software copyright
This commit is contained in:
jiahong
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# -*- coding: utf-8 -*-
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"""
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自然写手写识别与AI分析引擎软件 V1.0
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版权所有 (C) 2026
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软件全称：自然写手写识别与AI分析引擎软件
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版本号：V1.0
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主启动文件 - FastAPI 服务入口
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负责服务初始化、路由注册、中间件配置
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"""
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from fastapi import FastAPI, Request, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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from contextlib import asynccontextmanager
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import uvicorn
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import logging
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import time
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from typing import Dict, Any
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# 导入各业务模块路由
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from api.ocr_api import router as ocr_router
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from api.math_api import router as math_router
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from api.stroke_order_api import router as stroke_order_router
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from api.essay_api import router as essay_router
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from service.model_manager import ModelManager
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from config.settings import Settings
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# 日志配置
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logging.basicConfig(
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level=logging.INFO,
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format="%(asctime)s [%(levelname)s] %(name)s: %(message)s"
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)
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logger = logging.getLogger("writech-ai-engine")
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# 全局配置
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settings = Settings()
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# 全局模型管理器实例
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model_manager = ModelManager(settings)
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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"""
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应用生命周期管理
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启动时加载所有AI模型到GPU/CPU内存
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关闭时释放模型资源
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"""
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logger.info("自然写AI引擎启动中，加载模型...")
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# 启动时加载所有模型
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await model_manager.load_all_models()
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logger.info("所有模型加载完成，服务就绪")
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yield
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# 关闭时释放资源
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logger.info("服务关闭中，释放模型资源...")
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model_manager.release_all_models()
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logger.info("模型资源已释放")
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# 创建 FastAPI 应用实例
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app = FastAPI(
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title="自然写手写识别与AI分析引擎",
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description="对智能点阵笔采集的笔迹数据进行OCR识别、数学列式识别、笔顺分析及AI智能批改",
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version="1.0.0",
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lifespan=lifespan
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)
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# 跨域中间件配置
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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@app.middleware("http")
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async def request_logging_middleware(request: Request, call_next):
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"""
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请求日志与性能监控中间件
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记录每个请求的处理时间、状态码、推理耗时
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"""
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start_time = time.time()
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request_id = request.headers.get("X-Request-ID", str(time.time()))
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# 输入数据大小校验（防恶意攻击，最大10MB）
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content_length = request.headers.get("content-length")
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if content_length and int(content_length) > 10 * 1024 * 1024:
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return JSONResponse(
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status_code=413,
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content={"code": 413, "msg": "请求数据过大，最大支持10MB", "data": None}
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)
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response = await call_next(request)
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# 记录请求处理时间
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process_time = time.time() - start_time
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response.headers["X-Process-Time"] = f"{process_time:.4f}"
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response.headers["X-Request-ID"] = request_id
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logger.info(
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f"{request.method} {request.url.path} "
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f"status={response.status_code} "
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f"time={process_time:.4f}s"
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)
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return response
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@app.middleware("http")
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async def mtls_authentication_middleware(request: Request, call_next):
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"""
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mTLS 双向认证中间件
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内部服务间通信需携带有效的客户端证书
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安全设计：
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- 服务鉴权：内部服务间 mTLS 双向认证
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- 请求校验：输入数据格式校验与大小限制（防恶意攻击）
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"""
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# 检查是否为内部服务调用
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client_cert = request.headers.get("X-Client-Cert")
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api_key = request.headers.get("X-API-Key")
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# 白名单路径不需要认证
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whitelist_paths = ["/health", "/docs", "/openapi.json"]
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if request.url.path in whitelist_paths:
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return await call_next(request)
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# 验证API Key或客户端证书
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if not api_key and not client_cert:
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return JSONResponse(
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status_code=401,
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content={"code": 401, "msg": "缺少认证凭据", "data": None}
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)
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if api_key and api_key != settings.api_key:
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return JSONResponse(
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status_code=403,
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content={"code": 403, "msg": "API Key无效", "data": None}
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)
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return await call_next(request)
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# 注册各业务路由
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app.include_router(ocr_router, prefix="/api/v1/ocr", tags=["OCR识别"])
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app.include_router(math_router, prefix="/api/v1/math", tags=["数学识别"])
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app.include_router(stroke_order_router, prefix="/api/v1/stroke-order", tags=["笔顺评分"])
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app.include_router(essay_router, prefix="/api/v1/essay", tags=["作文批改"])
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@app.get("/health")
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async def health_check():
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"""健康检查端点"""
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model_status = model_manager.get_all_status()
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return {
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"code": 200,
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"msg": "success",
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"data": {
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"status": "healthy",
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"models": model_status,
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"version": "1.0.0"
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}
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}
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@app.get("/api/v1/model/status")
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async def get_model_status():
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"""
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查询各模型加载状态与版本
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GET /api/v1/model/status
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"""
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status = model_manager.get_all_status()
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return {
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"code": 200,
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"msg": "success",
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"data": status
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}
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@app.exception_handler(HTTPException)
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async def http_exception_handler(request: Request, exc: HTTPException):
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"""统一HTTP异常处理"""
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return JSONResponse(
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status_code=exc.status_code,
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content={
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"code": exc.status_code,
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"msg": exc.detail,
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"data": None
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}
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)
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@app.exception_handler(Exception)
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async def general_exception_handler(request: Request, exc: Exception):
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"""统一异常处理"""
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logger.error(f"未处理异常: {str(exc)}", exc_info=True)
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return JSONResponse(
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status_code=500,
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content={
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"code": 500,
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"msg": "AI引擎内部错误",
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"data": None
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}
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)
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if __name__ == "__main__":
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uvicorn.run(
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"main:app",
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host="0.0.0.0",
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port=8001,
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workers=4,
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log_level="info"
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)
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