
+    return f"""
+    <div style="border:1px solid #ddd; height:20px; border-radius:6px; overflow:hidden; margin-top:6px;">
+      <div style="height:100%; width:{pct}%; background:#3b82f6;"></div>
+    </div>
+    <div style="font-size:12px; margin-top:4px;">{done} / {total}</div>
+    """
+
+def _build_order_with_anchor(total:int, anchor_idx:int, repeats:int, pool_size:int, min_gap:int=1):
+    """
+    total:     TOTAL_PER_PARTICIPANT (e.g., 30)
+    anchor_idx: index of the anchor video in V (0 for first item)
+    repeats:   how many times to show anchor (e.g., 5)
+    pool_size: len(V)
+    min_gap:   최소 간격(인접 금지 => 1)
+
+    return: list of indices (length=total)
+    """
+    assert repeats <= total, "repeats must be <= total"
+    assert pool_size >= 1, "videos pool must be non-empty"
+
+    # 1) 다른 비디오 25개(중복 없이) 뽑기
+    others_needed = total - repeats
+    # anchor를 제외한 후보 인덱스
+    candidates = list(range(1, pool_size)) if anchor_idx == 0 else [i for i in range(pool_size) if i != anchor_idx]
+    if len(candidates) < others_needed:
+        raise ValueError("Not enough unique non-anchor videos to fill the schedule without duplication.")
+
+    others = random.sample(candidates, k=others_needed)
+
+    # 2) 기본 시퀀스(others)를 무작위로 섞기
+    random.shuffle(others)
+
+    # 3) 앵커를 min_gap를 만족하도록 삽입할 위치 선정
+    #    30개를 5구간으로 나눠, 각 구간 내에서 충돌 덜 나게 배치
+    #    (간단하고 안정적인 방식)
+    seq = others[:]  # 길이=25
+    anchor_positions = []
+    segment = total // repeats  # 30//5 = 6
+
+    for k in range(repeats):
+        # 각 구간 [k*segment, (k+1)*segment) 안에서 후보 위치를 고름
+        lo = k * segment
+        hi = (k + 1) * segment if k < repeats - 1 else total  # 마지막은 끝까지
+        # 경계 내 임의 오프셋 선택 (여유를 두고 충돌을 피함)
+        candidate_pos = random.randrange(lo, hi)
+
+        # 인접 금지 보정: 이미 배정된 anchor 위치들과의 거리가 min_gap 이상 되도록 조정
+        # 필요 시 좌우로 근접한 빈 슬롯 탐색
+        def ok(pos):
+            return all(abs(pos - p) >= min_gap + 1 for p in anchor_positions)  # 연속금지 => 거리 >= 2
+
+        # 근방 탐색 폭
+        found = None
+        for delta in range(0, segment):  # 구간 크기 내에서 탐색
+            # 좌/우 번갈아가며 후보 시도
+            for sign in (+1, -1):
+                pos = candidate_pos + sign * delta
+                if 0 <= pos < total and ok(pos):
+                    found = pos
+                    break
+            if found is not None:
+                break
+
+        if found is None:
+            # 최후: 0..total-1 범위에서 아무 데나 충돌 없는 곳 찾기
+            for pos in range(total):
+                if ok(pos):
+                    found = pos
+                    break
+        if found is None:
+            raise RuntimeError("Failed to place anchor without adjacency. Try different strategy or loosen min_gap.")
+
+        anchor_positions.append(found)
+
+    # 4) others를 기반으로 길이 total의 빈 시퀀스를 만들고 anchor를 주입
+    #    우선 빈 리스트를 만들고 anchor 위치를 채운 후, 나머지를 others로 채움
+    result = [None] * total
+    for pos in anchor_positions:
+        result[pos] = anchor_idx
+
+    # others 포인터
+    j = 0
+    for i in range(total):
+        if result[i] is None:
+            result[i] = others[j]
+            j += 1
+
+    # 안전체크
+    assert len(result) == total
+    # 인접 anchor 없는지 확인
+    for i in range(1, total):
+        assert not (result[i] == anchor_idx and result[i-1] == anchor_idx), "Adjacent anchors found."
+    # anchor 개수 확인
+    assert sum(1 for x in result if x == anchor_idx) == repeats, "Anchor count mismatch."
+
+    return result
+
+
+# -------------------- Example videos (download to local cache) --------------------
+EXAMPLES = {
+    "BodyWeightSquats": {
+        "real": "examples/BodyWeightSquats_real.mp4",
+        "bad":  "examples/BodyWeightSquats_bad.mp4",
+    },
+    "WallPushUps": {
+        "real": "examples/WallPushUps_real.mp4",
+        "bad":  "examples/WallPushUps_bad.mp4",
+    },
+}
+EX_CACHE = {}
+for cls, files in EXAMPLES.items():
+    EX_CACHE[cls] = {"real": None, "bad": None}
+    for kind, fname in files.items():
+        try:
+            EX_CACHE[cls][kind] = hf_hub_download(
+                repo_id=REPO_ID,
+                filename=fname,
+                repo_type="dataset",
+                token=HF_TOKEN,
+                local_dir="/tmp",
+                local_dir_use_symlinks=False,
+            )
+        except Exception as e:
+            print(f"[WARN] example missing: {cls} {kind} -> {fname}: {e}")
+
+for cls in EX_CACHE:
+    for kind in EX_CACHE[cls]:
+        if EX_CACHE[cls][kind]:
+            EX_CACHE[cls][kind] = ensure_muted_copy(EX_CACHE[cls][kind])
+
+
+CLEAN_BG_CSS = r"""
+/* 전체 페이지 배경을 흰색으로 (Spaces의 회색 배경 제거) */
+html, body { background:#ffffff !important; }
+
+/* Intro/Eval 섹션의 래퍼 박스만 투명 처리 (입력창/슬라이더 등 컨트롤은 놔둠) */
+#intro .gr-panel, #intro .gr-group, #intro .gr-box, #intro .gr-row, #intro .gr-column,
+#eval  .gr-panel, #eval  .gr-group, #eval  .gr-box, #eval  .gr-row, #eval  .gr-column {
+  background: transparent !important;
+  box-shadow: none !important;
+  border-color: transparent !important;
+}
+
+/* 비디오 카드 주변 툴바(작은 회색 박스)도 투명하게 */
+#intro [data-testid="block-video"] .prose, 
+#eval  [data-testid="block-video"] .prose {
+  background: transparent !important;
+  box-shadow: none !important;
+  border-color: transparent !important;
+}
+"""
+# -------------------- UI --------------------
+
+with gr.Blocks(css=CLEAN_BG_CSS) as demo:
+    # Blocks 안, 어디서든 한 번만 추가(권장 위치: Blocks 시작 직후)
+    gr.HTML("""
+    <script>
+    (function(){
+    // 모든 일반 DOM + Shadow DOM 안의 <video>까지 찾아 음소거
+    function eachNodeWithShadow(root, fn){
+        const walker = document.createTreeWalker(root, NodeFilter.SHOW_ELEMENT);
+        fn(root);
+        while (walker.nextNode()){
+        const el = walker.currentNode;
+        fn(el);
+        if (el.shadowRoot){
+            eachNodeWithShadow(el.shadowRoot, fn);
+        }
+        }
+    }
+
+    function muteVideoEl(v){
+        try{
+        // 재생 전에 반드시 muted 속성이 있어야 브라우저가 소리 차단
+        v.muted = true;
+        v.volume = 0.0;
+        v.setAttribute('muted','');
+        v.setAttribute('playsinline','');
+        v.setAttribute('preload','metadata');
+
+        // 사용자가 볼륨/음소거를 바꿔도 다시 0으로
+        if (!v._muteHooked){
+            v.addEventListener('volumechange', () => {
+            if (!v.muted || v.volume > 0) { v.muted = true; v.volume = 0.0; }
+            });
+            v.addEventListener('play', () => {
+            v.muted = true; v.volume = 0.0;
+            });
+            v._muteHooked = true;
+        }
+        }catch(e){}
+    }
+
+    function muteAll(root){
+        eachNodeWithShadow(root || document, (el)=>{
+        if (el.tagName && el.tagName.toLowerCase() === 'video'){
+            muteVideoEl(el);
+        }else if (el.querySelectorAll){
+            el.querySelectorAll('video').forEach(muteVideoEl);
+        }
+        });
+    }
+
+    // 초기 렌더에서
+    muteAll(document);
+
+    // 이후 DOM 변화 감시(Shadow DOM 내부 변화도 포착)
+    const obs = new MutationObserver(() => muteAll(document));
+    obs.observe(document, {subtree:true, childList:true, attributes:false});
+    })();
+    </script>
+    """)
+
+
+    order_state = gr.State(value=[])   # v4에서는 value= 권장
+    ptr_state   = gr.State(value=0)
+    cur_video_id = gr.State(value="")
+    reward_code_state = gr.State(value="")  # 완료 시 생성한 코드 저장(중복 생성 방지)
+
+    selected_action = gr.State(value=None)   # 아직 선택 없음
+    selected_phys   = gr.State(value=None)   # 아직 선택 없음
+
+    # ------------------ PAGE 1: Intro + Examples ------------------
+    # page_intro = gr.Group(visible=True)
+    page_intro = gr.Group(visible=True, elem_id="intro")
+    with page_intro:
+        # gr.Markdown("## 🎯 Action Consistency Human Evaluation")
+        gr.Markdown("## 🎯 Human Evaluation: Action Consistency & Physical Plausibility")
+
+        gr.Markdown(INSTRUCTION_MD)
+
+        understood = gr.Checkbox(label="I have read and understand the task.", value=False)
+        start_btn  = gr.Button("Yes, start", variant="secondary", interactive=False)
+
+        def _toggle_start(checked: bool):
+            return gr.update(interactive=checked, variant=("primary" if checked else "secondary"))
+        understood.change(_toggle_start, inputs=understood, outputs=start_btn)
+
+        # Examples: Squats
+        with gr.Group():
+            gr.Markdown("### Examples: BodyWeightSquats")
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("**Expected depiction of action**")
+                    gr.Video(value=EX_CACHE["BodyWeightSquats"]["real"], height=240, autoplay=False, elem_id="ex_squats_real",)
+                with gr.Column():
+                    gr.Markdown("**Poorly generated action**")
+                    gr.Video(value=EX_CACHE["BodyWeightSquats"]["bad"], height=240, autoplay=False)
+            if not (EX_CACHE["BodyWeightSquats"]["real"] and EX_CACHE["BodyWeightSquats"]["bad"]):
+                gr.Markdown("> ⚠️ Upload `examples/BodyWeightSquats_real.mp4` and `_bad.mp4` to show both samples.")
+
+        # Examples: WallPushUps
+        with gr.Group():
+            gr.Markdown("### Examples: WallPushUps")
+            with gr.Row():
+                with gr.Column():
+                    gr.Markdown("**Expected depiction of action**")
+                    gr.Video(value=EX_CACHE["WallPushUps"]["real"], height=240, autoplay=False, elem_id="ex_wallpushups_real",)
+                with gr.Column():
+                    gr.Markdown("**Poorly generated action**")
+                    gr.Video(value=EX_CACHE["WallPushUps"]["bad"], height=240, autoplay=False)
+            if not (EX_CACHE["WallPushUps"]["real"] and EX_CACHE["WallPushUps"]["bad"]):
+                gr.Markdown("> ⚠️ Upload `examples/WallPushUps_real.mp4` and `_bad.mp4` to show both samples.")
+
+        gr.HTML("""
+            <script>
+            (function(){
+            const ids = [
+                "ex_squats_real",
+                "ex_wallpushups_real",
+            ];
+            function muteById(id){
+                const host = document.getElementById(id);
+                if(!host) return;
+                // gr.Video는 Shadow DOM 내부에 <video>가 랜더링됨
+                const roots = [host, host.shadowRoot].filter(Boolean);
+                roots.forEach(root=>{
+                const vids = root.querySelectorAll('video');
+                vids.forEach(v=>{
+                    try{
+                    v.muted = true;
+                    v.volume = 0.0;
+                    v.setAttribute('muted','');
+                    v.setAttribute('playsinline','');
+                    // 사용자가 바꿔도 즉시 원복
+                    if(!v._exMuteHooked){
+                        v.addEventListener('volumechange', ()=>{
+                        if(!v.muted || v.volume > 0){ v.muted = true; v.volume = 0.0; }
+                        });
+                        v.addEventListener('play', ()=>{
+                        v.muted = true; v.volume = 0.0;
+                        });
+                        v._exMuteHooked = true;
+                    }
+                    }catch(e){}
+                });
+                });
+            }
+            function apply(){
+                ids.forEach(muteById);
+            }
+            // 초기 + DOM변경 시 적용
+            apply();
+            new MutationObserver(apply).observe(document, {subtree:true, childList:true});
+            })();
+            </script>
+            """)
+
+
+    # ------------------ PAGE 2: Evaluation ------------------
+    page_eval = gr.Group(visible=False, elem_id="eval")
+    with page_eval:
+        # PID 입력
+        with gr.Row():
+            pid = gr.Textbox(label="Participant ID (required)", placeholder="e.g., Youngsun-2025/10/24")
+
+        # 지침(원문) + 비디오 + 진행바 / 오른쪽에 슬라이더 + Save&Next
+        with gr.Row(): #equal_height=True
+            with gr.Column(scale=1):
+                gr.Markdown(INSTRUCTION_MD)  # 교수님 문구 그대로
+                video = gr.Video(label="Video", height=360)
+                progress = gr.HTML(_progress_html(0, TOTAL_PER_PARTICIPANT))
+
+            with gr.Column(scale=1):
+                action_tb = gr.Textbox(label="Expected action", interactive=False)
+                # NEW: two separate sliders
+                score_action = gr.Slider(
+                    minimum=0.0, maximum=10.0, step=0.1, value=5.0,
+                    label="Action Consistency (0.0 - 10.0)"
+                )
+                score_phys = gr.Slider(
+                    minimum=0.0, maximum=10.0, step=0.1, value=5.0,
+                    label="Physical Plausibility (0.0 - 10.0)"
+                )
+
+                save_next = gr.Button("💾 Save & Next ▶", variant="secondary", interactive=False)
+
+
+        status = gr.Markdown(visible=False)
+        done_state = gr.State(0)
+
+        # PID 입력에 따라 Save&Next 토글
+        def _toggle_by_pid(pid_text: str):
+            enabled = bool(pid_text and pid_text.strip())
+            return gr.update(interactive=enabled, variant=("primary" if enabled else "secondary"))
+        # pid.change(_toggle_by_pid, inputs=pid, outputs=save_next)
+
+
+    def _on_action_release(val: float, pid_text: str, sel_p):
+        # 사용자가 액션 슬라이더를 한 번이라도 놓으면 선택 완료
+        return val, _recompute_save(pid_text, val, sel_p)
+
+    def _on_phys_release(val: float, pid_text: str, sel_a):
+        return val, _recompute_save(pid_text, sel_a, val)
+
+    # release: 마우스를 놓을 때 1회 확정
+    score_action.release(_on_action_release,
+                         inputs=[score_action, pid, selected_phys],
+                         outputs=[selected_action, save_next])
+
+    score_phys.release(_on_phys_release,
+                       inputs=[score_phys, pid, selected_action],
+                       outputs=[selected_phys, save_next])
+
+    # PID가 바뀌면 현재 선택 상태로 Save 버튼 재계산
+    pid.change(_recompute_save,
+               inputs=[pid, selected_action, selected_phys],
+               outputs=save_next)
+
+    page_thanks = gr.Group(visible=False)
+    with page_thanks:
+        reward_msg = gr.Markdown(visible=False)
+        reward_code_box = gr.Textbox(label="Your reward code (copy & paste)", interactive=False, visible=False)
+        reward_pid_box = gr.Textbox(label="Your participant ID", interactive=False, visible=False)
+        
+        gr.Markdown("### Any comments (optional)")
+        feedback_tb = gr.Textbox(
+            label="Any comments (optional)",
+            placeholder="Leave any feedback for the study organizers (optional).",
+            lines=4
+        )
+        feedback_submit = gr.Button("Submit feedback")
+        feedback_status = gr.Markdown(visible=False)
+
+
+
+    # -------- 페이지 전환 & 첫 로드 --------
+    ANCHOR_IDX = 0       # videos.json의 맨 첫 비디오
+    ANCHOR_REPEATS = 5   # 앵커 5회
+    MIN_GAP = 1          # 앵커 연속 금지(인접 금지)
+
+    def _build_order_least_first_with_anchor(total:int, anchor_idx:int, repeats:int, min_gap:int=1):
+        """
+        - results.csv를 읽어 video_id별 카운트를 계산
+        - 앵커(첫 비디오) 5회 포함, 연속 금지
+        - 나머지는 '가장 적게 평가된 순'으로 중복 없이 채움
+        """
+        assert repeats <= total
+        N = len(V)
+        assert N >= 1
+
+        # 0) id 매핑
+        def vid_of(i): return _get_video_id(V[i])
+
+        # 1) 현재 누적 카운트 로드
+        counts = _load_eval_counts()
+
+        # 2) 앵커 제외 후보(중복 없이) 정렬: 카운트 오름차순, 동률은 랜덤 셔플
+        anchor_vid = vid_of(anchor_idx)
+        candidates = [i for i in range(N) if i != anchor_idx]
+        # 동률 랜덤화를 위해 일단 셔플
+        random.shuffle(candidates)
+        candidates.sort(key=lambda i: counts.get(vid_of(i), 0))
+
+        others_needed = total - repeats
+        if len(candidates) < others_needed:
+            raise ValueError("Not enough unique non-anchor videos to fill the schedule without duplication.")
+
+        others = candidates[:others_needed]  # 중복 없이 선택
+
+        # 3) others를 베이스 시퀀스로(랜덤 살짝 섞기)
+        random.shuffle(others)
+
+        # 4) 앵커를 구간 배치(연속 금지)
+        seq = [None] * total
+        segment = total // repeats if repeats > 0 else total
+        anchor_positions = []
+        for k in range(repeats):
+            lo = k * segment
+            hi = (k + 1) * segment if k < repeats - 1 else total
+            cand = random.randrange(lo, hi)
+
+            def ok(pos):
+                return all(abs(pos - p) >= (min_gap + 1) for p in anchor_positions)
+
+            found = None
+            for d in range(0, max(1, segment)):
+                for sgn in (+1, -1):
+                    pos = cand + sgn * d
+                    if 0 <= pos < total and ok(pos):
+                        found = pos
+                        break
+                if found is not None:
+                    break
+            if found is None:
+                # 마지막 수단: 전체 탐색
+                for pos in range(total):
+                    if ok(pos):
+                        found = pos
+                        break
+            if found is None:
+                raise RuntimeError("Failed to place anchor without adjacency.")
+            anchor_positions.append(found)
+
+        for pos in anchor_positions:
+            seq[pos] = anchor_idx
+
+        # 5) 빈 자리를 others로 채우기
+        j = 0
+        for i in range(total):
+            if seq[i] is None:
+                seq[i] = others[j]
+                j += 1
+
+        # 6) 안전 체크
+        assert sum(1 for x in seq if x == anchor_idx) == repeats
+        for i in range(1, total):
+            assert not (seq[i] == anchor_idx and seq[i-1] == anchor_idx), "Adjacent anchors found."
+
+        return seq
+
+    def _start_and_load_first():
+        total = TOTAL_PER_PARTICIPANT
+        order = _build_order_least_first_with_anchor(
+            total=total,
+            anchor_idx=ANCHOR_IDX,
+            repeats=ANCHOR_REPEATS,
+            min_gap=MIN_GAP
+        )
+        first_idx = order[0]
+        v0 = V[first_idx]
+        return (
+            gr.update(visible=False),
+            gr.update(visible=True),
+            gr.update(visible=False),
+            v0["url"],
+            _extract_action(v0),
+            5.0,   # score_action default
+            5.0,   # score_phys default  ✅ NEW
+            gr.update(visible=False, value=""),
+            0,
+            _progress_html(0, TOTAL_PER_PARTICIPANT),
+            order,
+            1,
+            _get_video_id(v0),  # cur_video_id
+            "",
+            None,  # ⬅️ selected_action 초기화(= 아직 선택 안됨)
+            None,  # ⬅️ selected_phys 초기화
+
+            gr.update(interactive=False, variant="secondary"),  # ⬅️ Save 버튼 잠금
+        )
+
+
+    start_btn.click(
+        _start_and_load_first,
+        inputs=[],
+        outputs=[
+            page_intro, page_eval, page_thanks,
+            video, action_tb,
+            score_action, score_phys,   # <-- two sliders
+            status,
+            done_state, progress, order_state, ptr_state, cur_video_id,
+            reward_code_state,
+            selected_action, selected_phys,
+            save_next,
+        ]
+    )
+
+
+
+    def save_and_next(participant_id, cur_vid, action_score, phys_score,
+                    done_cnt, order, ptr, reward_code):
+        try:
+            if not participant_id or not participant_id.strip():
+                return (
+                    gr.update(visible=True),
+                    gr.update(visible=False),
+                    gr.update(visible=True, value="❗ Please enter your Participant ID."),
+                    gr.update(), gr.update(),
+                    done_cnt,
+                    _progress_html(done_cnt, TOTAL_PER_PARTICIPANT),
+                    5.0,  # reset action slider
+                    5.0,  # reset phys slider  ✅
+                    ptr,
+                    cur_vid,
+                    reward_code,
+                    gr.update(visible=False),
+                    gr.update(visible=False),
+                    gr.update(visible=False),
+                    None,  # ⬅️ selected_action reset
+                    None,  # ⬅️ selected_phys reset
+                    gr.update(interactive=False, variant="secondary"),  # ⬅️ lock button
+                )
+
+            # save both scores
+            status_msg = push(participant_id, cur_vid, action_score, phys_score, "")
+            new_done = int(done_cnt) + 1
+
+            if new_done >= TOTAL_PER_PARTICIPANT or ptr >= len(order):
+                code = reward_code.strip() or _gen_reward_code(participant_id, length=10)
+                try:
+                    _persist_reward_code(participant_id, code, new_done)
+                except Exception:
+                    pass
+
+                thanks_text = (
+                    "## 🎉 Thank you so much!\n"
+                    "Your responses have been recorded. You may now close this window.\n\n"
+                    "**Below are your reward code and ID.** Please copy them and submit them to **AMT** to receive your payment."
+                )
+                return (
+                    gr.update(visible=False),
+                    gr.update(visible=True),
+                    status_msg,
+                    None,
+                    "",
+                    TOTAL_PER_PARTICIPANT,
+                    _progress_html(TOTAL_PER_PARTICIPANT, TOTAL_PER_PARTICIPANT),
+                    5.0,  # reset action
+                    5.0,  # reset phys
+                    len(order),
+                    cur_vid,
+                    code,
+                    gr.update(visible=True, value=thanks_text),
+                    gr.update(visible=True, value=code),
+                    gr.update(visible=True, value=participant_id.strip()),
+                    None,  # ⬅️ selected_action reset
+                    None,  # ⬅️ selected_phys reset
+                    gr.update(interactive=False, variant="secondary"),
+                )
+
+            # next video
+            next_idx = order[ptr]
+            v = V[next_idx]
+            next_vid = _get_video_id(v)
+
+            return (
+                gr.update(visible=True),
+                gr.update(visible=False),
+                status_msg,
+                v["url"],
+                _extract_action(v),
+                new_done,
+                _progress_html(new_done, TOTAL_PER_PARTICIPANT),
+                5.0,  # reset action
+                5.0,  # reset phys
+                ptr + 1,
+                next_vid,
+                reward_code,
+                gr.update(visible=False),
+                gr.update(visible=False),
+                gr.update(visible=False),
+                None,  # ⬅️ selected_action reset
+                None,  # ⬅️ selected_phys reset
+                gr.update(interactive=False, variant="secondary"),
+            )
+        except Exception as e:
+            return (
+                gr.update(visible=True),
+                gr.update(visible=False),
+                gr.update(visible=True, value=f"❌ Error: {type(e).__name__}: {e}"),
+                gr.update(), gr.update(),
+                done_cnt,
+                _progress_html(done_cnt, TOTAL_PER_PARTICIPANT),
+                5.0,
+                5.0,
+                ptr,
+                cur_vid,
+                reward_code,
+                gr.update(visible=False),
+                gr.update(visible=False),
+                gr.update(visible=False),
+                None,  # ⬅️ reset on error too
+                None,
+                gr.update(interactive=False, variant="secondary"),
+            )
+
+
+
+    save_next.click(
+        save_and_next,
+        inputs=[pid, cur_video_id, score_action, score_phys, done_state, order_state, ptr_state, reward_code_state],
+        outputs=[
+            page_eval, page_thanks,
+            status, video, action_tb,
+            done_state, progress, score_action, score_phys,
+            ptr_state, cur_video_id,
+            reward_code_state,
+            reward_msg, reward_code_box, reward_pid_box,
+
+            selected_action, selected_phys,   # ⬅️ 추가
+            save_next,                        # ⬅️ 버튼 상태 갱신
+        ]
+    )
+    feedback_submit.click(
+        push_final_feedback,
+        # 완료 후 page_thanks에서 보여주는 participant ID 박스를 사용 (값이 채워져 있음)
+        inputs=[reward_pid_box, feedback_tb],
+        outputs=feedback_status
+    )
+
+
+
+if __name__ == "__main__":
+    # demo.launch()
+    demo.launch(ssr_mode=False)

make_json.py

@@ -0,0 +1,105 @@
+# make_json.py (혹은 build_videos_json.py)
+
+import json, os, re, random
+from typing import Optional
+from huggingface_hub import list_repo_files
+
+REPO = os.getenv("VIDEO_DATASET_REPO", "SGTLIM/ucf101_eval_unified")
+INCLUDE_FOLDERS = [s.strip() for s in os.getenv("VIDEO_INCLUDE_FOLDERS", "").split(",") if s.strip()]
+OUT_PATH = "videos.json"
+ADD_DOWNLOAD_PARAM = False  # 원격 재생 문제 있으면 False 유지
+
+# 10개 액션의 표준명(대소문자 포함)
+ALLOWED_ACTIONS = {
+    "BodyWeightSquats","HulaHoop","JumpingJack","PullUps","PushUps",
+    "Shotput","SoccerJuggling","TennisSwing","ThrowDiscus","WallPushups",
+}
+
+# 흔한 철자/대소문자/언더스코어 변형 → 표준명으로 치환
+ALIAS = {
+    "bodyweightsquats":"BodyWeightSquats",
+    "body_weight_squats":"BodyWeightSquats",
+    "bodysquats":"BodyWeightSquats",
+
+    "hulahoop":"HulaHoop",
+
+    "jumpingjack":"JumpingJack",
+
+    "pullups":"PullUps",
+
+    "pushups":"PushUps",
+
+    "shotput":"Shotput",
+
+    "soccerjuggling":"SoccerJuggling",
+    "soccer_juggling":"SoccerJuggling",
+
+    "tennisswing":"TennisSwing",
+    "tennis_swing":"TennisSwing",
+
+    "throwdiscus":"ThrowDiscus",
+    "throw_discus":"ThrowDiscus",
+
+    "wallpushups":"WallPushups",
+    "wall_pushups":"WallPushups",
+}
+
+def normalize_action(s: str) -> Optional[str]:
+    key = re.sub(r"[^A-Za-z0-9]+", "_", s).lower().strip("_")
+    canon = ALIAS.get(key)
+    return canon if canon in ALLOWED_ACTIONS else None
+
+def is_index_token(tok: str) -> bool:
+    return bool(re.fullmatch(r"\d{2,3}", tok))
+
+def extract_action_from_id(path_in_repo: str) -> Optional[str]:
+    """
+    <Model>_(...optional tokens...)_<Action>_<Index>_(hash).mp4
+    → 오른쪽부터 스캔해 첫 숫자 토큰(2~3자리)을 'Index'로 간주,
+      그 바로 앞 토큰을 액션으로 사용.
+    """
+    name = path_in_repo.rsplit("/", 1)[-1]
+    stem = name.rsplit(".", 1)[0]
+    toks = stem.split("_")
+    # 오른쪽→왼쪽 방향으로 숫자 토큰을 찾는다 (예: 01, 10, 123)
+    for i in range(len(toks)-1, -1, -1):
+        t = toks[i]
+        if re.fullmatch(r"\d{2,3}", t):
+            if i - 1 >= 0:
+                action = toks[i - 1]
+                return normalize_action(action)
+            break
+    return None
+
+
+def main():
+    base_url = f"https://huggingface.co/datasets/{REPO}/resolve/main/"
+    files = list_repo_files(repo_id=REPO, repo_type="dataset")
+    mp4s = [f for f in files if f.lower().endswith(".mp4")]
+    if INCLUDE_FOLDERS:
+        mp4s = [f for f in mp4s if any(f.startswith(folder + "/") for folder in INCLUDE_FOLDERS)]
+
+    videos, bad = [], []
+    for p in mp4s:
+        action = extract_action_from_id(p)
+        if action is None or action not in ALLOWED_ACTIONS:
+            bad.append(p); continue
+        url = base_url + p + ("?download=1" if ADD_DOWNLOAD_PARAM else "")
+        videos.append({"url": url, "id": p, "action": action})
+
+    random.shuffle(videos)
+    with open(OUT_PATH, "w", encoding="utf-8") as fp:
+        json.dump(videos, fp, ensure_ascii=False, indent=2)
+
+    print(f"[DONE] wrote {OUT_PATH} with {len(videos)} items from repo={REPO}")
+    from collections import Counter
+    c = Counter(v["action"] for v in videos)
+    for a in sorted(ALLOWED_ACTIONS):
+        print(f"  {a:16s} = {c.get(a,0)}")
+    if bad:
+        print(f"[INFO] skipped (not matched to ALLOWED after normalize): {len(bad)}")
+        for x in bad[:10]:
+            print("   -", x)
+
+if __name__ == "__main__":
+    main()

make_my_repo_dataset.py

@@ -0,0 +1,279 @@
+#!/usr/bin/env python3
+import os, re, csv, hashlib, shutil, subprocess
+from pathlib import Path
+from collections import defaultdict
+from typing import List, Tuple
+from huggingface_hub import HfApi, list_repo_files, hf_hub_download, create_repo
+from huggingface_hub import whoami
+from huggingface_hub import CommitOperationAdd
+from huggingface_hub import snapshot_download
+import time, glob
+
+# ========= CONFIG =========
+SRC_REPO = "XThomasBU/video_evals_ucf101"
+SRC_SUBDIRS = ["Hunyuan_videos", "Opensora_768", "RunwayGen4", "wan21_videos"]  # 필요한 것만
+
+# 너의 로컬 Wan2.2(60개)도 포함
+WAN22_LOCAL_ROOT = Path("/projectnb/ivc-ml/youngsun/Video_Eval/Datasets/Wan2p2/Generated_UCF")
+WAN22_TARGET_SUBDIR = "Wan2.2"   # 목적지 리포의 상위 폴더명(우리가 쓰던 이름 유지)
+
+DEST_REPO = "SGTLIM/ucf101_eval_unified"   # ← 네 계정으로 고정
+DEST_REPO_IS_PRIVATE = False
+
+# 용량 넉넉한 작업 디렉토리( /tmp 대신 /projectnb )
+WORKDIR = Path("/projectnb/ivc-ml/youngsun/tmp_ucf_unified")
+STAGING = WORKDIR / "staging"      # 스테이징 루트
+DL_ROOT = WORKDIR / "downloads"    # 원본 다운로드 저장소
+MAKE_SILENT = False
+TOKEN = os.getenv("HF_TOKEN")
+# =========================
+
+# /tmp 대신 WORKDIR을 임시/캐시로 사용
+os.environ.setdefault("TMPDIR", str(WORKDIR / "_tmp"))
+os.environ.setdefault("HF_HOME", "/projectnb/ivc-ml/youngsun/.cache/huggingface")
+
+# 디렉토리 보장
+(WORKDIR / "_tmp").mkdir(parents=True, exist_ok=True)
+STAGING.mkdir(parents=True, exist_ok=True)
+DL_ROOT.mkdir(parents=True, exist_ok=True)
+
+
+# 액션명 정규화(필요시 추가)
+ALIAS = {
+    "bodyweightsquats":"body_weight_squats",
+    "bodysquats":"body_weight_squats",
+    "body_weight_squats":"body_weight_squats",
+    "hulahoop":"hula_hoop",
+    "jumpingjack":"jumping_jack",
+    "pullups":"pull_ups",
+    "pushups":"push_ups",
+    "throwdiscus":"throw_discus",
+    "wallpushups":"wall_pushups",
+}
+
+
+def camel_action(s: str) -> str:
+    # 'body_weight_squats' -> 'BodyWeightSquats'
+    parts = s.strip("_").split("_")
+    return "".join(p.capitalize() for p in parts if p)
+
+def extract_action_from_remote(rel_remote: str) -> str:
+    """
+    HF 리포의 '원본 경로'에서만 액션을 뽑는다.
+    예: Hunyuan_videos/v_BodyWeightSquats_g05_c01.mp4 -> body_weight_squats
+    """
+    base = os.path.basename(rel_remote)
+    m = re.match(r"^v_([A-Za-z0-9]+)_", base)  # 반드시 v_<Action>_...
+    if m:
+        return slugify_action(m.group(1))
+    # (예외 케이스 최소화: 원본 리포는 v_패턴이므로 여기까지 올 일 거의 없음)
+    return slugify_action(base.split("_", 1)[0])
+
+
+
+def slugify_action(s: str) -> str:
+    s = s.strip().lower().replace(" ", "_")
+    s = re.sub(r"[^a-z0-9_]+", "_", s)
+    s = re.sub(r"_+", "_", s).strip("_")
+    return ALIAS.get(s, s)
+
+def model_slug(s: str) -> str:
+    s = s.strip().lower()
+    s = s.replace(" ", "_")
+    s = re.sub(r"[^a-z0-9_]+", "_", s)
+    s = re.sub(r"_+", "_", s).strip("_")
+    return s
+
+def sha1_8(p: Path) -> str:
+    h = hashlib.sha1()
+    with open(p, "rb") as f:
+        for chunk in iter(lambda: f.read(1024*1024), b""):
+            h.update(chunk)
+    return h.hexdigest()[:8]
+
+def ensure_ffmpeg():
+    # MAKE_SILENT=False 이면 ffmpeg 체크 안 함
+    if MAKE_SILENT and not shutil.which("ffmpeg"):
+        raise RuntimeError("ffmpeg not found, but MAKE_SILENT=True")
+
+def mute_copy(src: Path, dst: Path):
+    # (지금은 쓰지 않지만, 옵션 되살리기 대비로 남겨둠)
+    cmd = ["ffmpeg","-y","-i",str(src),"-c:v","copy","-an",str(dst)]
+    try:
+        subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+    except subprocess.CalledProcessError:
+        cmd = [
+            "ffmpeg","-y","-i",str(src),
+            "-vf","format=yuv420p","-movflags","+faststart",
+            "-c:v","libx264","-crf","18","-preset","veryfast",
+            "-an",str(dst)
+        ]
+        subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
+
+def extract_action_from_filename(fn: str) -> str:
+    # 예: v_BodyWeightSquats_g05_c01.mp4 → BodyWeightSquats
+    m = re.match(r"v_([A-Za-z0-9]+)", fn)
+    if m:
+        return slugify_action(m.group(1))
+    parts = fn.split("/")
+    if len(parts) >= 2:
+        return slugify_action(parts[-2])
+    stem = Path(fn).stem
+    stem = re.sub(r"^\w+_", "", stem)
+    return slugify_action(stem)
+    
+def stage_from_hf_model(api, model_dir, rows):
+    # 1) 원본 리포 파일 목록에서 이 폴더의 mp4만 고르기
+    all_remote = list_repo_files(repo_id=SRC_REPO, repo_type="dataset", token=TOKEN)
+    remotes = [p for p in all_remote if p.startswith(model_dir + "/") and p.lower().endswith(".mp4")]
+    print(f"[FETCH] {model_dir}: {len(remotes)} remote mp4 files")
+    if not remotes:
+        print(f"[WARN] no matches under {model_dir}")
+        return
+
+    # 2) 이 모델 전용 다운로드 폴��를 매번 깨끗하게 생성
+    dl_dir = DL_ROOT / model_dir
+    if dl_dir.exists():
+        shutil.rmtree(dl_dir)
+    dl_dir.mkdir(parents=True, exist_ok=True)
+
+    # 3) (remote_path, local_path) 쌍으로 보관 (액션 추출은 remote_path로!)
+    pairs = []
+    for rel_remote in remotes:
+        local = hf_hub_download(
+            repo_id=SRC_REPO,
+            filename=rel_remote,          # ← 원본 경로 유지
+            repo_type="dataset",
+            token=TOKEN,
+            local_dir=str(dl_dir),
+            local_dir_use_symlinks=False,
+        )
+        pairs.append((rel_remote, local))
+
+    # 4) 스테이징으로 이동 + 올바른 이름으로 리네임
+    folder_name = model_dir                 # 예: 'Hunyuan_videos', 'RunwayGen4', ...
+    dst_dir = STAGING / folder_name
+    dst_dir.mkdir(parents=True, exist_ok=True)
+
+    counters = defaultdict(int)
+    moved = 0
+    for rel_remote, local in sorted(pairs):
+        # ✅ 액션은 '원본 경로'에서만 추출
+        action_slug = extract_action_from_remote(rel_remote)      # 'body_weight_squats'
+        print(action_slug)
+        action_camel = camel_action(action_slug)                  # 'BodyWeightSquats'
+        print(action_camel)
+        counters[action_slug] += 1
+        idx = counters[action_slug]
+        h8 = sha1_8(Path(local))
+
+        # ✅ 최종 규칙: Model_Action_두자리_해시8.mp4
+        new_name = f"{folder_name}_{action_camel}_{idx:02d}_{h8}.mp4"
+
+        dst = dst_dir / new_name
+        shutil.move(local, dst)
+        moved += 1
+
+        rows.append([f"hf://{SRC_REPO}/{rel_remote}", folder_name, action_slug, idx, h8, f"{folder_name}/{new_name}"])
+
+    print(f"[STAGED] {model_dir}: moved {moved} files to {dst_dir}")
+
+def stage_from_local_wan22(rows: List[List[str]]):
+    pretty_model = "Wan2.2"
+    counters = defaultdict(int)
+    for class_dir in sorted([p for p in WAN22_LOCAL_ROOT.iterdir() if p.is_dir()]):
+        action = slugify_action(class_dir.name)
+        for mp4 in sorted([p for p in class_dir.iterdir() if p.suffix.lower()==".mp4"]):
+            counters[action] += 1
+            idx = counters[action]
+            h8 = sha1_8(mp4)
+            # new_name = f"wan2p2_{action}_{idx:02d}_{h8}.mp4"
+            pretty_model = WAN22_TARGET_SUBDIR      # ex) 'Wan2.2'
+            camel = camel_action(action)            # ex) 'PushUps'
+            new_name = f"{pretty_model}_{camel}_{idx:02d}_{h8}.mp4"
+            dst_dir = STAGING / pretty_model
+            dst_dir.mkdir(parents=True, exist_ok=True)
+            dst = dst_dir / new_name
+            if MAKE_SILENT:
+                mute_copy(mp4, dst)
+            else:
+                shutil.copy2(mp4, dst)  # 🔊 원본 그대로
+            rows.append([str(mp4), pretty_model, action, idx, h8, f"{pretty_model}/{new_name}"])
+
+def main():
+    if not TOKEN:
+        raise SystemExit("Set HF_TOKEN with write permission.")
+    ensure_ffmpeg()
+
+    # 🔻 작업 디렉토리 초기화 (여기 추가)
+    if STAGING.exists():
+        shutil.rmtree(STAGING)
+    if DL_ROOT.exists():
+        shutil.rmtree(DL_ROOT)
+    STAGING.mkdir(parents=True, exist_ok=True)
+    DL_ROOT.mkdir(parents=True, exist_ok=True)
+
+    api = HfApi()
+    try:
+        create_repo(repo_id=DEST_REPO, repo_type="dataset", private=DEST_REPO_IS_PRIVATE, token=TOKEN, exist_ok=True)
+    except Exception:
+        pass
+
+    rows: List[List[str]] = []
+
+    # 1) 원본 리포에서 4개 모델 폴더만 스테이징
+    for sub in SRC_SUBDIRS:
+        stage_from_hf_model(api, sub, rows)
+
+    # 2) 로컬 Wan2.2(60개) 추가
+    stage_from_local_wan22(rows)
+
+    # 3) 매핑 csv 저장
+    with open(STAGING / "mapping.csv", "w", newline="", encoding="utf-8") as f:
+        w = csv.writer(f)
+        w.writerow(["source","model","action","idx","hash8","dest_path"])
+        w.writerows(rows)
+
+    # 4) 폴더별로 명시적 추가 커밋(변경감지 우회)
+    subdirs_to_push = ["Hunyuan_videos", "Opensora_768", "RunwayGen4", "wan21_videos"] #, "Wan2.2"
+    for sd in subdirs_to_push:
+        sd_path = STAGING / sd
+        if not sd_path.exists():
+            print(f"[WARN] skip missing subdir: {sd}")
+            continue
+        files = sorted(str(p) for p in sd_path.rglob("*.mp4"))
+        if not files:
+            print(f"[WARN] skip empty subdir: {sd}")
+            continue
+
+        print(f"[PUSH] {sd} ... ({len(files)} files)")
+        ops = []
+        for fp in files:
+            rel_in_repo = os.path.relpath(fp, start=STAGING)  # 예: Hunyuan_videos/xxx.mp4
+            ops.append(CommitOperationAdd(path_in_repo=rel_in_repo, path_or_fileobj=fp))
+
+        api.create_commit(
+            repo_id=DEST_REPO,
+            repo_type="dataset",
+            operations=ops,
+            commit_message=f"Add {sd} ({len(files)} files)",
+            token=TOKEN,
+        )
+
+    # mapping.csv 업로드
+    map_csv = STAGING / "mapping.csv"
+    if map_csv.exists():
+        api.upload_file(
+            path_or_fileobj=str(map_csv),
+            path_in_repo="mapping.csv",
+            repo_id=DEST_REPO,
+            repo_type="dataset",
+            token=TOKEN,
+            commit_message="Add mapping.csv",
+        )
+
+    print(f"[DONE] Pushed to https://huggingface.co/datasets/{DEST_REPO}")
+
+if __name__ == "__main__":
+    main()
+    

requirements.txt

@@ -0,0 +1,3 @@
+gradio==4.44.0
+huggingface_hub>=0.25.0
+

test.py

@@ -0,0 +1,21 @@
+from huggingface_hub import list_repo_files
+from collections import Counter
+
+REPO="SGTLIM/ucf101_eval_unified"
+files = list_repo_files(repo_id=REPO, repo_type="dataset")
+mp4s = [f for f in files if f.lower().endswith(".mp4")]
+by_top = Counter(f.split("/",1)[0] for f in mp4s)
+print(by_top)  # 각 최상위 폴더별 mp4 개수
+
+# 액션 파싱(두 자리 인덱스 앞 토큰)
+import re
+def act(p):
+    stem = p.rsplit("/",1)[-1].rsplit(".",1)[0].split("_")
+    for i,t in enumerate(stem):
+        if re.fullmatch(r"\d{2,3}", t):
+            return stem[i-1] if i>0 else None
+    return None
+
+for folder in ["Wan2.2","RunwayGen4","Hunyuan_videos","Opensora_768","wan21_videos"]:
+    xs = [f for f in mp4s if f.startswith(folder + "/")]
+    print(folder, len(xs), "files")

upload_videos_toHF.py

@@ -0,0 +1,53 @@
+import os
+from huggingface_hub import HfApi
+
+# 1) 환경변수/설정
+HF_TOKEN = os.environ["HF_TOKEN"]  # 반드시 Write 권한 토큰이어야 함
+# whoami()["name"] 결과(또는 조직명)로 맞춰 쓰기
+OWNER = os.getenv("HF_OWNER", "sgtlim")         # 필요시: export HF_OWNER=your_name_or_org
+REPO_ID = f"{OWNER}/videoeval_results"          # 업로드할 데이터셋 리포
+
+# 2) 로컬 파일 경로 (네가 준 경로 그대로)
+WALLPUSHUPS_REAL = "/projectnb/ivc-ml/xthomas/SHARED/video_evals/ucf101_10classes/WallPushups/v_WallPushups_g01_c01.mp4"
+WALLPUSHUPS_BAD  = "/projectnb/ivc-ml/xthomas/SHARED/video_evals/YOUTUBE_DATA/WallPushups__EE329FD74B.mp4"
+
+# 3) HF 내 저장 경로 (app.py에서 이 경로로 다운로드할 거임)
+WALLPUSHUPS_REAL_IN_REPO = "examples/WallPushUps_real.mp4"
+WALLPUSHUPS_BAD_IN_REPO  = "examples/WallPushUps_bad.mp4"
+
+api = HfApi()
+
+# A) 데이터셋 리포 생성(이미 있으면 패스)
+api.create_repo(
+    repo_id=REPO_ID,
+    repo_type="dataset",
+    private=True,
+    exist_ok=True,
+    token=HF_TOKEN,
+)
+
+# B) 업로드
+api.upload_file(
+    path_or_fileobj=WALLPUSHUPS_REAL,
+    path_in_repo=WALLPUSHUPS_REAL_IN_REPO,
+    repo_id=REPO_ID, repo_type="dataset", token=HF_TOKEN,
+    commit_message="add WallPushUps real/good example"
+)
+
+api.upload_file(
+    path_or_fileobj=WALLPUSHUPS_BAD,
+    path_in_repo=WALLPUSHUPS_BAD_IN_REPO,
+    repo_id=REPO_ID, repo_type="dataset", token=HF_TOKEN,
+    commit_message="add WallPushUps generated/bad example"
+)
+
+# C) 확인용 출력 (app.py에서 쓸 URL/파일명)
+print("✅ Uploaded to dataset:", REPO_ID)
+print("   -", WALLPUSHUPS_REAL_IN_REPO)
+print("   -", WALLPUSHUPS_BAD_IN_REPO)
+print("\nUse these in app.py hf_hub_download(filename=...):")
+print("   filename='examples/WallPushUps_real.mp4'")
+print("   filename='examples/WallPushUps_bad.mp4'")
+print("\nDirect URLs (repo public일 때만 브라우저 재생):")
+print(f"   https://huggingface.co/datasets/{REPO_ID}/resolve/main/examples/WallPushUps_real.mp4")
+print(f"   https://huggingface.co/datasets/{REPO_ID}/resolve/main/examples/WallPushUps_bad.mp4")

videos.json

@@ -0,0 +1,1502 @@
+[
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Wan2.2/Wan2.2_BodyWeightSquats_03_g09_c02.mp4",
+    "id": "Wan2.2/Wan2.2_BodyWeightSquats_03_g09_c02.mp4",
+    "action": "BodyWeightSquats"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Opensora_768/Opensora_768_WallPushups_02_df41b139.mp4",
+    "id": "Opensora_768/Opensora_768_WallPushups_02_df41b139.mp4",
+    "action": "WallPushups"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/wan21_videos/wan21_videos_PushUps_05_f710180a.mp4",
+    "id": "wan21_videos/wan21_videos_PushUps_05_f710180a.mp4",
+    "action": "PushUps"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Opensora_768/Opensora_768_PullUps_06_3e4cc58d.mp4",
+    "id": "Opensora_768/Opensora_768_PullUps_06_3e4cc58d.mp4",
+    "action": "PullUps"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/wan21_videos/wan21_videos_HulaHoop_02_832c4e43.mp4",
+    "id": "wan21_videos/wan21_videos_HulaHoop_02_832c4e43.mp4",
+    "action": "HulaHoop"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Opensora_768/Opensora_768_Shotput_01_11916263.mp4",
+    "id": "Opensora_768/Opensora_768_Shotput_01_11916263.mp4",
+    "action": "Shotput"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/wan21_videos/wan21_videos_PullUps_01_e5ac30e6.mp4",
+    "id": "wan21_videos/wan21_videos_PullUps_01_e5ac30e6.mp4",
+    "action": "PullUps"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_Shotput_01_90e8d35b.mp4",
+    "id": "RunwayGen4/RunwayGen4_Shotput_01_90e8d35b.mp4",
+    "action": "Shotput"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_SoccerJuggling_04_39be32d1.mp4",
+    "id": "RunwayGen4/RunwayGen4_SoccerJuggling_04_39be32d1.mp4",
+    "action": "SoccerJuggling"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/wan21_videos/wan21_videos_Soccerjuggling_03_a2b2002a.mp4",
+    "id": "wan21_videos/wan21_videos_Soccerjuggling_03_a2b2002a.mp4",
+    "action": "SoccerJuggling"
+  },
+  {
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+    "id": "wan21_videos/wan21_videos_PushUps_02_839540a7.mp4",
+    "action": "PushUps"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Hunyuan_videos/Hunyuan_videos_Tennisswing_06_bb80e7c4.mp4",
+    "id": "Hunyuan_videos/Hunyuan_videos_Tennisswing_06_bb80e7c4.mp4",
+    "action": "TennisSwing"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_PullUps_04_96757669.mp4",
+    "id": "RunwayGen4/RunwayGen4_PullUps_04_96757669.mp4",
+    "action": "PullUps"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Wan2.2/Wan2.2_SoccerJuggling_01_g02_c02.mp4",
+    "id": "Wan2.2/Wan2.2_SoccerJuggling_01_g02_c02.mp4",
+    "action": "SoccerJuggling"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_Shotput_02_455cfce3.mp4",
+    "id": "RunwayGen4/RunwayGen4_Shotput_02_455cfce3.mp4",
+    "action": "Shotput"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_WallPushups_01_a09ec727.mp4",
+    "id": "RunwayGen4/RunwayGen4_WallPushups_01_a09ec727.mp4",
+    "action": "WallPushups"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Hunyuan_videos/Hunyuan_videos_BodyWeightSquats_03_88c332fc.mp4",
+    "id": "Hunyuan_videos/Hunyuan_videos_BodyWeightSquats_03_88c332fc.mp4",
+    "action": "BodyWeightSquats"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/wan21_videos/wan21_videos_Shotput_06_c6441ef8.mp4",
+    "id": "wan21_videos/wan21_videos_Shotput_06_c6441ef8.mp4",
+    "action": "Shotput"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Opensora_768/Opensora_768_BodyWeightSquats_05_f31479e5.mp4",
+    "id": "Opensora_768/Opensora_768_BodyWeightSquats_05_f31479e5.mp4",
+    "action": "BodyWeightSquats"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Opensora_768/Opensora_768_Soccerjuggling_02_35358a25.mp4",
+    "id": "Opensora_768/Opensora_768_Soccerjuggling_02_35358a25.mp4",
+    "action": "SoccerJuggling"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Hunyuan_videos/Hunyuan_videos_ThrowDiscus_04_c598b9d1.mp4",
+    "id": "Hunyuan_videos/Hunyuan_videos_ThrowDiscus_04_c598b9d1.mp4",
+    "action": "ThrowDiscus"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_PushUps_02_582e8a8f.mp4",
+    "id": "RunwayGen4/RunwayGen4_PushUps_02_582e8a8f.mp4",
+    "action": "PushUps"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_Shotput_05_26ec7ac5.mp4",
+    "id": "RunwayGen4/RunwayGen4_Shotput_05_26ec7ac5.mp4",
+    "action": "Shotput"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Wan2.2/Wan2.2_SoccerJuggling_02_g03_c01.mp4",
+    "id": "Wan2.2/Wan2.2_SoccerJuggling_02_g03_c01.mp4",
+    "action": "SoccerJuggling"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/wan21_videos/wan21_videos_WallPushups_02_8f800db9.mp4",
+    "id": "wan21_videos/wan21_videos_WallPushups_02_8f800db9.mp4",
+    "action": "WallPushups"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Opensora_768/Opensora_768_ThrowDiscus_04_7d4c3099.mp4",
+    "id": "Opensora_768/Opensora_768_ThrowDiscus_04_7d4c3099.mp4",
+    "action": "ThrowDiscus"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Opensora_768/Opensora_768_JumpingJack_01_66318db4.mp4",
+    "id": "Opensora_768/Opensora_768_JumpingJack_01_66318db4.mp4",
+    "action": "JumpingJack"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Opensora_768/Opensora_768_Shotput_05_5e12b84d.mp4",
+    "id": "Opensora_768/Opensora_768_Shotput_05_5e12b84d.mp4",
+    "action": "Shotput"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Wan2.2/Wan2.2_WallPushups_02_g04_c01.mp4",
+    "id": "Wan2.2/Wan2.2_WallPushups_02_g04_c01.mp4",
+    "action": "WallPushups"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/wan21_videos/wan21_videos_JumpingJack_01_05a3068d.mp4",
+    "id": "wan21_videos/wan21_videos_JumpingJack_01_05a3068d.mp4",
+    "action": "JumpingJack"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_HulaHoop_05_e288ca8f.mp4",
+    "id": "RunwayGen4/RunwayGen4_HulaHoop_05_e288ca8f.mp4",
+    "action": "HulaHoop"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_WallPushups_03_1fb482ac.mp4",
+    "id": "RunwayGen4/RunwayGen4_WallPushups_03_1fb482ac.mp4",
+    "action": "WallPushups"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_PullUps_06_8ca9f347.mp4",
+    "id": "RunwayGen4/RunwayGen4_PullUps_06_8ca9f347.mp4",
+    "action": "PullUps"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Wan2.2/Wan2.2_SoccerJuggling_03_g09_c04.mp4",
+    "id": "Wan2.2/Wan2.2_SoccerJuggling_03_g09_c04.mp4",
+    "action": "SoccerJuggling"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/Wan2.2/Wan2.2_PushUps_04_g05_c02.mp4",
+    "id": "Wan2.2/Wan2.2_PushUps_04_g05_c02.mp4",
+    "action": "PushUps"
+  },
+  {
+    "url": "https://huggingface.co/datasets/SGTLIM/ucf101_eval_unified/resolve/main/RunwayGen4/RunwayGen4_BodyWeightSquats_03_e330d8c2.mp4",
+    "id": "RunwayGen4/RunwayGen4_BodyWeightSquats_03_e330d8c2.mp4",
+    "action": "BodyWeightSquats"
+  }
+]