"""大模型调用客户端：OpenAI / DeepSeek / 阿里百炼 qwen / OpenAI 兼容。"""
from __future__ import annotations

import httpx


PROVIDER_PRESETS = {
    "openai": {
        "label": "OpenAI",
        "base_url": "https://api.openai.com/v1",
        "model": "gpt-4o-mini",
    },
    "deepseek": {
        "label": "DeepSeek",
        "base_url": "https://api.deepseek.com/v1",
        "model": "deepseek-chat",
    },
    "qwen": {
        "label": "阿里千问（百炼）",
        "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
        "model": "qwen-plus",
    },
    "openai_compatible": {
        "label": "OpenAI 兼容（自定义）",
        "base_url": "",
        "model": "",
    },
}


def mask_key(api_key: str) -> str:
    if not api_key:
        return ""
    if len(api_key) <= 8:
        return "****"
    return f"****{api_key[-4:]}"


def _content_text(value) -> str:
    """兼容 content 为字符串或 OpenAI 多段数组（[{"type":"text","text":...}]）。"""
    if value is None:
        return ""
    if isinstance(value, str):
        return value
    if isinstance(value, list):
        chunks: list[str] = []
        for part in value:
            if isinstance(part, dict):
                text = part.get("text")
                if isinstance(text, str):
                    chunks.append(text)
            else:
                chunks.append(str(part))
        return "".join(chunks)
    if isinstance(value, dict):
        text = value.get("text")
        return text if isinstance(text, str) else ""
    return str(value)


def _message_text(message: dict) -> str:
    """OpenAI 兼容网关常见差异：正文可能在 content 或 reasoning/reasoning_content。"""
    text = _content_text(message.get("content"))
    if text.strip():
        return text.strip()
    for key in ("reasoning_content", "reasoning"):
        fallback = _content_text(message.get(key))
        if fallback.strip():
            return fallback.strip()
    refusal = message.get("refusal")
    if isinstance(refusal, str) and refusal.strip():
        raise ValueError(f"模型拒绝回答：{refusal[:200]}")
    return ""


def chat_completion(
    *,
    base_url: str,
    api_key: str,
    model: str,
    messages: list[dict[str, str]],
    temperature: float = 0.3,
    max_tokens: int = 1600,
    timeout: float = 120.0,
) -> str:
    if not base_url or not api_key or not model:
        raise ValueError("请先完整填写模型配置（地址、API Key、模型名）。")
    endpoint = f"{base_url.rstrip('/')}/chat/completions"
    payload = {
        "model": model,
        "messages": messages,
        "temperature": temperature,
        "max_tokens": max_tokens,
    }
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    }
    try:
        with httpx.Client(timeout=timeout) as client:
            response = client.post(endpoint, json=payload, headers=headers)
    except httpx.HTTPError as exc:
        raise ValueError(f"无法连接模型服务：{exc.__class__.__name__}") from exc

    if response.status_code != 200:
        detail = response.text[:300]
        raise ValueError(f"模型服务返回 {response.status_code}：{detail}")
    try:
        data = response.json()
        message = data["choices"][0].get("message")
        if not isinstance(message, dict):
            raise KeyError("message")
        content = _message_text(message)
        if not content:
            finish_reason = data["choices"][0].get("finish_reason")
            raise ValueError(
                "模型没有返回可见文本"
                f"（finish_reason={finish_reason}）。"
                "若模型启用了推理模式，请提高 max_tokens 或缩短提示。"
            )
        return content
    except ValueError:
        raise
    except (KeyError, IndexError, TypeError) as exc:
        raise ValueError("模型返回格式不符合 Chat Completions 规范") from exc


def quick_test(
    *,
    base_url: str,
    api_key: str,
    model: str,
    temperature: float = 0.0,
) -> str:
    return chat_completion(
        base_url=base_url,
        api_key=api_key,
        model=model,
        temperature=temperature,
        max_tokens=256,
        timeout=45.0,
        messages=[
            {
                "role": "system",
                "content": "你是连通性测试助手，只回复两个字：正常",
            },
            {"role": "user", "content": "请确认服务可用。"},
        ],
    )