bash -lc cat > /mnt/data/app.py <<'PY'
import atexit
import csv
import io
import os
import re
import shutil
import tempfile
import threading
import time
import unicodedata
import uuid
import wave
from datetime import datetime

import gradio as gr
import librosa
import numpy as np
from huggingface_hub import CommitOperationAdd, HfApi, hf_hub_download
from openwakeword.model import Model

# ============================================================
# CẤU HÌNH
# ============================================================
DATASET_ID = "lumiwakeword/lumioiv1"
HF_TOKEN = os.getenv("HF_TOKEN")
MODEL_PATH = "loo_mee_oy_v2.onnx"

# Buffer / batch upload
BUFFER_DIR = "buffer"
AUDIO_BUFFER_DIR = os.path.join(BUFFER_DIR, "audios")
PENDING_QUEUE_FILE = os.path.join(BUFFER_DIR, "pending_queue.csv")
FLUSH_INTERVAL_SEC = 600          # 10 phút
MAX_PENDING_BEFORE_FLUSH = 100    # flush sớm nếu đủ nhiều mẫu

# Metadata trên Hub: giữ nguyên schema cũ để tương thích pipeline hiện tại
HUB_METADATA_HEADER = ["file_name"]

api = HfApi(token=HF_TOKEN)

os.makedirs(AUDIO_BUFFER_DIR, exist_ok=True)

buffer_lock = threading.Lock()
flush_in_progress = threading.Event()
shutdown_event = threading.Event()

# --- KHỞI TẠO MODEL ---
oww_model = None
model_ready = threading.Event()


# ============================================================
# MODEL LOADER
# ============================================================
def load_model():
    global oww_model
    try:
        t_start = time.perf_counter()
        dummy_wav = np.zeros(16000, dtype=np.float32)
        with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as f:
            tmp_path = f.name
        with wave.open(tmp_path, "w") as wf:
            wf.setnchannels(1)
            wf.setsampwidth(2)
            wf.setframerate(16000)
            wf.writeframes((dummy_wav * 32767).astype(np.int16).tobytes())
        librosa.load(tmp_path, sr=16000)
        os.unlink(tmp_path)
        print(f"  librosa warm-up: {(time.perf_counter()-t_start)*1000:.0f}ms")

        t2 = time.perf_counter()
        m = Model(wakeword_model_paths=[MODEL_PATH])
        print(f"  Model load: {(time.perf_counter()-t2)*1000:.0f}ms")

        stable_count = 0
        for i in range(80):
            t_iter = time.perf_counter()
            m.predict(np.zeros(1280, dtype=np.int16))
            elapsed_ms = (time.perf_counter() - t_iter) * 1000
            if i < 3:
                continue
            if elapsed_ms < 5.0:
                stable_count += 1
                if stable_count >= 5:
                    print(f"  ONNX JIT stable sau {i+1} iters ({elapsed_ms:.1f}ms/iter)")
                    break
            else:
                stable_count = 0

        oww_model = m
        print(f"✅ Fully ready — tổng boot: {(time.perf_counter()-t_start):.1f}s")
    except Exception as e:
        print(f"❌ Model Error: {e}")
    finally:
        model_ready.set()


threading.Thread(target=load_model, daemon=True).start()


# ============================================================
# BUFFER / QUEUE HELPERS
# ============================================================
def ensure_pending_queue():
    if not os.path.exists(PENDING_QUEUE_FILE):
        with open(PENDING_QUEUE_FILE, "w", newline="", encoding="utf-8") as f:
            writer = csv.writer(f)
            writer.writerow(["local_path", "repo_path", "speaker", "score", "created_at"])


def read_pending_queue():
    ensure_pending_queue()
    with open(PENDING_QUEUE_FILE, "r", encoding="utf-8") as f:
        rows = list(csv.reader(f))
    if not rows:
        return ["local_path", "repo_path", "speaker", "score", "created_at"], []
    return rows[0], rows[1:]


def write_pending_queue(header, rows):
    with open(PENDING_QUEUE_FILE, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(header)
        writer.writerows(rows)


def get_pending_count():
    with buffer_lock:
        _, rows = read_pending_queue()
        return len(rows)


def fetch_existing_metadata_rows():
    try:
        existing = hf_hub_download(
            repo_id=DATASET_ID,
            filename="metadata.csv",
            repo_type="dataset",
            token=HF_TOKEN,
        )
        with open(existing, "r", encoding="utf-8") as f:
            rows = list(csv.reader(f))
        if not rows:
            return [HUB_METADATA_HEADER]
        # Nếu file cũ chỉ có 1 cột hoặc header khác, vẫn cố chuẩn hóa tối thiểu.
        header = rows[0] if rows[0] else HUB_METADATA_HEADER
        if len(header) == 1:
            normalized = [HUB_METADATA_HEADER]
            for row in rows[1:]:
                if row:
                    normalized.append([row[0]])
            return normalized
        return rows
    except Exception:
        return [HUB_METADATA_HEADER]


def enqueue_sample_local(audio_path, score, speaker_name):
    ensure_pending_queue()

    spk = slugify(speaker_name) if speaker_name else "unknown"
    created_at = datetime.utcnow().replace(microsecond=0).isoformat() + "Z"
    ext = ".wav"
    filename = f"lumi_{spk}_{uuid.uuid4().hex[:8]}{ext}"
    local_buffer_path = os.path.join(AUDIO_BUFFER_DIR, filename)
    repo_audio_path = f"audios/{filename}"

    # Chuẩn hóa thành WAV 16k mono để dataset đồng nhất.
    y, _ = librosa.load(audio_path, sr=16000, mono=True)
    y = np.clip(y, -1.0, 1.0)
    audio_int16 = (y * 32767).astype(np.int16)

    with wave.open(local_buffer_path, "wb") as wf:
        wf.setnchannels(1)
        wf.setsampwidth(2)
        wf.setframerate(16000)
        wf.writeframes(audio_int16.tobytes())

    with buffer_lock:
        header, rows = read_pending_queue()
        rows.append([local_buffer_path, repo_audio_path, spk, f"{score:.4f}", created_at])
        write_pending_queue(header, rows)

    return filename, local_buffer_path, repo_audio_path


def flush_buffer_to_hf():
    if flush_in_progress.is_set():
        return False, "flush đang chạy"

    if not HF_TOKEN:
        return False, "HF_TOKEN chưa được cấu hình"

    flush_in_progress.set()
    try:
        with buffer_lock:
            header, pending_rows = read_pending_queue()
            if not pending_rows:
                return True, "không có mẫu chờ"
            snapshot_rows = list(pending_rows)

        valid_rows = []
        missing_rows = []
        for row in snapshot_rows:
            if not row or len(row) < 2:
                continue
            local_path = row[0]
            if os.path.exists(local_path):
                valid_rows.append(row)
            else:
                missing_rows.append(row)

        if not valid_rows and missing_rows:
            with buffer_lock:
                cur_header, cur_rows = read_pending_queue()
                missing_keys = {(r[0], r[1]) for r in missing_rows if len(r) >= 2}
                remaining_rows = [
                    r for r in cur_rows
                    if len(r) >= 2 and (r[0], r[1]) not in missing_keys
                ]
                write_pending_queue(cur_header, remaining_rows)
            return False, "có bản ghi pending bị mất file local"

        if not valid_rows:
            return True, "không có file hợp lệ để flush"

        existing_rows = fetch_existing_metadata_rows()
        existing_data = existing_rows[1:] if len(existing_rows) > 1 else []
        existing_set = {row[0] for row in existing_data if row}

        operations = []
        new_metadata_rows = []
        for row in valid_rows:
            local_path, repo_path = row[0], row[1]
            operations.append(
                CommitOperationAdd(
                    path_in_repo=repo_path,
                    path_or_fileobj=local_path,
                )
            )
            if repo_path not in existing_set:
                new_metadata_rows.append([repo_path])

        merged_rows = [HUB_METADATA_HEADER] + existing_data + new_metadata_rows
        buf = io.StringIO()
        csv.writer(buf).writerows(merged_rows)
        csv_bytes = buf.getvalue().encode("utf-8")

        operations.append(
            CommitOperationAdd(
                path_in_repo="metadata.csv",
                path_or_fileobj=io.BytesIO(csv_bytes),
            )
        )

        commit_msg = (
            f"batch upload {len(valid_rows)} samples @ "
            f"{datetime.utcnow().replace(microsecond=0).isoformat()}Z"
        )

        api.create_commit(
            repo_id=DATASET_ID,
            repo_type="dataset",
            operations=operations,
            commit_message=commit_msg,
            token=HF_TOKEN,
        )

        # Chỉ xóa queue/file local sau khi commit thành công.
        with buffer_lock:
            cur_header, cur_rows = read_pending_queue()
            flushed_keys = {(r[0], r[1]) for r in valid_rows if len(r) >= 2}
            missing_keys = {(r[0], r[1]) for r in missing_rows if len(r) >= 2}

            remaining_rows = [
                r for r in cur_rows
                if len(r) >= 2 and (r[0], r[1]) not in flushed_keys and (r[0], r[1]) not in missing_keys
            ]
            write_pending_queue(cur_header, remaining_rows)

        for row in valid_rows:
            local_path = row[0]
            try:
                if os.path.exists(local_path):
                    os.remove(local_path)
            except Exception as rm_err:
                print(f"⚠️ Không xóa được file buffer {local_path}: {rm_err}")

        print(f"✅ Flush thành công {len(valid_rows)} mẫu lên Hugging Face")
        return True, f"đã flush {len(valid_rows)} mẫu"

    except Exception as e:
        print(f"❌ Flush lỗi: {e}")
        return False, str(e)
    finally:
        flush_in_progress.clear()


def flush_worker():
    while not shutdown_event.is_set():
        try:
            shutdown_event.wait(FLUSH_INTERVAL_SEC)
            if shutdown_event.is_set():
                break
            ok, msg = flush_buffer_to_hf()
            print(f"[flush_worker] ok={ok} msg={msg}")
        except Exception as e:
            print(f"❌ Flush worker error: {e}")


threading.Thread(target=flush_worker, daemon=True).start()


def shutdown_cleanup():
    shutdown_event.set()
    try:
        pending = get_pending_count()
        if pending > 0:
            ok, msg = flush_buffer_to_hf()
            print(f"[shutdown_flush] ok={ok} msg={msg}")
    except Exception as e:
        print(f"⚠️ shutdown cleanup error: {e}")


atexit.register(shutdown_cleanup)


# ============================================================
# PYTHON LOGIC
# ============================================================
def verify_audio(audio_path):
    if not model_ready.wait(timeout=30):
        return "⏳ Model đang khởi động, thử lại sau vài giây...", gr.update(interactive=False), 0.0
    if not audio_path:
        return "⚠️ Hãy ghi âm trước khi kiểm tra!", gr.update(interactive=False), 0.0
    if oww_model is None:
        return "❌ Model lỗi, vui lòng reload trang.", gr.update(interactive=False), 0.0
    try:
        y, sr = librosa.load(audio_path, sr=16000)
        audio_int16 = (y * 32767).astype(np.int16)
        oww_model.reset()
        max_score = 0.0
        chunk_size = 1280
        for i in range(0, len(audio_int16), chunk_size):
            chunk = audio_int16[i: i + chunk_size]
            if len(chunk) < chunk_size:
                chunk = np.pad(chunk, (0, chunk_size - len(chunk)))
            prediction = oww_model.predict(chunk)
            score_val = list(prediction.values())[0]
            current_score = float(score_val[0]) if isinstance(score_val, (np.ndarray, list)) else float(score_val)
            if current_score > max_score:
                max_score = current_score
        for _ in range(7):
            oww_model.predict(np.zeros(1280, dtype=np.int16))
        if max_score >= 0.8:
            return f"✅ Hợp lệ! Điểm: {max_score:.2f} — Nhấn GỬI để lưu mẫu", gr.update(interactive=True), max_score
        else:
            return f"❌ Chưa đạt (Điểm: {max_score:.2f}). Hãy nói rõ hơn và thử lại!", gr.update(interactive=False), max_score
    except Exception as e:
        return f"❌ Lỗi xử lý: {str(e)}", gr.update(interactive=False), 0.0


def upload_final(audio_path, score, speaker_name):
    if not audio_path:
        return "❌ Không có file để gửi.", gr.update(interactive=False), None
    try:
        _, _, _ = enqueue_sample_local(audio_path, score, speaker_name)
        pending_count = get_pending_count()

        # Flush sớm nếu hàng đợi đạt ngưỡng.
        if pending_count >= MAX_PENDING_BEFORE_FLUSH and not flush_in_progress.is_set():
            threading.Thread(target=flush_buffer_to_hf, daemon=True).start()

        return (
            f"🎉 Đã nhận mẫu! Đang chờ đồng bộ. Số mẫu trong hàng đợi: {pending_count}",
            gr.update(interactive=False),
            None,
        )
    except Exception as e:
        return f"❌ Lỗi lưu buffer: {e}", gr.update(interactive=True), audio_path


def slugify(text):
    text = unicodedata.normalize('NFD', text or '')
    text = ''.join(c for c in text if unicodedata.category(c) != 'Mn')
    text = text.lower().strip()
    text = re.sub(r'[^a-z0-9]+', '_', text)
    return text.strip('_') or 'unknown'


def reset_ui():
    return (
        None,
        'Sẵn sàng — Nhấn mic và đọc "Lumi ơi"',
        gr.update(interactive=False),
        0.0,
    )


# ============================================================
# CSS
# ============================================================
CSS = """
@import url('https://fonts.googleapis.com/css2?family=DM+Sans:ital,opsz,wght@0,9..40,300;0,9..40,400;0,9..40,500;0,9..40,600;1,9..40,300&family=DM+Mono:wght@400;500&display=swap');
*, *::before, *::after { box-sizing: border-box; margin: 0; padding: 0; }
:root {
    --bg:      #f4f6fb;
    --surface: #ffffff;
    --surface2:#eef1f8;
    --border:  #dde2ef;
    --border2: #c8d0e4;
    --accent:  #3b7ef4;
    --accent2: #1a5fe0;
    --text:    #1a2236;
    --text2:   #4a5778;
    --text3:   #8a96b0;
    --mono:    'DM Mono', monospace;
    --fs-xs:   clamp(0.72rem, 1.8vw, 0.85rem);
    --fs-sm:   clamp(0.85rem, 2vw,   1rem);
    --fs-base: clamp(0.95rem, 2.2vw, 1.15rem);
    --fs-lg:   clamp(1.1rem,  2.6vw, 1.4rem);
    --fs-xl:   clamp(1.3rem,  3vw,   1.7rem);
    --sp-xs: clamp(4px,  1vw,  8px);
    --sp-sm: clamp(8px,  2vw,  14px);
    --sp-md: clamp(12px, 2.5vw,20px);
    --sp-lg: clamp(16px, 3vw,  28px);
}
body, .gradio-container {
    background: var(--bg) !important;
    font-family: 'DM Sans', sans-serif !important;
    color: var(--text) !important;
    font-size: var(--fs-base);
}
.gradio-container {
    max-width: min(96vw, 860px) !important;
    margin: 0 auto !important;
    padding: clamp(16px,4vw,40px) clamp(12px,4vw,36px) 60px !important;
}
.lumi-header {
    display: flex; align-items: center; gap: var(--sp-md);
    background: linear-gradient(135deg, #eef3ff 0%, #f4f6fb 100%);
    border: 1px solid var(--border);
    border-radius: clamp(10px,2vw,16px);
    padding: var(--sp-md) var(--sp-lg);
    margin-bottom: var(--sp-md);
}
.lumi-icon-wrap {
    width: clamp(38px,8vw,52px); height: clamp(38px,8vw,52px);
    background: var(--surface2); border: 1px solid var(--border2);
    border-radius: clamp(8px,2vw,13px);
    display: flex; align-items: center; justify-content: center;
    font-size: clamp(16px,4vw,24px); flex-shrink: 0;
    position: relative; overflow: hidden;
}
.lumi-icon-wrap::before {
    content: ''; position: absolute; inset: 0;
    background: radial-gradient(circle at 30% 30%, rgba(59,126,244,0.12), transparent 70%);
}
.lumi-header-text h1 {
    font-size: var(--fs-xl); font-weight: 600; color: var(--text);
    letter-spacing: -0.3px; line-height: 1.25;
}
.lumi-header-text p {
    font-size: var(--fs-sm); color: var(--text3); margin-top: 2px;
}
.lumi-progress {
    display: flex; align-items: center;
    margin-bottom: var(--sp-md);
}
.lumi-prog-step { display: flex; align-items: center; gap: var(--sp-xs); flex: 1; }
.lumi-prog-dot {
    width: clamp(24px,5vw,32px); height: clamp(24px,5vw,32px);
    border-radius: 50%; background: var(--surface2); border: 1px solid var(--border);
    display: flex; align-items: center; justify-content: center;
    font-size: var(--fs-xs); font-weight: 600; color: var(--text3);
    font-family: var(--mono); flex-shrink: 0;
}
.lumi-prog-info { flex: 1; }
.lumi-prog-title { font-size: var(--fs-xs); font-weight: 600; color: var(--text3); letter-spacing: 0.4px; }
.lumi-prog-line {
    height: 1px; background: var(--border);
    width: clamp(14px,3vw,28px); flex-shrink: 0; margin: 0 var(--sp-xs);
}
.lumi-guide {
    background: var(--surface); border: 1px solid var(--border);
    border-radius: clamp(10px,2vw,14px);
    padding: var(--sp-md) var(--sp-lg);
    margin-bottom: var(--sp-md);
    display: flex; gap: var(--sp-sm); align-items: flex-start;
}
.lumi-guide-icon { font-size: var(--fs-lg); margin-top: 2px; flex-shrink: 0; opacity: 0.8; }
.lumi-guide-body { flex: 1; }
.lumi-guide-title {
    font-size: var(--fs-xs); font-weight: 700; color: var(--text3);
    text-transform: uppercase; letter-spacing: 0.9px; margin-bottom: var(--sp-xs);
}
.lumi-guide-steps { display: flex; flex-direction: column; gap: var(--sp-xs); }
.lumi-guide-step {
    display: flex; align-items: baseline; gap: var(--sp-xs);
    font-size: var(--fs-base); color: var(--text2); line-height: 1.5;
}
.lumi-guide-step b {
    color: var(--text);
    font-weight: 700;
}
.lumi-guide-num {
    font-family: var(--mono); font-size: var(--fs-xs);
    color: var(--accent); background: rgba(59,126,244,0.08);
    border: 1px solid rgba(59,126,244,0.2);
    border-radius: 4px; padding: 1px 5px; flex-shrink: 0;
}
.lumi-keyword {
    color: var(--accent2); font-weight: 600;
    background: rgba(59,126,244,0.08); border-radius: 4px; padding: 0 4px;
}
.lumi-tips {
    margin-top: var(--sp-sm); padding-top: var(--sp-sm);
    border-top: 1px solid var(--border);
    display: flex; flex-direction: column; gap: var(--sp-xs);
}
.lumi-tip {
    display: flex; align-items: center; gap: var(--sp-xs);
    font-size: var(--fs-sm); color: var(--text2);
    background: rgba(59,126,244,0.05);
    border: 1px solid rgba(59,126,244,0.12);
    border-radius: 7px; padding: var(--sp-xs) var(--sp-sm); line-height: 1.45;
}
.lumi-tip b { color: var(--text); font-weight: 600; }
.tip-icon { font-size: var(--fs-base); flex-shrink: 0; }
.lumi-recorder-card {
    background: linear-gradient(145deg, #1e2d5a 0%, #162447 55%, #1a3060 100%);
    border: 1px solid rgba(99,140,255,0.25);
    border-radius: clamp(12px,3vw,20px);
    padding: var(--sp-lg);
    margin-bottom: var(--sp-md);
    box-shadow: 0 6px 28px rgba(30,45,90,0.16), inset 0 1px 0 rgba(255,255,255,0.06);
    position: relative; overflow: hidden;
}
.lumi-recorder-card::before {
    content: ''; position: absolute; top: -50px; right: -50px;
    width: clamp(100px,20vw,180px); height: clamp(100px,20vw,180px);
    border-radius: 50%;
    background: radial-gradient(circle, rgba(99,140,255,0.1) 0%, transparent 70%);
    pointer-events: none;
}
.lumi-recorder-card::after {
    content: ''; position: absolute; bottom: -30px; left: -30px;
    width: clamp(80px,16vw,140px); height: clamp(80px,16vw,140px);
    border-radius: 50%;
    background: radial-gradient(circle, rgba(139,92,246,0.07) 0%, transparent 70%);
    pointer-events: none;
}
#lumi-recorder-ui {
    display: flex; flex-direction: column;
    align-items: center; gap: var(--sp-md);
    position: relative; z-index: 1;
}
#lumi-mic-btn {
    width: clamp(60px,13vw,88px); height: clamp(60px,13vw,88px);
    border-radius: 50%;
    background: rgba(255,255,255,0.09);
    border: 1.5px solid rgba(255,255,255,0.18);
    cursor: pointer; display: flex; align-items: center; justify-content: center;
    font-size: clamp(24px,5.5vw,36px);
    outline: none; transition: all 0.2s cubic-bezier(.4,0,.2,1);
    position: relative;
}
#lumi-mic-btn::after {
    content: ''; position: absolute; inset: -1px;
    border-radius: 50%; border: 1px solid transparent; transition: all 0.3s ease;
}
#lumi-mic-btn:hover {
    background: rgba(99,140,255,0.22); border-color: rgba(99,140,255,0.6);
    transform: scale(1.06); box-shadow: 0 0 20px rgba(99,140,255,0.28);
}
#lumi-mic-btn:hover::after { border-color: rgba(99,140,255,0.18); inset: -6px; }
#lumi-mic-btn.recording {
    background: rgba(248,113,113,0.2); border-color: rgba(248,113,113,0.6);
    animation: rec-pulse 1.8s ease infinite;
}
@keyframes rec-pulse {
    0%,100% { box-shadow: 0 0 0 0    rgba(248,113,113,0.4); }
    50%      { box-shadow: 0 0 0 14px rgba(248,113,113,0);   }
}
#lumi-rec-label {
    font-size: var(--fs-sm); color: rgba(255,255,255,0.45);
    font-weight: 500; letter-spacing: 0.3px; text-align: center;
}
#lumi-rec-label.recording { color: #fca5a5; }
#lumi-canvas {
    width: 100%; height: clamp(44px,7vw,68px);
    border-radius: 8px; background: rgba(0,0,0,0.22);
    border: 1px solid rgba(255,255,255,0.07); display: block;
}
#lumi-proc-bar { display: none; width: 100%; }
#lumi-proc-bar .p-label {
    font-size: var(--fs-xs); font-family: var(--mono);
    color: rgba(255,255,255,0.4); margin-bottom: 5px;
    display: flex; align-items: center; gap: 6px;
}
#lumi-proc-bar .p-dot {
    width: 6px; height: 6px; border-radius: 50%;
    background: #7eb8ff; animation: blink 1s ease infinite;
}
@keyframes blink { 0%,100%{opacity:1} 50%{opacity:0.2} }
#lumi-proc-bar .p-track {
    height: 2px; background: rgba(255,255,255,0.1);
    border-radius: 99px; overflow: hidden;
}
#lumi-proc-bar .p-fill {
    height: 100%; background: linear-gradient(90deg, #638cff, #a78bfa);
    border-radius: 99px; animation: slide 1.4s ease-in-out infinite;
}
@keyframes slide {
    0%  {width:0%;  margin-left:0%;}
    50% {width:50%; margin-left:25%;}
    100%{width:0%;  margin-left:100%;}
}
#lumi-audio-playback { width: 100%; display: none; flex-direction: column; gap: 5px; }
#lumi-audio-playback .play-label {
    font-size: var(--fs-xs); font-family: var(--mono); color: rgba(255,255,255,0.35);
}
#lumi-audio-playback audio {
    width: 100%; height: 30px; border-radius: 6px; outline: none; accent-color: #638cff;
}
.lumi-status {
    background: var(--surface) !important; border: 1px solid var(--border) !important;
    border-radius: 10px !important;
    padding: var(--sp-sm) var(--sp-md) !important;
    color: var(--text2) !important; font-size: var(--fs-base) !important;
    font-weight: 500 !important; min-height: 48px !important;
    font-family: var(--mono) !important;
}
.lumi-status .output-class {
    font-size: var(--fs-base) !important; color: var(--text2) !important;
    font-family: var(--mono) !important;
}
.lumi-btn-row {
    display: grid; grid-template-columns: 1fr 1fr;
    gap: var(--sp-xs); margin-bottom: var(--sp-xs);
}
button.lb-check, button.lb-reset, button.lb-send {
    border-radius: 9px !important;
    font-family: 'DM Sans', sans-serif !important;
    font-weight: 500 !important; font-size: var(--fs-base) !important;
    padding: clamp(10px,2vw,14px) clamp(12px,3vw,20px) !important;
    border: none !important; cursor: pointer !important;
    transition: all 0.18s ease !important; width: 100% !important;
}
button.lb-check {
    background: var(--surface2) !important; color: var(--text2) !important;
    border: 1px solid var(--border2) !important;
}
button.lb-check:hover:not(:disabled) {
    background: #dce8ff !important; border-color: var(--accent) !important;
    color: var(--accent2) !important;
}
button.lb-reset {
    background: transparent !important; color: var(--text3) !important;
    border: 1px solid var(--border) !important;
}
button.lb-reset:hover:not(:disabled) {
    color: var(--text2) !important; border-color: var(--border2) !important;
    background: var(--surface2) !important;
}
button.lb-send {
    background: var(--accent) !important; color: #fff !important;
    font-weight: 600 !important;
}
button.lb-send:hover:not(:disabled) {
    background: var(--accent2) !important; transform: translateY(-1px) !important;
    box-shadow: 0 4px 14px rgba(59,126,244,0.3) !important;
}
button.lb-send:disabled {
    background: var(--surface2) !important; color: var(--text3) !important;
    cursor: not-allowed !important;
}
.hidden-audio-wrap {
    position: absolute; width: 1px; height: 1px;
    overflow: hidden; opacity: 0; pointer-events: none;
}
.lumi-speaker-input { margin-bottom: var(--sp-xs) !important; }
.lumi-speaker-input input {
    background: var(--surface) !important;
    border: 1px solid var(--border) !important;
    border-radius: 10px !important;
    font-family: 'DM Sans', sans-serif !important;
    font-size: var(--fs-base) !important;
    color: var(--text) !important;
    padding: var(--sp-sm) var(--sp-md) !important;
}
.lumi-speaker-input input:focus {
    border-color: var(--accent) !important;
    box-shadow: 0 0 0 3px rgba(59,126,244,0.12) !important;
    outline: none !important;
}
.lumi-speaker-input label span {
    font-size: var(--fs-xs) !important;
    font-weight: 600 !important;
    color: var(--text3) !important;
    text-transform: uppercase !important;
    letter-spacing: 0.8px !important;
}
footer { display: none !important; }
.contain { background: transparent !important; }
"""


# ============================================================
# RECORDER HTML
# ============================================================
RECORDER_HTML = """
<div id="lumi-recorder-ui">
    <button id="lumi-mic-btn" title="Nhấn để ghi âm">🎙</button>
    <div id="lumi-rec-label">Nhấn để bắt đầu ghi âm</div>
    <canvas id="lumi-canvas" width="520" height="60"></canvas>
    <div id="lumi-proc-bar">
        <div class="p-label"><span class="p-dot"></span>Đang xử lý...</div>
        <div class="p-track"><div class="p-fill"></div></div>
    </div>
    <div id="lumi-audio-playback">
        <div class="play-label">// PLAYBACK</div>
        <audio id="lumi-audio-el" controls></audio>
    </div>
</div>
<script>
(function () {
    const micBtn   = document.getElementById('lumi-mic-btn');
    const recLabel = document.getElementById('lumi-rec-label');
    const canvas   = document.getElementById('lumi-canvas');
    const ctx2d    = canvas.getContext('2d');
    const procBar  = document.getElementById('lumi-proc-bar');
    const playWrap = document.getElementById('lumi-audio-playback');
    const audioEl  = document.getElementById('lumi-audio-el');
    let isRecording = false, mediaRecorder = null, audioChunks = [];
    let audioCtx = null, analyser = null, micStream = null, animId = null;
    micBtn.addEventListener('click', () => isRecording ? stopRecording() : startRecording());
    async function startRecording() {
        try {
            micStream = await navigator.mediaDevices.getUserMedia({ audio: true, video: false });
        } catch (e) {
            recLabel.textContent = '✕ Không có quyền truy cập micro';
            return;
        }
        audioCtx = new (window.AudioContext || window.webkitAudioContext)();
        analyser = audioCtx.createAnalyser();
        analyser.fftSize = 256;
        audioCtx.createMediaStreamSource(micStream).connect(analyser);
        audioChunks = [];
        const mime = ['audio/webm;codecs=opus','audio/webm','audio/ogg']
            .find(t => MediaRecorder.isTypeSupported(t)) || '';
        mediaRecorder = new MediaRecorder(micStream, mime ? { mimeType: mime } : {});
        mediaRecorder.ondataavailable = e => { if (e.data.size > 0) audioChunks.push(e.data); };
        mediaRecorder.onstop = onDone;
        mediaRecorder.start(100);
        isRecording = true;
        micBtn.classList.add('recording');
        micBtn.textContent = '⏹';
        recLabel.textContent = '● Đang ghi — nhấn để dừng';
        recLabel.classList.add('recording');
        playWrap.style.display = 'none';
        procBar.style.display  = 'none';
        drawWave();
    }
    function stopRecording() {
        mediaRecorder && mediaRecorder.state !== 'inactive' && mediaRecorder.stop();
        micStream && micStream.getTracks().forEach(t => t.stop());
        audioCtx && audioCtx.close();
        animId && cancelAnimationFrame(animId);
        micStream = audioCtx = analyser = null;
        isRecording = false;
        micBtn.classList.remove('recording');
        micBtn.textContent = '🎙';
        recLabel.textContent = 'Nhấn để ghi lại';
        recLabel.classList.remove('recording');
        procBar.style.display = 'block';
        drawFlatLine();
    }
    function onDone() {
        const mime = audioChunks[0]?.type || 'audio/webm';
        const blob = new Blob(audioChunks, { type: mime });
        const url  = URL.createObjectURL(blob);
        audioEl.src = url;
        audioEl.oncanplay = () => {
            procBar.style.display  = 'none';
            playWrap.style.display = 'flex';
        };
        injectFile(blob, mime);
    }
    function injectFile(blob, mime) {
        const ext  = mime.includes('ogg') ? 'ogg' : 'webm';
        const file = new File([blob], 'recording.' + ext, { type: mime });
        function attempt(tries) {
            const inp = document.querySelector('.hidden-audio-wrap input[type="file"]');
            if (inp) {
                const dt = new DataTransfer();
                dt.items.add(file);
                inp.files = dt.files;
                inp.dispatchEvent(new Event('change', { bubbles: true }));
            } else if (tries > 0) {
                setTimeout(() => attempt(tries - 1), 300);
            }
        }
        attempt(10);
    }
    function drawWave() {
        if (!analyser) return;
        animId = requestAnimationFrame(drawWave);
        const buf  = analyser.frequencyBinCount;
        const data = new Uint8Array(buf);
        analyser.getByteTimeDomainData(data);
        const W = canvas.width, H = canvas.height;
        ctx2d.clearRect(0, 0, W, H);
        const rms       = Math.sqrt(data.reduce((s,v) => s+(v-128)**2, 0) / buf);
        const intensity = Math.min(rms / 28, 1);
        if (intensity > 0.05) {
            const cx = W/2, cy = H/2;
            const grd = ctx2d.createRadialGradient(cx, cy, 0, cx, cy, W/2);
            grd.addColorStop(0, `rgba(59,126,244,${0.06 * intensity})`);
            grd.addColorStop(1, 'transparent');
            ctx2d.fillStyle = grd;
            ctx2d.fillRect(0, 0, W, H);
        }
        const grad = ctx2d.createLinearGradient(0, 0, W, 0);
        grad.addColorStop(0,    'rgba(59,126,244,0)');
        grad.addColorStop(0.15, `rgba(59,126,244,${0.5 + intensity * 0.5})`);
        grad.addColorStop(0.5,  `rgba(26,95,224,${0.7 + intensity * 0.3})`);
        grad.addColorStop(0.85, `rgba(59,126,244,${0.5 + intensity * 0.5})`);
        grad.addColorStop(1,    'rgba(59,126,244,0)');
        ctx2d.beginPath();
        ctx2d.lineWidth   = 2;
        ctx2d.strokeStyle = grad;
        ctx2d.shadowColor = '#3b7ef4';
        ctx2d.shadowBlur  = 4 * intensity;
        const sw = W / buf;
        for (let i = 0; i < buf; i++) {
            const y = ((data[i]/128)*H)/2;
            i === 0 ? ctx2d.moveTo(0, y) : ctx2d.lineTo(i*sw, y);
        }
        ctx2d.stroke();
        ctx2d.shadowBlur = 0;
    }
    function drawFlatLine() {
        ctx2d.clearRect(0, 0, canvas.width, canvas.height);
        ctx2d.beginPath();
        ctx2d.moveTo(0, canvas.height/2);
        ctx2d.lineTo(canvas.width, canvas.height/2);
        ctx2d.strokeStyle = 'rgba(255,255,255,0.12)';
        ctx2d.lineWidth   = 1;
        ctx2d.stroke();
    }
    drawFlatLine();
})();
</script>
"""


# ============================================================
# GRADIO BLOCKS
# ============================================================
with gr.Blocks(theme=gr.themes.Base(), css=CSS, title="Lumi Voice Collector") as demo:

    gr.HTML("""
    <div class="lumi-header">
        <div class="lumi-icon-wrap">🎙</div>
        <div class="lumi-header-text">
            <h1>Lumi Wake Word Collector</h1>
            <p>Thu thập giọng nói để huấn luyện trợ lý Lumi</p>
        </div>
    </div>
    """)

    gr.HTML("""
    <div class="lumi-progress">
        <div class="lumi-prog-step">
            <div class="lumi-prog-dot">01</div>
            <div class="lumi-prog-info">
                <div class="lumi-prog-title">GHI ÂM</div>
            </div>
        </div>
        <div class="lumi-prog-line"></div>
        <div class="lumi-prog-step">
            <div class="lumi-prog-dot">02</div>
            <div class="lumi-prog-info">
                <div class="lumi-prog-title">KIỂM TRA</div>
            </div>
        </div>
        <div class="lumi-prog-line"></div>
        <div class="lumi-prog-step">
            <div class="lumi-prog-dot">03</div>
            <div class="lumi-prog-info">
                <div class="lumi-prog-title">GỬI LÊN</div>
            </div>
        </div>
    </div>
    """)

    gr.HTML("""
    <div class="lumi-guide">
        <div class="lumi-guide-icon">📋</div>
        <div class="lumi-guide-body">
            <div class="lumi-guide-title">Hướng dẫn</div>
            <div class="lumi-guide-steps">
                <div class="lumi-guide-step">
                    <span class="lumi-guide-num">01</span>
                    Nhấn nút mic để bắt đầu ghi âm
                </div>
                <div class="lumi-guide-step">
                    <span class="lumi-guide-num">02</span>
                    Nói rõ <span class="lumi-keyword">Lumi ơi</span> vào micro — chỉ <b>1 lần</b> mỗi mẫu
                </div>
                <div class="lumi-guide-step">
                    <span class="lumi-guide-num">03</span>
                    Nhấn lại để dừng — đợi xử lý xong
                </div>
                <div class="lumi-guide-step">
                    <span class="lumi-guide-num">04</span>
                    Kiểm tra → đạt ≥ 0.8 → Gửi lên
                </div>
            </div>
            <div class="lumi-tips">
                <div class="lumi-tip"><span class="tip-icon">⏱</span> Độ dài lý tưởng: <b>1 – 3 giây</b> mỗi mẫu</div>
                <div class="lumi-tip"><span class="tip-icon">🗣</span> Giữ <b>âm lượng tự nhiên</b>, không cần nói to hay nhỏ hơn bình thường</div>
            </div>
        </div>
    </div>
    """)

    gr.HTML("""
    <div class="lumi-guide">
        <div class="lumi-guide-icon">📐</div>
        <div class="lumi-guide-body">
            <div class="lumi-guide-title">Khoảng cách & Góc thu âm</div>
            <div class="lumi-guide-steps">
                <div class="lumi-guide-step">
                    <span class="lumi-guide-num">0–1m</span>
                    Thu khoảng <b>35%</b> tổng số mẫu
                </div>
                <div class="lumi-guide-step">
                    <span class="lumi-guide-num">1–2m</span>
                    Thu khoảng <b>30%</b> tổng số mẫu
                </div>
                <div class="lumi-guide-step">
                    <span class="lumi-guide-num">2–5m</span>
                    Thu khoảng <b>25%</b> tổng số mẫu
                </div>
                <div class="lumi-guide-step">
                    <span class="lumi-guide-num">&gt;5m</span>
                    Thu khoảng <b>10%</b> tổng số mẫu
                </div>
            </div>
            <div class="lumi-tips">
                <div class="lumi-tip"><span class="tip-icon">🔄</span> Nói ở <b>nhiều góc khác nhau</b> — thẳng, lệch trái, lệch phải</div>
                <div class="lumi-tip"><span class="tip-icon">🏠</span> Thu trong <b>nhiều phòng / môi trường</b> khác nhau</div>
            </div>
        </div>
    </div>
    """)

    with gr.Group(elem_classes="lumi-recorder-card"):
        gr.HTML(RECORDER_HTML)

    with gr.Group(elem_classes="hidden-audio-wrap"):
        audio_inst = gr.Audio(sources=["upload"], type="filepath", label="hidden", visible=False)

    speaker_input = gr.Textbox(
        placeholder="Nhập tên của bạn...",
        label="Tên người thu âm",
        max_lines=1,
        elem_classes="lumi-speaker-input",
    )

    status_txt = gr.Label(
        value='Sẵn sàng — Nhấn mic và đọc "Lumi ơi"',
        elem_classes="lumi-status",
        show_label=False,
    )

    with gr.Row(elem_classes="lumi-btn-row"):
        btn_check = gr.Button("Kiểm tra mẫu", variant="secondary", elem_classes="lb-check")
        btn_reset = gr.Button("Ghi lại", variant="stop", elem_classes="lb-reset")

    btn_send = gr.Button(
        "Gửi lên hệ thống →",
        variant="primary",
        interactive=False,
        elem_classes="lb-send",
    )

    score_state = gr.State(0.0)

    btn_check.click(fn=verify_audio, inputs=audio_inst, outputs=[status_txt, btn_send, score_state])
    btn_send.click(fn=upload_final, inputs=[audio_inst, score_state, speaker_input], outputs=[status_txt, btn_send, audio_inst])
    btn_reset.click(fn=reset_ui, outputs=[audio_inst, status_txt, btn_send, score_state])
    audio_inst.change(lambda: gr.update(interactive=False), None, btn_send)


if __name__ == "__main__":
    print("🚀 Starting Lumi Wake Word Collector")
    print(f"   DATASET_ID={DATASET_ID}")
    print(f"   FLUSH_INTERVAL_SEC={FLUSH_INTERVAL_SEC}")
    print(f"   MAX_PENDING_BEFORE_FLUSH={MAX_PENDING_BEFORE_FLUSH}")
    print(f"   HF_TOKEN={'OK' if HF_TOKEN else 'MISSING'}")
    demo.launch()
PY