"""大模型调用：OpenAI 兼容接口（默认）或本机 Ollama 二选一。

配置从 os.environ 惰性读取：各实例 app.py 在 import 本模块后才 load_env_file(.env)，
若在 import 时缓存变量会导致 OPENAI_API_KEY 始终为空。
"""
from __future__ import annotations

import base64
import os
import re
from typing import List, Optional, Sequence, Tuple

import requests


def _env_str(name: str, default: str = "") -> str:
    v = os.getenv(name)
    if v is None:
        return default
    return str(v).strip()


def _ai_timeout_seconds(*, image_count: int = 0, chat: bool = False) -> int:
    if chat:
        try:
            return max(30, int(_env_str("CHAT_AI_TIMEOUT_SECONDS", "300") or "300"))
        except ValueError:
            return 300
    if image_count > 0:
        try:
            return max(30, int(_env_str("AI_REVIEW_TIMEOUT_SECONDS", "300") or "300"))
        except ValueError:
            return 300
    try:
        return max(10, int(_env_str("AI_TIMEOUT_SECONDS", "120") or "120"))
    except ValueError:
        return 120


def _ai_provider() -> str:
    return (_env_str("AI_PROVIDER", "openai") or "openai").lower()


def _openai_api_base() -> str:
    base = _env_str("OPENAI_API_BASE", "https://op.bz121.com/v1") or "https://op.bz121.com/v1"
    return base.rstrip("/")


def _openai_api_key() -> str:
    return _env_str("OPENAI_API_KEY") or _env_str("AI_API_KEY")


def _openai_model() -> str:
    return _env_str("OPENAI_MODEL", "gemma4:e4b") or "gemma4:e4b"


def _ollama_api() -> str:
    return _env_str("OLLAMA_API", "http://127.0.0.1:11434/api/generate") or "http://127.0.0.1:11434/api/generate"


def _ollama_model() -> str:
    return _env_str("AI_MODEL", "huihui_ai/deepseek-r1-abliterated:latest") or "huihui_ai/deepseek-r1-abliterated:latest"


def _use_openai() -> bool:
    return _ai_provider() in ("openai", "openai_compatible", "gateway")


def _image_mime_for_path(path: str) -> str:
    ext = os.path.splitext(str(path or ""))[1].lower()
    if ext == ".png":
        return "image/png"
    if ext in (".jpg", ".jpeg"):
        return "image/jpeg"
    if ext == ".webp":
        return "image/webp"
    if ext == ".gif":
        return "image/gif"
    return "image/jpeg"


def _read_image_base64(image_path: str) -> Optional[tuple]:
    try:
        with open(image_path, "rb") as f:
            b64 = base64.b64encode(f.read()).decode("utf-8")
        return b64, _image_mime_for_path(image_path)
    except Exception:
        return None


def _collect_images(
    image_paths: Optional[Sequence[str]] = None,
    images_b64: Optional[Sequence[str]] = None,
) -> List[tuple]:
    out: List[tuple] = []
    for p in image_paths or []:
        item = _read_image_base64(p)
        if item:
            out.append(item)
    for b in images_b64 or []:
        if b:
            out.append((str(b), "image/jpeg"))
    return out


def _openai_chat_url() -> str:
    base = _openai_api_base()
    if base.endswith("/chat/completions"):
        return base
    return f"{base}/chat/completions"


def _openai_message_text(msg: dict) -> str:
    content = msg.get("content")
    if isinstance(content, list):
        parts: list[str] = []
        for part in content:
            if isinstance(part, dict) and part.get("type") == "text":
                parts.append(str(part.get("text") or ""))
        content = "".join(parts)
    text = str(content or "").strip()
    if not text:
        text = str(msg.get("reasoning_content") or "").strip()
    return text


def _apply_max_tokens(body: dict, max_tokens: int | None, *, chat: bool = False) -> None:
    if max_tokens is not None and max_tokens > 0:
        mt = int(max_tokens)
        body["max_tokens"] = mt
        if not chat:
            body["max_completion_tokens"] = mt


def _openai_chat_completion(
    messages: list[dict],
    *,
    temperature: float,
    max_tokens: int | None = None,
    image_count: int = 0,
    chat: bool = False,
) -> Tuple[str, str]:
    api_key = _openai_api_key()
    if not api_key:
        return "AI 调用失败：未配置 OPENAI_API_KEY（请在当前实例目录 .env 中设置，修改后需重启服务）", "error"
    headers = {
        "Authorization": f"Bearer {api_key}",
        "Content-Type": "application/json",
    }
    body: dict = {
        "model": _openai_model(),
        "messages": messages,
        "temperature": temperature,
        "stream": False,
    }
    _apply_max_tokens(body, max_tokens, chat=chat)
    r = requests.post(
        _openai_chat_url(),
        headers=headers,
        json=body,
        timeout=_ai_timeout_seconds(image_count=image_count, chat=chat),
    )
    r.raise_for_status()
    data = r.json()
    choices = data.get("choices") or []
    if not choices:
        return "AI 生成失败：响应无 choices", "error"
    choice = choices[0] or {}
    msg = choice.get("message") or {}
    text = _openai_message_text(msg)
    finish = str(choice.get("finish_reason") or "")
    if not text and chat and max_tokens:
        retry_body = dict(body)
        retry_body.pop("max_completion_tokens", None)
        r2 = requests.post(
            _openai_chat_url(),
            headers=headers,
            json=retry_body,
            timeout=_ai_timeout_seconds(image_count=image_count, chat=chat),
        )
        r2.raise_for_status()
        data2 = r2.json()
        choices2 = data2.get("choices") or []
        if choices2:
            msg2 = (choices2[0] or {}).get("message") or {}
            text2 = _openai_message_text(msg2)
            if text2:
                return text2, str((choices2[0] or {}).get("finish_reason") or finish)
    if not text:
        return "AI 生成失败：空内容", finish or "error"
    return text, finish


def _generate_openai(
    prompt: str,
    images: List[tuple],
    temperature: float,
    *,
    max_tokens: int | None = None,
) -> str:
    if images:
        content: List[dict] = [{"type": "text", "text": prompt}]
        for b64, mime in images:
            content.append(
                {
                    "type": "image_url",
                    "image_url": {"url": f"data:{mime};base64,{b64}"},
                }
            )
        messages = [{"role": "user", "content": content}]
    else:
        messages = [{"role": "user", "content": prompt}]
    text, _reason = _openai_chat_completion(
        messages,
        temperature=temperature,
        max_tokens=max_tokens,
        image_count=len(images),
    )
    return text


def _generate_ollama(
    prompt: str,
    images: List[tuple],
    temperature: float,
    *,
    max_tokens: int | None = None,
    chat: bool = False,
) -> Tuple[str, str]:
    options: dict = {"temperature": temperature}
    if max_tokens is not None and max_tokens > 0:
        options["num_predict"] = int(max_tokens)
    payload = {
        "model": _ollama_model(),
        "prompt": prompt,
        "stream": False,
        "options": options,
    }
    if images:
        payload["images"] = [b64 for b64, _mime in images]
    r = requests.post(
        _ollama_api(),
        json=payload,
        timeout=_ai_timeout_seconds(image_count=len(images), chat=chat),
    )
    r.raise_for_status()
    data = r.json()
    text = (data.get("response") or "").strip() or "AI 生成失败"
    return text, str(data.get("done_reason") or "")


def ai_generate(
    prompt: str,
    *,
    image_paths: Optional[Sequence[str]] = None,
    images_b64: Optional[Sequence[str]] = None,
    temperature: float = 0.2,
    max_tokens: int | None = None,
) -> str:
    """统一文本生成；失败时返回以「AI 调用失败」开头的说明。"""
    images = _collect_images(image_paths, images_b64)
    try:
        if _use_openai():
            return _generate_openai(prompt, images, temperature, max_tokens=max_tokens)
        text, _reason = _generate_ollama(prompt, images, temperature, max_tokens=max_tokens)
        return text
    except requests.HTTPError as e:
        detail = ""
        try:
            detail = (e.response.text or "")[:500]
        except Exception:
            pass
        prov = "OpenAI" if _use_openai() else "Ollama"
        return f"AI 调用失败（{prov} HTTP {e.response.status_code if e.response else '?'}）：{detail or str(e)}"
    except Exception as e:
        prov = "OpenAI" if _use_openai() else "Ollama"
        return f"AI 调用失败（{prov}）：{str(e)}"


_CHAT_CONTINUE_USER = (
    "你上一条回复在中途截断了。请从断点处继续写完，不要重复已写内容，"
    "保持同一语气；编号列表每条单独一行。"
)
_CHAT_END_CHARS = "。！？.!?\"」』）)>】"
_INCOMPLETE_TAIL_RE = re.compile(
    r"(不会|不能|没有|会不会|是不是|够不够|能不能|要不要|如何|怎么|什么|哪里|多少|对吗|怎么样|"
    r"这个\.\.\.|这个…|\.\.\.\d+\.|\d+\.)$"
)


def _looks_truncated(text: str) -> bool:
    t = (text or "").rstrip()
    if len(t) < 16:
        return False
    if t[-1] in _CHAT_END_CHARS:
        return False
    if _INCOMPLETE_TAIL_RE.search(t):
        return True
    if t.endswith("…") or t.endswith("..."):
        return True
    if re.search(r"\d+\.\s*$", t):
        return True
    return t[-1] not in "，、,;；:：\n"


def _should_continue(reason: str, full_text: str) -> bool:
    if reason in ("length", "max_tokens", "model_length"):
        return True
    return _looks_truncated(full_text)


def _chat_continue_message(full_text: str) -> str:
    tail = full_text[-500:] if len(full_text) > 500 else full_text
    return (
        f"{_CHAT_CONTINUE_USER}\n\n"
        f"已写到最后这几句：\n「{tail}」\n\n"
        f"请从断点接着写完。不要重复前文；最后一句话必须以句号、问号或感叹号结束。"
    )


def _chat_continue_system(system: str) -> str:
    return (
        f"{system.strip()}\n\n"
        "【续写模式】只输出断点后的剩余内容，不要重复前文；"
        "列表每条单独一行；必须以句号、问号或感叹号收尾。"
    )


def ai_generate_chat(
    *,
    system: str,
    user: str,
    temperature: float = 0.5,
    images_b64: Optional[Sequence[str]] = None,
    max_tokens: int = 8192,
    max_continuations: int = 4,
) -> str:
    """聊天专用：system/user 分消息；输出触顶时轻量续写（不重复巨型上下文）。"""
    images = _collect_images(None, images_b64)
    max_rounds = max(1, int(max_continuations) + 1)
    try:
        if _use_openai():
            if images:
                user_content: List[dict] | str = [{"type": "text", "text": user.strip()}]
                for b64, mime in images:
                    user_content.append(
                        {
                            "type": "image_url",
                            "image_url": {"url": f"data:{mime};base64,{b64}"},
                        }
                    )
            else:
                user_content = user.strip()
            base_user_msg = {"role": "user", "content": user_content}
            messages: list[dict] = [
                {"role": "system", "content": system.strip()},
                base_user_msg,
            ]

            parts: list[str] = []
            for attempt in range(max_rounds):
                chunk, reason = _openai_chat_completion(
                    messages,
                    temperature=temperature,
                    max_tokens=max_tokens,
                    image_count=len(images) if attempt == 0 else 0,
                    chat=True,
                )
                if chunk.startswith("AI 调用失败") or chunk.startswith("AI 生成失败"):
                    return chunk if not parts else "".join(parts).strip()
                parts.append(chunk)
                full = "".join(parts)
                if not _should_continue(reason, full) or attempt >= max_rounds - 1:
                    break
                messages = [
                    {"role": "system", "content": _chat_continue_system(system)},
                    {"role": "assistant", "content": full},
                    {"role": "user", "content": _chat_continue_message(full)},
                ]
            return "".join(parts).strip() or "AI 生成失败：空内容"

        prompt = f"{system.strip()}\n\n---\n\n{user.strip()}"
        parts: list[str] = []
        for attempt in range(max_rounds):
            if parts:
                full = "".join(parts)
                current_prompt = (
                    f"{_chat_continue_system(system)}\n\n"
                    f"【你已写道】\n{full}\n\n{_chat_continue_message(full)}"
                )
            else:
                current_prompt = prompt
            chunk, reason = _generate_ollama(
                current_prompt,
                images if not parts else [],
                temperature,
                max_tokens=max_tokens,
                chat=True,
            )
            if chunk.startswith("AI 生成失败") and not parts:
                return chunk
            if chunk.startswith("AI 生成失败"):
                break
            parts.append(chunk)
            full = "".join(parts)
            if not _should_continue(reason, full) or attempt >= max_rounds - 1:
                break
        return "".join(parts).strip() or "AI 生成失败：空内容"
    except requests.HTTPError as e:
        detail = ""
        try:
            detail = (e.response.text or "")[:500]
        except Exception:
            pass
        prov = "OpenAI" if _use_openai() else "Ollama"
        return f"AI 调用失败（{prov} HTTP {e.response.status_code if e.response else '?'}）：{detail or str(e)}"
    except Exception as e:
        prov = "OpenAI" if _use_openai() else "Ollama"
        return f"AI 调用失败（{prov}）：{str(e)}"


def ai_review(trades_text: str, period_title: str, image_paths=None) -> str:
    n_img = len(image_paths or [])
    period_label = "周" if "周" in str(period_title) else "日"
    attach_note = (
        f"ℹ️ 【系统说明：已向模型附带 {n_img} 张复盘附图（自动K线或上传截图），请结合附图分析第5节。】\n\n"
        if n_img
        else "ℹ️ 【系统说明：本次未附带复盘附图，第5节请写明「无附图，无法看图」；保存复盘记录时可勾选「自动生成K线图」。】\n\n"
    )
    prompt = f"""
你是一位专业交易教练。下面是用户的{period_title}交易记录，请做简洁、可执行的复盘（中文）。

【硬性规则 — 必须遵守】
- 你只能根据「交易记录」里**明确出现的字段**陈述事实；禁止编造：是否触发止损、是否扛单、亏损是否扩大、图上具体结构/进出场点位等记录里**没有**的信息。
- 「平仓/离场」只是交易员自述摘要，不是客观成交明细；若记录未写明代币是否打到止损价、是否软件平仓等，不要断言执行路径，可用「在记录有限前提下，一种可能是……」或简短写「执行路径记录不足，无法判断」。
- 「提前离场」类结论必须优先依据记录中的「提前离场记录」字段；若该段全为「无」或未出现有效内容，不得写道「明显扛单」「拒不止损」「未执行硬止损」等。
- 实际RR为负只说明结果相对于预期RR不利，不等同于「风控失灵」或「止损纪律崩溃」，除非记录里另有依据。
- 禁止用语：人身攻击、夸张定性（如「致命伤」「灾难」）；语气克制、对事不对人。
- 若有截图且你能辨认，再结合图讨论；看不清或无明确定位则明确说「无法从图确认」，不得虚构 K 线故事。

【输出格式 — Markdown，必须严格遵守】
- 第一行：**交易复盘报告（{period_label}度）**
- 五个大节标题必须**完全一致**（含 emoji，不要用其它编号或改名）：
  **1. 📊 总体盈亏结构**
  **2. 🧠 心态与执行**
  **3. 🏷️ 行为标签**
  **4. ✅ 改进建议**
  **5. 📈 图表分析**
- 每节正文用 `- **子项名**：内容` 列表；第4节改进建议用有序列表 `1. 2. 3.`
- 第1节至少包含：**笔数/盈亏**、**风险回报比**、**总结**
- 第2节至少包含：**得分**（1–10）、**依据**（对应记录字段）
- 第5节至少包含：**趋势确认**、**执行路径**（记录不足则写明）
- 语气简洁，少形容词；不要输出代码块、不要表格

交易记录：
{trades_text}
""".strip()
    return attach_note + ai_generate(prompt, image_paths=image_paths, temperature=0.2)


def ai_short_advice(prompt_text: str) -> str:
    prompt = f"""
你是交易风控助理。请用中文给出**最多 3 条**提醒，要求：
- 每条不超过 25 个字
- 语气克制、具体、可执行
- 不要输出 Markdown，不要编号前缀以外的废话

场景：
{prompt_text}
""".strip()
    return ai_generate(prompt, temperature=0.2)


def ai_provider_label() -> str:
    if _use_openai():
        return f"OpenAI 兼容 · {_openai_model()} @ {_openai_api_base()}"
    return f"Ollama · {_ollama_model()}"


def ai_config_status() -> dict:
    """调试用：当前进程内读到的 AI 配置（不含密钥明文）。"""
    key = _openai_api_key()
    return {
        "provider": _ai_provider(),
        "openai_base": _openai_api_base(),
        "openai_model": _openai_model(),
        "openai_key_configured": bool(key),
        "ollama_api": _ollama_api(),
        "ollama_model": _ollama_model(),
    }