app.py

import gradio as gr
import datetime
import pandas as pd
import warnings
import joblib
import hdbscan
import numpy as np
import logger_service
import os
import urllib.request
import matplotlib.pyplot as plt
import seaborn as sns
import matplotlib.font_manager as fm
import shutil
import io
import base64

# --- 🌟 解決 Matplotlib 中文顯示問題 ---
font_path = 'SimHei.ttf'
if not os.path.exists(font_path):
    try:
        print("📥 正在下載中文字型...")
        urllib.request.urlretrieve('https://github.com/StellarCN/scp_zh/raw/master/fonts/SimHei.ttf', font_path)
    except Exception as e:
        print(f"⚠️ 字型下載失敗: {e}")

if os.path.exists(font_path):
    fm.fontManager.addfont(font_path)
    plt.rc('font', family='SimHei')
plt.rcParams['axes.unicode_minus'] = False

# Seaborn 近期會對舊的 pandas option 發出 FutureWarning（純警告、非功能錯誤）
# 重整前端會觸發繪圖端點，因此你會在 terminal 看到重複訊息。
warnings.filterwarnings(
    "ignore",
    category=FutureWarning,
    message="use_inf_as_na option is deprecated*",
)

# --- 全域統計與狀態變數 ---
global_stats = {
    "total_count": 0,
    "abnormal_count": 0,
    "start_time": datetime.datetime.now()
}

system_weights = {
    "ow": 75.0, "tw": 80.0, "iw": 65.0, 
    "dw": 45.0, "gw": 50.0, "fw": 60.0
}

def update_system_weights(ow, tw, iw, dw, gw, fw):
    system_weights.update({
        "ow": float(ow), "tw": float(tw), "iw": float(iw), 
        "dw": float(dw), "gw": float(gw), "fw": float(fw)
    })
    return f"✅ 系統權重已更新 - 總體: {ow}%, 時間: {tw}%, IP: {iw}%, 裝置: {dw}%, 地理: {gw}%, 頻率: {fw}%"

ACTION_MAP = {
    "教務系統: 期末網路教學評量": "click_eval_system",
    "教務系統: 期末網路預選系統": "click_pre_select_system",
    "教務系統: 開學後加退選系統": "click_add_drop_system",
    "教務系統: 北科i學園PLUS": "click_istudy",
    "教務系統: 學生證掛失及補發系統": "click_card_loss",
    "教務系統: 課程系統": "click_course_system",
    "教務系統: 學業成績查詢系統": "click_score_query",
    "學務系統: 學生請假系統": "click_leave_system",
    "學務系統: 學生宿舍登錄(抽籤)系統": "click_dorm_system",
    "學務系統: 獎助學金申請系統": "click_scholarship",
    "資訊服務: 網路郵局 WebMail": "click_webmail",
    "資訊服務: 北科軟體雲": "click_vdesk",
    "惡意測試: 嘗試偷改成績 (SQL Injection)": "malicious_sql_injection"
}

log_history = []
initial_df_abn = pd.DataFrame(columns=["timestamp", "ip_address", "cookie_id", "account", "role", "action", "status"])

# HDBSCAN 特徵分群用的 IP 分類
def get_ip_category(ip_str):
    ip = str(ip_str).split(" ")[0]
    if ip.startswith("140.124."): return 0.0    
    elif ip.startswith("61.228."): return 1.0   
    elif ip.startswith("210.61."): return 2.0   
    elif ip.startswith("45.33."): return 3.0    
    else: return 4.0  

def get_geo_level(ip_str):
    ip = str(ip_str).split(" ")[0]
    if ip == "140.124.71.55": return 0       
    elif ip == "140.124.18.22": return 1     
    elif ip.startswith("61.228."): return 2  
    elif ip.startswith("210.61."): return 3  
    elif ip.startswith("45.33."): return 4   
    else: return 5                           

# --- 1. 載入模型與歷史資料 ---
try:
    le_action = joblib.load('le_action.pkl')
    
    print("📂 正在讀取本地歷史資料庫...")
    if os.path.exists('baseline_logs.csv'): 
        baseline_df = pd.read_csv('baseline_logs.csv')
    else: 
        baseline_df = pd.DataFrame(columns=["timestamp", "ip_address", "cookie_id", "account", "role", "action", "status"])
        baseline_df.to_csv('baseline_logs.csv', index=False)

    baseline_df['timestamp'] = pd.to_datetime(baseline_df['timestamp'])
    baseline_df['clean_ip'] = baseline_df['ip_address'].astype(str).apply(lambda x: x.split(" ")[0])
    
    global_stats["total_count"] = len(baseline_df)
    global_stats["abnormal_count"] = baseline_df[baseline_df['status'].str.contains("異常", na=False)].shape[0]
    print(f"📊 統計初始化：總監測量 {global_stats['total_count']}, 異常數 {global_stats['abnormal_count']}")

    display_df = baseline_df.sort_values(by='timestamp', ascending=False).head(20).copy()
    display_df['timestamp'] = display_df['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')
    log_history = display_df[['timestamp', 'ip_address', 'cookie_id', 'account', 'role', 'action', 'status']].to_dict('records')
    
    abnormal_mask = baseline_df['status'].astype(str).str.contains("異常", na=False)
    if abnormal_mask.any():
        abn_df_temp = baseline_df[abnormal_mask].sort_values(by='timestamp', ascending=False).copy()
        abn_df_temp['timestamp'] = abn_df_temp['timestamp'].dt.strftime('%Y-%m-%d %H:%M:%S')
        initial_df_abn = abn_df_temp[['timestamp', 'ip_address', 'cookie_id', 'account', 'role', 'action', 'status']]
    
    baseline_df['month'] = baseline_df['timestamp'].dt.month
    baseline_df['day'] = baseline_df['timestamp'].dt.day
    baseline_df['hour'] = baseline_df['timestamp'].dt.hour
    
    ip_codes = np.array([get_ip_category(ip) for ip in baseline_df['clean_ip'].values])
        
    actions = baseline_df['action'].astype(str).values
    known_actions = set(le_action.classes_)
    unknown_actions = set(actions) - known_actions
    if unknown_actions: le_action.classes_ = np.append(le_action.classes_, list(unknown_actions))
    
    baseline_X = np.column_stack((
        baseline_df['month'].values, baseline_df['day'].values, baseline_df['hour'].values,
        ip_codes, le_action.transform(actions)
    ))
    
    AI_READY = True
    print("✅ AI 模組與歷史基準資料載入成功！")
except Exception as e:
    AI_READY = False
    global_stats["total_count"] = 0; global_stats["abnormal_count"] = 0
    print(f"⚠️ AI 模組載入失敗，請確認檔案。錯誤: {e}")

USER_DB = {"student": ["1234", "學生", "同學"], "admin": ["1234", "管理", "管理員"]}


# =====================================================================
# 👉 新增：純淨 API 端點專用函式 (只回傳 JSON 字典，給隊友抓資料用)
# =====================================================================
def api_get_stats():
    """取得系統狀態與權重"""
    total = global_stats["total_count"]
    abnormal = global_stats["abnormal_count"]
    rate = round((abnormal / total * 100), 2) if total > 0 else 0
    return {
        "status": "success",
        "ai_ready": AI_READY,
        "data": {
            "total_logs": total,
            "abnormal_logs": abnormal,
            "anomaly_rate_percent": rate,
            "current_weights": system_weights
        }
    }

def api_get_logs(limit=20):
    """取得最新的 N 筆 Log"""
    try: limit = int(limit)
    except: limit = 20
    return {
        "status": "success", 
        "count": len(log_history[:limit]), 
        "data": log_history[:limit]
    }

def api_get_chart_data(action_name):
    """取得特定系統的圖表原始數據 (供前端自行繪圖)"""
    if not AI_READY: return {"status": "error", "message": "AI 模型未載入"}
    action_code = ACTION_MAP.get(action_name)
    if not action_code: return {"status": "error", "message": f"找不到系統: {action_name}"}
    
    df_hist = baseline_df[baseline_df['action'] == action_code].copy()
    df_live = pd.DataFrame(log_history)
    if not df_live.empty:
        df_live['timestamp'] = pd.to_datetime(df_live['timestamp'], errors='coerce')
        df_live = df_live.dropna(subset=['timestamp'])
        df_live = df_live[df_live['action'] == action_code].copy()
        
    if not df_live.empty:
        df_combined = pd.concat([df_hist, df_live], ignore_index=True)
        df_combined = df_combined.drop_duplicates(subset=['timestamp', 'account', 'action'])
    else:
        df_combined = df_hist.copy()
        
    if df_combined.empty:
        return {"status": "success", "action": action_code, "data": {"normal": [], "abnormal": []}}
        
    df_combined['month'] = df_combined['timestamp'].dt.month
    df_combined['time_of_day'] = df_combined['timestamp'].dt.hour + df_combined['timestamp'].dt.minute / 60.0
    
    is_abnormal = df_combined['status'].astype(str).str.contains("異常", na=False)
    
    normal_data = df_combined[~is_abnormal][['month', 'time_of_day', 'timestamp']].astype(str).to_dict('records')
    abnormal_data = df_combined[is_abnormal][['month', 'time_of_day', 'timestamp', 'status']].astype(str).to_dict('records')
    
    return {
        "status": "success", 
        "action_code": action_code, 
        "data": {"normal": normal_data, "abnormal": abnormal_data}
    }

def api_get_abnormal_logs():
    """取得所有異常 Log"""
    abnormal_logs = [log for log in log_history if "異常" in str(log.get("status", "")) or "錯誤" in str(log.get("status", "")) or "受限" in str(log.get("status", ""))]
    return {
        "status": "success",
        "count": len(abnormal_logs),
        "data": abnormal_logs
    }
# =====================================================================


def get_dashboard_html():
    total = global_stats["total_count"]
    abnormal = global_stats["abnormal_count"]
    rate = (abnormal / total * 100) if total > 0 else 0
    
    if not AI_READY: status_icon, status_text, status_color, status_desc = "🔴", "系統錯誤", "#ef4444", "AI 模型載入失敗，防護停用中。"
    elif rate > 10: status_icon, status_text, status_color, status_desc = "🟡", "高風險警告", "#f59e0b", "近期異常行為頻發，請立刻檢查 Log。"
    else: status_icon, status_text, status_color, status_desc = "🟢", "正常執行", "#10b981", "HDBSCAN 與 Agent 服務運作正常。"

    return f"""
    <div class="stat-dashboard">
        <div class="stat-card card-total">
            <div class="card-icon">📊</div>
            <div class="card-content">
                <div class="card-label">總監測量 (本地歷史資料庫)</div>
                <div class="card-value">{total:,} <span class="card-unit">筆 Log</span></div>
                <div class="card-sub-text">自系統啟動起統計 (+今日即時)</div>
            </div>
        </div>
        <div class="stat-card card-anomaly">
            <div class="card-icon">⚠️</div>
            <div class="card-content">
                <div class="card-label">異常率 (平均數值)</div>
                <div class="card-value-container">
                    <div class="card-value" style="color: #ef4444;">{rate:.2f} %</div>
                    <div class="card-trend trend-up">↑即時</div>
                </div>
                <div class="card-sub-text">共 {abnormal:,} 筆監測到異常行為</div>
            </div>
        </div>
        <div class="stat-card card-status" style="border-top-color: {status_color};">
            <div class="card-icon">{status_icon}</div>
            <div class="card-content">
                <div class="card-label">系統狀態</div>
                <div class="card-value" style="font-size: 24px; color: {status_color};">{status_text}</div>
                <div class="card-sub-text">{status_desc}</div>
            </div>
        </div>
    </div>
    """

def get_plot_data_for_api(selected_key):
    """API專用版本，返回Base64圖片字符串"""
    if not selected_key or not AI_READY: return None
    action_code = ACTION_MAP.get(selected_key)
    try:
        plt.close('all') 
        df_hist = baseline_df[baseline_df['action'] == action_code].copy()
        
        df_live = pd.DataFrame(log_history)
        if not df_live.empty:
            df_live['timestamp'] = pd.to_datetime(df_live['timestamp'], errors='coerce')
            df_live = df_live.dropna(subset=['timestamp']) 
            df_live = df_live[df_live['action'] == action_code].copy()
        
        if not df_live.empty:
            df_combined = pd.concat([df_hist, df_live], ignore_index=True)
            df_combined = df_combined.drop_duplicates(subset=['timestamp', 'account', 'action'])
        else:
            df_combined = df_hist.copy()
            
        if df_combined.empty:
            fig, ax = plt.subplots(figsize=(12, 6))
            if os.path.exists(font_path): plt.rc('font', family='SimHei')
            ax.text(0.5, 0.5, f"【{selected_key}】目前尚無資料", ha='center', va='center', fontsize=15)
            # 將圖片轉換為 Base64
            buf = io.BytesIO()
            fig.savefig(buf, format='png', dpi=100, bbox_inches='tight')
            buf.seek(0)
            img_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
            buf.close()
            plt.close(fig)
            return f"data:image/png;base64,{img_base64}"

        df_combined['month'] = df_combined['timestamp'].dt.month
        df_combined['time_of_day'] = df_combined['timestamp'].dt.hour + df_combined['timestamp'].dt.minute / 60.0
        
        is_abnormal = df_combined['status'].astype(str).str.contains("異常", na=False)
        df_normal = df_combined[~is_abnormal]
        df_abnormal = df_combined[is_abnormal]

        fig, ax = plt.subplots(figsize=(12, 6))
        sns.set_theme(style="whitegrid")
        if os.path.exists(font_path): plt.rc('font', family='SimHei')
        plt.rcParams['axes.unicode_minus'] = False

        has_data = False
        months_order = list(range(1, 13)) 

        if not df_normal.empty:
            has_data = True
            sns.stripplot(data=df_normal, x='month', y='time_of_day', jitter=0.3, alpha=0.4, size=5, color='#10b981', order=months_order, ax=ax, label='正常資料')

        if not df_abnormal.empty:
            has_data = True
            sns.stripplot(data=df_abnormal, x='month', y='time_of_day', jitter=0.3, alpha=0.9, size=9, color='#ef4444', marker='X', order=months_order, ax=ax, label='異常資料')
            
        if not has_data:
            ax.text(0.5, 0.5, f"【{selected_key}】目前尚無資料", ha='center', va='center', fontsize=15)
            # 將圖片轉換為 Base64
            buf = io.BytesIO()
            fig.savefig(buf, format='png', dpi=100, bbox_inches='tight')
            buf.seek(0)
            img_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
            buf.close()
            plt.close(fig)
            return f"data:image/png;base64,{img_base64}"

        ax.set_xlabel('月份 (1 ~ 12月)', fontsize=12)
        ax.set_ylabel('時間 (24小時制)', fontsize=12)
        ax.set_yticks(np.arange(0, 25, 2))
        ax.set_ylim(24.5, -0.5)

        handles, labels = ax.get_legend_handles_labels()
        by_label = dict(zip(labels, handles))
        if by_label: ax.legend(by_label.values(), by_label.keys(), loc='upper right', bbox_to_anchor=(1.15, 1))

        plt.tight_layout()
        
        # 將圖片轉換為 Base64
        buf = io.BytesIO()
        fig.savefig(buf, format='png', dpi=100, bbox_inches='tight')
        buf.seek(0)
        img_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
        buf.close()
        plt.close(fig)
        return f"data:image/png;base64,{img_base64}"
    except Exception as e:
        print(f"繪圖失敗: {e}")
        # 返回一個簡單的錯誤圖片
        fig, ax = plt.subplots(figsize=(8, 4))
        ax.text(0.5, 0.5, f"圖表生成失敗: {str(e)}", ha='center', va='center', fontsize=12, color='red')
        ax.set_xlim(0, 1)
        ax.set_ylim(0, 1)
        ax.axis('off')
        
        buf = io.BytesIO()
        fig.savefig(buf, format='png', dpi=100, bbox_inches='tight')
        buf.seek(0)
        img_base64 = base64.b64encode(buf.getvalue()).decode('utf-8')
        buf.close()
        plt.close(fig)
        return f"data:image/png;base64,{img_base64}"

def get_plot_data(selected_key):
    """給Gradio界面使用，返回Figure對象"""
    if not selected_key or not AI_READY: return None
    action_code = ACTION_MAP.get(selected_key)
    try:
        plt.close('all') 
        df_hist = baseline_df[baseline_df['action'] == action_code].copy()
        
        df_live = pd.DataFrame(log_history)
        if not df_live.empty:
            df_live['timestamp'] = pd.to_datetime(df_live['timestamp'], errors='coerce')
            df_live = df_live.dropna(subset=['timestamp']) 
            df_live = df_live[df_live['action'] == action_code].copy()
        
        if not df_live.empty:
            df_combined = pd.concat([df_hist, df_live], ignore_index=True)
            df_combined = df_combined.drop_duplicates(subset=['timestamp', 'account', 'action'])
        else:
            df_combined = df_hist.copy()
            
        if df_combined.empty:
            fig, ax = plt.subplots(figsize=(12, 6))
            if os.path.exists(font_path): plt.rc('font', family='SimHei')
            ax.text(0.5, 0.5, f"【{selected_key}】目前尚無資料", ha='center', va='center', fontsize=15)
            return fig

        df_combined['month'] = df_combined['timestamp'].dt.month
        df_combined['time_of_day'] = df_combined['timestamp'].dt.hour + df_combined['timestamp'].dt.minute / 60.0
        
        is_abnormal = df_combined['status'].astype(str).str.contains("異常", na=False)
        df_normal = df_combined[~is_abnormal]
        df_abnormal = df_combined[is_abnormal]

        fig, ax = plt.subplots(figsize=(12, 6))
        sns.set_theme(style="whitegrid")
        if os.path.exists(font_path): plt.rc('font', family='SimHei')
        plt.rcParams['axes.unicode_minus'] = False

        has_data = False
        months_order = list(range(1, 13)) 

        if not df_normal.empty:
            has_data = True
            sns.stripplot(data=df_normal, x='month', y='time_of_day', jitter=0.3, alpha=0.4, size=5, color='#10b981', order=months_order, ax=ax, label='正常資料')

        if not df_abnormal.empty:
            has_data = True
            sns.stripplot(data=df_abnormal, x='month', y='time_of_day', jitter=0.3, alpha=0.9, size=9, color='#ef4444', marker='X', order=months_order, ax=ax, label='異常資料')
            
        if not has_data:
            ax.text(0.5, 0.5, f"【{selected_key}】目前尚無資料", ha='center', va='center', fontsize=15)
            return fig

        ax.set_xlabel('月份 (1 ~ 12月)', fontsize=12)
        ax.set_ylabel('時間 (24小時制)', fontsize=12)
        ax.set_yticks(np.arange(0, 25, 2))
        ax.set_ylim(24.5, -0.5)

        handles, labels = ax.get_legend_handles_labels()
        by_label = dict(zip(labels, handles))
        if by_label: ax.legend(by_label.values(), by_label.keys(), loc='upper right', bbox_to_anchor=(1.15, 1))

        plt.tight_layout()
        return fig
    except Exception as e:
        print(f"繪圖失敗: {e}")
        return None

def timer_update(selected_key): return get_dashboard_html(), get_plot_data(selected_key)

def explain_abnormal_log(log_text):
    if not log_text or log_text.strip() == "": return "⚠️ 請先提供異常 Log 資訊以進行分析。"
    try: from groq import Groq
    except ImportError: return "⚠️ 未安裝 groq 套件。請在終端機執行 `pip install groq`。"
    groq_api_key = os.environ.get("GROQ_API_KEY", "")
    if not groq_api_key: return "⚠️ 未設定 GROQ_API_KEY。"
    try:
        client = Groq(api_key=groq_api_key)
        response = client.chat.completions.create(
            messages=[
                {"role": "system", "content": "你是一位專業的資安分析師。請簡單解釋為什麼這筆 Log 異常，並指出風險(如撞庫攻擊、作息異常等)。"},
                {"role": "user", "content": f"請分析這筆異常 Log：\n{log_text}"}
            ],
            model="llama-3.1-8b-instant", temperature=0.5, max_tokens=300
        )
        return response.choices[0].message.content
    except Exception as e: return f"⚠️ 呼叫 API 發生錯誤: {str(e)}"

# --- 2. 核心邏輯與真實 HDBSCAN 異常偵測 ---
def check_anomaly(ip, account, action, log_time):
    if not AI_READY: return "success" 
    if action in ["login_attempt", "logout"]: return "success"

    try:
        ow = float(system_weights["ow"])
        tw = float(system_weights["tw"])
        iw = float(system_weights["iw"])
        dw = float(system_weights["dw"])
        gw = float(system_weights["gw"])
        fw = float(system_weights["fw"])
        
        geo_level = get_geo_level(ip)
        
        if gw == 100 and geo_level > 0:
            return "⚠️異常_地理位置受限 (100%絕對鎖定: 僅限校內專網存取)"
        elif gw >= 90 and geo_level > 1:
            return "⚠️異常_地理位置受限 (高敏感防護: 僅限校園網路與宿舍)"
        elif gw >= 70 and geo_level > 2:
            return "⚠️異常_地理位置受限 (進階防護: 禁止公共場所與海外連線)"
        elif gw >= 50 and geo_level > 3:
            return "⚠️異常_地理位置受限 (預設防護: 禁止海外異常 IP)"
        
        current_dt = pd.to_datetime(str(log_time))
        cutoff_dt = current_dt - pd.Timedelta(seconds=60)
        
        recent_clicks = 0
        for log in log_history:
            log_dt = pd.to_datetime(log['timestamp'])
            if log_dt >= cutoff_dt:
                if log.get('account') == account and log.get('action') == action:
                    recent_clicks += 1
            else:
                break
                
        if fw <= 0: max_allowed_clicks = 100
        elif fw <= 10: max_allowed_clicks = 90
        elif fw <= 20: max_allowed_clicks = 80
        elif fw <= 30: max_allowed_clicks = 70
        elif fw <= 40: max_allowed_clicks = 60
        elif fw <= 50: max_allowed_clicks = 50
        elif fw <= 60: max_allowed_clicks = 40
        elif fw <= 70: max_allowed_clicks = 30
        elif fw <= 80: max_allowed_clicks = 15
        elif fw <= 90: max_allowed_clicks = 9
        else: max_allowed_clicks = 3

        if (recent_clicks + 1) > max_allowed_clicks:
            return f"⚠️異常_單一操作頻率過高 ({recent_clicks+1}次/分)"
            
        ip_code = float(get_ip_category(ip))
        action_code = float(le_action.transform([action])[0])
        
        current_month = float(current_dt.month)
        current_day = float(current_dt.day)
        current_hour = float(current_dt.hour)
        
        time_multiplier = tw / 100.0
        action_scale = (dw / 100.0) * 100.0 
        
        scaled_baseline = baseline_X.copy().astype(float)
        scaled_baseline[:, 0] *= time_multiplier * 10.0  
        scaled_baseline[:, 1] *= time_multiplier * 0.5   
        scaled_baseline[:, 2] *= time_multiplier * 2.0   
        scaled_baseline[:, 3] *= (iw / 100.0) * 10.0     
        scaled_baseline[:, 4] *= action_scale            
        
        v_month = current_month * time_multiplier * 10.0
        v_day = current_day * time_multiplier * 0.5
        v_hour = current_hour * time_multiplier * 2.0
        v_ip = ip_code * (iw / 100.0) * 10.0
        v_action = action_code * action_scale
        
        new_data_point = np.array([[v_month, v_day, v_hour, v_ip, v_action]], dtype=float)
        
        same_action_mask = (baseline_X[:, 4] == action_code)
        relevant_history = scaled_baseline[same_action_mask]
        
        other_history = scaled_baseline[~same_action_mask]
        if len(other_history) > 800:
            np.random.seed(int(current_day + current_hour)) 
            indices = np.random.choice(len(other_history), 800, replace=False)
            other_history = other_history[indices]
            
        fit_X = np.vstack((other_history, relevant_history, new_data_point))
        strictness = max(2, int(2 + (ow / 100.0) * 4))
        
        model = hdbscan.HDBSCAN(min_cluster_size=strictness, min_samples=2)
        clusters = model.fit_predict(fit_X)
        
        if clusters[-1] == -1: return "⚠️異常_作息或行為不符"

        if np.any(same_action_mask):
            distances = np.linalg.norm(relevant_history - new_data_point[0], axis=1)
            min_distance = np.min(distances)
            max_tolerance_distance = time_multiplier * 15.0 + 1.0
            
            if ip_code >= 3.0: max_tolerance_distance *= (1 - (iw / 100.0) * 0.6)
                
            if min_distance > max_tolerance_distance:
                return "⚠️異常_作息或行為不符"
                
        return "success"
    except ValueError:
        return "⚠️異常_未知特徵 (出現未授權的 IP 或操作)"
    except Exception as e:
        return f"⚠️系統錯誤: {str(e)}"

def record_log(ip, cookie, account, role, action, status, time_mode, custom_time):
    global baseline_X 
    
    if time_mode == "自訂時間 (模擬過去/未來)" and custom_time and str(custom_time).strip() != "": 
        try:
            valid_time = pd.to_datetime(str(custom_time))
            log_time = valid_time.strftime("%Y-%m-%d %H:%M:%S")
        except Exception:
            log_time = datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=8))).strftime("%Y-%m-%d %H:%M:%S")
            status = "⚠️格式錯誤_無效的自訂時間"
    else: 
        log_time = datetime.datetime.now(datetime.timezone(datetime.timedelta(hours=8))).strftime("%Y-%m-%d %H:%M:%S")

    if status == "success":
        ai_judgment = check_anomaly(ip, account, action, log_time)
        if ai_judgment != "success": status = ai_judgment

    new_log = {"timestamp": log_time, "ip_address": ip, "cookie_id": cookie, "account": account, "role": role, "action": action, "status": status}

    new_df = pd.DataFrame([new_log])
    new_df.to_csv('baseline_logs.csv', mode='a', header=not os.path.exists('baseline_logs.csv'), index=False)
    
    try: logger_service.push_new_log(new_log)
    except: pass

    global_stats["total_count"] += 1
    if "異常" in str(status) or "錯誤" in str(status) or "受限" in str(status): global_stats["abnormal_count"] += 1

    log_history.insert(0, new_log)

    if status == "success" and AI_READY:
        try:
            current_time = pd.to_datetime(str(log_time))
            if action not in le_action.classes_: le_action.classes_ = np.append(le_action.classes_, action)
            new_row = np.array([[float(current_time.month), float(current_time.day), float(current_time.hour), float(get_ip_category(ip)), float(le_action.transform([action])[0])]], dtype=float)
            baseline_X = np.vstack((baseline_X, new_row))
        except: pass 

    df_all = pd.DataFrame(log_history)
    abnormal_logs = [log for log in log_history if "異常" in str(log.get("status", "")) or "錯誤" in str(log.get("status", "")) or "受限" in str(log.get("status", ""))]
    df_abnormal = pd.DataFrame(abnormal_logs) if abnormal_logs else pd.DataFrame(columns=df_all.columns)
    
    input_update = gr.update(value=f"時間: {log_time} | IP: {ip} | 帳號: {account} | 動作: {action} | 狀態: {status}") if "異常" in str(status) or "錯誤" in str(status) or "受限" in str(status) else gr.update()
    return df_all, df_abnormal, input_update

def fast_login(target_account, ip, cookie, time_mode, custom_time):
    role, name = USER_DB[target_account][1], USER_DB[target_account][2]
    df_all, df_abn, inp_upd = record_log(ip, cookie, target_account, role, "login_attempt", "success", time_mode, custom_time)
    target_tab = "student_tab" if role == "學生" else "admin_tab"
    return gr.update(selected="dashboard_route"), gr.update(selected=target_tab), "", f"**{name}** 歡迎您  |  線上人數: 938 (⚡快速登入模式)", df_all, df_abn, inp_upd, target_account

def process_login(ip, cookie, account, password, captcha, time_mode, custom_time):
    def fail_response(msg, df_all, df_abn, inp_upd): return gr.update(selected="login_route"), gr.update(), msg, gr.update(), df_all, df_abn, inp_upd
    if account not in USER_DB: 
        df_all, df_abn, inp_upd = record_log(ip, cookie, account, "Unknown", "login_attempt", "failed_no_account", time_mode, custom_time)
        return fail_response("帳號不存在！", df_all, df_abn, inp_upd)
    role, name = USER_DB[account][1], USER_DB[account][2]
    if captcha.upper() != "FEIK": 
        df_all, df_abn, inp_upd = record_log(ip, cookie, account, role, "login_attempt", "failed_captcha", time_mode, custom_time)
        return fail_response("驗證碼錯誤！", df_all, df_abn, inp_upd)
    if USER_DB[account][0] == password:
        df_all, df_abn, inp_upd = record_log(ip, cookie, account, role, "login_attempt", "success", time_mode, custom_time)
        return gr.update(selected="dashboard_route"), gr.update(selected="student_tab" if role == "學生" else "admin_tab"), "", f"**{name}** 歡迎您  |  線上人數: 938", df_all, df_abn, inp_upd
    df_all, df_abn, inp_upd = record_log(ip, cookie, account, role, "login_attempt", "failed_password", time_mode, custom_time)
    return fail_response("密碼錯誤！", df_all, df_abn, inp_upd)

def toggle_role(current_account, ip, cookie, time_mode, custom_time):
    target_account = "student" if current_account == "admin" else "admin"
    role, name = USER_DB[target_account][1], USER_DB[target_account][2]
    df_all, df_abn, inp_upd = record_log(ip, cookie, target_account, role, "login_attempt", "success", time_mode, custom_time)
    return gr.update(selected="student_tab" if role == "學生" else "admin_tab"), f"**{name}** 歡迎您  |  線上人數: 938 (⚡單鍵切換模式)", df_all, df_abn, inp_upd, target_account

def logout(ip, cookie, account, time_mode, custom_time):
    role = USER_DB.get(account, ["", "Unknown"])[1]
    df_all, df_abn, inp_upd = record_log(ip, cookie, account, role, "logout", "success", time_mode, custom_time)
    return gr.update(selected="login_route"), "", "", "", "", df_all, df_abn, inp_upd

def simulate_click(ip, cookie, account, button_name, time_mode, custom_time):
    role = USER_DB.get(account, ["", "Unknown"])[1]
    return record_log(ip, cookie, account, role, button_name, "success", time_mode, custom_time)

def _extract_gr_update_value(update_obj):
    """
    Gradio 的 gr.update(...) 可能回傳 dict 形式或物件形式。
    這裡統一抓出其中的 `value`，否則回傳 None。
    """
    if update_obj is None:
        return None
    if isinstance(update_obj, dict):
        return update_obj.get("value")
    # Fallback: try attribute access
    try:
        return getattr(update_obj, "value", None)
    except Exception:
        return None

def api_run_student_action(ip, c, acc, button_name, tm, ct):
    """
    統一的 student action API handler：
    - 呼叫 simulate_click 真正寫入 log
    - 只回傳異常/受限/錯誤時 abnormal_log_input 的字串（正常時回傳空字串）
    """
    df_all, df_abn, inp_upd = simulate_click(ip, c, acc, button_name, tm, ct)
    val = _extract_gr_update_value(inp_upd)
    return val or ""

def api_run_student_action_code(ip, c, acc, action_code, tm, ct):
    """
    Generic student action API.
    Vue 會直接傳 action_code（例如 click_eval_system / malicious_sql_injection）
    """
    df_all, df_abn, inp_upd = simulate_click(ip, c, acc, action_code, tm, ct)
    val = _extract_gr_update_value(inp_upd)
    # 回傳 store 的新資料（讓前端 update_dataframes 能讀到），以及異常字串給 Vue 顯示
    return df_all, df_abn, val or ""

def api_lambda(ip, c, tm, ct):
    # Vue: lambda(期末教學評量) 沒有傳 acc => 預設用學生帳號
    default_acc = USER_DB["student"][0]
    return api_run_student_action(ip, c, default_acc, "click_eval_system", tm, ct)

def api_lambda_1(ip, c, tm, ct):
    default_acc = USER_DB["student"][0]
    return api_run_student_action(ip, c, default_acc, "click_pre_select_system", tm, ct)

def api_lambda_7(ip, c, acc, tm, ct):
    return api_run_student_action(ip, c, acc, "click_leave_system", tm, ct)

def api_lambda_8(ip, c, acc, tm, ct):
    return api_run_student_action(ip, c, acc, "click_dorm_system", tm, ct)

def api_lambda_10(ip, c, acc, tm, ct):
    return api_run_student_action(ip, c, acc, "click_webmail", tm, ct)

def api_lambda_11(ip, c, acc, tm, ct):
    return api_run_student_action(ip, c, acc, "click_vdesk", tm, ct)

def api_lambda_12(ip, c, acc, tm, ct):
    return api_run_student_action(ip, c, acc, "malicious_sql_injection", tm, ct)

def test_anomaly(ip, cookie, account, action_name, test_time_str):
    role = USER_DB.get(account, ["", "Unknown"])[1]
    return record_log(ip, cookie, account, role, action_name, "success", "自訂時間 (模擬過去/未來)", test_time_str)

def toggle_time_input(mode): return gr.update(visible=(mode == "自訂時間 (模擬過去/未來)"))

# --- 4. UI 介面設計 ---
custom_css = """
.mock-panel { background-color: var(--background-fill-secondary); padding: 15px; border-radius: 8px; border: 1px dashed var(--border-color-primary); }
.login-container { max-width: 500px; margin: 0 auto; padding: 20px; }
.ntut-title { color: #d9534f; font-size: 22px; font-weight: bold; text-align: center; margin-bottom: 20px;}
.weight-card { padding: 10px; background: white; border-radius: 8px; box-shadow: 0 1px 3px rgba(0,0,0,0.1); margin-top: 10px; }
.headless-tabs > div:first-child { display: none !important; }
.headless-tabs { border: none !important; background: transparent !important; }
.stat-dashboard { display: flex; gap: 15px; margin-bottom: 20px; justify-content: space-between; }
.stat-card { flex: 1; background-color: #ffffff !important; padding: 15px; border-radius: 10px; box-shadow: 0 4px 6px rgba(0,0,0,0.05); display: flex; align-items: center; gap: 15px; border-top: 4px solid transparent; }
.card-total { border-left: 4px solid #10b981; }
.card-anomaly { border-left: 4px solid #ef4444; }
.card-status { border-left: 4px solid #3b82f6; }
.card-icon { font-size: 30px; width: 40px; text-align: center; }
.card-content { flex: 1; }
.card-label { color: #374151 !important; font-size: 13px; margin-bottom: 2px; font-weight: bold !important; }
.card-value-container { display: flex; align-items: baseline; gap: 8px; }
.card-value { font-size: 24px; font-weight: bold; }
.card-total .card-value { color: #000000 !important; }
.card-unit { font-size: 14px; color: #4b5563 !important; font-weight: normal; }
.card-trend { font-size: 12px; padding: 2px 6px; border-radius: 4px; }
.trend-up { background-color: #fee2e2 !important; color: #ef4444 !important; }
.card-sub-text { color: #6b7280 !important; font-size: 11px; margin-top: 2px; }
"""

with gr.Blocks(title="模擬校園入口與資安防護系統") as demo:
    store_df_all = gr.State(value=pd.DataFrame(log_history))
    store_df_abn = gr.State(value=initial_df_abn)
    dashboard_timer = gr.Timer(value=60)

    with gr.Row():
        with gr.Column(scale=1, elem_classes="mock-panel"):
            gr.Markdown("### ⚙️ 環境變數模擬器")
            mock_ip = gr.Dropdown(choices=["140.124.71.55 (校內預設)", "140.124.18.22 (宿舍)", "61.228.45.112 (家裡)", "210.61.47.88 (公共場所)", "45.33.22.11 (國外異常IP)"], value="140.124.71.55 (校內預設)", allow_custom_value=True, label="模擬來源 IP")
            mock_cookie = gr.Textbox(label="模擬 Cookie Session ID", value="sess_baseline")
            gr.Markdown("---")
            time_mode = gr.Radio(label="時間設定模式", choices=["真實時間 (目前時間)", "自訂時間 (模擬過去/未來)"], value="真實時間 (目前時間)")
            custom_time_input = gr.Textbox(label="輸入自訂時間", placeholder="格式: YYYY-MM-DD HH:MM:SS", value="2026-03-17 03:00:00", visible=False)
            gr.Markdown("---")
            gr.Markdown("### 📊 即時 Log 與 AI 判定區")
            log_display = gr.Dataframe(value=pd.DataFrame(log_history), headers=["timestamp", "ip_address", "cookie_id", "account", "role", "action", "status"], datatype=["str", "str", "str", "str", "str", "str", "str"], interactive=False, wrap=True)

        with gr.Column(scale=2):
            with gr.Tabs(selected="login_route", elem_classes="headless-tabs") as main_router:
                with gr.Tab("Login", id="login_route"):
                    with gr.Column(elem_classes="login-container"):
                        gr.Markdown("<div class='ntut-title'>校園入口網站 Taipei Tech Portal</div>")
                        login_msg = gr.Markdown(value="", visible=True)
                        with gr.Row(): acc_input = gr.Textbox(label="使用者帳號 (Account)", placeholder="student or admin")
                        with gr.Row(): pwd_input = gr.Textbox(label="使用者密碼 (Password)", type="password", placeholder="password : 1234")
                        with gr.Row(): gr.HTML("<div style='background:var(--background-fill-secondary); color:var(--body-text-color); padding:10px; font-weight:bold; letter-spacing: 3px; border:1px solid var(--border-color-primary); text-align:center;'>F E I K</div>")
                        with gr.Row(): captcha_input = gr.Textbox(label="請輸入驗證碼 (Keyin Code)")
                        login_btn = gr.Button("登入 Login", variant="primary")
                        
                        gr.Markdown("---")
                        gr.Markdown("<div style='text-align:center; color:gray; font-size: 0.9em;'>🛠️ 開發與測試專用捷徑</div>")
                        with gr.Row():
                            btn_fast_student = gr.Button("🚀 快速登入 (學生)", variant="secondary")
                            btn_fast_admin = gr.Button("🚀 快速登入 (管理員)", variant="secondary")

                with gr.Tab("Dashboard", id="dashboard_route"):
                    with gr.Column():
                        with gr.Row():
                            gr.Markdown("### 資訊系統")
                            welcome_text = gr.Markdown(value="", elem_classes="text-right")
                            logout_btn = gr.Button("登出", size="sm")
                        with gr.Row(): btn_toggle_role = gr.Button("🔄 Student ↔ Admin", size="sm", variant="secondary")
                        gr.Markdown("---")
                        
                        with gr.Tabs(selected="student_tab", elem_classes="headless-tabs") as role_tabs:
                            with gr.Tab("👨‍🎓 學生專區", id="student_tab"):
                                with gr.Accordion("▼ 1. 教務系統", open=True):
                                    with gr.Row(): btn_eval = gr.Button("▶ 期末網路教學評量", size="sm"); btn_pre_select = gr.Button("▶ 期末網路預選系統", size="sm"); btn_add_drop = gr.Button("▶ 開學後加退選系統", size="sm")
                                    with gr.Row(): btn_istudy = gr.Button("▶ 北科i學園PLUS", size="sm"); btn_card = gr.Button("▶ 學生證掛失及補發系統", size="sm"); btn_course = gr.Button("▶ 課程系統", size="sm")
                                    with gr.Row(): btn_score = gr.Button("▶ 學業成績查詢系統", size="sm")
                                with gr.Accordion("▼ 2. 學務系統", open=True):
                                    with gr.Row(): btn_leave = gr.Button("▶ 學生請假系統", size="sm"); btn_dorm = gr.Button("▶ 學生宿舍登錄(抽籤)系統", size="sm"); btn_scholarship = gr.Button("▶ 獎助學金申請系統", size="sm")
                                with gr.Accordion("▼ 3. 資訊服務", open=True):
                                    with gr.Row(): btn_webmail = gr.Button("▶ 網路郵局 WebMail", size="sm"); btn_vdesk = gr.Button("▶ 北科軟體雲", size="sm")
                                with gr.Accordion("▼ 惡意操作測試區", open=True):
                                    btn_hack_score = gr.Button("💀 嘗試偷改成績 (SQL Injection)", variant="stop", size="sm")

                            with gr.Tab("🛡️ 管理員專區", id="admin_tab"):
                                stat_dashboard_display = gr.HTML(value=get_dashboard_html())
                                with gr.Accordion("▼ 系統操作時間分佈圖 (即時監控)", open=True, elem_classes="weight-card"):
                                    action_dropdown = gr.Dropdown(choices=list(ACTION_MAP.keys()), label="選擇要檢視的系統資料分佈", value=None)
                                    action_plot = gr.Plot(show_label=False)

                                with gr.Accordion("▼ 防護強度微調 (AI 敏感度)", open=True, elem_classes="weight-card"):
                                    gr.Markdown("<span style='color:gray; font-size:0.9em;'>動態防護已全面啟動：包含 HDBSCAN 空間異常偵測、IP 聲譽聯防，以及動態視窗頻率攔截。</span>")
                                    with gr.Row():
                                        overall_weight = gr.Number(label="總體防護強度 (%)", value=75, minimum=0, maximum=100)
                                        time_weight = gr.Number(label="時間特徵權重 (%)", value=80, minimum=0, maximum=100)
                                        ip_weight = gr.Number(label="IP 聲譽權重 (%)", value=65, minimum=0, maximum=100)
                                    with gr.Row():
                                        device_weight = gr.Number(label="裝置指紋/行為權重 (%)", value=45, minimum=0, maximum=100)
                                        geo_weight = gr.Number(label="🌍 地理偏移敏感度 (%)", value=50, minimum=0, maximum=100)
                                        freq_weight = gr.Number(label="⚡ 行為頻率敏感度 (%)", value=60, minimum=0, maximum=100)
                                    weights_status = gr.Textbox(value="", visible=False)
                                
                                with gr.Accordion("▼ 異常 Log 分析 (Groq AI Agent)", open=True, elem_classes="weight-card"):
                                    abnormal_log_display = gr.Dataframe(value=initial_df_abn, headers=["timestamp", "ip_address", "cookie_id", "account", "role", "action", "status"], datatype=["str", "str", "str", "str", "str", "str", "str"], interactive=False, wrap=True)
                                    with gr.Row():
                                        abnormal_log_input = gr.Textbox(label="要分析的異常 Log", placeholder="系統若偵測到新異常將會自動填入...", scale=4)
                                        btn_explain_log = gr.Button("🧠 Groq AI 分析", variant="primary", scale=1)
                                    ai_explanation_output = gr.Textbox(label="AI 分析結果與說明", lines=4, interactive=False)
                                
                                with gr.Accordion("▼ HDBSCAN 異常辨識自動化測試區", open=True, elem_classes="weight-card"):
                                    gr.Markdown("<span style='color:gray; font-size:0.9em;'>點擊按鈕將直接以指定時間點執行該系統的操作，結果將顯示於左側 Log 區。</span>")
                                    gr.Markdown("#### 1. 選課相關系統 (具備強烈季節性)")
                                    with gr.Row():
                                        t_pre_sel_norm = gr.Button("✅ 正常: 期末網路預選系統 (06-13 11:00)", size="sm"); t_pre_sel_anom = gr.Button("⚠️ 異常: 期末網路預選系統 (10-15 13:00)", size="sm")
                                    with gr.Row():
                                        t_course_norm = gr.Button("✅ 正常: 課程系統 (02-25 07:50)", size="sm"); t_course_anom = gr.Button("⚠️ 異常: 課程系統 (07-15 05:00)", size="sm")
                                    with gr.Row():
                                        t_add_drop_norm = gr.Button("✅ 正常: 開學後加退選系統 (02-18 14:00)", size="sm"); t_add_drop_anom = gr.Button("⚠️ 異常: 開學後加退選系統 (05-15 12:00)", size="sm")
                                    gr.Markdown("#### 2. 期末與成績相關 (具備週期性)")
                                    with gr.Row():
                                        t_eval_norm = gr.Button("✅ 正常: 期末網路教學評量 (12-17 13:20)", size="sm"); t_eval_anom = gr.Button("⚠️ 異常: 期末網路教學評量 (03-15 03:00)", size="sm")
                                    with gr.Row():
                                        t_score_norm = gr.Button("✅ 正常: 學業成績查詢系統 (11-17 18:50)", size="sm"); t_score_anom = gr.Button("⚠️ 異常: 學業成績查詢系統 (02-15 03:00)", size="sm")
                                    gr.Markdown("#### 3. 校務與行政服務 (生活與常規)")
                                    with gr.Row():
                                        t_dorm_norm = gr.Button("✅ 正常: 學生宿舍登錄(抽籤)系統 (07-21 23:10)", size="sm"); t_dorm_anom = gr.Button("⚠️ 異常: 學生宿舍登錄(抽籤)系統 (11-15 16:00)", size="sm")
                                    with gr.Row():
                                        t_schol_norm = gr.Button("✅ 正常: 獎助學金申請系統 (03-12 14:00)", size="sm"); t_schol_anom = gr.Button("⚠️ 異常: 獎助學金申請系統 (08-15 02:00)", size="sm")
                                    with gr.Row():
                                        t_card_norm = gr.Button("✅ 正常: 學生證掛失 (10-23 13:40)", size="sm"); t_card_anom = gr.Button("⚠️ 異常: 學生證掛失 (08-10 03:00)", size="sm")
                                    gr.Markdown("#### 4. 學習與日常輔助系統 (具備作息規律)")
                                    with gr.Row():
                                        t_istudy_norm = gr.Button("✅ 正常: 北科i學園PLUS (10-18 16:00)", size="sm"); t_istudy_anom = gr.Button("⚠️ 異常: 北科i學園PLUS (08-20 02:00)", size="sm")
                                    with gr.Row():
                                        t_leave_norm = gr.Button("✅ 正常: 學生請假系統 (03-29 08:45)", size="sm"); t_leave_anom = gr.Button("⚠️ 異常: 學生請假系統 (08-15 13:00)", size="sm")
                                    with gr.Row():
                                        t_vdesk_norm = gr.Button("✅ 正常: 軟體雲/WebMail (03-22 18:00)", size="sm"); t_vdesk_anom = gr.Button("⚠️ 異常: 軟體雲/WebMail (08-10 00:00)", size="sm")

    def update_dataframes(df_all, df_abn): return gr.update(value=df_all), gr.update(value=df_abn)
    store_df_all.change(fn=update_dataframes, inputs=[store_df_all, store_df_abn], outputs=[log_display, abnormal_log_display])

    demo.load(fn=timer_update, inputs=[action_dropdown], outputs=[stat_dashboard_display, action_plot])
    dashboard_timer.tick(fn=timer_update, inputs=[action_dropdown], outputs=[stat_dashboard_display, action_plot])
    action_dropdown.change(fn=get_plot_data, inputs=[action_dropdown], outputs=[action_plot], api_name="getPlotData")
    time_mode.change(fn=toggle_time_input, inputs=time_mode, outputs=custom_time_input)
    btn_explain_log.click(fn=explain_abnormal_log, inputs=[abnormal_log_input], outputs=[ai_explanation_output])
    
    # weight_inputs = [overall_weight, time_weight, ip_weight, device_weight, geo_weight, freq_weight]
    # 移除所有 UI 欄位的 update_system_weights 綁定，僅保留 API 專用端點

    update_outputs = [main_router, role_tabs, login_msg, welcome_text, store_df_all, store_df_abn, abnormal_log_input]
    
    login_btn.click(fn=process_login, inputs=[mock_ip, mock_cookie, acc_input, pwd_input, captcha_input, time_mode, custom_time_input], outputs=update_outputs)
    logout_btn.click(fn=logout, inputs=[mock_ip, mock_cookie, acc_input, time_mode, custom_time_input], outputs=[main_router, acc_input, pwd_input, captcha_input, login_msg, store_df_all, store_df_abn, abnormal_log_input])
    
    fast_inputs = [mock_ip, mock_cookie, time_mode, custom_time_input]
    fast_outputs = update_outputs + [acc_input] 
    
    btn_fast_student.click(
        fn=lambda ip, c, tm, ct: fast_login("student", ip, c, tm, ct),
        inputs=fast_inputs,
        outputs=fast_outputs,
        api_name=None
    )
    btn_fast_admin.click(
        fn=lambda ip, c, tm, ct: fast_login("admin", ip, c, tm, ct),
        inputs=fast_inputs,
        outputs=fast_outputs,
        api_name=None
    )
    btn_toggle_role.click(fn=toggle_role, inputs=[acc_input, mock_ip, mock_cookie, time_mode, custom_time_input], outputs=[role_tabs, welcome_text, store_df_all, store_df_abn, abnormal_log_input, acc_input])

    action_mapping = [
        (btn_eval, "click_eval_system"), (btn_pre_select, "click_pre_select_system"), (btn_add_drop, "click_add_drop_system"),
        (btn_istudy, "click_istudy"), (btn_card, "click_card_loss"), (btn_course, "click_course_system"),
        (btn_score, "click_score_query"), (btn_leave, "click_leave_system"), (btn_dorm, "click_dorm_system"),
        (btn_scholarship, "click_scholarship"), (btn_webmail, "click_webmail"), (btn_vdesk, "click_vdesk"),
        (btn_hack_score, "malicious_sql_injection")
    ]

    for btn, action_name in action_mapping:
        btn.click(
            fn=lambda ip, c, acc, tm, ct, n=action_name: simulate_click(ip, c, acc, n, tm, ct), 
            inputs=[mock_ip, mock_cookie, acc_input, time_mode, custom_time_input], 
            outputs=[store_df_all, store_df_abn, abnormal_log_input],
            api_name=None
        )
        
    test_mapping = [
        (t_pre_sel_norm, "click_pre_select_system", "2026-06-13 11:00:00"), (t_pre_sel_anom, "click_pre_select_system", "2026-10-15 13:00:00"),
        (t_course_norm, "click_course_system", "2026-02-25 07:50:00"), (t_course_anom, "click_course_system", "2026-07-15 05:00:00"),
        (t_add_drop_norm, "click_add_drop_system", "2026-02-18 14:00:00"), (t_add_drop_anom, "click_add_drop_system", "2026-05-15 12:00:00"),
        (t_eval_norm, "click_eval_system", "2026-12-17 13:20:00"), (t_eval_anom, "click_eval_system", "2026-03-15 03:00:00"),
        (t_score_norm, "click_score_query", "2026-11-17 18:50:00"), (t_score_anom, "click_score_query", "2026-02-15 03:00:00")
    ]
    
    for btn, action_name, test_time_str in test_mapping:
        btn.click(
            fn=lambda ip, c, acc, n=action_name, t_str=test_time_str: test_anomaly(ip, c, acc, n, t_str),
            inputs=[mock_ip, mock_cookie, acc_input],
            outputs=[store_df_all, store_df_abn, abnormal_log_input],
            api_name=None
        )

    # =====================================================================
    # 👉 新增：隱藏的 UI 綁定區 (讓外部能透過 api_name 存取資料)
    # =====================================================================
    with gr.Group(visible=False):
        api_stats_btn = gr.Button("API_Stats")
        api_stats_out = gr.JSON()
        api_stats_btn.click(fn=api_get_stats, inputs=[], outputs=[api_stats_out], api_name="get_stats")
        
        api_logs_btn = gr.Button("API_Logs")
        api_logs_in = gr.Number(value=20)
        api_logs_out = gr.JSON()
        api_logs_btn.click(fn=api_get_logs, inputs=[api_logs_in], outputs=[api_logs_out], api_name="get_logs")
        
        api_chart_btn = gr.Button("API_Chart")
        api_chart_in = gr.Textbox()
        api_chart_out = gr.JSON()
        api_chart_btn.click(fn=api_get_chart_data, inputs=[api_chart_in], outputs=[api_chart_out], api_name="get_chart_data")
        
        api_abnormal_logs_btn = gr.Button("API_Abnormal_Logs")
        api_abnormal_logs_out = gr.JSON()
        api_abnormal_logs_btn.click(fn=api_get_abnormal_logs, inputs=[], outputs=[api_abnormal_logs_out], api_name="get_abnormal_logs")
        
        api_plot_btn = gr.Button("API_Plot")
        api_plot_in = gr.Textbox()
        api_plot_out = gr.Textbox()
        api_plot_btn.click(fn=get_plot_data_for_api, inputs=[api_plot_in], outputs=[api_plot_out], api_name="get_plot_data_api")
        
        # System weights update API endpoint
        api_update_weights_btn = gr.Button("API_Update_Weights")
        api_update_weights_ow = gr.Number(label="Overall Weight")
        api_update_weights_tw = gr.Number(label="Time Weight") 
        api_update_weights_iw = gr.Number(label="IP Weight")
        api_update_weights_dw = gr.Number(label="Device Weight")
        api_update_weights_gw = gr.Number(label="Geo Weight")
        api_update_weights_fw = gr.Number(label="Frequency Weight")
        api_update_weights_in = [api_update_weights_ow, api_update_weights_tw, api_update_weights_iw, api_update_weights_dw, api_update_weights_gw, api_update_weights_fw]
        api_update_weights_out = gr.Textbox()
        api_update_weights_btn.click(fn=update_system_weights, inputs=api_update_weights_in, outputs=[api_update_weights_out], api_name="update_system_weights")

        # ================================================================
        # Vue student actions API endpoints
        #   - 對應 Digital-Bodyguard-Fontend/src/App.vue 裡的 ACTION_ENDPOINTS
        #   - 對應 Digital-Bodyguard-Fontend/src/services/gradioService.ts 的 lambda*
        # ================================================================
        api_student_ip = gr.Textbox(label="ip")
        api_student_cookie = gr.Textbox(label="c")
        api_student_time_mode = gr.Radio(label="tm", choices=["真實時間 (目前時間)", "自訂時間 (模擬過去/未來)"], value="真實時間 (目前時間)")
        api_student_custom_time = gr.Textbox(label="ct")

        # lambda (期末教學評量) => api_name="lambda"
        api_lambda_out = gr.Textbox()
        gr.Button("API_lambda", visible=False).click(
            fn=api_lambda,
            inputs=[api_student_ip, api_student_cookie, api_student_time_mode, api_student_custom_time],
            outputs=[api_lambda_out],
            api_name="lambda",
        )

        # lambda_1 (預選系統)
        api_lambda_1_out = gr.Textbox()
        gr.Button("API_lambda_1", visible=False).click(
            fn=api_lambda_1,
            inputs=[api_student_ip, api_student_cookie, api_student_time_mode, api_student_custom_time],
            outputs=[api_lambda_1_out],
            api_name="lambda_1",
        )

        # lambda_7 (請假系統)
        api_lambda_acc = gr.Textbox(label="acc")
        api_lambda_7_out = gr.Textbox()
        gr.Button("API_lambda_7", visible=False).click(
            fn=api_lambda_7,
            inputs=[api_student_ip, api_student_cookie, api_lambda_acc, api_student_time_mode, api_student_custom_time],
            outputs=[api_lambda_7_out],
            api_name="lambda_7",
        )

        # lambda_8 (宿舍登錄)
        api_lambda_8_out = gr.Textbox()
        gr.Button("API_lambda_8", visible=False).click(
            fn=api_lambda_8,
            inputs=[api_student_ip, api_student_cookie, api_lambda_acc, api_student_time_mode, api_student_custom_time],
            outputs=[api_lambda_8_out],
            api_name="lambda_8",
        )

        # lambda_10 (網路郵局)
        api_lambda_10_out = gr.Textbox()
        gr.Button("API_lambda_10", visible=False).click(
            fn=api_lambda_10,
            inputs=[api_student_ip, api_student_cookie, api_lambda_acc, api_student_time_mode, api_student_custom_time],
            outputs=[api_lambda_10_out],
            api_name="lambda_10",
        )

        # lambda_11 (軟體雲)
        api_lambda_11_out = gr.Textbox()
        gr.Button("API_lambda_11", visible=False).click(
            fn=api_lambda_11,
            inputs=[api_student_ip, api_student_cookie, api_lambda_acc, api_student_time_mode, api_student_custom_time],
            outputs=[api_lambda_11_out],
            api_name="lambda_11",
        )

        # lambda_12 (SQL Injection)
        api_lambda_12_out = gr.Textbox()
        gr.Button("API_lambda_12", visible=False).click(
            fn=api_lambda_12,
            inputs=[api_student_ip, api_student_cookie, api_lambda_acc, api_student_time_mode, api_student_custom_time],
            outputs=[api_lambda_12_out],
            api_name="lambda_12",
        )

        # ================================================================
        # Generic student action API (Vue 一次呼叫任意系統按鈕)
        # ================================================================
        api_student_action_code = gr.Textbox(label="action_code")
        api_student_run_out = gr.Textbox()
        gr.Button("API_RunStudentAction", visible=False).click(
            fn=api_run_student_action_code,
            inputs=[
                api_student_ip,
                api_student_cookie,
                api_lambda_acc,
                api_student_action_code,
                api_student_time_mode,
                api_student_custom_time,
            ],
            outputs=[store_df_all, store_df_abn, api_student_run_out],
            api_name="run_student_action",
        )
    # =====================================================================

if __name__ == "__main__":
    demo.launch(css=custom_css)