225 lines
6.7 KiB
Python
225 lines
6.7 KiB
Python
"""AI 图片生成服务：后端请求 + 本地缓存管理。
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API 端点待接入，当前通过 ``set_api_caller`` 注入具体实现。
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缓存目录：``settings/ai_image_cache/``，每张图片有同名的 ``.json`` 侧车记录。
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"""
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from __future__ import annotations
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import datetime as _dt
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import hashlib
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import json
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import os
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import shutil
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import threading
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from dataclasses import dataclass, asdict
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from typing import Callable, List, Optional
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# ---------- 常量 ----------
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_CACHE_DIRNAME = os.path.join("settings", "ai_image_cache")
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_META_SUFFIX = ".json"
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_SUPPORTED_IMG_EXT = (".png", ".jpg", ".jpeg", ".bmp", ".webp")
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# ---------- 数据结构 ----------
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@dataclass
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class AIImageRecord:
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"""一条缓存记录。"""
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id: str
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prompt: str
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image_path: str
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created_at: str # ISO8601
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extra: Optional[dict] = None
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def to_json(self) -> str:
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return json.dumps(asdict(self), ensure_ascii=False, indent=2)
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# ---------- API 注入 ----------
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# 调用签名: ``fn(prompt: str) -> (image_bytes: bytes, image_ext: str, extra: dict|None)``
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# ``image_ext`` 例如 ``".png"``；``extra`` 可为 None。
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_ApiCaller = Callable[[str], tuple]
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_api_caller: Optional[_ApiCaller] = None
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def set_api_caller(fn: Optional[_ApiCaller]) -> None:
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"""注入真实的后端 API 调用函数。在 API 就绪前可保持为 None。"""
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global _api_caller
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_api_caller = fn
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def has_api() -> bool:
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return _api_caller is not None
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# ---------- 缓存路径工具 ----------
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def get_cache_dir(base_dir: Optional[str] = None) -> str:
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"""返回缓存目录，如不存在则创建。``base_dir`` 默认使用当前工作目录。"""
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root = base_dir if base_dir else os.getcwd()
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path = os.path.join(root, _CACHE_DIRNAME)
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os.makedirs(path, exist_ok=True)
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return path
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def _make_id(prompt: str) -> str:
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stamp = _dt.datetime.now().strftime("%Y%m%d_%H%M%S_%f")
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digest = hashlib.md5(prompt.encode("utf-8")).hexdigest()[:8]
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return f"{stamp}_{digest}"
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def _meta_path_for(image_path: str) -> str:
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return os.path.splitext(image_path)[0] + _META_SUFFIX
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# ---------- 读写 ----------
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def list_records(base_dir: Optional[str] = None) -> List[AIImageRecord]:
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"""列出缓存目录下的所有记录，按创建时间倒序（最新在前）。"""
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cache_dir = get_cache_dir(base_dir)
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records: List[AIImageRecord] = []
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for name in os.listdir(cache_dir):
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full = os.path.join(cache_dir, name)
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if not (os.path.isfile(full) and name.lower().endswith(_SUPPORTED_IMG_EXT)):
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continue
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meta_path = _meta_path_for(full)
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prompt = ""
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created_at = ""
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extra = None
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rec_id = os.path.splitext(name)[0]
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if os.path.isfile(meta_path):
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try:
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with open(meta_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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prompt = data.get("prompt", "")
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created_at = data.get("created_at", "")
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extra = data.get("extra")
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rec_id = data.get("id", rec_id)
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except Exception:
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pass
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if not created_at:
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# fallback 到文件 mtime
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try:
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mtime = os.path.getmtime(full)
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created_at = _dt.datetime.fromtimestamp(mtime).isoformat()
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except Exception:
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created_at = ""
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records.append(
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AIImageRecord(
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id=rec_id,
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prompt=prompt,
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image_path=full,
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created_at=created_at,
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extra=extra,
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)
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)
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records.sort(key=lambda r: r.created_at, reverse=True)
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return records
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def save_image_to_cache(
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prompt: str,
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image_bytes: bytes,
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image_ext: str = ".png",
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extra: Optional[dict] = None,
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base_dir: Optional[str] = None,
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) -> AIImageRecord:
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"""把生成的图片字节写入缓存，返回记录。"""
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if not image_ext.startswith("."):
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image_ext = "." + image_ext
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if image_ext.lower() not in _SUPPORTED_IMG_EXT:
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image_ext = ".png"
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cache_dir = get_cache_dir(base_dir)
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rec_id = _make_id(prompt)
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image_path = os.path.join(cache_dir, f"{rec_id}{image_ext}")
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with open(image_path, "wb") as f:
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f.write(image_bytes)
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record = AIImageRecord(
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id=rec_id,
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prompt=prompt,
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image_path=image_path,
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created_at=_dt.datetime.now().isoformat(timespec="seconds"),
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extra=extra,
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)
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try:
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with open(_meta_path_for(image_path), "w", encoding="utf-8") as f:
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f.write(record.to_json())
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except Exception:
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pass
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return record
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def delete_record(record: AIImageRecord) -> bool:
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"""删除一条缓存记录（图片 + 侧车）。返回是否成功。"""
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ok = True
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for p in (record.image_path, _meta_path_for(record.image_path)):
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try:
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if os.path.isfile(p):
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os.remove(p)
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except Exception:
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ok = False
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return ok
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def export_record(record: AIImageRecord, dest_path: str) -> None:
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"""把缓存中的图片另存到 ``dest_path``。"""
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shutil.copyfile(record.image_path, dest_path)
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# ---------- 异步请求 ----------
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def request_image_async(
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prompt: str,
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on_success: Callable[[AIImageRecord], None],
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on_error: Callable[[Exception], None],
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base_dir: Optional[str] = None,
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) -> threading.Thread:
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"""在后台线程请求 API → 写入缓存 → 回调。
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``on_success`` / ``on_error`` 会在 **工作线程** 中被调用；UI 侧若需
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切回主线程，请在回调内部自行用 ``root.after(0, ...)``。
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"""
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def _worker():
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try:
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if _api_caller is None:
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raise RuntimeError("AI 图片 API 尚未接入，请调用 set_api_caller 注入")
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image_bytes, image_ext, extra = _normalize_api_result(_api_caller(prompt))
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record = save_image_to_cache(
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prompt=prompt,
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image_bytes=image_bytes,
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image_ext=image_ext,
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extra=extra,
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base_dir=base_dir,
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)
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on_success(record)
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except Exception as exc:
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on_error(exc)
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t = threading.Thread(target=_worker, daemon=True)
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t.start()
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return t
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def _normalize_api_result(result):
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"""允许 API 返回 ``bytes`` 或 ``(bytes, ext)`` 或 ``(bytes, ext, extra)``。"""
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if isinstance(result, (bytes, bytearray)):
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return bytes(result), ".png", None
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if isinstance(result, tuple):
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if len(result) == 2:
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return bytes(result[0]), str(result[1]), None
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if len(result) == 3:
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return bytes(result[0]), str(result[1]), result[2]
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raise ValueError("API 返回格式不支持，需为 bytes 或 (bytes, ext[, extra])")
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