from flask import Flask, request, jsonify, Response, render_template_string, render_template, redirect, url_for, session as flask_session
import requests
import time
import json
import uuid
import random
import io
import re
from functools import wraps
import hashlib
import jwt  
import os
import threading
from datetime import datetime, timedelta
import tiktoken  # 导入tiktoken来计算token数量

app = Flask(__name__, template_folder='templates')
app.secret_key = os.environ.get("SECRET_KEY", "abacus_chat_proxy_secret_key")
app.config['PERMANENT_SESSION_LIFETIME'] = timedelta(days=7)


API_ENDPOINT_URL = "https://abacus.ai/api/v0/describeDeployment"
MODEL_LIST_URL = "https://abacus.ai/api/v0/listExternalApplications"
CHAT_URL = "https://apps.abacus.ai/api/_chatLLMSendMessageSSE"
USER_INFO_URL = "https://abacus.ai/api/v0/_getUserInfo"
COMPUTE_POINTS_URL = "https://apps.abacus.ai/api/_getOrganizationComputePoints"
COMPUTE_POINTS_LOG_URL = "https://abacus.ai/api/v0/_getOrganizationComputePointLog"
CREATE_CONVERSATION_URL = "https://apps.abacus.ai/api/createDeploymentConversation"
DELETE_CONVERSATION_URL = "https://apps.abacus.ai/api/deleteDeploymentConversation"


USER_AGENTS = [
    "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/116.0.0.0 Safari/537.36"
]


PASSWORD = None
USER_NUM = 0
USER_DATA = []
CURRENT_USER = -1
MODELS = set()
# 添加变量跟踪是否删除上一个对话以及上一个对话ID
DELETE_CHAT = True # 默认启用删除功能
PREVIOUS_CONVERSATION_IDS = {} # 用于跟踪每个用户的上一个conversation_id


TRACE_ID = "3042e28b3abf475d8d973c7e904935af"
SENTRY_TRACE = f"{TRACE_ID}-80d9d2538b2682d0"


# 添加一个计数器记录健康检查次数
health_check_counter = 0


# 添加统计变量
model_usage_stats = {}  # 模型使用次数统计
total_tokens = {
    "prompt": 0,       # 输入token统计
    "completion": 0,   # 输出token统计
    "total": 0         # 总token统计
}

# 模型调用记录
model_usage_records = []  # 每次调用详细记录
MODEL_USAGE_RECORDS_FILE = "/app/model_usage_records.json"  # 调用记录保存文件，使用/app目录确保HF中持久化

# 计算点信息
compute_points = {
    "left": 0,          # 剩余计算点
    "total": 0,         # 总计算点
    "used": 0,          # 已使用计算点
    "percentage": 0,    # 使用百分比
    "last_update": None # 最后更新时间
}

# 计算点使用日志
compute_points_log = {
    "columns": {},      # 列名
    "log": []           # 日志数据
}

# 多用户计算点信息
users_compute_points = []

# 记录启动时间
START_TIME = datetime.utcnow() + timedelta(hours=8)  # 北京时间

# 自定义JSON编码器，处理datetime对象
class DateTimeEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, datetime):
            return obj.strftime('%Y-%m-%d %H:%M:%S')
        return super(DateTimeEncoder, self).default(obj)


def update_conversation_id(user_index, conversation_id):
    """更新用户的conversation_id并保存到配置文件"""
    try:
        with open("config.json", "r") as f:
            config = json.load(f)
        
        if "config" in config and user_index < len(config["config"]):
            config["config"][user_index]["conversation_id"] = conversation_id
            
            # 保存到配置文件
            with open("config.json", "w") as f:
                json.dump(config, f, indent=4)
                
            print(f"已将用户 {user_index+1} 的conversation_id更新为: {conversation_id}")
        else:
            print(f"更新conversation_id失败: 配置文件格式错误或用户索引越界")
    except Exception as e:
        print(f"更新conversation_id失败: {e}")


def load_config():
    """从配置文件加载配置"""
    global USER_DATA, CURRENT_USER
    try:
        # 读取配置文件
        with open('config.json', 'r') as f:
            config = json.load(f)
        
        # 确保USER_DATA为空列表
        USER_DATA = []
        
        # 如果配置文件中有配置项，则加载
        if "config" in config and isinstance(config["config"], list):
            for i, user_config in enumerate(config["config"]):
                # 获取必要的配置项
                cookies = user_config.get("cookies", "")
                models = user_config.get("models", {})
                
                # 不再从配置文件读取conversation_id，确保每次启动都使用新会话
                conversation_id = None
                
                # 创建会话对象
                session = requests.Session()
                
                # 刷新token
                session_token = refresh_token(session, cookies)
                if not session_token:
                    print(f"无法获取用户 {i+1} 的token，跳过该用户")
                    continue
                
                try:
                    # 获取模型映射
                    model_map, models_set = get_model_map(session, cookies, session_token)
                    
                    # 添加到USER_DATA
                    USER_DATA.append((session, cookies, session_token, conversation_id, model_map, i))
                    print(f"用户 {i+1} 配置成功，可用模型: {', '.join(models_set)}")
                except Exception as e:
                    print(f"配置用户 {i+1} 失败: {e}")
                    continue
                
            print(f"成功加载了 {len(USER_DATA)} 个用户配置")
            
            # 设置当前用户为第一个用户
            CURRENT_USER = 0
            
            return True
        else:
            print("配置文件格式错误")
            return False
    except Exception as e:
        print(f"加载配置失败: {e}")
        return False


def get_password():
    global PASSWORD
    # 从环境变量读取密码
    env_password = os.environ.get("password")
    if env_password:
        PASSWORD = hashlib.sha256(env_password.encode()).hexdigest()
        return

    # 如果环境变量不存在，从文件读取
    try:
        with open("password.txt", "r") as f:
            PASSWORD = f.read().strip()
    except FileNotFoundError:
        with open("password.txt", "w") as f:
            PASSWORD = None


def require_auth(f):
    @wraps(f)
    def decorated(*args, **kwargs):
        if not PASSWORD:
            return f(*args, **kwargs)
        
        # 检查Flask会话是否已登录
        if flask_session.get('logged_in'):
            return f(*args, **kwargs)
            
        # 如果是API请求，检查Authorization头
        auth = request.authorization
        if not auth or not check_auth(auth.token):
            # 如果是浏览器请求，重定向到登录页面
            if request.headers.get('Accept', '').find('text/html') >= 0:
                return redirect(url_for('login'))
            return jsonify({"error": "Unauthorized access"}), 401
        return f(*args, **kwargs)

    return decorated


def check_auth(token):
    return hashlib.sha256(token.encode()).hexdigest() == PASSWORD


def is_token_expired(token):
    if not token:
        return True
    
    try:
        # Malkodi tokenon sen validigo de subskribo
        payload = jwt.decode(token, options={"verify_signature": False})
        # Akiru eksvalidiĝan tempon, konsideru eksvalidiĝinta 5 minutojn antaŭe
        return payload.get('exp', 0) - time.time() < 300
    except:
        return True


def refresh_token(session, cookies):
    """Uzu kuketon por refreŝigi session token, nur revenigu novan tokenon"""
    headers = {
        "accept": "application/json, text/plain, */*",
        "accept-language": "zh-CN,zh;q=0.9",
        "content-type": "application/json",
        "reai-ui": "1",
        "sec-ch-ua": "\"Chromium\";v=\"116\", \"Not)A;Brand\";v=\"24\", \"Google Chrome\";v=\"116\"",
        "sec-ch-ua-mobile": "?0",
        "sec-ch-ua-platform": "\"Windows\"",
        "sec-fetch-dest": "empty",
        "sec-fetch-mode": "cors",
        "sec-fetch-site": "same-site",
        "x-abacus-org-host": "apps",
        "user-agent": random.choice(USER_AGENTS),
        "origin": "https://apps.abacus.ai",
        "referer": "https://apps.abacus.ai/",
        "cookie": cookies
    }
    
    try:
        response = requests.post(
            USER_INFO_URL,
            headers=headers,
            json={}
        )
        
        if response.status_code == 200:
            response_data = response.json()
            if response_data.get('success') and 'sessionToken' in response_data.get('result', {}):
                return response_data['result']['sessionToken']
            else:
                print(f"刷新token失败: {response_data.get('error', '未知错误')}")
                return None
        else:
            print(f"刷新token失败，状态码: {response.status_code}")
            return None
    except Exception as e:
        print(f"刷新token异常: {e}")
        return None


def get_model_map(session, cookies, session_token):
    """Akiru disponeblan modelan liston kaj ĝiajn mapajn rilatojn"""
    headers = {
        "accept": "application/json, text/plain, */*",
        "accept-language": "zh-CN,zh;q=0.9",
        "content-type": "application/json",
        "reai-ui": "1",
        "sec-ch-ua": "\"Chromium\";v=\"116\", \"Not)A;Brand\";v=\"24\", \"Google Chrome\";v=\"116\"",
        "sec-ch-ua-mobile": "?0",
        "sec-ch-ua-platform": "\"Windows\"",
        "sec-fetch-dest": "empty",
        "sec-fetch-mode": "cors",
        "sec-fetch-site": "same-site",
        "x-abacus-org-host": "apps",
        "user-agent": random.choice(USER_AGENTS),
        "origin": "https://apps.abacus.ai",
        "referer": "https://apps.abacus.ai/",
        "cookie": cookies
    }
    
    if session_token:
        headers["session-token"] = session_token
    
    model_map = {}
    models_set = set()
    
    try:
        response = requests.post(
            MODEL_LIST_URL,
            headers=headers,
            json={}
        )
        
        if response.status_code != 200:
            print(f"获取模型列表失败，状态码: {response.status_code}")
            raise Exception("API请求失败")
        
        data = response.json()
        if not data.get('success'):
            print(f"获取模型列表失败: {data.get('error', '未知错误')}")
            raise Exception("API返回错误")
        
        applications = []
        if isinstance(data.get('result'), dict):
            applications = data.get('result', {}).get('externalApplications', [])
        elif isinstance(data.get('result'), list):
            applications = data.get('result', [])
        
        for app in applications:
            app_name = app.get('name', '')
            app_id = app.get('externalApplicationId', '')
            prediction_overrides = app.get('predictionOverrides', {})
            llm_name = prediction_overrides.get('llmName', '') if prediction_overrides else ''
            
            if not (app_name and app_id and llm_name):
                continue
                
            model_name = app_name
            model_map[model_name] = (app_id, llm_name)
            models_set.add(model_name)
        
        if not model_map:
            raise Exception("未找到任何可用模型")
        
        return model_map, models_set
    
    except Exception as e:
        print(f"获取模型列表异常: {e}")
        raise


def init_session():
    get_password()
    global USER_NUM, MODELS, USER_DATA
    
    # 调用load_config代替resolve_config
    if not load_config():
        print("配置加载失败，退出...")
        exit(1)
    
    # 更新USER_NUM和MODELS
    USER_NUM = len(USER_DATA)
    if USER_NUM == 0:
        print("No user available, exiting...")
        exit(1)
    
    # 获取所有可用模型
    all_models = set()
    for session, cookies, session_token, conversation_id, model_map, i in USER_DATA:
        all_models.update(model_map.keys())
    
    MODELS = all_models
    print(f"启动完成，共配置 {USER_NUM} 个用户")


def update_cookie(session, cookies):
    cookie_jar = {}
    for key, value in session.cookies.items():
        cookie_jar[key] = value
    cookie_dict = {}
    for item in cookies.split(";"):
        key, value = item.strip().split("=", 1)
        cookie_dict[key] = value
    cookie_dict.update(cookie_jar)
    cookies = "; ".join([f"{key}={value}" for key, value in cookie_dict.items()])
    return cookies


# 获取空间URL
SPACE_URL = get_space_url()
if SPACE_URL:
    print(f"Space URL: {SPACE_URL}")
    print("注意：Hugging Face生成的URL会自动将空间名称中的下划线(_)替换为连字符(-)")

# 初始化会话
init_session()


@app.route("/v1/models", methods=["GET"])
@require_auth
def get_models():
    if len(MODELS) == 0:
        return jsonify({"error": "No models available"}), 500
    model_list = []
    for model in MODELS:
        model_list.append(
            {
                "id": model,
                "object": "model",
                "created": int(time.time()),
                "owned_by": "Elbert",
                "name": model,
            }
        )
    return jsonify({"object": "list", "data": model_list})


@app.route("/v1/chat/completions", methods=["POST"])
@require_auth
def chat_completions():
    openai_request = request.get_json()
    stream = openai_request.get("stream", False)
    messages = openai_request.get("messages")
    if messages is None:
        return jsonify({"error": "Messages is required", "status": 400}), 400
    model = openai_request.get("model")
    if model not in MODELS:
        return (
            jsonify(
                {
                    "error": "Model not available, check if it is configured properly",
                    "status": 404,
                }
            ),
            404,
        )
    message = format_message(messages)
    think = (
        openai_request.get("think", False) if model == "Claude Sonnet 3.7" else False
    )
    return (
        send_message(message, model, think)
        if stream
        else send_message_non_stream(message, model, think)
    )


def get_user_data():
    global CURRENT_USER
    CURRENT_USER = (CURRENT_USER + 1) % USER_NUM
    print(f"使用配置 {CURRENT_USER+1}")
    
    # Akiru uzantajn datumojn
    session, cookies, session_token, conversation_id, model_map, user_index = USER_DATA[CURRENT_USER]
    
    # Kontrolu ĉu la tokeno eksvalidiĝis, se jes, refreŝigu ĝin
    if is_token_expired(session_token):
        print(f"Cookie {CURRENT_USER+1}的token已过期或即将过期，正在刷新...")
        new_token = refresh_token(session, cookies)
        if new_token:
            # Ĝisdatigu la globale konservitan tokenon
            USER_DATA[CURRENT_USER] = (session, cookies, new_token, conversation_id, model_map, user_index)
            session_token = new_token
            print(f"成功更新token: {session_token[:15]}...{session_token[-15:]}")
        else:
            print(f"警告：无法刷新Cookie {CURRENT_USER+1}的token，继续使用当前token")
    
    return (session, cookies, session_token, conversation_id, model_map, user_index)


def create_conversation(session, cookies, session_token, external_application_id=None, deployment_id=None):
    """创建新的会话"""
    if not (external_application_id and deployment_id):
        print("无法创建新会话: 缺少必要参数")
        return None
    
    headers = {
        "accept": "application/json, text/plain, */*",
        "accept-language": "zh-CN,zh;q=0.9",
        "content-type": "application/json",
        "cookie": cookies,
        "user-agent": random.choice(USER_AGENTS),
        "x-abacus-org-host": "apps"
    }
    
    if session_token:
        headers["session-token"] = session_token
    
    create_payload = {
        "deploymentId": deployment_id,
        "name": "New Chat",
        "externalApplicationId": external_application_id
    }
    
    try:
        response = requests.post(
            CREATE_CONVERSATION_URL,
            headers=headers,
            json=create_payload
        )
        
        if response.status_code == 200:
            data = response.json()
            if data.get("success", False):
                new_conversation_id = data.get("result", {}).get("deploymentConversationId")
                if new_conversation_id:
                    print(f"成功创建新的conversation: {new_conversation_id}")
                    return new_conversation_id
        
        print(f"创建会话失败: {response.status_code} - {response.text[:100]}")
        return None
    except Exception as e:
        print(f"创建会话时出错: {e}")
        return None


def delete_conversation(session, cookies, session_token, conversation_id):
    """删除指定的会话"""
    if not conversation_id:
        print("无法删除会话: conversation_id为空")
        return False
    
    headers = {
        "accept": "application/json, text/plain, */*",
        "accept-language": "zh-CN,zh;q=0.9",
        "content-type": "application/json",
        "cookie": cookies,
        "user-agent": random.choice(USER_AGENTS),
        "x-abacus-org-host": "apps"
    }
    
    if session_token:
        headers["session-token"] = session_token
    
    delete_payload = {
        "deploymentId": "14b2a314cc",
        "deploymentConversationId": conversation_id
    }
    
    try:
        response = requests.post(
            DELETE_CONVERSATION_URL,
            headers=headers,
            json=delete_payload
        )
        
        if response.status_code == 200:
            data = response.json()
            if data.get("success", False):
                print(f"成功删除conversation: {conversation_id}")
                return True
        
        print(f"删除会话失败: {response.status_code} - {response.text[:100]}")
        return False
    except Exception as e:
        print(f"删除会话时出错: {e}")
        return False


def is_conversation_valid(session, cookies, session_token, conversation_id, model_map, model):
    """检查会话ID是否有效"""
    if not conversation_id:
        return False
    
    # 如果没有这些信息，无法验证
    if not (model in model_map and len(model_map[model]) >= 2):
        return False
        
    external_app_id = model_map[model][0]
    
    # 尝试发送一个空消息来测试会话ID是否有效
    headers = {
        "accept": "text/event-stream",
        "content-type": "text/plain;charset=UTF-8",
        "cookie": cookies,
        "user-agent": random.choice(USER_AGENTS)
    }
    
    if session_token:
        headers["session-token"] = session_token
    
    payload = {
        "requestId": str(uuid.uuid4()),
        "deploymentConversationId": conversation_id,
        "message": "",  # 空消息
        "isDesktop": False,
        "externalApplicationId": external_app_id
    }
    
    try:
        # 直接发送请求
        response = requests.post(
            CHAT_URL,
            headers=headers,
            data=json.dumps(payload),
            stream=False
        )
        
        # 即使返回错误，只要不是缺少ID的错误，也说明ID是有效的
        if response.status_code == 200:
            return True
        
        error_text = response.text
        if "Missing required parameter" in error_text:
            return False
            
        # 其他类型的错误，可能ID是有效的但有其他问题
        return True
    except:
        # 如果请求出错，无法确定，返回False让系统创建新ID
        return False


def get_or_create_conversation(session, cookies, session_token, conversation_id, model_map, model, user_index):
    """获取有效的会话ID，如果无效则创建新会话"""
    # 始终创建新会话，不再检查现有会话是否有效
    print("正在创建新会话...")
    
    if model in model_map and len(model_map[model]) >= 2:
        external_app_id = model_map[model][0]
        # 创建会话时需要deployment_id，我们先使用一个固定值
        # 在实际应用中应从API响应中获取
        deployment_id = "14b2a314cc"  # 这是从您提供的请求中获取的
        
        new_conversation_id = create_conversation(
            session, cookies, session_token, 
            external_application_id=external_app_id,
            deployment_id=deployment_id
        )
        
        if new_conversation_id:
            # 更新全局存储的会话ID
            global USER_DATA, CURRENT_USER
            session, cookies, session_token, _, model_map, _ = USER_DATA[CURRENT_USER]
            USER_DATA[CURRENT_USER] = (session, cookies, session_token, new_conversation_id, model_map, user_index)
            
            # 不再保存到配置文件，确保每次都创建新会话
            # update_conversation_id(user_index, new_conversation_id)
            
            return new_conversation_id
    
    # 如果创建失败，返回原始ID
    return conversation_id


def generate_trace_id():
    """Generu novan trace_id kaj sentry_trace"""
    trace_id = str(uuid.uuid4()).replace('-', '')
    sentry_trace = f"{trace_id}-{str(uuid.uuid4())[:16]}"
    return trace_id, sentry_trace


def send_message(message, model, think=False):
    """Flua traktado kaj plusendo de mesaĝoj"""
    (session, cookies, session_token, conversation_id, model_map, user_index) = get_user_data()
    
    # 保存当前用户的上一个对话ID用于稍后删除
    previous_conversation_id = PREVIOUS_CONVERSATION_IDS.get(user_index)
    
    # 确保有有效的会话ID，现在总是会创建新的
    new_conversation_id = get_or_create_conversation(session, cookies, session_token, conversation_id, model_map, model, user_index)
    
    # 更新当前用户的会话ID记录
    PREVIOUS_CONVERSATION_IDS[user_index] = new_conversation_id
    
    trace_id, sentry_trace = generate_trace_id()
    
    # 计算输入token
    prompt_tokens = num_tokens_from_string(message)
    completion_buffer = io.StringIO()  # 收集所有输出用于计算token
    
    headers = {
        "accept": "text/event-stream",
        "accept-language": "zh-CN,zh;q=0.9",
        "baggage": f"sentry-environment=production,sentry-release=975eec6685013679c139fc88db2c48e123d5c604,sentry-public_key=3476ea6df1585dd10e92cdae3a66ff49,sentry-trace_id={trace_id}",
        "content-type": "text/plain;charset=UTF-8",
        "cookie": cookies,
        "sec-ch-ua": "\"Chromium\";v=\"116\", \"Not)A;Brand\";v=\"24\", \"Google Chrome\";v=\"116\"",
        "sec-ch-ua-mobile": "?0",
        "sec-ch-ua-platform": "\"Windows\"",
        "sec-fetch-dest": "empty",
        "sec-fetch-mode": "cors",
        "sec-fetch-site": "same-origin",
        "sentry-trace": sentry_trace,
        "user-agent": random.choice(USER_AGENTS)
    }
    
    if session_token:
        headers["session-token"] = session_token
    
    payload = {
        "requestId": str(uuid.uuid4()),
        "deploymentConversationId": new_conversation_id,
        "message": message,
        "isDesktop": False,
        "chatConfig": {
            "timezone": "Asia/Shanghai",
            "language": "zh-CN"
        },
        "llmName": model_map[model][1],
        "externalApplicationId": model_map[model][0],
        "regenerate": True,
        "editPrompt": True
    }
    
    if think:
        payload["useThinking"] = think
    
    try:
        response = requests.post(
            CHAT_URL,
            headers=headers,
            data=json.dumps(payload),
            stream=True
        )
        
        response.raise_for_status()
        
        def extract_segment(line_data):
            try:
                data = json.loads(line_data)
                if "segment" in data:
                    if isinstance(data["segment"], str):
                        return data["segment"]
                    elif isinstance(data["segment"], dict) and "segment" in data["segment"]:
                        return data["segment"]["segment"]
                return ""
            except:
                return ""
        
        def generate():
            id = ""
            think_state = 2
            
            yield "data: " + json.dumps({"object": "chat.completion.chunk", "choices": [{"delta": {"role": "assistant"}}]}) + "\n\n"
            
            for line in response.iter_lines():
                if line:
                    decoded_line = line.decode("utf-8")
                    try:
                        if think:
                            data = json.loads(decoded_line)
                            if data.get("type") != "text":
                                continue
                            elif think_state == 2:
                                id = data.get("messageId")
                                segment = "<think>\n" + data.get("segment", "")
                                completion_buffer.write(segment)  # 收集输出
                                yield f"data: {json.dumps({'object': 'chat.completion.chunk', 'choices': [{'delta': {'content': segment}}]})}\n\n"
                                think_state = 1
                            elif think_state == 1:
                                if data.get("messageId") != id:
                                    segment = data.get("segment", "")
                                    completion_buffer.write(segment)  # 收集输出
                                    yield f"data: {json.dumps({'object': 'chat.completion.chunk', 'choices': [{'delta': {'content': segment}}]})}\n\n"
                                else:
                                    segment = "\n</think>\n" + data.get("segment", "")
                                    completion_buffer.write(segment)  # 收集输出
                                    yield f"data: {json.dumps({'object': 'chat.completion.chunk', 'choices': [{'delta': {'content': segment}}]})}\n\n"
                                    think_state = 0
                            else:
                                segment = data.get("segment", "")
                                completion_buffer.write(segment)  # 收集输出
                                yield f"data: {json.dumps({'object': 'chat.completion.chunk', 'choices': [{'delta': {'content': segment}}]})}\n\n"
                        else:
                            segment = extract_segment(decoded_line)
                            if segment:
                                completion_buffer.write(segment)  # 收集输出
                                yield f"data: {json.dumps({'object': 'chat.completion.chunk', 'choices': [{'delta': {'content': segment}}]})}\n\n"
                    except Exception as e:
                        print(f"处理响应出错: {e}")
            
            yield "data: " + json.dumps({"object": "chat.completion.chunk", "choices": [{"delta": {}, "finish_reason": "stop"}]}) + "\n\n"
            yield "data: [DONE]\n\n"
            
            # 在流式传输完成后计算token并更新统计
            completion_tokens = num_tokens_from_string(completion_buffer.getvalue())
            update_model_stats(model, prompt_tokens, completion_tokens)
            
            # 在流式传输完成后删除上一个对话（如果存在且启用了删除功能）
            if DELETE_CHAT and previous_conversation_id and previous_conversation_id != new_conversation_id:
                delete_conversation(session, cookies, session_token, previous_conversation_id)
        
        return Response(generate(), mimetype="text/event-stream")
    except requests.exceptions.RequestException as e:
        error_details = str(e)
        if hasattr(e, 'response') and e.response is not None:
            if hasattr(e.response, 'text'):
                error_details += f" - Response: {e.response.text[:200]}"
        print(f"发送消息失败: {error_details}")
        return jsonify({"error": f"Failed to send message: {error_details}"}), 500


def send_message_non_stream(message, model, think=False):
    """Ne-flua traktado de mesaĝoj"""
    (session, cookies, session_token, conversation_id, model_map, user_index) = get_user_data()
    
    # 保存当前用户的上一个对话ID用于稍后删除
    previous_conversation_id = PREVIOUS_CONVERSATION_IDS.get(user_index)
    
    # 确保有有效的会话ID，现在总是会创建新的
    new_conversation_id = get_or_create_conversation(session, cookies, session_token, conversation_id, model_map, model, user_index)
    
    # 更新当前用户的会话ID记录
    PREVIOUS_CONVERSATION_IDS[user_index] = new_conversation_id
    
    trace_id, sentry_trace = generate_trace_id()
    
    # 计算输入token
    prompt_tokens = num_tokens_from_string(message)
    
    headers = {
        "accept": "text/event-stream",
        "accept-language": "zh-CN,zh;q=0.9",
        "baggage": f"sentry-environment=production,sentry-release=975eec6685013679c139fc88db2c48e123d5c604,sentry-public_key=3476ea6df1585dd10e92cdae3a66ff49,sentry-trace_id={trace_id}",
        "content-type": "text/plain;charset=UTF-8",
        "cookie": cookies,
        "sec-ch-ua": "\"Chromium\";v=\"116\", \"Not)A;Brand\";v=\"24\", \"Google Chrome\";v=\"116\"",
        "sec-ch-ua-mobile": "?0",
        "sec-ch-ua-platform": "\"Windows\"",
        "sec-fetch-dest": "empty",
        "sec-fetch-mode": "cors",
        "sec-fetch-site": "same-origin",
        "sentry-trace": sentry_trace,
        "user-agent": random.choice(USER_AGENTS)
    }
    
    if session_token:
        headers["session-token"] = session_token
    
    payload = {
        "requestId": str(uuid.uuid4()),
        "deploymentConversationId": new_conversation_id,
        "message": message,
        "isDesktop": False,
        "chatConfig": {
            "timezone": "Asia/Shanghai",
            "language": "zh-CN"
        },
        "llmName": model_map[model][1],
        "externalApplicationId": model_map[model][0],
        "regenerate": True,
        "editPrompt": True
    }
    
    if think:
        payload["useThinking"] = think
    
    try:
        response = requests.post(
            CHAT_URL,
            headers=headers,
            data=json.dumps(payload),
            stream=True
        )
        
        response.raise_for_status()
        buffer = io.StringIO()
        
        def extract_segment(line_data):
            try:
                data = json.loads(line_data)
                if "segment" in data:
                    if isinstance(data["segment"], str):
                        return data["segment"]
                    elif isinstance(data["segment"], dict) and "segment" in data["segment"]:
                        return data["segment"]["segment"]
                return ""
            except:
                return ""
        
        if think:
            id = ""
            think_state = 2
            think_buffer = io.StringIO()
            content_buffer = io.StringIO()
            
            for line in response.iter_lines():
                if line:
                    decoded_line = line.decode("utf-8")
                    try:
                        data = json.loads(decoded_line)
                        if data.get("type") != "text":
                            continue
                        elif think_state == 2:
                            id = data.get("messageId")
                            segment = data.get("segment", "")
                            think_buffer.write(segment)
                            think_state = 1
                        elif think_state == 1:
                            if data.get("messageId") != id:
                                segment = data.get("segment", "")
                                content_buffer.write(segment)
                            else:
                                segment = data.get("segment", "")
                                think_buffer.write(segment)
                                think_state = 0
                        else:
                            segment = data.get("segment", "")
                            content_buffer.write(segment)
                    except Exception as e:
                        print(f"处理响应出错: {e}")
            
            think_content = think_buffer.getvalue()
            response_content = content_buffer.getvalue()
            
            # 计算输出token并更新统计信息
            completion_tokens = num_tokens_from_string(think_content + response_content)
            update_model_stats(model, prompt_tokens, completion_tokens)
            
            # 删除上一个对话（如果存在且启用了删除功能）
            if DELETE_CHAT and previous_conversation_id and previous_conversation_id != new_conversation_id:
                delete_conversation(session, cookies, session_token, previous_conversation_id)
            
            return jsonify({
                "id": f"chatcmpl-{str(uuid.uuid4())}",
                "object": "chat.completion",
                "created": int(time.time()),
                "model": model,
                "choices": [{
                    "index": 0,
                    "message": {
                        "role": "assistant",
                        "content": f"<think>\n{think_content}\n</think>\n{response_content}"
                    },
                    "finish_reason": "stop"
                }],
                "usage": {
                    "prompt_tokens": prompt_tokens,
                    "completion_tokens": completion_tokens,
                    "total_tokens": prompt_tokens + completion_tokens
                }
            })
        else:
            for line in response.iter_lines():
                if line:
                    decoded_line = line.decode("utf-8")
                    segment = extract_segment(decoded_line)
                    if segment:
                        buffer.write(segment)
            
            response_content = buffer.getvalue()
            
            # 计算输出token并更新统计信息
            completion_tokens = num_tokens_from_string(response_content)
            update_model_stats(model, prompt_tokens, completion_tokens)
            
            # 删除上一个对话（如果存在且启用了删除功能）
            if DELETE_CHAT and previous_conversation_id and previous_conversation_id != new_conversation_id:
                delete_conversation(session, cookies, session_token, previous_conversation_id)
            
            return jsonify({
                "id": f"chatcmpl-{str(uuid.uuid4())}",
                "object": "chat.completion",
                "created": int(time.time()),
                "model": model,
                "choices": [{
                    "index": 0,
                    "message": {
                        "role": "assistant",
                        "content": response_content
                    },
                    "finish_reason": "stop"
                }],
                "usage": {
                    "prompt_tokens": prompt_tokens,
                    "completion_tokens": completion_tokens,
                    "total_tokens": prompt_tokens + completion_tokens
                }
            })
    except requests.exceptions.RequestException as e:
        error_details = str(e)
        if hasattr(e, 'response') and e.response is not None:
            if hasattr(e.response, 'text'):
                error_details += f" - Response: {e.response.text[:200]}"
        print(f"发送消息失败: {error_details}")
        return jsonify({"error": f"Failed to send message: {error_details}"}), 500


def format_message(messages):
    buffer = io.StringIO()
    role_map, prefix, messages = extract_role(messages)
    for message in messages:
        role = message.get("role")
        role = "\b" + role_map[role] if prefix else role_map[role]
        content = message.get("content").replace("\\n", "\n")
        pattern = re.compile(r"<\|removeRole\|>\n")
        if pattern.match(content):
            content = pattern.sub("", content)
            buffer.write(f"{content}\n")
        else:
            buffer.write(f"{role}: {content}\n\n")
    formatted_message = buffer.getvalue()
    return formatted_message


def extract_role(messages):
    role_map = {"user": "Human", "assistant": "Assistant", "system": "System"}
    prefix = False
    first_message = messages[0]["content"]
    pattern = re.compile(
        r"""
        <roleInfo>\s*
        user:\s*(?P<user>[^\n]*)\s*
        assistant:\s*(?P<assistant>[^\n]*)\s*
        system:\s*(?P<system>[^\n]*)\s*
        prefix:\s*(?P<prefix>[^\n]*)\s*
        </roleInfo>\n
    """,
        re.VERBOSE,
    )
    match = pattern.search(first_message)
    if match:
        role_map = {
            "user": match.group("user"),
            "assistant": match.group("assistant"),
            "system": match.group("system"),
        }
        prefix = match.group("prefix") == "1"
        messages[0]["content"] = pattern.sub("", first_message)
        print(f"Extracted role map:")
        print(
            f"User: {role_map['user']}, Assistant: {role_map['assistant']}, System: {role_map['system']}"
        )
        print(f"Using prefix: {prefix}")
    return (role_map, prefix, messages)


@app.route("/health", methods=["GET"])
def health_check():
    global health_check_counter
    health_check_counter += 1
    return jsonify({
        "status": "healthy",
        "timestamp": datetime.now().isoformat(),
        "checks": health_check_counter
    })


def keep_alive():
    """每20分钟进行一次自我健康检查"""
    while True:
        try:
            # 获取当前端口
            port = int(os.environ.get("PORT", 9876))
            # 请求本地健康检查端点
            requests.get(f"http://127.0.0.1:{port}/health")
            time.sleep(1200)  # 20分钟
        except:
            # 忽略错误，保持运行
            time.sleep(1200)  # 即使失败也等待20分钟


@app.route("/", methods=["GET"])
def index():
    # 如果需要密码且用户未登录，重定向到登录页面
    if PASSWORD and not flask_session.get('logged_in'):
        return redirect(url_for('login'))
    
    # 否则重定向到仪表盘
    return redirect(url_for('dashboard'))


# 使用tiktoken计算字符串的token数量
def num_tokens_from_string(text):
    """使用tiktoken计算字符串的token数量"""
    try:
        # 使用cl100k_base编码器，这是gpt-4和gpt-3.5-turbo使用的编码器
        enc = tiktoken.get_encoding("cl100k_base")
        tokens = enc.encode(text)
        return len(tokens)
    except Exception as e:
        # 如果出错，使用字符长度作为粗略估计（大约每4个字符1个token）
        print(f"计算token数量出错: {e}，使用估算方法")
        return len(text) // 4

# 更新模型使用统计
def update_model_stats(model, prompt_tokens, completion_tokens):
    """更新模型使用统计数据"""
    global model_usage_stats, total_tokens, model_usage_records
    
    # 获取北京时间
    beijing_now = datetime.utcnow() + timedelta(hours=8)
    call_time = beijing_now.strftime("%Y-%m-%d %H:%M:%S")
    
    # 首次使用该模型时初始化统计
    if model not in model_usage_stats:
        model_usage_stats[model] = {
            "count": 0,
            "prompt_tokens": 0,
            "completion_tokens": 0
        }
    
    # 更新模型统计
    model_usage_stats[model]["count"] += 1
    model_usage_stats[model]["prompt_tokens"] += prompt_tokens
    model_usage_stats[model]["completion_tokens"] += completion_tokens
    
    # 更新总token统计
    total_tokens["prompt"] += prompt_tokens
    total_tokens["completion"] += completion_tokens
    total_tokens["total"] += (prompt_tokens + completion_tokens)
    
    # 添加使用记录
    usage_record = {
        "call_time": call_time,
        "model": model,
        "prompt_tokens": prompt_tokens,
        "completion_tokens": completion_tokens,
        "total_tokens": prompt_tokens + completion_tokens,
        "calculation_method": "tiktoken"
    }
    model_usage_records.append(usage_record)
    
    # 保存记录到文件，确保数据持久化
    save_model_usage_records()
    
    print(f"模型使用记录: {model}, 输入: {prompt_tokens}, 输出: {completion_tokens}, 时间: {call_time}")


# 获取计算点信息
def get_compute_points():
    global compute_points, USER_DATA, users_compute_points
    
    if USER_NUM == 0:
        return
    
    # 清空用户计算点列表
    users_compute_points = []
    
    # 累计总计算点
    total_left = 0
    total_points = 0
    
    # 获取每个用户的计算点信息
    for i, user_data in enumerate(USER_DATA):
        try:
            session, cookies, session_token, _, _, _ = user_data
            
            # 检查token是否有效
            if is_token_expired(session_token):
                session_token = refresh_token(session, cookies)
                if not session_token:
                    print(f"用户{i+1}刷新token失败，无法获取计算点信息")
                    continue
                USER_DATA[i] = (session, cookies, session_token, user_data[3], user_data[4], i)
            
            headers = {
                "accept": "application/json, text/plain, */*",
                "accept-language": "zh-CN,zh;q=0.9",
                "baggage": f"sentry-environment=production,sentry-release=93da8385541a6ce339b1f41b0c94428c70657e22,sentry-public_key=3476ea6df1585dd10e92cdae3a66ff49,sentry-trace_id={TRACE_ID}",
                "reai-ui": "1",
                "sec-ch-ua": "\"Chromium\";v=\"116\", \"Not)A;Brand\";v=\"24\", \"Google Chrome\";v=\"116\"",
                "sec-ch-ua-mobile": "?0",
                "sec-ch-ua-platform": "\"Windows\"",
                "sec-fetch-dest": "empty",
                "sec-fetch-mode": "cors",
                "sec-fetch-site": "same-origin",
                "sentry-trace": SENTRY_TRACE,
                "session-token": session_token,
                "x-abacus-org-host": "apps",
                "cookie": cookies
            }
            
            response = requests.get(
                COMPUTE_POINTS_URL,
                headers=headers
            )
            
            if response.status_code == 200:
                result = response.json()
                if result.get("success") and "result" in result:
                    data = result["result"]
                    left = data.get("computePointsLeft", 0)
                    total = data.get("totalComputePoints", 0)
                    used = total - left
                    percentage = round((used / total) * 100, 2) if total > 0 else 0
                    
                    # 获取北京时间
                    beijing_now = datetime.utcnow() + timedelta(hours=8)
                    
                    # 添加到用户列表
                    user_points = {
                        "user_id": i + 1,  # 用户ID从1开始
                        "left": left,
                        "total": total,
                        "used": used,
                        "percentage": percentage,
                        "last_update": beijing_now
                    }
                    users_compute_points.append(user_points)
                    
                    # 累计总数
                    total_left += left
                    total_points += total
                    
                    print(f"用户{i+1}计算点信息更新成功: 剩余 {left}, 总计 {total}")
                    
                    # 对于第一个用户，获取计算点使用日志
                    if i == 0:
                        get_compute_points_log(session, cookies, session_token)
                else:
                    print(f"获取用户{i+1}计算点信息失败: {result.get('error', '未知错误')}")
            else:
                print(f"获取用户{i+1}计算点信息失败，状态码: {response.status_code}")
        except Exception as e:
            print(f"获取用户{i+1}计算点信息异常: {e}")
    
    # 更新全局计算点信息（所有用户总和）
    if users_compute_points:
        compute_points["left"] = total_left
        compute_points["total"] = total_points
        compute_points["used"] = total_points - total_left
        compute_points["percentage"] = round((compute_points["used"] / compute_points["total"]) * 100, 2) if compute_points["total"] > 0 else 0
        compute_points["last_update"] = datetime.utcnow() + timedelta(hours=8)  # 北京时间
        print(f"所有用户计算点总计: 剩余 {total_left}, 总计 {total_points}")

# 获取计算点使用日志
def get_compute_points_log(session, cookies, session_token):
    global compute_points_log
    
    try:
        headers = {
            "accept": "application/json, text/plain, */*",
            "accept-language": "zh-CN,zh;q=0.9",
            "content-type": "application/json",
            "reai-ui": "1",
            "sec-ch-ua": "\"Chromium\";v=\"116\", \"Not)A;Brand\";v=\"24\", \"Google Chrome\";v=\"116\"",
            "sec-ch-ua-mobile": "?0",
            "sec-ch-ua-platform": "\"Windows\"",
            "sec-fetch-dest": "empty",
            "sec-fetch-mode": "cors",
            "sec-fetch-site": "same-site",
            "session-token": session_token,
            "x-abacus-org-host": "apps",
            "cookie": cookies
        }
        
        response = requests.post(
            COMPUTE_POINTS_LOG_URL,
            headers=headers,
            json={"byLlm": True}
        )
        
        if response.status_code == 200:
            result = response.json()
            if result.get("success") and "result" in result:
                data = result["result"]
                compute_points_log["columns"] = data.get("columns", {})
                compute_points_log["log"] = data.get("log", [])
                print(f"计算点使用日志更新成功，获取到 {len(compute_points_log['log'])} 条记录")
            else:
                print(f"获取计算点使用日志失败: {result.get('error', '未知错误')}")
        else:
            print(f"获取计算点使用日志失败，状态码: {response.status_code}")
    except Exception as e:
        print(f"获取计算点使用日志异常: {e}")


# 添加登录相关路由
@app.route("/login", methods=["GET", "POST"])
def login():
    error = None
    if request.method == "POST":
        password = request.form.get("password")
        if password and hashlib.sha256(password.encode()).hexdigest() == PASSWORD:
            flask_session['logged_in'] = True
            flask_session.permanent = True
            return redirect(url_for('dashboard'))
        else:
            # 密码错误时提示使用环境变量密码
            error = "密码不正确。请使用设置的环境变量 password 或 password.txt 中的值作为密码和API认证密钥。"
    
    # 传递空间URL给模板
    return render_template('login.html', error=error, space_url=SPACE_URL)


@app.route("/logout")
def logout():
    flask_session.clear()
    return redirect(url_for('login'))


@app.route("/dashboard")
@require_auth
def dashboard():
    # 在每次访问仪表盘时更新计算点信息
    get_compute_points()
    
    # 计算运行时间（使用北京时间）
    beijing_now = datetime.utcnow() + timedelta(hours=8)
    uptime = beijing_now - START_TIME
    days = uptime.days
    hours, remainder = divmod(uptime.seconds, 3600)
    minutes, seconds = divmod(remainder, 60)
    
    if days > 0:
        uptime_str = f"{days}天 {hours}小时 {minutes}分钟"
    elif hours > 0:
        uptime_str = f"{hours}小时 {minutes}分钟"
    else:
        uptime_str = f"{minutes}分钟 {seconds}秒"

    # 当前北京年份
    beijing_year = beijing_now.year

    return render_template(
        'dashboard.html',
        uptime=uptime_str,
        health_checks=health_check_counter,
        user_count=USER_NUM,
        models=sorted(list(MODELS)),
        year=beijing_year,
        model_stats=model_usage_stats,
        total_tokens=total_tokens,
        compute_points=compute_points,
        compute_points_log=compute_points_log,
        space_url=SPACE_URL,  # 传递空间URL
        users_compute_points=users_compute_points,  # 传递用户计算点信息
        model_usage_records=model_usage_records,  # 传递模型使用记录
        delete_chat=DELETE_CHAT  # 传递删除对话开关状态
    )


@app.route("/api/set_delete_chat", methods=["POST"])
@require_auth
def set_delete_chat():
    """设置是否启用自动删除对话功能"""
    global DELETE_CHAT
    try:
        data = request.get_json()
        if "delete_chat" in data:
            DELETE_CHAT = bool(data["delete_chat"])
            return jsonify({"success": True, "delete_chat": DELETE_CHAT})
        return jsonify({"success": False, "error": "Missing delete_chat parameter"}), 400
    except Exception as e:
        return jsonify({"success": False, "error": str(e)}), 500


# 获取Hugging Face Space URL
def get_space_url():
    # 尝试从环境变量获取
    space_url = os.environ.get("SPACE_URL")
    if space_url:
        return space_url
    
    # 如果SPACE_URL不存在，尝试从SPACE_ID构建
    space_id = os.environ.get("SPACE_ID")
    if space_id:
        username, space_name = space_id.split("/")
        # 将空间名称中的下划线替换为连字符
        # 注意：Hugging Face生成的URL会自动将空间名称中的下划线(_)替换为连字符(-)
        # 例如："abacus_chat_proxy" 会变成 "abacus-chat-proxy"
        space_name = space_name.replace("_", "-")
        return f"https://{username}-{space_name}.hf.space"
    
    # 如果以上都不存在，尝试从单独的用户名和空间名构建
    username = os.environ.get("SPACE_USERNAME")
    space_name = os.environ.get("SPACE_NAME")
    if username and space_name:
        # 将空间名称中的下划线替换为连字符
        # 同上，Hugging Face会自动进行此转换
        space_name = space_name.replace("_", "-")
        return f"https://{username}-{space_name}.hf.space"
    
    # 默认返回None
    return None

# 定期保存token使用统计
def auto_save_stats():
    """定期自动保存token使用统计数据的线程函数"""
    while True:
        try:
            # 每10分钟保存一次
            time.sleep(600)
            # 保存模型使用记录
            save_model_usage_records()
            print(f"已自动保存模型使用记录: {datetime.utcnow() + timedelta(hours=8)}")
            
            # 每小时尝试重新加载记录，确保数据不丢失
            if datetime.utcnow().minute < 10:  # 每小时的前10分钟执行
                print("尝试重新加载模型使用记录...")
                load_model_usage_records()
        except Exception as e:
            print(f"自动保存/加载模型使用记录出错: {e}")


# 加载模型调用记录
def load_model_usage_records():
    global model_usage_records
    try:
        if os.path.exists(MODEL_USAGE_RECORDS_FILE):
            with open(MODEL_USAGE_RECORDS_FILE, 'r', encoding='utf-8') as f:
                records = json.load(f)
                if isinstance(records, list):
                    model_usage_records = records
                    print(f"成功加载 {len(model_usage_records)} 条模型调用记录")
                else:
                    print("调用记录文件格式不正确，初始化为空列表")
    except Exception as e:
        print(f"加载模型调用记录失败: {e}")
        model_usage_records = []

# 保存模型调用记录
def save_model_usage_records():
    try:
        with open(MODEL_USAGE_RECORDS_FILE, 'w', encoding='utf-8') as f:
            json.dump(model_usage_records, f, ensure_ascii=False, indent=2, cls=DateTimeEncoder)
        print(f"成功保存 {len(model_usage_records)} 条模型调用记录")
    except Exception as e:
        print(f"保存模型调用记录失败: {e}")


if __name__ == "__main__":
    # 启动保活线程
    threading.Thread(target=keep_alive, daemon=True).start()
    
    # 启动自动保存统计数据的线程
    threading.Thread(target=auto_save_stats, daemon=True).start()
    
    # 加载历史模型调用记录
    load_model_usage_records()
    
    # 获取初始计算点信息
    get_compute_points()
    
    port = int(os.environ.get("PORT", 9876))
    app.run(port=port, host="0.0.0.0")