620 lines
26 KiB
Python
620 lines
26 KiB
Python
"""
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开版订单 AI 解析服务
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Flask + pydantic-ai + 豆包（方舟 OpenAI 兼容协议 /chat/completions）
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功能：
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1. 代理企微消息接口（解决 CORS）
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2. AI 解析消息内容 → 开版订单字段
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3. 字典枚举值缓存（启动时拉取，每日刷新）
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4. 布料/客户智能匹配：搜索 → 不存在则自动新增
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启动：
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python app.py
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"""
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import os
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import json
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import logging
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import threading
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import time
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from typing import Optional
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import requests
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from datetime import datetime, timedelta
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from flask import Flask, request, jsonify
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from flask_cors import CORS
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from dotenv import load_dotenv
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from pydantic import BaseModel, Field, field_validator
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from pydantic_ai import Agent
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.providers.openai import OpenAIProvider
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load_dotenv()
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# ─── 日志 ─────────────────────────────────────────────────────────────────────
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(message)s",
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)
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logger = logging.getLogger(__name__)
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# ─── Flask ────────────────────────────────────────────────────────────────────
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app = Flask(__name__)
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CORS(app, origins="*")
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# ─── 豆包 AI 配置（通过 pydantic-ai + OpenAI 兼容协议调用方舟）─────────────────
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AI_API_KEY = os.getenv("AI_API_KEY", "")
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AI_BASE_URL = os.getenv("AI_BASE_URL", "https://ark.cn-beijing.volces.com/api/v3")
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AI_MODEL = os.getenv("AI_MODEL", "doubao-seed-2-0-mini-260428")
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# ─── 企微消息服务配置 ─────────────────────────────────────────────────────────
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WECHAT_API_BASE = "https://test.ruicaiyinhua.online"
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WECHAT_AUTH = "hophopkk"
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WORK_ORDER_ROOM = "wrW_mXZwAAaGwXBWvzbiAfEjy3ZM7ong"
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# ─── ERP 后端配置 ─────────────────────────────────────────────────────────────
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ERP_BASE_URL = os.getenv("ERP_BASE_URL", "https://yuwenerp.yuwen.cloud")
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ERP_USERNAME = os.getenv("ERP_USERNAME", "jimi")
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ERP_PASSWORD = os.getenv("ERP_PASSWORD", "zuowei1216")
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# 需要全量缓存的小字典分组（用于 AI prompt 约束）
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SMALL_DICT_GROUPS = [
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"起版情况", "紧急程度", "做货方式", "开版方式",
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"地区", "画图评级", "调色评级", "套样评级",
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"难度评级", "布源", "幅宽",
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]
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# ─── ERP Token 管理 ──────────────────────────────────────────────────────────
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class ErpAuth:
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def __init__(self):
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self.access_token = None
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self.refresh_token = None
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self.expires_at = 0
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self._lock = threading.Lock()
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def login(self):
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with self._lock:
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try:
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r = requests.post(
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f"{ERP_BASE_URL}/api/auth/login/",
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json={"username": ERP_USERNAME, "password": ERP_PASSWORD},
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timeout=15,
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)
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r.raise_for_status()
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data = r.json()
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self.access_token = data["access"]
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self.refresh_token = data.get("refresh")
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# JWT 默认 7 天，提前 1 小时刷新
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self.expires_at = time.time() + 6 * 24 * 3600
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logger.info("ERP 登录成功")
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return self.access_token
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except Exception as e:
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logger.error(f"ERP 登录失败: {e}")
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raise
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def get_token(self):
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if not self.access_token or time.time() >= self.expires_at:
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return self.login()
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return self.access_token
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def headers(self):
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return {"Authorization": f"Bearer {self.get_token()}"}
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erp_auth = ErpAuth()
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def erp_get(path: str, params: dict = None):
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"""GET ERP 接口，自动处理 401"""
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url = f"{ERP_BASE_URL}{path}"
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r = requests.get(url, headers=erp_auth.headers(), params=params or {}, timeout=30)
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if r.status_code == 401:
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erp_auth.login()
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r = requests.get(url, headers=erp_auth.headers(), params=params or {}, timeout=30)
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r.raise_for_status()
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return r.json()
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def erp_post(path: str, data: dict):
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"""POST ERP 接口"""
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url = f"{ERP_BASE_URL}{path}"
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r = requests.post(url, headers={**erp_auth.headers(), "Content-Type": "application/json"},
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json=data, timeout=30)
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if r.status_code == 401:
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erp_auth.login()
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r = requests.post(url, headers={**erp_auth.headers(), "Content-Type": "application/json"},
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json=data, timeout=30)
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r.raise_for_status()
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return r.json()
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# ─── 字典缓存 ─────────────────────────────────────────────────────────────────
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class DictCache:
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"""小字典分组的内存缓存，启动时加载，每日刷新"""
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def __init__(self):
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self.cache = {} # {group: [name1, name2, ...]}
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self.processes = [] # [{id, name, description}] 关联流程
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self.last_loaded = 0
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self.ttl = 24 * 3600 # 24 小时
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self._lock = threading.Lock()
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def load(self):
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with self._lock:
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logger.info("加载字典缓存...")
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new_cache = {}
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for group in SMALL_DICT_GROUPS:
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try:
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data = erp_get("/api/backend/quick-inputs/", {"group": group, "limit": 200})
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items = data.get("results", [])
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new_cache[group] = [it["name"] for it in items if it.get("name")]
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logger.info(f" [{group}] {len(new_cache[group])} 项")
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except Exception as e:
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logger.warning(f" [{group}] 加载失败: {e}")
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new_cache[group] = self.cache.get(group, [])
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self.cache = new_cache
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# 加载关联流程
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try:
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data = erp_get("/api/v1/stateflow/processes/", {"limit": 100})
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self.processes = [
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{"id": p["id"], "name": p["name"], "description": p.get("description", "")}
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for p in data.get("results", [])
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]
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logger.info(f" [关联流程] {len(self.processes)} 项")
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except Exception as e:
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logger.warning(f" [关联流程] 加载失败: {e}")
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self.last_loaded = time.time()
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logger.info("字典缓存加载完成")
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def get(self, group: str):
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if time.time() - self.last_loaded > self.ttl:
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try:
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self.load()
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except Exception:
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pass
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return self.cache.get(group, [])
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def get_processes(self):
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return self.processes
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def find_process_id(self, name: str):
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"""根据名称查找流程 ID（精确 + 模糊）"""
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if not name:
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return None
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for p in self.processes:
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if p["name"] == name:
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return p["id"]
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# 模糊匹配
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for p in self.processes:
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if name in p["name"] or p["name"] in name:
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return p["id"]
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return None
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def all(self):
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return dict(self.cache)
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dict_cache = DictCache()
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def background_init():
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"""后台线程：登录 ERP + 加载字典缓存（不阻塞 Flask 启动）"""
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try:
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erp_auth.login()
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dict_cache.load()
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except Exception as e:
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logger.error(f"初始化失败: {e}")
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# ─── 布料/客户 智能匹配 ───────────────────────────────────────────────────────
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def find_or_create_fabric(name: str) -> dict:
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"""
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精确匹配优先；找不到则新增（不做模糊匹配，避免错配）
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返回 {id, name, exists, created}
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"""
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if not name:
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return None
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name = name.strip()
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try:
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# 精确搜索（按 name 参数过滤）
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data = erp_get("/api/backend/quick-inputs/", {"group": "布料", "name": name, "limit": 20})
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items = data.get("results", [])
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# 只接受精确匹配
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exact = next((it for it in items if it.get("name") == name), None)
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if exact:
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return {"id": exact["id"], "name": exact["name"], "exists": True, "created": False}
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# 找不到精确 → 新增
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new_item = erp_post("/api/backend/quick-inputs/", {
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"group": "布料",
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"name": name,
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"value": name,
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})
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logger.info(f"新增布料: {name} (id={new_item.get('id')})")
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return {"id": new_item.get("id"), "name": name, "exists": False, "created": True}
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except Exception as e:
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logger.error(f"匹配/新增布料失败 [{name}]: {e}")
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return None
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def find_customer(name: str) -> dict:
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"""
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搜索客户：精确匹配优先；模糊匹配只在精确未命中时使用，并标记 fuzzy
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返回 {id, name, exists, ...}
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"""
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if not name:
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return None
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name = name.strip()
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try:
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data = erp_get("/api/backend/customers/", {"name": name, "limit": 10})
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items = data.get("results", []) if isinstance(data, dict) else data
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if not items:
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return {"id": None, "name": name, "exists": False}
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# 精确匹配
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exact = next((it for it in items if it.get("name") == name), None)
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if exact:
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return {
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"id": exact["id"], "name": exact["name"], "exists": True,
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"area": exact.get("area"),
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"visible_employees": exact.get("visible_employees"),
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}
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# 没有精确匹配 → 返回未找到（让前端决定是否模糊建议）
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return {
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"id": None, "name": name, "exists": False,
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"suggestions": [{"id": it["id"], "name": it["name"]} for it in items[:5]],
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}
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except Exception as e:
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logger.error(f"搜索客户失败 [{name}]: {e}")
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return None
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# ─── AI 解析（带枚举约束）────────────────────────────────────────────────────
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def build_system_prompt() -> str:
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"""根据缓存的字典动态构建 prompt，让 AI 严格选枚举"""
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def opts(group):
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vals = dict_cache.get(group)
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return "/".join(vals) if vals else "（待加载）"
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# 流程选项（带描述）
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process_lines = []
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for p in dict_cache.get_processes():
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desc = f"({p['description']})" if p.get("description") else ""
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process_lines.append(f" - {p['name']}{desc}")
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process_text = "\n".join(process_lines) if process_lines else "（待加载）"
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return f"""你是一个印染厂开版订单信息提取助手。
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用户会发给你一条企业微信群聊消息，消息内容是跟单员发给设计师的工单指令。
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你需要从消息中提取以下字段，返回 JSON 格式。
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【字段说明（**必须严格使用以下枚举值**，不在列表中的返回 null）】
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- customer_name: 客户名称（自由文本，消息开头通常是客户名，如"華鑫"、"林庆福"、"糖糖"）
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- plate_type: 起版情况，**只能选**：{opts('起版情况')}
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映射规则：图片起版/样衣起版/图片开版/样衣开版/找图开发/起版 → 首版
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修改/加色/调色/调整/改版 → 修改单
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套大货/套纸样/套样/重新套 → 复版/开版
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- urgency_level: 紧急程度，**只能选**：{opts('紧急程度')}
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映射规则：消息有"加急"→ 加急；有"特急"→ 特急；"加急已下单"等 → 紧急已下单；否则 → 正常
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- production_method: 做货方式，**只能选**：{opts('做货方式')}
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- plate_method: 开版方式，**只能选**：{opts('开版方式')}
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映射规则：消息有"图片起版/图片开版"→ 图片开版
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消息有"样衣起版/样板起版/样衣开版"→ 样衣开版
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消息有"文件开版"→ 文件开版
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其他原始词如"开发"、"开板"→ 开发
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- area: 客户区域，**只能选**：{opts('地区')}（消息里很少提及，多数返回 null）
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- fabric: 面料名称（自由文本，完整保留克重和布料类型，如"140g四面弹"、"120克本白四面弹"、"180克牛奶丝"、"泡泡皱"）
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- width: 面料幅宽，**只能选**：{opts('幅宽')}（如"幅宽151"匹配"1.51"，"120"匹配"120"，找不到则 null）
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- fabric_source: 面料来源，**只能选**：{opts('布源')}
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- drawing_rating: 画图评级，**只能选**：{opts('画图评级')}（消息里几乎不会有，多返 null）
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- color_matching_rating: 调色评级，**只能选**：{opts('调色评级')}（多返 null）
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- sample_rating: 套样评级，**只能选**：{opts('套样评级')}（多返 null）
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- difficulty_rating: 难度评级，**只能选**：{opts('难度评级')}（多返 null）
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- process_name: 关联流程，**只能选**以下名称之一（直接返回流程名字符串，不要带括号注释）：
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{process_text}
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选择规则：根据 plate_type 和操作内容综合判断
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- 套大货/套纸样 → "开版(套米样)" 或 "开版(套大货)"
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- 加色/调色 → "开版(配色)" 或 "开版(绘图调色)"
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- P图/抠图 → "开版(P图)"
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- 描图/找图开发 → "开版(描图开版)" 或 "开版(绘图调色套样)"
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- 不确定时返回 null（让用户手动选）
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- plate_notes: 打版备注（自由文本，把消息中的具体要求、注意事项原文保留：克重、颜色要求、特殊工艺等）
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- style_name: 款号名称（如"NSY1090"、"G67单600-20款"、"6576改"等款号，没有则 null）
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- original_design_code: 原订单ID（如果消息提到要"套大货/套纸样/修改"等，且消息中有 5-7 位的纯数字编号，那就是原订单号；否则 null）
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- merchandiser_name: 跟单员（直接返回 sender 字段的原值）
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【重要规则】
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1. 枚举字段（plate_type、urgency_level、production_method、plate_method、area、width、fabric_source、各评级、process_name）**必须**从给定选项里选，不能自创；判断不出就返回 null
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2. 自由文本字段（customer_name、fabric、plate_notes、style_name、original_design_code）保持原文
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3. 一律返回 JSON 对象，不要任何其它文字"""
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class PlateOrder(BaseModel):
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"""开版订单解析结果。所有字段均可为 null（无法判断时返回 None）。"""
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customer_name: Optional[str] = Field(None, description="客户名称（自由文本，消息开头）")
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plate_type: Optional[str] = Field(None, description="起版情况枚举")
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urgency_level: Optional[str] = Field(None, description="紧急程度枚举")
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production_method: Optional[str] = Field(None, description="做货方式枚举")
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plate_method: Optional[str] = Field(None, description="开版方式枚举")
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area: Optional[str] = Field(None, description="客户区域枚举")
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fabric: Optional[str] = Field(None, description="面料名称（自由文本，保留克重与布类）")
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width: Optional[str] = Field(None, description="面料幅宽枚举")
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fabric_source: Optional[str] = Field(None, description="面料来源枚举")
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drawing_rating: Optional[str] = Field(None, description="画图评级枚举")
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color_matching_rating: Optional[str] = Field(None, description="调色评级枚举")
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sample_rating: Optional[str] = Field(None, description="套样评级枚举")
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difficulty_rating: Optional[str] = Field(None, description="难度评级枚举")
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process_name: Optional[str] = Field(None, description="关联流程名称")
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plate_notes: Optional[str] = Field(None, description="打版备注（自由文本，原文保留）")
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style_name: Optional[str] = Field(None, description="款号名称")
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original_design_code: Optional[str] = Field(None, description="原订单ID（5-7位纯数字）")
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merchandiser_name: Optional[str] = Field(None, description="跟单员（取 sender 原值）")
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@field_validator("*", mode="before")
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@classmethod
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def _normalize_empty(cls, v):
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"""模型有时会填字符串 'null'/'none'/空串，统一归一为 None"""
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if isinstance(v, str) and v.strip().lower() in ("", "null", "none", "n/a", "无"):
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return None
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return v
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# pydantic-ai Agent：通过 OpenAI 兼容协议调用方舟（豆包）
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_provider = OpenAIProvider(base_url=AI_BASE_URL, api_key=AI_API_KEY)
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_model = OpenAIChatModel(AI_MODEL, provider=_provider)
|
||
plate_agent = Agent(
|
||
_model,
|
||
output_type=PlateOrder,
|
||
model_settings={"temperature": 0.1, "max_tokens": 4096},
|
||
retries=2,
|
||
)
|
||
|
||
|
||
@plate_agent.system_prompt
|
||
def _dynamic_system_prompt() -> str:
|
||
"""系统提示词依赖运行时加载的字典缓存，故每次调用动态生成"""
|
||
return build_system_prompt()
|
||
|
||
|
||
def call_doubao(user_text: str) -> dict:
|
||
"""通过 pydantic-ai 调用豆包，返回结构化结果 dict"""
|
||
result = plate_agent.run_sync(user_text)
|
||
return result.output.model_dump()
|
||
|
||
|
||
# ─── 路由 ─────────────────────────────────────────────────────────────────────
|
||
|
||
@app.route("/health", methods=["GET"])
|
||
def health():
|
||
return jsonify({
|
||
"status": "ok",
|
||
"model": AI_MODEL,
|
||
"dict_cache_loaded": bool(dict_cache.cache),
|
||
"dict_groups": list(dict_cache.cache.keys()),
|
||
"erp_logged_in": bool(erp_auth.access_token),
|
||
})
|
||
|
||
|
||
@app.route("/api/v1/dict-cache/", methods=["GET"])
|
||
def get_dict_cache():
|
||
"""查看当前缓存（调试用）"""
|
||
return jsonify(dict_cache.all())
|
||
|
||
|
||
@app.route("/api/v1/dict-cache/refresh/", methods=["POST"])
|
||
def refresh_dict_cache():
|
||
"""手动刷新字典缓存"""
|
||
try:
|
||
dict_cache.load()
|
||
return jsonify({"status": "ok", "groups": list(dict_cache.cache.keys())})
|
||
except Exception as e:
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
|
||
# ─── 企微消息代理 ─────────────────────────────────────────────────────────────
|
||
|
||
@app.route("/api/v1/wechat/messages/", methods=["GET"])
|
||
def proxy_wechat_messages():
|
||
limit = request.args.get("limit", "30")
|
||
offset = request.args.get("offset", "0")
|
||
room_id = request.args.get("room_id", WORK_ORDER_ROOM)
|
||
with_image = request.args.get("with_image", "false")
|
||
|
||
try:
|
||
r = requests.get(
|
||
f"{WECHAT_API_BASE}/messages",
|
||
headers={"Authorization": WECHAT_AUTH},
|
||
params={"room_id": room_id, "limit": limit, "offset": offset, "with_image": with_image},
|
||
timeout=60,
|
||
)
|
||
r.raise_for_status()
|
||
return jsonify(r.json())
|
||
except requests.Timeout:
|
||
return jsonify({"error": "企微接口超时"}), 504
|
||
except Exception as e:
|
||
logger.error(f"代理请求失败: {e}")
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
|
||
@app.route("/api/v1/wechat/messages/<path:msg_id>/with-image", methods=["GET"])
|
||
def proxy_get_message_with_image(msg_id):
|
||
"""
|
||
代理 GET /messages/:msg_id?with_image=true
|
||
企微服务直接支持按 msg_id 查单条消息，速度极快（< 0.1s）
|
||
"""
|
||
try:
|
||
r = requests.get(
|
||
f"{WECHAT_API_BASE}/messages/{msg_id}",
|
||
headers={"Authorization": WECHAT_AUTH},
|
||
params={"with_image": "true"},
|
||
timeout=30,
|
||
)
|
||
r.raise_for_status()
|
||
return jsonify(r.json())
|
||
except requests.Timeout:
|
||
return jsonify({"error": "企微接口超时"}), 504
|
||
except requests.HTTPError as e:
|
||
return jsonify({"error": f"企微接口错误 {e.response.status_code}"}), e.response.status_code
|
||
except Exception as e:
|
||
logger.error(f"获取消息图片失败 msg_id={msg_id}: {e}")
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
|
||
@app.route("/api/v1/wechat/messages/<path:msg_id>/tags", methods=["PUT"])
|
||
def proxy_update_tags(msg_id):
|
||
try:
|
||
body = request.get_data()
|
||
r = requests.put(
|
||
f"{WECHAT_API_BASE}/messages/{msg_id}/tags",
|
||
headers={"Authorization": WECHAT_AUTH, "Content-Type": "application/json"},
|
||
data=body,
|
||
timeout=10,
|
||
)
|
||
return jsonify(r.json()), r.status_code
|
||
except Exception as e:
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
|
||
@app.route("/api/v1/wechat/messages/<path:msg_id>/parse-record/", methods=["POST"])
|
||
def record_parse(msg_id):
|
||
try:
|
||
body = request.get_json(silent=True) or {}
|
||
old_tags = body.get("old_tags") or {}
|
||
new_count = int(old_tags.get("parse_count", 0)) + 1
|
||
new_tags = {
|
||
**old_tags,
|
||
"parse_count": new_count,
|
||
"last_parsed_at": datetime.utcnow().isoformat() + "Z",
|
||
"last_parsed_by": body.get("user", "anonymous"),
|
||
}
|
||
r = requests.put(
|
||
f"{WECHAT_API_BASE}/messages/{msg_id}/tags",
|
||
headers={"Authorization": WECHAT_AUTH, "Content-Type": "application/json"},
|
||
json=new_tags,
|
||
timeout=10,
|
||
)
|
||
r.raise_for_status()
|
||
return jsonify(r.json())
|
||
except Exception as e:
|
||
logger.error(f"记录解析失败 msg_id={msg_id}: {e}")
|
||
return jsonify({"error": str(e)}), 500
|
||
|
||
|
||
# ─── 主接口：解析开版订单 ────────────────────────────────────────────────────
|
||
|
||
@app.route("/api/v1/ai/parse-plate-order/", methods=["POST"])
|
||
def parse_plate_order():
|
||
"""
|
||
解析企业微信消息 → 开版订单字段
|
||
自动匹配/新增布料、搜索客户
|
||
|
||
请求体：
|
||
{
|
||
"text": "消息文字",
|
||
"sender": "发送者",
|
||
"has_images": true,
|
||
"image_count": 2
|
||
}
|
||
"""
|
||
data = request.get_json(silent=True) or {}
|
||
text = data.get("text", "").strip()
|
||
sender = data.get("sender", "")
|
||
has_images = data.get("has_images", False)
|
||
image_count = data.get("image_count", 0)
|
||
|
||
if not text and not has_images:
|
||
return jsonify({"error": "消息内容为空"}), 400
|
||
|
||
user_content = f"发送者：{sender}\n"
|
||
if has_images:
|
||
user_content += f"（消息含 {image_count} 张图片）\n"
|
||
user_content += f"消息内容：{text or '（纯图片消息，无文字）'}"
|
||
|
||
logger.info(f"解析请求 | sender={sender} | text={text[:80]}")
|
||
|
||
try:
|
||
result = call_doubao(user_content)
|
||
except Exception as e:
|
||
logger.error(f"AI 解析失败: {e}")
|
||
return jsonify({"error": f"AI 解析失败: {e}"}), 500
|
||
|
||
# 清理空值
|
||
cleaned = {k: v for k, v in result.items() if v not in (None, "", "null")}
|
||
|
||
# 跟单员兜底
|
||
if "merchandiser_name" not in cleaned and sender:
|
||
cleaned["merchandiser_name"] = sender
|
||
|
||
# 校验枚举字段（不在缓存里就剔除）
|
||
enum_fields = {
|
||
"plate_type": "起版情况",
|
||
"urgency_level": "紧急程度",
|
||
"production_method": "做货方式",
|
||
"plate_method": "开版方式",
|
||
"area": "地区",
|
||
"width": "幅宽",
|
||
"fabric_source": "布源",
|
||
"drawing_rating": "画图评级",
|
||
"color_matching_rating": "调色评级",
|
||
"sample_rating": "套样评级",
|
||
"difficulty_rating": "难度评级",
|
||
}
|
||
for field, group in enum_fields.items():
|
||
if field in cleaned:
|
||
valid = dict_cache.get(group)
|
||
if valid and cleaned[field] not in valid:
|
||
logger.warning(f"AI 返回 {field}={cleaned[field]} 不在枚举中，剔除")
|
||
cleaned.pop(field, None)
|
||
|
||
# 处理布料：搜索 → 不存在则新增
|
||
if cleaned.get("fabric"):
|
||
match = find_or_create_fabric(cleaned["fabric"])
|
||
if match:
|
||
cleaned["fabric"] = match["name"] # 用规范化的名称
|
||
cleaned["fabric_id"] = match["id"]
|
||
cleaned["fabric_created"] = match.get("created", False)
|
||
|
||
# 处理客户：精确匹配优先；找不到时附带模糊建议供前端选择
|
||
if cleaned.get("customer_name"):
|
||
match = find_customer(cleaned["customer_name"])
|
||
if match and match.get("id"):
|
||
cleaned["customer_id"] = match["id"]
|
||
cleaned["customer_name"] = match["name"]
|
||
if match.get("area"):
|
||
cleaned["customer_area"] = match["area"]
|
||
if match.get("visible_employees"):
|
||
cleaned["customer_visible_employees"] = match["visible_employees"]
|
||
else:
|
||
cleaned["customer_not_found"] = True
|
||
if match and match.get("suggestions"):
|
||
cleaned["customer_suggestions"] = match["suggestions"]
|
||
|
||
# 处理关联流程：根据 process_name 查找 ID
|
||
if cleaned.get("process_name"):
|
||
pid = dict_cache.find_process_id(cleaned["process_name"])
|
||
if pid:
|
||
cleaned["process_id"] = pid
|
||
else:
|
||
logger.warning(f"未找到流程：{cleaned['process_name']}")
|
||
cleaned.pop("process_name", None)
|
||
|
||
logger.info(f"解析结果：{cleaned}")
|
||
return jsonify(cleaned)
|
||
|
||
|
||
# ─── 入口 ─────────────────────────────────────────────────────────────────────
|
||
|
||
if __name__ == "__main__":
|
||
port = int(os.getenv("PORT", 5050))
|
||
debug = os.getenv("DEBUG", "false").lower() == "true"
|
||
|
||
# 后台线程初始化（不阻塞 Flask 启动）
|
||
threading.Thread(target=background_init, daemon=True).start()
|
||
|
||
logger.info(f"启动 plate-ai-service | port={port} | model={AI_MODEL}")
|
||
app.run(host="0.0.0.0", port=port, debug=debug, use_reloader=False)
|