import time
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 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
ZOOM_FACTOR = 1.5

DETECTOR_BACKEND = "mtcnn"     # 想快一點可改 "opencv"
ALIGN_FACE = True


# =========================
# 情緒中文 + 分數映射（7 -> 5）
# =========================
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",
}

# 你要的：把 7 種情緒塞進 5 分心情分數
EMO_TO_SCORE = {
    "angry": 1,
    "disgust": 1,
    "fear": 1,
    "sad": 2,
    "neutral": 3,
    "surprise": 4,
    "happy": 5,
    "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）",
    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

    final_score: int = 3  


# =========================
# 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_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 _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("）")

    # ✅ 強制設定 color: #000000
    return f"""
    <div style="
        border-radius:16px;
        padding:20px;
        border:1px solid #E6E6E6;
        background:#FFFFFF;
        color:#000000; 
    ">
      <div style="font-size:14px;color:#000000;margin-bottom:8px;">
        辨識結果
      </div>

      <div style="
        font-size:42px;
        font-weight:800;
        line-height:1.1;
        margin-bottom:14px;
        color:#000000;
      ">
        {zh}
      </div>

      <div style="font-size:16px;color:#000000;margin-bottom:6px;">
        心情分數：{score}（{score_desc}）
      </div>

      <div style="font-size:14px;color:#000000;">
        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 _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 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]


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
    return st


# =========================
# Camera streaming
# =========================
def on_stream(frame_rgb: np.ndarray, st: AppState):
    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"),
        )
    
    frame_rgb = _zoom_center(frame_rgb, ZOOM_FACTOR)

    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:
        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

    # 延遲確認
    if st.candidate_emo != stable_key:
        st.candidate_emo = stable_key
        st.candidate_since = now
    else:
        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
        st.final_score = EMO_TO_SCORE.get(stable_key, 3)

        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"),
        )

    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"),
    )


# =========================
# Mode / Buttons
# =========================

def on_restart(st: AppState):
    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"),                      
    )


def on_stop(st: AppState):
    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"),
        )

    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"),
    )


# =========================
# Gradio UI
# =========================

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",
            visible=True,
        )

        with gr.Row():
            btn_restart = gr.Button("重新攝影機辨識", variant="primary")
            btn_stop = gr.Button("停止", variant="secondary")

        gr.Markdown("### 辨識結果")
        result = gr.HTML(_hint_html("等待影像…（尚未開始串流）"))

        gr.Markdown("### 心情分數（系統判定）")
        mood = gr.Radio(
            choices=SCORE_CHOICES,
            value=_score_radio_value(3),
            label="心情分數",
            interactive=False,
        )

        cam.stream(
            fn=on_stream,
            inputs=[cam, st],
            outputs=[cam, result, mood, hint, st, btn_restart],
            show_progress="minimal",
        )

        btn_restart.click(
            fn=on_restart,
            inputs=[st],
            outputs=[cam, result, mood, hint, st, btn_restart],
            show_progress="minimal",
        )

        btn_stop.click(
            fn=on_stop,
            inputs=[st],
            outputs=[cam, result, mood, hint, st, btn_restart],
            show_progress="minimal",
        )

    demo.launch(server_name="0.0.0.0", server_port=7860, share=False)