Upload emotion.py

emotion.py

@@ -1,15 +1,10 @@
 import time
-import threading
 from dataclasses import dataclass, field
-from typing import Optional, List, Tuple, Dict
+from typing import Optional, Dict, Tuple, List
 
 import gradio as gr
 import numpy as np
-import cv2
-
-import mediapipe as mp
-from mediapipe.tasks import python
-from mediapipe.tasks.python import vision
+from PIL import Image
 
 from deepface import DeepFace
 
@@ -17,18 +12,16 @@ from deepface import DeepFace
 # =========================
 # 參數（可微調）
 # =========================
-ANALYZE_EVERY_SEC = 1.5          # ⭐ 建議 1.5~2.0：HF CPU 比較順
+ANALYZE_EVERY_SEC = 0.8
+DOWNSAMPLE_W = 720
 EMA_ALPHA = 0.45
-CONF_TH = 40.0                   # DeepFace emotion confidence 門檻
-SWITCH_CONFIRM_SEC = 2.5         # 連續穩定多久才算完成
-ZOOM_FACTOR = 1.5                # ✅ 保留：只用在「顯示」
-UI_TARGET_W = 720                # ✅ 回傳畫面縮小寬度，提升順暢度
-
-# MediaPipe face detector 門檻（太高很容易漏）
-MP_MIN_DET_CONF = 0.3
+CONF_TH = 40.0
+SWITCH_CONFIRM_SEC = 2.5
+IOU_TH = 0.25
+ZOOM_FACTOR = 1.5
 
-# 模型路徑（確認 repo 中模型真的在這裡）
-MP_MODEL_PATH = "blaze_face_short_range.tflite"
+DETECTOR_BACKEND = "mtcnn"     # 想快一點可改 "opencv"
+ALIGN_FACE = True
 
 
 # =========================
@@ -45,6 +38,7 @@ EMO_ZH = {
     "unknown": "未知",
 }
 
+# 你要的：把 7 種情緒塞進 5 分心情分數
 EMO_TO_SCORE = {
     "angry": 1,
     "disgust": 1,
@@ -56,6 +50,9 @@ EMO_TO_SCORE = {
     "unknown": 3,
 }
 
+def get_emotion_score(emo: str) -> int:
+    return EMO_TO_SCORE.get(emo, 3)
+
 SCORE_LABELS = {
     1: "1（心情差）",
     2: "2（不太好）",
@@ -71,12 +68,15 @@ SCORE_CHOICES = [SCORE_LABELS[i] for i in [1, 2, 3, 4, 5]]
 # =========================
 @dataclass
 class AppState:
-    mode: str = "攝影機辨識"
+    mode: str = "攝影機辨識" 
 
     running: bool = True
     finished: bool = False
     last_analyze_t: float = 0.0
 
+    # tracking
+    track_bbox: Optional[Tuple[int, int, int, int]] = None
+
     # smoothing
     ema_scores: Optional[np.ndarray] = None
     emo_keys: List[str] = field(default_factory=list)
@@ -89,40 +89,36 @@ class AppState:
     # final
     final_emo: Optional[str] = None
     final_conf: Optional[float] = None
-    final_score: int = 3
+
+    final_score: int = 3  
 
 
 # =========================
-# UI HTML
+# UI HTML (強制黑色文字)
 # =========================
 def _hint_html(msg: str) -> str:
+    # 加入 color:#000000 強制黑色
     return f"""
     <div style="border-radius:14px;padding:14px;border:1px dashed #DADADA;background:#FAFAFA;color:#000000;">
       {msg}
     </div>
     """
 
+
 def _camera_hint_before_start() -> str:
     return _hint_html(
         "請先按「Click to access webcam / 允許」開啟攝影機；"
         "接著，請按「錄製（Record）」開始。"
     )
 
-def _camera_hint_need_face() -> str:
-    return _hint_html(
-        "⚠️ 目前未偵測到人臉，請將臉部移至畫面中央、靠近鏡頭，並避免瀏海遮住眉眼，才可進行情緒辨識。"
-    )
 
 def _camera_hint_done() -> str:
     return _hint_html("已完成辨識，可按「重新攝影機辨識」再次進行辨識。")
 
+
 def _camera_hint_stopped() -> str:
     return _hint_html("已停止辨識。可按「重新攝影機辨識」再試一次。")
 
-def _score_radio_value(score: int) -> str:
-    score = int(score)
-    score = min(5, max(1, score))
-    return SCORE_LABELS[score]
 
 def _result_card_html(emo_key: str, conf: Optional[float]) -> str:
     zh = EMO_ZH.get(emo_key, emo_key)
@@ -130,13 +126,14 @@ def _result_card_html(emo_key: str, conf: Optional[float]) -> str:
     conf_txt = "" if conf is None else f"{conf:.1f}%"
     score_desc = SCORE_LABELS[score].split("（")[1].rstrip("）")
 
+    # ✅ 強制設定 color: #000000
     return f"""
     <div style="
         border-radius:16px;
         padding:20px;
         border:1px solid #E6E6E6;
         background:#FFFFFF;
-        color:#000000;
+        color:#000000; 
     ">
       <div style="font-size:14px;color:#000000;margin-bottom:8px;">
         辨識結果
@@ -163,206 +160,118 @@ def _result_card_html(emo_key: str, conf: Optional[float]) -> str:
     """
 
 
-# =========================
-# 影像處理（保命 + 顯示優化）
-# =========================
-def _to_rgb_uint8(frame: np.ndarray) -> np.ndarray:
-    """統一成 MediaPipe Tasks 最穩格式：RGB、uint8、0~255、contiguous。"""
-    if frame is None:
-        return frame
-
-    # RGBA -> RGB
-    if frame.ndim == 3 and frame.shape[2] == 4:
-        frame = frame[:, :, :3]
-
-    # 灰階 -> RGB
-    if frame.ndim == 2:
-        frame = np.stack([frame, frame, frame], axis=-1)
+def _score_radio_value(score: int) -> str:
+    score = int(score)
+    score = min(5, max(1, score))
+    return SCORE_LABELS[score]
 
-    # dtype / range 修正
-    if frame.dtype != np.uint8:
-        mx = float(np.max(frame)) if frame.size else 0.0
-        if mx <= 1.5:  # 多半是 0~1
-            frame = (frame * 255.0).clip(0, 255).astype(np.uint8)
-        else:          # 多半是 0~255 但 float
-            frame = np.clip(frame, 0, 255).astype(np.uint8)
 
-    return np.ascontiguousarray(frame)
+def _downsample_rgb(frame: np.ndarray, target_w: int) -> np.ndarray:
+    if frame is None:
+        return None
+    h, w = frame.shape[:2]
+    if w <= target_w:
+        return frame
+    scale = target_w / w
+    new_h = max(1, int(h * scale))
+    img = Image.fromarray(frame)
+    img = img.resize((target_w, new_h))
+    return np.array(img)
 
 def _zoom_center(frame: np.ndarray, zoom: float) -> np.ndarray:
+    """
+    裁切畫面中心以模擬數位變焦。
+    zoom: 放大倍率 (例如 1.5)
+    """
     if frame is None or zoom <= 1.0:
         return frame
+        
     h, w = frame.shape[:2]
+    
+    # 計算新的寬高
     new_w = int(w / zoom)
     new_h = int(h / zoom)
+    
+    # 計算起始點 (讓裁切框置中)
     start_x = (w - new_w) // 2
     start_y = (h - new_h) // 2
+    
+    # 進行裁切
     cropped = frame[start_y : start_y + new_h, start_x : start_x + new_w]
-    return np.ascontiguousarray(cropped)
-
-def _resize_for_ui(frame_rgb: np.ndarray, target_w: int = UI_TARGET_W) -> np.ndarray:
-    """回傳給 Gradio 的畫面縮到固定寬度，減少傳輸/渲染負擔。"""
-    if frame_rgb is None:
-        return frame_rgb
-    h, w = frame_rgb.shape[:2]
-    if w <= target_w:
-        return frame_rgb
-    scale = target_w / w
-    new_h = max(1, int(h * scale))
-    return cv2.resize(frame_rgb, (target_w, new_h), interpolation=cv2.INTER_AREA)
-
-
-# =========================
-# MediaPipe Tasks Face Detector
-# =========================
-BaseOptions = python.BaseOptions
-FaceDetector = vision.FaceDetector
-FaceDetectorOptions = vision.FaceDetectorOptions
-VisionRunningMode = vision.RunningMode
-
-_face_detector = FaceDetector.create_from_options(
-    FaceDetectorOptions(
-        base_options=BaseOptions(model_asset_path=MP_MODEL_PATH),
-        running_mode=VisionRunningMode.IMAGE,
-        min_detection_confidence=MP_MIN_DET_CONF,
-    )
-)
-
-def mp_tasks_detect_and_draw(frame_rgb: np.ndarray):
-    """
-    - 最大臉策略（bbox 面積最大）
-    - 有臉：畫白框 + 紅點 keypoints
-    - 回 bbox_px (x, y, w, h) 供 ROI 裁切
-    """
-    frame_rgb = _to_rgb_uint8(frame_rgb)
-    h, w = frame_rgb.shape[:2]
-
-    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=frame_rgb)
-    result = _face_detector.detect(mp_image)
+    return cropped
+
+
+def _iou(a: Tuple[int, int, int, int], b: Tuple[int, int, int, int]) -> float:
+    ax, ay, aw, ah = a
+    bx, by, bw, bh = b
+    ax2, ay2 = ax + aw, ay + ah
+    bx2, by2 = bx + bw, by + bh
+
+    ix1, iy1 = max(ax, bx), max(ay, by)
+    ix2, iy2 = min(ax2, bx2), min(ay2, by2)
+    iw, ih = max(0, ix2 - ix1), max(0, iy2 - iy1)
+    inter = iw * ih
+    if inter <= 0:
+        return 0.0
+    union = aw * ah + bw * bh - inter
+    return inter / max(1e-6, union)
+
+
+def _pick_face_by_tracking(
+    faces: List[Dict],
+    prev_bbox: Optional[Tuple[int, int, int, int]]
+) -> Tuple[Optional[Dict], Optional[Tuple[int, int, int, int]]]:
+    if not faces:
+        return None, None
+
+    cand = []
+    for f in faces:
+        fa = f.get("facial_area", {}) or {}
+        x = int(fa.get("x", 0) or 0)
+        y = int(fa.get("y", 0) or 0)
+        w = int(fa.get("w", 0) or 0)
+        h = int(fa.get("h", 0) or 0)
+        if w > 0 and h > 0 and f.get("face") is not None:
+            cand.append((f, (x, y, w, h), w * h))
+
+    if not cand:
+        return None, None
+
+    if prev_bbox is not None:
+        best = None
+        best_i = 0.0
+        for f, bb, area in cand:
+            i = _iou(prev_bbox, bb)
+            if i > best_i:
+                best_i = i
+                best = (f, bb)
+        if best is not None and best_i >= IOU_TH:
+            return best[0], best[1]
+
+    cand.sort(key=lambda x: x[2], reverse=True)
+    return cand[0][0], cand[0][1]
 
-    if not result.detections:
-        return frame_rgb, None
 
-    best_det = None
-    best_area = -1.0
-    best_bbox = None
-
-    for det in result.detections:
-        bbox = det.bounding_box
-        area = float(bbox.width) * float(bbox.height)
-        if area > best_area:
-            best_area = area
-            best_det = det
-            best_bbox = bbox
-
-    if best_det is None or best_bbox is None:
-        return frame_rgb, None
-
-    lx = int(best_bbox.origin_x)
-    ly = int(best_bbox.origin_y)
-    bw = int(best_bbox.width)
-    bh = int(best_bbox.height)
-
-    lx = max(0, min(w - 1, lx))
-    ly = max(0, min(h - 1, ly))
-    bw = max(1, min(w - lx, bw))
-    bh = max(1, min(h - ly, bh))
-
-    bgr = cv2.cvtColor(frame_rgb, cv2.COLOR_RGB2BGR)
-    cv2.rectangle(bgr, (lx, ly), (lx + bw, ly + bh), (255, 255, 255), 3)
-
-    for kp in best_det.keypoints:
-        cx = int(kp.x * w)
-        cy = int(kp.y * h)
-        cv2.circle(bgr, (cx, cy), 6, (0, 0, 255), -1)
-
-    drawn = cv2.cvtColor(bgr, cv2.COLOR_BGR2RGB)
-    return drawn, (lx, ly, bw, bh)
-
-
-# =========================
-# 背景推論（避免卡頓） + ✅ 沒臉時清空
-# =========================
-_ANALYZE_LOCK = threading.Lock()
-_ANALYZE_THREAD: Optional[threading.Thread] = None
-_PENDING_FACE: Optional[np.ndarray] = None
-_LAST_ANALYZE: Dict[str, Optional[float]] = {"ts": 0.0, "conf": None, "emo": "unknown"}
-
-def _clear_last_analyze():
-    """✅ 沒有人臉時，清空上一筆情緒，避免殘留顯示（你影片遇到的 bug）"""
-    global _LAST_ANALYZE
-    _LAST_ANALYZE = {"ts": 0.0, "conf": None, "emo": "unknown"}
-
-def _analyze_worker():
-    global _PENDING_FACE, _ANALYZE_THREAD, _LAST_ANALYZE
-
-    while True:
-        with _ANALYZE_LOCK:
-            face = _PENDING_FACE
-            _PENDING_FACE = None
-
-        if face is None:
-            break
-
-        try:
-            r = DeepFace.analyze(
-                img_path=face,
-                actions=["emotion"],
-                enforce_detection=False,
-                detector_backend="skip",  # ⭐ 你已裁 ROI，別再偵測一次
-            )
-            if isinstance(r, list):
-                r = r[0]
-            emo_dict = r.get("emotion", None)
-
-            if isinstance(emo_dict, dict) and len(emo_dict) > 0:
-                top_key = max(emo_dict, key=lambda k: float(emo_dict[k]))
-                top_conf = float(emo_dict[top_key])
-                _LAST_ANALYZE = {"ts": time.time(), "emo": top_key, "conf": top_conf}
-        except Exception:
-            pass
-
-    _ANALYZE_THREAD = None
-
-def _enqueue_analyze(face_roi: np.ndarray):
-    global _PENDING_FACE, _ANALYZE_THREAD
-
-    with _ANALYZE_LOCK:
-        _PENDING_FACE = face_roi
-
-    if _ANALYZE_THREAD is None:
-        _ANALYZE_THREAD = threading.Thread(target=_analyze_worker, daemon=True)
-        _ANALYZE_THREAD.start()
-
-
-# =========================
-# state helper
-# =========================
 def _reset_state_for_camera(st: AppState) -> AppState:
     st.running = True
     st.finished = False
     st.last_analyze_t = 0.0
-
+    st.track_bbox = None
     st.ema_scores = None
     st.emo_keys = []
-
     st.stable_emo = "unknown"
     st.candidate_emo = None
     st.candidate_since = None
-
     st.final_emo = None
     st.final_conf = None
     st.final_score = 3
-
-    _clear_last_analyze()
     return st
 
 
 # =========================
-# streaming callback
+# Camera streaming
 # =========================
 def on_stream(frame_rgb: np.ndarray, st: AppState):
-    # stopped / finished
     if st.finished or (not st.running):
         cam_visible = False
 
@@ -376,7 +285,7 @@ def on_stream(frame_rgb: np.ndarray, st: AppState):
             hint_html = _camera_hint_stopped()
 
         return (
-            gr.update(value=None, visible=cam_visible),
+            gr.update(visible=cam_visible),
             gr.update(value=result_html),
             gr.update(value=mood_val),
             gr.update(value=hint_html),
@@ -384,108 +293,105 @@ def on_stream(frame_rgb: np.ndarray, st: AppState):
             gr.update(value="重新攝影機辨識"),
         )
 
-    # no frame yet
     if frame_rgb is None:
         return (
-            gr.update(value=None, visible=True),
+            gr.update(visible=True),
             gr.update(value=_hint_html("等待影像…（尚未開始串流）")),
             gr.update(value=_score_radio_value(3)),
             gr.update(value=_camera_hint_before_start()),
             st,
             gr.update(value="重新攝影機辨識"),
         )
+    
+    frame_rgb = _zoom_center(frame_rgb, ZOOM_FACTOR)
 
-    frame_rgb = _to_rgb_uint8(frame_rgb)
-
-    # ✅ 偵測用原圖
-    frame_drawn, bbox_px = mp_tasks_detect_and_draw(frame_rgb)
-
-    # ✅ 顯示用 zoom
-    frame_show = _zoom_center(frame_drawn if bbox_px is not None else frame_rgb, ZOOM_FACTOR)
-    frame_show = _resize_for_ui(frame_show, UI_TARGET_W)
-
-    # ✅ 沒有臉：清空殘留情緒 + 重置候選狀態
-    if bbox_px is None:
-        _clear_last_analyze()
-
-        st.stable_emo = "unknown"
-        st.candidate_emo = None
-        st.candidate_since = None
-        st.ema_scores = None
-        st.emo_keys = []
-
+    now = time.time()
+    if now - st.last_analyze_t < ANALYZE_EVERY_SEC:
+        show = st.stable_emo or "unknown"
         return (
-            gr.update(value=frame_show, visible=True),
-            gr.update(value=_hint_html("尚未偵測到人臉")),
-            gr.update(value=_score_radio_value(3)),
-            gr.update(value=_camera_hint_need_face()),
+            gr.update(visible=True),
+            gr.update(value=_result_card_html(show, st.final_conf)),
+            gr.update(value=_score_radio_value(EMO_TO_SCORE.get(show, 3))),
+            gr.update(value=_camera_hint_before_start()),
             st,
             gr.update(value="重新攝影機辨識"),
         )
 
-    now = time.time()
+    st.last_analyze_t = now
 
-    # 到分析時間：只排隊給背景 thread
-    if now - st.last_analyze_t >= ANALYZE_EVERY_SEC:
-        st.last_analyze_t = now
+    small = _downsample_rgb(frame_rgb, DOWNSAMPLE_W)
 
-        x, y, bw, bh = bbox_px
-        pad_x = int(bw * 0.10)
-        pad_y = int(bh * 0.10)
-        H, W = frame_rgb.shape[:2]
-        x1 = max(0, x - pad_x)
-        y1 = max(0, y - pad_y)
-        x2 = min(W, x + bw + pad_x)
-        y2 = min(H, y + bh + pad_y)
-        face_roi = frame_rgb[y1:y2, x1:x2].astype(np.uint8)
+    faces = DeepFace.extract_faces(
+        img_path=small,
+        detector_backend=DETECTOR_BACKEND,
+        enforce_detection=False,
+        align=ALIGN_FACE,
+    )
+    pick, bbox = _pick_face_by_tracking(faces, st.track_bbox)
+    if pick is None or bbox is None or pick.get("face") is None:
+        show = st.stable_emo or "unknown"
+        return (
+            gr.update(visible=True),
+            gr.update(value=_result_card_html(show, st.final_conf)),
+            gr.update(value=_score_radio_value(EMO_TO_SCORE.get(show, 3))),
+            gr.update(value=_camera_hint_before_start()),
+            st,
+            gr.update(value="重新攝影機辨識"),
+        )
 
-        _enqueue_analyze(face_roi)
+    st.track_bbox = bbox
+    face = pick["face"]
+    if face.dtype != np.uint8:
+        face = np.clip(face * 255.0, 0, 255).astype(np.uint8)
 
-    # ✅ Gate：只有「有臉」才讀取 _LAST_ANALYZE（避免殘留）
-    top_key = _LAST_ANALYZE.get("emo", "unknown") or "unknown"
-    top_conf = _LAST_ANALYZE.get("conf", None)
+    result = DeepFace.analyze(
+        img_path=face,
+        actions=["emotion"],
+        enforce_detection=False,
+    )
+    if isinstance(result, list):
+        result = result[0]
+    emo_dict = result.get("emotion", None)
 
-    # 尚未推論到結果：先顯示 unknown
-    if top_conf is None:
+    if not isinstance(emo_dict, dict) or len(emo_dict) == 0:
         show = st.stable_emo or "unknown"
         return (
-            gr.update(value=frame_show, visible=True),
-            gr.update(value=_result_card_html(show, None)),
+            gr.update(visible=True),
+            gr.update(value=_result_card_html(show, st.final_conf)),
             gr.update(value=_score_radio_value(EMO_TO_SCORE.get(show, 3))),
             gr.update(value=_camera_hint_before_start()),
             st,
             gr.update(value="重新攝影機辨識"),
         )
 
-    # 低信心就不切換
+    top_key = max(emo_dict, key=lambda k: float(emo_dict[k]))
+    top_conf = float(emo_dict[top_key])
+
     if top_conf < CONF_TH:
         show = st.stable_emo or "unknown"
         return (
-            gr.update(value=frame_show, visible=True),
-            gr.update(value=_result_card_html(show, None)),
+            gr.update(visible=True),
+            gr.update(value=_result_card_html(show, st.final_conf)),
             gr.update(value=_score_radio_value(EMO_TO_SCORE.get(show, 3))),
             gr.update(value=_camera_hint_before_start()),
             st,
             gr.update(value="重新攝影機辨識"),
         )
 
-    # === EMA smoothing（用 7 類別向量）===
-    all_keys = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
-    vec = np.zeros((len(all_keys),), dtype=np.float32)
-    if top_key in all_keys:
-        vec[all_keys.index(top_key)] = float(top_conf)
+    keys = list(emo_dict.keys())
+    vec = np.array([float(emo_dict[k]) for k in keys], dtype=np.float32)
 
-    if st.ema_scores is None or st.emo_keys != all_keys:
+    if st.ema_scores is None or st.emo_keys != keys:
         st.ema_scores = vec
-        st.emo_keys = all_keys
+        st.emo_keys = keys
     else:
         st.ema_scores = EMA_ALPHA * vec + (1.0 - EMA_ALPHA) * st.ema_scores
 
     stable_idx = int(np.argmax(st.ema_scores))
-    stable_key = st.emo_keys[stable_idx] if st.emo_keys else "unknown"
+    stable_key = st.emo_keys[stable_idx]
     st.stable_emo = stable_key
 
-    # 延遲確認，避免跳動
+    # 延遲確認
     if st.candidate_emo != stable_key:
         st.candidate_emo = stable_key
         st.candidate_since = now
@@ -498,18 +404,17 @@ def on_stream(frame_rgb: np.ndarray, st: AppState):
         and (now - st.candidate_since) >= SWITCH_CONFIRM_SEC
     )
 
-    # ✅ 完成一次辨識後停止
     if confirmed and st.final_emo is None:
         st.final_emo = stable_key
-        st.final_conf = float(top_conf) if top_conf is not None else None
+        st.final_conf = top_conf
         st.final_score = EMO_TO_SCORE.get(stable_key, 3)
 
         st.running = False
         st.finished = True
 
         return (
-            gr.update(value=None, visible=False),
-            gr.update(value=_result_card_html(stable_key, st.final_conf)),
+            gr.update(visible=False),
+            gr.update(value=_result_card_html(stable_key, top_conf)),
             gr.update(value=_score_radio_value(EMO_TO_SCORE.get(stable_key, 3))),
             gr.update(value=_camera_hint_done()),
             st,
@@ -517,8 +422,8 @@ def on_stream(frame_rgb: np.ndarray, st: AppState):
         )
 
     return (
-        gr.update(value=frame_show, visible=True),
-        gr.update(value=_result_card_html(stable_key, float(top_conf))),
+        gr.update(visible=True),
+        gr.update(value=_result_card_html(stable_key, top_conf)),
         gr.update(value=_score_radio_value(EMO_TO_SCORE.get(stable_key, 3))),
         gr.update(value=_camera_hint_before_start()),
         st,
@@ -527,26 +432,27 @@ def on_stream(frame_rgb: np.ndarray, st: AppState):
 
 
 # =========================
-# Buttons
+# Mode / Buttons
 # =========================
+
 def on_restart(st: AppState):
     st = _reset_state_for_camera(st)
     return (
-        gr.update(value=None, visible=True),
-        gr.update(value=_hint_html("等待影像…（尚未開始串流）")),
-        gr.update(value=_score_radio_value(3)),
-        gr.update(value=_camera_hint_before_start()),
+        gr.update(visible=True),                               
+        gr.update(value=_hint_html("等待影像…（尚未開始串流）")),  
+        gr.update(value=_score_radio_value(3)),                
+        gr.update(value=_camera_hint_before_start()),          
         st,
-        gr.update(value="重新攝影機辨識"),
+        gr.update(value="重新攝影機辨識"),                      
     )
 
+
 def on_stop(st: AppState):
     st.running = False
     st.finished = True
-
     if st.final_emo is None:
         return (
-            gr.update(value=None, visible=False),
+            gr.update(visible=False),
             gr.update(value=_hint_html("已停止辨識。")),
             gr.update(value=_score_radio_value(3)),
             gr.update(value=_camera_hint_stopped()),
@@ -555,7 +461,7 @@ def on_stop(st: AppState):
         )
 
     return (
-        gr.update(value=None, visible=False),
+        gr.update(visible=False),
         gr.update(value=_result_card_html(st.final_emo, st.final_conf)),
         gr.update(value=_score_radio_value(EMO_TO_SCORE.get(st.final_emo, 3))),
         gr.update(value=_camera_hint_done()),
@@ -567,20 +473,20 @@ def on_stop(st: AppState):
 # =========================
 # Gradio UI
 # =========================
-css = ""
 
 if __name__ == "__main__":
     with gr.Blocks(title="Emotion Detector", css=css) as demo:
         st = gr.State(AppState())
 
         gr.Markdown("## 情緒辨識")
+        
         hint = gr.HTML(_camera_hint_before_start())
 
         cam = gr.Image(
             sources=["webcam"],
             streaming=True,
             type="numpy",
-            label="攝影機畫面",
+            label="攝影機",
             visible=True,
         )
 
@@ -620,4 +526,4 @@ if __name__ == "__main__":
             show_progress="minimal",
         )
 
-    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)