651 lines
21 KiB
Python
651 lines
21 KiB
Python
#!/usr/bin/env python3
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from __future__ import annotations
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import argparse
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import base64
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import json
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import os
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import re
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import subprocess
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import sys
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import tempfile
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import time
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import traceback
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import urllib.parse
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import urllib.request
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from pathlib import Path
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# The skill runner consumes stdout, so route Python error output there as well.
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sys.stderr = sys.stdout
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def _skill_root() -> Path:
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script_dir = Path(__file__).resolve().parent
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return script_dir.parent
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def _skill_venv_python() -> Path:
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venv_dir = _skill_root() / ".venv"
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if sys.platform == "win32":
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return venv_dir / "Scripts" / "python.exe"
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return venv_dir / "bin" / "python"
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def _get_python_executable() -> str:
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if sys.executable:
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return sys.executable
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import shutil
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for candidate in ("python3", "python"):
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found = shutil.which(candidate)
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if found:
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return found
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raise RuntimeError("无法找到 Python 解释器路径")
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def _run_bootstrap() -> None:
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bootstrap = Path(__file__).resolve().parent / "bootstrap.py"
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result = subprocess.run([_get_python_executable(), str(bootstrap)])
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if result.returncode != 0:
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raise SystemExit(result.returncode)
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def _ensure_skill_venv_python() -> None:
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venv_python = _skill_venv_python()
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if not venv_python.is_file():
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_run_bootstrap()
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venv_python = _skill_venv_python()
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if not venv_python.is_file():
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sys.stdout.write("bootstrap 后仍未找到虚拟环境\n")
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raise SystemExit(1)
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venv_dir = _skill_root() / ".venv"
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if Path(sys.prefix) == venv_dir.resolve():
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return
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os.execv(str(venv_python), [str(venv_python), str(Path(__file__).resolve()), *sys.argv[1:]])
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_ensure_skill_venv_python()
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try:
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import pymysql # type: ignore # noqa: E402
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from openai import OpenAI # type: ignore # noqa: E402
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except ModuleNotFoundError:
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_run_bootstrap()
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_py = _get_python_executable()
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os.execv(_py, [_py, str(Path(__file__).resolve()), *sys.argv[1:]])
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# ---------------------------------------------------------------------------
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# Database helpers
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# ---------------------------------------------------------------------------
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def _mysql_connect():
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host = os.environ.get("MYSQL_HOST", "127.0.0.1")
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port = int(os.environ.get("MYSQL_PORT", "3306"))
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user = os.environ.get("MYSQL_USER", "root")
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password = os.environ.get("MYSQL_PASSWORD", "")
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database = os.environ.get("ROBOT_CODE", "")
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if not database:
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raise RuntimeError("环境变量 ROBOT_CODE 未配置")
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return pymysql.connect(
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host=host, port=port, user=user, password=password,
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database=database, charset="utf8mb4",
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connect_timeout=10, read_timeout=30,
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)
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def _query_one(conn, sql: str, params: tuple = ()) -> dict | None:
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cur = conn.cursor()
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cur.execute(sql, params)
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columns = [desc[0] for desc in cur.description] if cur.description else []
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row = cur.fetchone()
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cur.close()
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if row is None:
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return None
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return dict(zip(columns, row))
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# ---------------------------------------------------------------------------
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# Settings resolution (mirrors the Go service logic)
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# ---------------------------------------------------------------------------
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def load_drawing_settings(conn, from_wx_id: str) -> tuple[bool, dict]:
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"""Return (enabled, image_ai_settings_dict)."""
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# 1. global_settings
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gs = _query_one(conn, "SELECT image_ai_enabled, image_ai_settings FROM global_settings LIMIT 1")
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enabled = False
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settings_json: dict = {}
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if gs:
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if gs.get("image_ai_enabled"):
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enabled = bool(gs["image_ai_enabled"])
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raw = gs.get("image_ai_settings")
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if raw:
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if isinstance(raw, (bytes, bytearray)):
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raw = raw.decode("utf-8")
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if isinstance(raw, str) and raw.strip():
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settings_json = json.loads(raw)
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# 2. override from chatroom / friend settings
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if from_wx_id.endswith("@chatroom"):
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override = _query_one(
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conn,
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"SELECT image_ai_enabled, image_ai_settings FROM chat_room_settings WHERE chat_room_id = %s LIMIT 1",
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(from_wx_id,),
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)
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else:
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override = _query_one(
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conn,
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"SELECT image_ai_enabled, image_ai_settings FROM friend_settings WHERE wechat_id = %s LIMIT 1",
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(from_wx_id,),
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)
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if override:
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if override.get("image_ai_enabled") is not None:
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enabled = bool(override["image_ai_enabled"])
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raw = override.get("image_ai_settings")
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if raw:
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if isinstance(raw, (bytes, bytearray)):
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raw = raw.decode("utf-8")
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if isinstance(raw, str) and raw.strip():
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settings_json = json.loads(raw)
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return enabled, settings_json
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# ---------------------------------------------------------------------------
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# API callers
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# ---------------------------------------------------------------------------
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def _http_post_json(url: str, body: dict, headers: dict, timeout: int = 300) -> dict:
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data = json.dumps(body).encode("utf-8")
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req = urllib.request.Request(url, data=data, headers=headers, method="POST")
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with urllib.request.urlopen(req, timeout=timeout) as resp:
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return json.loads(resp.read().decode("utf-8"))
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def _http_get_json(url: str, headers: dict, timeout: int = 30) -> dict:
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req = urllib.request.Request(url, headers=headers, method="GET")
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with urllib.request.urlopen(req, timeout=timeout) as resp:
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return json.loads(resp.read().decode("utf-8"))
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def _coerce_int(value, default: int, minimum: int, maximum: int) -> int:
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try:
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parsed = int(value)
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except (TypeError, ValueError):
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parsed = default
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return min(max(parsed, minimum), maximum)
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def _openai_output_format(config: dict) -> str:
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output_format = str(config.get("output_format", "png") or "png").lower()
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if output_format not in {"png", "jpeg", "webp"}:
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return "png"
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return output_format
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def _openai_size(config: dict, ratio: str, resolution: str) -> str:
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configured = str(config.get("size", "") or "").strip()
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if configured:
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return configured
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normalized_ratio = (ratio or "").replace(" ", "").lower()
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normalized_resolution = (resolution or "").replace(" ", "").lower()
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if normalized_resolution in {"4k", "2160p", "3840x2160"}:
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sizes = {
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"16:9": "3840x2160",
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"9:16": "2160x3840",
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"1:1": "2048x2048",
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"3:2": "3072x2048",
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"2:3": "2048x3072",
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}
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elif normalized_resolution in {"2k", "1440p", "2048"}:
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sizes = {
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"16:9": "2048x1152",
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"9:16": "1152x2048",
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"1:1": "2048x2048",
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"3:2": "2048x1360",
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"2:3": "1360x2048",
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}
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elif normalized_resolution in {"1k", "1024", "1024p"}:
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sizes = {
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"16:9": "1536x864",
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"9:16": "864x1536",
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"1:1": "1024x1024",
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"3:2": "1536x1024",
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"2:3": "1024x1536",
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}
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else:
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return "auto"
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return sizes.get(normalized_ratio, "auto")
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def _openai_prompt(prompt: str, negative_prompt: str) -> str:
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if not negative_prompt:
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return prompt
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return f"{prompt}\n\n不要包含: {negative_prompt}"
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def _openai_client(config: dict) -> OpenAI:
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api_key = str(config.get("api_key", "")).strip()
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if not api_key:
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raise RuntimeError("OpenAI 绘图配置缺少 api_key")
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kwargs: dict[str, str | float] = {"api_key": api_key}
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base_url = str(config.get("base_url", "") or "").strip()
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if base_url:
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kwargs["base_url"] = base_url
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organization = str(config.get("organization", "") or "").strip()
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if organization:
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kwargs["organization"] = organization
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project = str(config.get("project", "") or "").strip()
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if project:
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kwargs["project"] = project
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timeout_value = config.get("timeout")
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if timeout_value not in (None, ""):
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kwargs["timeout"] = float(timeout_value)
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return OpenAI(**kwargs)
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def _openai_image_suffix(output_format: str) -> str:
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if output_format == "jpeg":
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return ".jpg"
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return f".{output_format}"
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def _write_openai_image_file(b64_json: str, output_format: str) -> str:
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image_bytes = base64.b64decode(b64_json)
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with tempfile.NamedTemporaryFile(
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prefix="wechat-openai-image-",
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suffix=_openai_image_suffix(output_format),
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delete=False,
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) as image_file:
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image_file.write(image_bytes)
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return image_file.name
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def _openai_images_from_response(response, output_format: str) -> list[str]:
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outputs: list[str] = []
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for item in getattr(response, "data", []) or []:
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b64_json = getattr(item, "b64_json", None)
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if b64_json:
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outputs.append(_write_openai_image_file(b64_json, output_format))
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continue
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url = getattr(item, "url", None)
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if url:
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outputs.append(url)
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return outputs
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def _is_remote_image_url(value: str) -> bool:
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return urllib.parse.urlparse(value).scheme in {"http", "https"}
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def _send_image_outputs(client_port: str, from_wx_id: str, image_outputs: list[str]) -> None:
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remote_urls = [value for value in image_outputs if value and _is_remote_image_url(value)]
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local_paths = [value for value in image_outputs if value and not _is_remote_image_url(value)]
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if remote_urls:
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send_url = f"http://127.0.0.1:{client_port}/api/v1/robot/message/send/image/url"
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send_body = {
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"to_wxid": from_wx_id,
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"image_urls": remote_urls,
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}
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_http_post_json(send_url, send_body, {"Content-Type": "application/json"}, timeout=60)
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if local_paths:
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send_url = f"http://127.0.0.1:{client_port}/api/v1/robot/message/send/image/local"
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send_body = {
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"to_wxid": from_wx_id,
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"file_path": local_paths,
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}
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_http_post_json(send_url, send_body, {"Content-Type": "application/json"}, timeout=60)
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def _cleanup_openai_temp_files(image_outputs: list[str]) -> None:
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for value in image_outputs:
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path = Path(value)
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if path.name.startswith("wechat-openai-image-") and path.is_file():
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try:
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path.unlink()
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except OSError:
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pass
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def call_jimeng(config: dict, prompt: str, model: str,
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negative_prompt: str, ratio: str, resolution: str) -> list[str]:
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"""Call JiMeng (即梦) image generation API."""
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base_url = config.get("base_url", "").rstrip("/")
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session_ids = config.get("sessionid", [])
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if not base_url or not session_ids:
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raise RuntimeError("即梦绘图配置缺少 base_url 或 sessionid")
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if not model or model == "none":
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model = "jimeng-5.0"
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if not ratio:
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ratio = "16:9"
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if not resolution:
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resolution = "2k"
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# 如果分辨率大于4k，重置为2k
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m = re.search(r"(\d+)", resolution)
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if m and int(m.group(1)) > 4:
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resolution = "2k"
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token = ",".join(session_ids)
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body = {
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"model": model,
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"prompt": prompt,
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"ratio": ratio,
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"resolution": resolution,
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"response_format": "url",
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"sample_strength": 0.5,
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}
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if negative_prompt:
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body["negative_prompt"] = negative_prompt
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resp = _http_post_json(
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f"{base_url}/v1/images/generations",
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body,
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{"Content-Type": "application/json", "Authorization": f"Bearer {token}"},
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timeout=300,
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)
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urls = [item["url"] for item in resp.get("data", []) if item.get("url")]
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return urls
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def call_doubao(config: dict, prompt: str, model: str) -> list[str]:
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"""Call DouBao (豆包) image generation API."""
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api_key = config.get("api_key", "")
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if not api_key:
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raise RuntimeError("豆包绘图配置缺少 api_key")
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if not model or model == "none":
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model = "doubao-seedream-4.5"
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# Map friendly model names to actual endpoint model IDs
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model_map = {
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"doubao-seedream-4.5": "doubao-seedream-4-5-251128",
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"doubao-seedream-4.0": "doubao-seedream-4-0-251128",
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"doubao-seedream-3.0-t2i": "doubao-seedream-3-0-t2i-250415",
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"doubao-seededit-3.0-i2i": "doubao-seededit-3-0-i2i-250628",
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}
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actual_model = model_map.get(model, model)
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body = {
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"model": actual_model,
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"prompt": prompt,
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"response_format": "url",
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"size": config.get("size", "2K"),
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"sequential_image_generation": config.get("sequential_image_generation", "auto"),
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"watermark": config.get("watermark", False),
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}
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image_val = config.get("image", "")
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if image_val:
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body["image"] = image_val
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resp = _http_post_json(
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"https://ark.cn-beijing.volces.com/api/v3/images/generations",
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body,
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{"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"},
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timeout=300,
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)
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urls = []
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for item in resp.get("data", []):
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url = item.get("url")
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if url:
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urls.append(url)
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return urls
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def call_zimage(config: dict, prompt: str, model: str) -> list[str]:
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"""Call Z-Image (造相) image generation API (async task-based)."""
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base_url = config.get("base_url", "").rstrip("/")
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api_key = config.get("api_key", "")
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if not base_url or not api_key:
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raise RuntimeError("造相绘图配置缺少 base_url 或 api_key")
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if not model or model == "none":
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model = "Z-Image-Turbo"
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# Map model names
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model_map = {
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"Z-Image": "Tongyi-MAI/Z-Image",
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"Z-Image-Turbo": "Tongyi-MAI/Z-Image-Turbo",
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"Qwen-Image-Edit-2511": "Qwen/Qwen-Image-Edit-2511",
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}
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actual_model = model_map.get(model)
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if actual_model is None:
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raise RuntimeError(f"不支持的造相模型: {model}")
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body = {
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"model": actual_model,
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"prompt": prompt,
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"image_url": config.get("image_url", []),
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}
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}",
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"X-ModelScope-Async-Mode": "true",
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}
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# Step 1: create task
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resp = _http_post_json(f"{base_url}/v1/images/generations", body, headers, timeout=30)
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task_id = resp.get("task_id", "")
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if not task_id:
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raise RuntimeError("造相接口未返回 task_id")
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# Step 2: poll for result
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poll_headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {api_key}",
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"X-ModelScope-Task-Type": "image_generation",
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}
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deadline = time.time() + 15 * 60 # 15 minutes
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while time.time() < deadline:
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task_resp = _http_get_json(f"{base_url}/v1/tasks/{task_id}", poll_headers, timeout=30)
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status = task_resp.get("task_status", "")
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if status == "SUCCEED":
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images = task_resp.get("output_images", [])
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if images:
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return images
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raise RuntimeError("造相任务成功但未返回图片")
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if status == "FAILED":
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raise RuntimeError("造相绘图任务失败")
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time.sleep(5)
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raise RuntimeError("造相绘图任务超时")
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def call_openai(config: dict, prompt: str, model: str,
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negative_prompt: str, ratio: str, resolution: str) -> list[str]:
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"""Call OpenAI GPT Image API for text-to-image generation."""
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client = _openai_client(config)
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output_format = _openai_output_format(config)
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quality = str(config.get("quality", "auto") or "auto")
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moderation = str(config.get("moderation", "auto") or "auto")
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background = str(config.get("background", "auto") or "auto")
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if background == "transparent":
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background = "auto"
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kwargs = {
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"model": model or "gpt-image-2",
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"prompt": _openai_prompt(prompt, negative_prompt),
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"n": _coerce_int(config.get("n"), 1, 1, 10),
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"size": _openai_size(config, ratio, resolution),
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"quality": quality,
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"background": background,
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"moderation": moderation,
|
||
"output_format": output_format,
|
||
}
|
||
if output_format in {"jpeg", "webp"} and config.get("output_compression") is not None:
|
||
kwargs["output_compression"] = _coerce_int(config.get("output_compression"), 100, 0, 100)
|
||
|
||
response = client.images.generate(**kwargs)
|
||
return _openai_images_from_response(response, output_format)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Main
|
||
# ---------------------------------------------------------------------------
|
||
|
||
JIMENG_MODELS = {"jimeng-4.5", "jimeng-4.6", "jimeng-5.0"}
|
||
DOUBAO_MODELS = {"doubao-seedream-4.5", "doubao-seedream-4.0", "doubao-seedream-3.0-t2i", "doubao-seededit-3.0-i2i"}
|
||
ZIMAGE_MODELS = {"Z-Image", "Z-Image-Turbo", "Qwen-Image-Edit-2511"}
|
||
OPENAI_MODELS = {"gpt-image-2"}
|
||
|
||
|
||
def _parse_cli_params(argv: list[str]) -> dict[str, str]:
|
||
parser = argparse.ArgumentParser(add_help=False)
|
||
parser.add_argument("--prompt", default="")
|
||
parser.add_argument("--model", default="")
|
||
parser.add_argument("--negative_prompt", default="")
|
||
parser.add_argument("--ratio", default="")
|
||
parser.add_argument("--resolution", default="")
|
||
|
||
namespace, unknown = parser.parse_known_args(argv)
|
||
if unknown:
|
||
raise ValueError(f"存在不支持的参数: {' '.join(unknown)}")
|
||
|
||
return {
|
||
"prompt": namespace.prompt,
|
||
"model": namespace.model,
|
||
"negative_prompt": namespace.negative_prompt,
|
||
"ratio": namespace.ratio,
|
||
"resolution": namespace.resolution,
|
||
}
|
||
|
||
|
||
def main() -> int:
|
||
if len(sys.argv) < 2:
|
||
sys.stdout.write("缺少输入参数\n")
|
||
return 1
|
||
|
||
try:
|
||
params = _parse_cli_params(sys.argv[1:])
|
||
except ValueError as exc:
|
||
sys.stdout.write(f"参数格式错误: {exc}\n")
|
||
return 1
|
||
|
||
prompt = params.get("prompt", "").strip()
|
||
if not prompt:
|
||
sys.stdout.write("缺少画图提示词\n")
|
||
return 1
|
||
|
||
model = params.get("model", "").strip()
|
||
negative_prompt = params.get("negative_prompt", "").strip()
|
||
ratio = params.get("ratio", "").strip()
|
||
resolution = params.get("resolution", "").strip()
|
||
|
||
from_wx_id = os.environ.get("ROBOT_FROM_WX_ID", "").strip()
|
||
if not from_wx_id:
|
||
sys.stdout.write("环境变量 ROBOT_FROM_WX_ID 未配置\n")
|
||
return 1
|
||
|
||
# Connect to DB and load settings
|
||
try:
|
||
conn = _mysql_connect()
|
||
except Exception as exc:
|
||
sys.stdout.write(f"数据库连接失败: {exc}\n")
|
||
return 1
|
||
|
||
try:
|
||
enabled, settings_json = load_drawing_settings(conn, from_wx_id)
|
||
except Exception as exc:
|
||
conn.close()
|
||
sys.stdout.write(f"加载绘图配置失败: {exc}\n")
|
||
return 1
|
||
finally:
|
||
try:
|
||
conn.close()
|
||
except Exception:
|
||
pass
|
||
|
||
if not enabled:
|
||
sys.stdout.write("AI 绘图未开启\n")
|
||
return 0
|
||
|
||
# Default model
|
||
if not model or model == "none":
|
||
model = "jimeng-5.0"
|
||
|
||
# Route to correct API
|
||
try:
|
||
image_urls: list[str] = []
|
||
|
||
if model in JIMENG_MODELS:
|
||
jimeng_config = settings_json.get("JiMeng", {})
|
||
if not jimeng_config.get("enabled", False):
|
||
sys.stdout.write("即梦绘图未开启\n")
|
||
return 0
|
||
image_urls = call_jimeng(jimeng_config, prompt, model, negative_prompt, ratio, resolution)
|
||
|
||
elif model in DOUBAO_MODELS:
|
||
doubao_config = settings_json.get("DouBao", {})
|
||
if not doubao_config.get("enabled", False):
|
||
sys.stdout.write("豆包绘图未开启\n")
|
||
return 0
|
||
image_urls = call_doubao(doubao_config, prompt, model)
|
||
|
||
elif model in ZIMAGE_MODELS:
|
||
zimage_config = settings_json.get("Z-Image", {})
|
||
if not zimage_config.get("enabled", False):
|
||
sys.stdout.write("造相绘图未开启\n")
|
||
return 0
|
||
image_urls = call_zimage(zimage_config, prompt, model)
|
||
|
||
elif model in OPENAI_MODELS:
|
||
openai_config = settings_json.get("OpenAI", {})
|
||
if not openai_config.get("enabled", False):
|
||
sys.stdout.write("OpenAI 绘图未开启\n")
|
||
return 0
|
||
image_urls = call_openai(openai_config, prompt, model, negative_prompt, ratio, resolution)
|
||
|
||
else:
|
||
sys.stdout.write("不支持的 AI 图像模型\n")
|
||
return 1
|
||
|
||
except Exception as exc:
|
||
sys.stdout.write(f"调用绘图接口失败: {exc}\n")
|
||
return 1
|
||
|
||
if not image_urls:
|
||
sys.stdout.write("未生成任何图像\n")
|
||
return 1
|
||
|
||
# 通过客户端接口发送图片
|
||
client_port = os.environ.get("ROBOT_WECHAT_CLIENT_PORT", "").strip()
|
||
if not client_port:
|
||
_cleanup_openai_temp_files(image_urls)
|
||
sys.stdout.write("环境变量 ROBOT_WECHAT_CLIENT_PORT 未配置\n")
|
||
return 1
|
||
|
||
try:
|
||
_send_image_outputs(client_port, from_wx_id, image_urls)
|
||
sys.stdout.write("图片发送成功\n")
|
||
except Exception as exc:
|
||
sys.stdout.write(f"发送图片失败: {exc}\n")
|
||
return 1
|
||
finally:
|
||
_cleanup_openai_temp_files(image_urls)
|
||
|
||
return 0
|
||
|
||
|
||
if __name__ == "__main__":
|
||
try:
|
||
raise SystemExit(main())
|
||
except SystemExit:
|
||
raise
|
||
except Exception:
|
||
traceback.print_exc(file=sys.stdout)
|
||
raise SystemExit(1)
|