import os
import sys
import threading
import random
import time
from contextlib import contextmanager

# When running as a PyInstaller bundle:
#   sys._MEIPASS  →  read-only bundle dir (templates, static, prompts)
#   sys.executable dir  →  writable dir next to the .exe (data, settings, db)
if getattr(sys, 'frozen', False):
    _BUNDLE_DIR = sys._MEIPASS                        # bundled app files
    BASE_DIR    = os.path.dirname(sys.executable)     # writable runtime dir
else:
    _BUNDLE_DIR = os.path.dirname(os.path.abspath(__file__))
    BASE_DIR    = _BUNDLE_DIR

DATA_DIR     = os.path.join(BASE_DIR,    'data')
UPLOAD_DIR   = os.path.join(DATA_DIR,   'uploads')
EXPORT_DIR   = os.path.join(DATA_DIR,   'exports')
KNOWLEDGE_DIR= os.path.join(DATA_DIR,   'knowledge')
DB_PATH      = os.path.join(DATA_DIR,   'projects.db')
CHROMA_DIR   = os.path.join(DATA_DIR,   'chroma')
PROMPTS_DIR  = os.path.join(_BUNDLE_DIR, 'prompts')

# ==================== AI 模型配置 ====================
# 模型选择：'openai' | 'qwen' | 'deepseek' | 'ollama'
MODEL_PROVIDER = os.environ.get('MODEL_PROVIDER', 'qwen')

# OpenAI
OPENAI_API_KEY  = os.environ.get('OPENAI_API_KEY',  'sk-your-openai-key')
OPENAI_MODEL    = os.environ.get('OPENAI_MODEL',    'gpt-4.1')
OPENAI_BASE_URL = os.environ.get('OPENAI_BASE_URL', 'https://api.openai.com/v1')

# 阿里云通义千问
QWEN_API_KEY  = os.environ.get('QWEN_API_KEY',  'sk-your-qwen-key')
QWEN_MODEL    = os.environ.get('QWEN_MODEL',    'qwen3.6-plus')
QWEN_BASE_URL = os.environ.get('QWEN_BASE_URL', 'https://dashscope.aliyuncs.com/compatible-mode/v1')
# 文生图（多模态生成）与 Chat 接口不同：北京地域见 Model Studio 文档
QWEN_MULTIMODAL_BASE = os.environ.get(
    'QWEN_MULTIMODAL_BASE',
    'https://dashscope.aliyuncs.com/api/v1',
)
# 投标附件插图默认模型（同步文生图）
QWEN_IMAGE_MODEL = os.environ.get('QWEN_IMAGE_MODEL', 'qwen-image-2.0-pro')
QWEN_IMAGE_SIZE = os.environ.get('QWEN_IMAGE_SIZE', '1536*1024')
QWEN_IMAGE_PROMPT_EXTEND = os.environ.get('QWEN_IMAGE_PROMPT_EXTEND', 'true').lower() in (
    '1', 'true', 'yes',
)
QWEN_IMAGE_WATERMARK = os.environ.get('QWEN_IMAGE_WATERMARK', 'false').lower() in (
    '1', 'true', 'yes',
)
QWEN_IMAGE_NEGATIVE_PROMPT = os.environ.get(
    'QWEN_IMAGE_NEGATIVE_PROMPT',
    '低分辨率, 模糊, 畸形肢体, 过度饱和, 蜡像感, 杂乱构图, 扭曲文字, 公司商标, LOGO, 投标人名称, '
    '塔吊, 塔式起重机, 起重机, 吊车, 挖掘机, 装载机, 压路机, 泵车, 搅拌车, 推土机, 施工机械, 工程车辆, '
    'crane, excavator, tower crane, bulldozer',
)

# DeepSeek
DEEPSEEK_API_KEY  = os.environ.get('DEEPSEEK_API_KEY',  'sk-your-deepseek-key')
DEEPSEEK_MODEL    = os.environ.get('DEEPSEEK_MODEL',    'deepseek-chat')
DEEPSEEK_BASE_URL = os.environ.get('DEEPSEEK_BASE_URL', 'https://api.deepseek.com/v1')

# Ollama 本地（OpenAI 兼容接口）
OLLAMA_BASE_URL = os.environ.get('OLLAMA_BASE_URL', 'http://localhost:11434/v1')
OLLAMA_MODEL    = os.environ.get('OLLAMA_MODEL',    'qwen3:8b')

# 豆包 / 火山引擎（字节跳动，OpenAI 兼容接口）
DOUBAO_API_KEY  = os.environ.get('DOUBAO_API_KEY',  'sk-your-doubao-key')
DOUBAO_MODEL    = os.environ.get('DOUBAO_MODEL',    'doubao-1-5-pro-32k')
DOUBAO_BASE_URL = os.environ.get('DOUBAO_BASE_URL', 'https://ark.cn-beijing.volces.com/api/v3')

# Kimi / Moonshot AI（OpenAI 兼容接口，支持 Embedding）
KIMI_API_KEY  = os.environ.get('KIMI_API_KEY',  'sk-your-kimi-key')
KIMI_MODEL    = os.environ.get('KIMI_MODEL',    'moonshot-v1-32k')
KIMI_BASE_URL = os.environ.get('KIMI_BASE_URL', 'https://api.moonshot.cn/v1')

# Embedding 模型
OPENAI_EMBEDDING_MODEL = 'text-embedding-3-small'
QWEN_EMBEDDING_MODEL   = 'text-embedding-v3'
KIMI_EMBEDDING_MODEL   = 'moonshot-v1-embedding'

# ==================== 应用配置 ====================
MAX_FILE_SIZE_MB = 50
ALLOWED_EXTENSIONS = {'pdf', 'doc', 'docx'}
SECRET_KEY = 'bidhuo-partner-secret-2024'

# ==================== 生成配置 ====================
MAX_RETRIES = 3
REQUEST_TIMEOUT = int(os.environ.get('REQUEST_TIMEOUT', '180'))
# 大纲生成单次提示词长、输出大，适当延长读超时（秒），避免接口未返回即被客户端断开
OUTLINE_REQUEST_TIMEOUT = int(os.environ.get('OUTLINE_REQUEST_TIMEOUT', '300'))
CHUNK_SIZE = 2000      # 知识库文本分块大小（字符数）
CHUNK_OVERLAP = 200    # 分块重叠大小
TOP_K_KNOWLEDGE = 3   # 知识库检索数量
CONTENT_VOLUME = os.environ.get('CONTENT_VOLUME', 'standard')  # 篇幅档位: concise / standard / detailed / full
TARGET_PAGES = int(os.environ.get('TARGET_PAGES', '0') or '0')  # 目标页数（0=不启用）
PAGE_CHAR_ESTIMATE = int(os.environ.get('PAGE_CHAR_ESTIMATE', '700') or '700')  # 粗略每页字数估算

# ==================== 并发控制 (极速优化核心) ====================
# 全局 LLM 调用信号量与章节生成线程池共用同一上限，默认 40 路真实并发。
# 可通过环境变量 LLM_CONCURRENCY_CAP 提高硬上限（界面保存仍不可超过该值）。
LLM_CONCURRENCY_CAP = int(os.environ.get('LLM_CONCURRENCY_CAP', '40'))
_env_def = str(LLM_CONCURRENCY_CAP)
_raw_llm = int(os.environ.get('LLM_CONCURRENCY_LIMIT', _env_def))
_raw_sec = int(os.environ.get('MAX_CONCURRENT_SECTIONS', _env_def))
_cv = max(1, min(_raw_llm, _raw_sec, LLM_CONCURRENCY_CAP))
LLM_CONCURRENCY_LIMIT = _cv
MAX_CONCURRENT_SECTIONS = _cv
_llm_semaphore = threading.Semaphore(_cv)


def refresh_llm_semaphore() -> None:
    """按当前 LLM/章节并发配置重建信号量（加载 settings.json 后须调用）。"""
    global _llm_semaphore, LLM_CONCURRENCY_LIMIT, MAX_CONCURRENT_SECTIONS
    v = max(1, min(int(LLM_CONCURRENCY_LIMIT), int(MAX_CONCURRENT_SECTIONS), int(LLM_CONCURRENCY_CAP)))
    LLM_CONCURRENCY_LIMIT = v
    MAX_CONCURRENT_SECTIONS = v
    _llm_semaphore = threading.Semaphore(v)


def set_concurrency(n: int) -> None:
    """统一设置章节池并发与 LLM 信号量上限，并重置信号量。"""
    global MAX_CONCURRENT_SECTIONS, LLM_CONCURRENCY_LIMIT, _llm_semaphore
    v = max(1, min(int(n), int(LLM_CONCURRENCY_CAP)))
    MAX_CONCURRENT_SECTIONS = v
    LLM_CONCURRENCY_LIMIT = v
    _llm_semaphore = threading.Semaphore(v)


@contextmanager
def llm_call():
    """全局LLM调用信号量。所有 ai_client.chat / embedding 必须使用。
    针对Qwen云API增加轻微jitter避免429。超时60s防止死锁。"""
    acquired = _llm_semaphore.acquire(blocking=True, timeout=60.0)
    if not acquired:
        raise TimeoutError(f"LLM并发已达上限({LLM_CONCURRENCY_LIMIT})，请稍后重试")
    try:
        # Qwen RPM敏感，增加极小jitter (0-0.08s) 避免429限流
        if MODEL_PROVIDER == 'qwen':
            time.sleep(random.uniform(0, 0.08))
        yield
    finally:
        _llm_semaphore.release()