Update app.py

app.py

@@ -1,267 +1,373 @@
-import gradio as gr
-from openai import OpenAI
-from huggingface_hub import InferenceClient
-import os
 import time
-import requests
-import urllib.parse
-import pandas as pd
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import FAISS
+from dataclasses import dataclass, field
+from typing import Optional, Dict, Tuple, List
+
+import gradio as gr
+import numpy as np
 from PIL import Image
-from dotenv import load_dotenv
-
-# --- 匯入組員的模組 ---
-import emotion 
-# ---------------------------
-
-load_dotenv()
-
-# ==========================================
-# 0. 環境變數 & 1. 系統初始化 (保持不變)
-# ==========================================
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-HF_TOKEN = os.getenv("HF_TOKEN")
-
-global_df = None
-global_mood_df = None
-global_retriever = None
-rag_initialized = False
-
-def init_rag_system():
-    global global_df, global_mood_df, global_retriever, rag_initialized
-    if rag_initialized: return
-    try:
-        global_df = pd.read_csv('restaurants.csv')
-        global_df['RAG_Content'] = global_df['RAG_Content'].fillna("")
-        global_df['Category'] = global_df['Category'].fillna("其他")
-        global_mood_df = pd.read_csv('mood_food_guide.csv')
-    except Exception: pass
-    if os.path.exists("faiss_index"):
-        try:
-            embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
-            vectorstore = FAISS.load_local("faiss_index", embeddings, allow_dangerous_deserialization=True)
-            global_retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-        except Exception: pass
-    rag_initialized = True
-
-# ==========================================
-# 2. 核心功能 (保持不變)
-# ==========================================
-def get_restaurant_data(mood_score_str, food_choice):
-    init_rag_system()
-    if global_df is None or global_df.empty: return None, True, "資料庫未載入", "無建議"
-    try: score = int(str(mood_score_str).split(' ')[0])
-    except: score = 3
-    mood_info = global_mood_df[global_mood_df['分數'] == score]
-    if not mood_info.empty:
-        rec_categories = mood_info.iloc[0]['推薦料理類別']
-        mood_reason = mood_info.iloc[0]['原因']
+
+from deepface import DeepFace
+
+
+# =========================
+# 參數（可微調）
+# =========================
+ANALYZE_EVERY_SEC = 0.8
+DOWNSAMPLE_W = 720
+EMA_ALPHA = 0.45
+CONF_TH = 40.0
+SWITCH_CONFIRM_SEC = 2.5
+IOU_TH = 0.25
+
+# 建議改為 "opencv" 穩定性較高，若想維持原狀可改回 "mtcnn"
+DETECTOR_BACKEND = "opencv" 
+ALIGN_FACE = True
+
+
+# =========================
+# 情緒中文 + 分數映射
+# =========================
+EMO_ZH = {
+    "angry": "生氣",
+    "disgust": "厭惡",
+    "fear": "害怕",
+    "happy": "開心",
+    "sad": "難過",
+    "surprise": "驚訝",
+    "neutral": "平靜",
+    "unknown": "未知",
+}
+
+EMO_TO_SCORE = {
+    "angry": 1,
+    "disgust": 1,
+    "fear": 1,
+    "sad": 2,
+    "neutral": 3,
+    "surprise": 4,
+    "happy": 5,
+    "unknown": 3,
+}
+
+# ### [整合需求 新增] ###
+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）",
+    3: "3（普通）",
+    4: "4（不錯）",
+    5: "5（超棒）",
+}
+SCORE_CHOICES = [SCORE_LABELS[i] for i in [1, 2, 3, 4, 5]]
+
+
+# =========================
+# 狀態
+# =========================
+@dataclass
+class AppState:
+    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)
+
+    # stable/candidate
+    stable_emo: str = "unknown"
+    candidate_emo: Optional[str] = None
+    candidate_since: Optional[float] = None
+
+    # final
+    final_emo: Optional[str] = None
+    final_conf: Optional[float] = None
+
+    # ### [整合需求 新增] 用來儲存整數分數 ###
+    final_score: int = 3  
+    # ######################
+
+
+# =========================
+# UI HTML
+# =========================
+def _hint_html(msg: str) -> str:
+    return f"""<div style="border-radius:14px;padding:14px;border:1px dashed #DADADA;background:#FAFAFA;color:#111;">{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_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 _upload_hint_before() -> str:
+    return _hint_html("請上傳一張清楚的臉部照片（臉越大越準）���")
+
+def _upload_hint_done() -> str:
+    return _hint_html("已完成圖片辨識，可按「重新上傳圖片辨識」再次辨識。")
+
+def _upload_hint_stopped() -> str:
+    return _hint_html("已停止圖片辨識。可按「重新上傳圖片辨識」再試一次。")
+
+def _result_card_html(emo_key: str, conf: Optional[float]) -> str:
+    zh = EMO_ZH.get(emo_key, emo_key)
+    score = EMO_TO_SCORE.get(emo_key, 3)
+    conf_txt = "" if conf is None else f"{conf:.1f}%"
+    score_desc = SCORE_LABELS[score].split("（")[1].rstrip("）")
+
+    return f"""
+    <div style="border-radius:16px;padding:20px;border:1px solid #E6E6E6;background:#FFFFFF;color:#111;">
+      <div style="font-size:14px;color:#444;margin-bottom:8px;">辨識結果</div>
+      <div style="font-size:42px;font-weight:800;line-height:1.1;margin-bottom:14px;color:#000;">{zh}</div>
+      <div style="font-size:16px;color:#111;margin-bottom:6px;">心情分數：<b>{score}</b>（{score_desc}）</div>
+      <div style="font-size:14px;color:#111;">信心值：{conf_txt}</div>
+    </div>
+    """
+
+def _score_radio_value(score: int) -> str:
+    score = int(score)
+    score = min(5, max(1, score))
+    return SCORE_LABELS[score]
+
+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 _iou(a, b) -> 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, prev_bbox):
+    if not faces: return None, None
+    cand = []
+    for f in faces:
+        fa = f.get("facial_area", {}) or {}
+        x, y, w, h = int(fa.get("x",0)), int(fa.get("y",0)), int(fa.get("w",0)), int(fa.get("h",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, best_i = None, 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]
+
+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
+    # ### [整合需求] 重置分數
+    st.final_score = 3
+    return st
+
+def _reset_state_for_upload(st: AppState) -> AppState:
+    st.running = False; 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
+    # ### [整合需求] 重置分數
+    st.final_score = 3
+    return st
+
+
+# =========================
+# Camera streaming
+# =========================
+def on_stream(frame_rgb: np.ndarray, st: AppState):
+    if st.mode != "攝影機辨識":
+        return gr.update(), gr.update(), gr.update(), gr.update(value=_upload_hint_before()), st, gr.update(value="重新上傳圖片辨識")
+
+    if st.finished or (not st.running):
+        cam_visible = False
+        if st.final_emo:
+            result_html = _result_card_html(st.final_emo, st.final_conf)
+            mood_val = _score_radio_value(EMO_TO_SCORE.get(st.final_emo, 3))
+            hint_html = _camera_hint_done()
+        else:
+            result_html = _hint_html("尚未完成辨識。")
+            mood_val = _score_radio_value(3)
+            hint_html = _camera_hint_stopped()
+        return gr.update(visible=cam_visible), gr.update(value=result_html), gr.update(value=mood_val), gr.update(value=hint_html), st, gr.update(value="重新攝影機辨識")
+
+    if frame_rgb is None:
+        return 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")
+
+    now = time.time()
+    if now - st.last_analyze_t < ANALYZE_EVERY_SEC:
+        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")
+
+    st.last_analyze_t = now
+    small = _downsample_rgb(frame_rgb, DOWNSAMPLE_W)
+    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")
+
+    st.track_bbox = bbox
+    face = pick["face"]
+    if face.dtype != np.uint8: face = np.clip(face * 255.0, 0, 255).astype(np.uint8)
+
+    result = DeepFace.analyze(img_path=face, actions=["emotion"], enforce_detection=False)
+    if isinstance(result, list): result = result[0]
+    emo_dict = result.get("emotion", None)
+
+    if not isinstance(emo_dict, dict) or len(emo_dict) == 0:
+        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")
+
+    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(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")
+
+    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 != keys:
+        st.ema_scores = vec; st.emo_keys = keys
     else:
-        rec_categories = ""
-        mood_reason = "隨意探索"
-    candidates = global_df.copy()
-    if rec_categories:
-        candidates = candidates[candidates['Category'].apply(lambda x: str(x) in str(rec_categories) or str(rec_categories) in str(x))]
-    food_keyword = "飯" if food_choice == "吃飯" else "麵" if food_choice == "吃麵" else ""
-    if food_keyword:
-        candidates = candidates[candidates['Name'].str.contains(food_keyword, case=False, na=False) | candidates['RAG_Content'].str.contains(food_keyword, case=False, na=False)]
-    if candidates.empty:
-        result = global_df.sample(1).iloc[0]; is_random = True
+        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]
+    st.stable_emo = stable_key
+
+    if st.candidate_emo != stable_key:
+        st.candidate_emo = stable_key; st.candidate_since = now
     else:
-        result = candidates.sample(1).iloc[0]; is_random = False
-    return result, is_random, rec_categories, mood_reason
-
-def generate_content_with_groq(restaurant_name, restaurant_detail, user_diary, mood_score, mood_guide_reason, debug_mode=False):
-    if not GROQ_API_KEY: return "⚠️ 請設定 GROQ_API_KEY"
-    client = OpenAI(api_key=GROQ_API_KEY, base_url="https://api.groq.com/openai/v1")
-    system_prompt = "你是一個幽默、懂吃且善解人意的 AI 朋友..."
-    user_msg = f"【狀態】心情分數：{mood_score}\n【日記】{user_diary}\n【建議原因】{mood_guide_reason}\n【餐廳】{restaurant_name}\n資料：{restaurant_detail}"
-    try:
-        response = client.chat.completions.create(model="llama-3.3-70b-versatile", messages=[{"role": "system", "content": system_prompt}, {"role": "user", "content": user_msg}])
-        return response.choices[0].message.content
-    except Exception as e: return f"Groq Error: {str(e)}"
-
-def generate_image_huggingface(prompt):
-    if not HF_TOKEN: return None
-    try:
-        hf_client = InferenceClient(token=HF_TOKEN)
-        return hf_client.text_to_image(prompt=prompt, model="stabilityai/stable-diffusion-xl-base-1.0")
-    except: return None
-
-def mood_agent_logic(score_input, food_input, diary_input, debug_mode):
-    restaurant, is_random, rec_categories, mood_reason = get_restaurant_data(score_input, food_input)
-    if restaurant is None: yield "資料庫讀取錯誤", None, ""; return
-    name = restaurant['Name']; address = restaurant['Address']; url = restaurant['URL']; img_prompt = restaurant.get('Visual_prompt')
-    rag_info = str(restaurant.get('RAG_Content', ''))
-    if global_retriever:
-        docs = global_retriever.invoke(name)
-        if docs: rag_info = "\n".join([d.page_content for d in docs])
-    ai_text = generate_content_with_groq(name, rag_info, diary_input, score_input, mood_reason, debug_mode)
-    final_response = f"### 🍽️ 推薦：{name}\n\n{ai_text}"
-    map_html = f'<div style="text-align:center"><a href="{url}" target="_blank" style="background:#4CAF50;color:white;padding:8px 16px;border-radius:20px;text-decoration:none">🗺️ Google Map 導航</a></div>'
-    yield final_response, None, map_html
-    image_output = generate_image_huggingface(img_prompt)
-    yield final_response, image_output, map_html
-
-
-# ==========================================
-# 3. [修正版] 橋接邏輯 (Bridge Functions)
-# ==========================================
-
-# 這是 app.py 自己的格式 (注意：這裡是半形括號)
-def _score_to_radio_value(score):
-    mapping = {1: "1 (心情差)", 2: "2 (不太好)", 3: "3 (普通)", 4: "4 (不錯)", 5: "5 (超棒)"}
-    # 預防萬一 score 是 None 或其他怪怪的值
-    try:
-        score = int(score)
-    except:
-        score = 3
-    return mapping.get(score, "3 (普通)")
-
-# 橋接 1：開始
-def bridge_start_click(st):
-    res = emotion.on_restart(st)
-    return res[1], res[0], gr.update(visible=True), res[5], res[4]
-
-# 橋接 2：停止
-def bridge_stop_click(st):
-    res = emotion.on_stop(st)
-    return res[1], res[0], gr.update(visible=False), res[5], res[4]
-
-# 橋接 3：Webcam 串流 (修正分數填入問題)
-def bridge_predict_frame(frame, st):
-    res = emotion.on_stream(frame, st)
-    
-    out_cam = res[0]
-    out_result = res[1]
-    # out_mood = res[2] # ❌ 我們不使用隊友回傳的 UI 更新，因為括號格式不同
-    out_st = res[4]
-    out_btn_start = res[5]
-    
-    score_update = gr.update()
-    tabs_update = gr.update()
-    out_btn_stop = gr.update()
-
-    # 如果辨識完成，我們自己產生一個符合 app.py 格式的 UI 更新
-    if out_st.finished and hasattr(out_st, 'final_score'):
-        # ✅ 使用 _score_to_radio_value 將整數轉換為半形括號字串
-        new_val = _score_to_radio_value(out_st.final_score)
-        score_update = gr.update(value=new_val)
+        if st.candidate_since is None: st.candidate_since = now
+
+    confirmed = (st.candidate_since is not None and (now - st.candidate_since) >= SWITCH_CONFIRM_SEC)
+
+    if confirmed and st.final_emo is None:
+        st.final_emo = stable_key; st.final_conf = top_conf
         
-        tabs_update = gr.Tabs(selected=1)
-        out_btn_stop = gr.update(visible=False)
+        # ### [整合需求] 存入分數 ###
+        st.final_score = EMO_TO_SCORE.get(stable_key, 3)
+        # #######################
         
-    return out_cam, out_result, out_st, out_btn_stop, out_btn_start, score_update, tabs_update
+        st.running = False; st.finished = True
+        return 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, gr.update(value="重新攝影機辨識")
 
-# 橋接 4：圖片上傳 (修正分數填入問題)
-def bridge_predict_upload(img, st):
-    res = emotion.on_upload(img, st)
-    
-    out_result = res[0]
-    # out_mood = res[1] # ❌ 同樣忽略隊友的格式
-    out_st = res[2]
-    
-    score_update = gr.update()
-    tabs_update = gr.update()
+    return 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, gr.update(value="重新攝影機辨識")
 
-    if out_st.finished and hasattr(out_st, 'final_score'):
-        # ✅ 使用 _score_to_radio_value 將整數轉換為半形括號字串
-        new_val = _score_to_radio_value(out_st.final_score)
-        score_update = gr.update(value=new_val)
-        
-        tabs_update = gr.Tabs(selected=1)
+
+# =========================
+# Upload image
+# =========================
+def on_upload(img: np.ndarray, st: AppState):
+    if st.mode != "上傳圖片辨識":
+        return gr.update(), gr.update(), st, gr.update(value=_camera_hint_before_start()), gr.update(value="重新攝影機辨識")
+
+    st = _reset_state_for_upload(st)
+    if img is None:
+        return gr.update(value=_upload_hint_before()), gr.update(value=_score_radio_value(3)), st, gr.update(value=_upload_hint_before()), gr.update(value="重新上傳圖片辨識")
+
+    # ### [修正] 圖片格式處理 (解決 PNG/灰階 問題) ###
+    if img.ndim == 2:
+        img = np.stack([img, img, img], axis=-1)
+    if img.shape[-1] == 4:
+        img = img[:, :, :3]
+    # ###########################################
+
+    small = _downsample_rgb(img, DOWNSAMPLE_W)
+    faces = DeepFace.extract_faces(img_path=small, detector_backend=DETECTOR_BACKEND, enforce_detection=False, align=ALIGN_FACE)
+    pick, bbox = _pick_face_by_tracking(faces, None)
     
-    return out_result, score_update, tabs_update, out_st
+    if pick is None or pick.get("face") is None:
+        return gr.update(value=_hint_html("沒有偵測到人臉，請換一張更清楚、臉更大的照片。")), gr.update(value=_score_radio_value(3)), st, gr.update(value=_upload_hint_before()), gr.update(value="重新上傳圖片辨識")
 
+    face = pick["face"]
+    if face.dtype != np.uint8: face = np.clip(face * 255.0, 0, 255).astype(np.uint8)
 
-# ==========================================
-# 4. Gradio 介面建構
-# ==========================================
+    result = DeepFace.analyze(img_path=face, actions=["emotion"], enforce_detection=False)
+    if isinstance(result, list): result = result[0]
+    emo_dict = result.get("emotion", None)
+    if not isinstance(emo_dict, dict) or len(emo_dict) == 0:
+        return gr.update(value=_hint_html("辨識失敗，請換一張照片再試一次。")), gr.update(value=_score_radio_value(3)), st, gr.update(value=_upload_hint_before()), gr.update(value="重新上傳圖片辨識")
 
-combined_css = emotion.css 
+    top_key = max(emo_dict, key=lambda k: float(emo_dict[k]))
+    top_conf = float(emo_dict[top_key])
 
-with gr.Blocks(title="AI 心情食堂", css=combined_css) as demo:
+    st.final_emo = top_key; st.final_conf = top_conf
+    
+    # ### [整合需求] 存入分數 ###
+    st.final_score = EMO_TO_SCORE.get(top_key, 3)
+    # #######################
     
-    st_state = gr.State(emotion.AppState())
+    st.finished = True
+    return gr.update(value=_result_card_html(top_key, top_conf)), gr.update(value=_score_radio_value(EMO_TO_SCORE.get(top_key, 3))), st, gr.update(value=_upload_hint_done()), gr.update(value="重新上傳圖片辨識")
+
+
+# =========================
+# Mode / Buttons
+# =========================
+def on_mode_change(mode: str, st: AppState):
+    st.mode = mode
+    if mode == "攝影機辨識":
+        st = _reset_state_for_camera(st)
+        return gr.update(visible=True), gr.update(visible=False, value=None), 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")
+    else:
+        st = _reset_state_for_upload(st)
+        return gr.update(visible=False), gr.update(visible=True, value=None), gr.update(value=_upload_hint_before()), gr.update(value=_score_radio_value(3)), gr.update(value=_upload_hint_before()), st, gr.update(value="重新上傳圖片辨識")
 
-    with gr.Tabs() as tabs:
-        
-        # Tab 1: 情緒辨識
-        with gr.TabItem("😊 情緒辨識 (Step 1)", id=0):
-            with gr.Column(elem_id="app_container"):
-                gr.Markdown("### 第一步：測測你的心情能量\n讓 AI 看看你的表情，自動幫你決定心情分數！(辨識完畢會自動跳轉)")
-                
-                with gr.Row():
-                    btn_start = gr.Button("📸 開啟攝影機辨識", variant="primary")
-                    btn_stop  = gr.Button("⏹️ 停止", variant="secondary", visible=False)
-
-                cam = gr.Image(sources=["webcam"], streaming=True, type="numpy", label="攝影機畫面", visible=False)
-                result_markdown = gr.Markdown(emotion._hint_html("請按「開啟攝影機辨識」或下方上傳照片。"))
-                
-                gr.Markdown("---")
-                gr.Markdown("### 或者：上傳照片")
-                upload_img = gr.Image(sources=["upload"], type="numpy", label="上傳照片")
-
-        # Tab 2: 主功能區
-        with gr.TabItem("🍽️ AI 心情食堂 (Step 2)", id=1):
-            with gr.Column():
-                gr.Markdown(f"## 🍱 今天想吃點什麼？")
-                with gr.Row():
-                    with gr.Column(scale=1):
-                        # 这里的 Radio 选项使用的是半形括号
-                        score_input = gr.Radio(
-                            ["1 (心情差)", "2 (不太好)", "3 (普通)", "4 (不錯)", "5 (超棒)"], 
-                            label="1. 心情分數 (由 Tab 1 自動填入)", 
-                            value="3 (普通)"
-                        )
-                        food_input = gr.Radio(["吃飯", "吃麵", "隨便"], label="2. 想吃什麼", value="隨便")
-                        diary_input = gr.Textbox(lines=4, label="3. 心情日記", placeholder="寫下今天發生的事...")
-                        debug_mode_btn = gr.Checkbox(label="🔧 開啟除錯模式", value=False)
-                        submit_btn = gr.Button("🍱 送出給 Agent", variant="primary")
-                    
-                    with gr.Column(scale=1):
-                        agent_output = gr.Markdown(label="AI 回應")
-                        image_output = gr.Image(label="AI 推薦美食圖", type="pil", width=400)
-                        map_output = gr.HTML(label="地圖導航")
-
-    # ==========================================
-    # 事件綁定
-    # ==========================================
-
-    btn_start.click(
-        fn=bridge_start_click,
-        inputs=[st_state],
-        outputs=[result_markdown, cam, btn_stop, btn_start, st_state],
-        show_progress="minimal"
-    )
-
-    btn_stop.click(
-        fn=bridge_stop_click,
-        inputs=[st_state],
-        outputs=[result_markdown, cam, btn_stop, btn_start, st_state],
-        show_progress="minimal"
-    )
-
-    cam.stream(
-        fn=bridge_predict_frame,
-        inputs=[cam, st_state],
-        outputs=[cam, result_markdown, st_state, btn_stop, btn_start, score_input, tabs], 
-        show_progress="minimal"
-    )
-
-    upload_img.change(
-        fn=bridge_predict_upload,
-        inputs=[upload_img, st_state],
-        outputs=[result_markdown, score_input, tabs, st_state], 
-        show_progress="minimal"
-    )
-
-    submit_btn.click(
-        fn=mood_agent_logic,
-        inputs=[score_input, food_input, diary_input, debug_mode_btn],
-        outputs=[agent_output, image_output, map_output]
-    )
+def on_restart(st: AppState):
+    if st.mode == "攝影機辨識":
+        st = _reset_state_for_camera(st)
+        return 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()
+    else:
+        st = _reset_state_for_upload(st)
+        return gr.update(), gr.update(value=_upload_hint_before()), gr.update(value=_score_radio_value(3)), gr.update(value=_upload_hint_before()), st, gr.update(value="重新上傳圖片辨識"), gr.update(value=None)
+
+def on_stop(st: AppState):
+    if st.mode == "攝影機辨識":
+        st.running = False; st.finished = True
+        if st.final_emo is None:
+            return 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()), st, gr.update(value="重新攝影機辨識"), gr.update()
+        return 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()), st, gr.update(value="重新攝影機辨識"), gr.update()
+    
+    st = _reset_state_for_upload(st)
+    return gr.update(), gr.update(value=_hint_html("已停止圖片辨識。")), gr.update(value=_score_radio_value(3)), gr.update(value=_upload_hint_stopped()), st, gr.update(value="重新上傳圖片辨識"), gr.update(value=None)
+
+css = """#app_container { max-width: 960px; margin: 0 auto; } .gradio-container { background: #f1f5f9 !important; }"""
 
 if __name__ == "__main__":
-    demo.launch(ssr_mode=False)
+    with gr.Blocks(title="Emotion Detector", css=css) as demo:
+        # 隊友的單獨測試介面 (略)
+        pass 
+    # demo.launch()