1556 lines
62 KiB
Python
1556 lines
62 KiB
Python
from fastapi import FastAPI, Depends, HTTPException, status, Body, UploadFile, File
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
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from fastapi.staticfiles import StaticFiles
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from jose import JWTError, jwt
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from passlib.context import CryptContext
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from datetime import datetime, timedelta
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from pydantic import BaseModel
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from typing import List, Optional, Dict, Any
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import json
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import requests
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from bs4 import BeautifulSoup
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from fastapi.responses import JSONResponse
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import os
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import pickle
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import random
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import urllib.parse
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import uuid
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import shutil
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from sqlalchemy.orm import Session
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import base64
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import docx # 用于解析Word文档
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import re # 用于正则表达式匹配
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# 导入数据库相关模块
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from database import get_db, engine
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import models
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import schemas
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import crud
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# JWT配置
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SECRET_KEY = "09d25e094faa6ca2556c818166b7a9563b93f7099f6f0f4caa6cf63b88e8d3e7"
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ALGORITHM = "HS256"
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ACCESS_TOKEN_EXPIRE_MINUTES = 60
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# 创建FastAPI应用
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app = FastAPI(title="科研评估管理系统API")
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# 配置CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # 在生产环境应该设置为具体的前端域名
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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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# 创建静态文件目录
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STATIC_DIR = "static"
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IMAGES_DIR = os.path.join(STATIC_DIR, "images")
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os.makedirs(IMAGES_DIR, exist_ok=True)
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# 挂载静态文件目录
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app.mount("/static", StaticFiles(directory=STATIC_DIR), name="static")
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# 密码处理上下文
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pwd_context = CryptContext(schemes=["bcrypt"], deprecated="auto")
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oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")
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# 数据模型
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class User(BaseModel):
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username: str
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email: Optional[str] = None
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full_name: Optional[str] = None
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disabled: Optional[bool] = None
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class UserInDB(User):
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hashed_password: str
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class Token(BaseModel):
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access_token: str
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token_type: str
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class TokenData(BaseModel):
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username: str
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class Talent(BaseModel):
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id: str
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idcode: Optional[str] = None
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name: str
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gender: Optional[str] = None
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birthDate: Optional[str] = None
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title: Optional[str] = None
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position: Optional[str] = None
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education: Optional[str] = None
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address: Optional[str] = None
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academicDirection: Optional[str] = None
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talentPlan: Optional[str] = None
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officeLocation: Optional[str] = None
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email: Optional[str] = None
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phone: Optional[str] = None
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tutorType: Optional[str] = None
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papers: Optional[str] = None
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projects: Optional[str] = None
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photo: Optional[str] = None
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eduWorkHistory: Optional[str] = None
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researchDirection: Optional[str] = None
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recentProjects: Optional[str] = None
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representativePapers: Optional[str] = None
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patents: Optional[str] = None
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evaluationData: Optional[List[float]] = None
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class Lab(BaseModel):
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id: str
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idcode: Optional[str] = None
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name: str
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personnel: Optional[str] = None
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nationalProjects: Optional[str] = None
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otherProjects: Optional[str] = None
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achievements: Optional[str] = None
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labAchievements: Optional[str] = None
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image: Optional[str] = None
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score: Optional[int] = None
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evaluationData: Optional[List[float]] = None
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class DashboardData(BaseModel):
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paperCount: int
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patentCount: int
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highImpactPapers: int
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keyProjects: int
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fundingAmount: str
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researcherStats: dict
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newsData: List[dict]
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# URL抓取模型
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class ScrapeRequest(BaseModel):
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url: str
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# 保存评估数据模型
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class SaveDataRequest(BaseModel):
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data_type: str # "talent" 或 "lab"
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data: dict
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# 文件存储路径
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DATA_DIR = "data"
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TALENT_DATA_FILE = os.path.join(DATA_DIR, "talents.pkl")
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LAB_DATA_FILE = os.path.join(DATA_DIR, "labs.pkl")
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# 确保数据目录存在
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os.makedirs(DATA_DIR, exist_ok=True)
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# 从文件加载数据
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def load_data(file_path, default_data):
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if os.path.exists(file_path):
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try:
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with open(file_path, 'rb') as f:
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return pickle.load(f)
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except Exception as e:
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print(f"Error loading data from {file_path}: {e}")
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return default_data
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# 保存数据到文件
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def save_data(file_path, data):
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try:
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with open(file_path, 'wb') as f:
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pickle.dump(data, f)
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return True
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except Exception as e:
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print(f"Error saving data to {file_path}: {e}")
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return False
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# 加载已保存的数据
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talents = load_data(TALENT_DATA_FILE, [])
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labs = load_data(LAB_DATA_FILE, [])
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# 模拟数据库
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fake_users_db = {
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"admin": {
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"username": "admin",
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"full_name": "管理员",
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"email": "admin@example.com",
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"hashed_password": pwd_context.hash("123456"),
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"disabled": False,
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}
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}
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# 模拟人才数据
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talents = [
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{
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"id": "BLG45187",
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"name": "张三",
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"gender": "男",
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"birthDate": "1980.01.01",
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"title": "教授",
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"position": "副院长",
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"education": "博士",
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"address": "北京市海淀区中关村南大街5号",
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"academicDirection": "人工智能",
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"talentPlan": "国家杰出青年",
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"officeLocation": "理工大厦A座201",
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"email": "zhangsan@example.com",
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"phone": "13800138000",
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"tutorType": "博士生导师",
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"papers": "15篇",
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"projects": "23项",
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"photo": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='100%25' height='100%25' viewBox='0 0 100 100'%3E%3Crect width='100' height='100' fill='%234986ff' opacity='0.2'/%3E%3Ccircle cx='50' cy='40' r='20' fill='%234986ff' opacity='0.5'/%3E%3Cpath d='M30,80 Q50,60 70,80 L70,100 L30,100 Z' fill='%234986ff' opacity='0.5'/%3E%3C/svg%3E",
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"eduWorkHistory": "2005年毕业于北京理工大学，获博士学位\n2005-2010年在清华大学从事博士后研究\n2010年至今在北京理工大学任教",
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"researchDirection": "机器学习、深度学习、计算机视觉",
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"recentProjects": "国家自然科学基金重点项目：深度学习在计算机视觉中的应用研究\n国家重点研发计划项目：智能机器人视觉感知系统研发",
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"representativePapers": "[1] 机器学习在自动化控制中的应用\n[2] 深度强化学习研究进展",
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"patents": "一种基于深度学习的图像识别方法\n一种智能控制系统及其控制方法",
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"evaluationData": [85, 90, 78, 82, 76, 88]
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},
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{
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"id": "BLG45188",
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"name": "李四",
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"gender": "男",
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"birthDate": "1982.05.15",
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"title": "副教授",
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"position": "系主任",
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"education": "博士",
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"address": "北京市海淀区中关村南大街5号",
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"academicDirection": "材料科学",
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"talentPlan": "青年千人计划",
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"officeLocation": "理工大厦B座305",
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"email": "lisi@example.com",
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"phone": "13900139000",
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"tutorType": "硕士生导师",
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"papers": "12篇",
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"projects": "18项",
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"photo": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='100%25' height='100%25' viewBox='0 0 100 100'%3E%3Crect width='100' height='100' fill='%234986ff' opacity='0.2'/%3E%3Ccircle cx='50' cy='40' r='20' fill='%234986ff' opacity='0.5'/%3E%3Cpath d='M30,80 Q50,60 70,80 L70,100 L30,100 Z' fill='%234986ff' opacity='0.5'/%3E%3C/svg%3E",
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"eduWorkHistory": "2008年毕业于中国科学院，获博士学位\n2008-2013年在美国麻省理工学院从事博士后研究\n2013年至今在北京理工大学任教",
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"researchDirection": "新能源材料、纳米材料、催化材料",
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"recentProjects": "国家自然科学基金面上项目：高性能催化材料的设计与合成\n企业合作项目：新型锂电池材料开发",
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||
"representativePapers": "[1] 高性能催化材料的设计与合成\n[2] 纳米材料在新能源领域的应用",
|
||
"patents": "一种高效催化材料的制备方法\n一种纳米材料的合成工艺",
|
||
"evaluationData": [92, 85, 76, 89, 78, 82]
|
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}
|
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]
|
||
|
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# 模拟工程研究中心数据
|
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labs = [
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{
|
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"id": "BLG45187",
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"name": "基础力学教学实验中心",
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"personnel": "30人",
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"nationalProjects": "10项",
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"otherProjects": "46项",
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"achievements": "28项",
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"labAchievements": "中心面向全校本科生开设力学类实验课程，获国家级教学成果奖1项，省部级教学成果奖2项。开发研制教学实验装置20余台套，获得国家专利15项。建设国家级精品课程2门，国家级精品资源共享课1门。",
|
||
"image": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='100%25' height='100%25' viewBox='0 0 300 200'%3E%3Crect width='300' height='200' fill='%234986ff' opacity='0.2'/%3E%3Crect x='20' y='40' width='260' height='120' fill='%234986ff' opacity='0.3'/%3E%3Ccircle cx='70' cy='70' r='20' fill='%234986ff' opacity='0.5'/%3E%3Crect x='120' y='50' width='140' height='40' fill='%234986ff' opacity='0.4'/%3E%3Crect x='120' y='110' width='140' height='30' fill='%234986ff' opacity='0.4'/%3E%3C/svg%3E",
|
||
"score": 98,
|
||
"evaluationData": [85, 90, 78, 82, 76, 88]
|
||
},
|
||
{
|
||
"id": "BLG45188",
|
||
"name": "高性能计算工程研究中心",
|
||
"personnel": "25人",
|
||
"nationalProjects": "8项",
|
||
"otherProjects": "37项",
|
||
"achievements": "22项",
|
||
"labAchievements": "工程研究中心围绕高性能计算、并行计算、分布式系统等方向开展研究。建有超级计算机集群，计算能力达到100 TFLOPS。在国际顶级期刊和会议发表论文50余篇，获国家发明专利12项。与多家知名IT企业建立了产学研合作关系。",
|
||
"image": "data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='100%25' height='100%25' viewBox='0 0 300 200'%3E%3Crect width='300' height='200' fill='%234986ff' opacity='0.1'/%3E%3Cpath d='M60,20 L240,20 L240,180 L60,180 Z' fill='%234986ff' opacity='0.2'/%3E%3Cpath d='M80,40 L140,40 L140,160 L80,160 Z' fill='%234986ff' opacity='0.3'/%3E%3Cpath d='M160,40 L220,40 L220,160 L160,160 Z' fill='%234986ff' opacity='0.3'/%3E%3Cline x1='80' y1='60' x2='140' y2='60' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='80' y1='80' x2='140' y2='80' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='80' y1='100' x2='140' y2='100' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='80' y1='120' x2='140' y2='120' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='80' y1='140' x2='140' y2='140' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='160' y1='60' x2='220' y2='60' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='160' y1='80' x2='220' y2='80' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='160' y1='100' x2='220' y2='100' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='160' y1='120' x2='220' y2='120' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3Cline x1='160' y1='140' x2='220' y2='140' stroke='%23fff' stroke-width='1' opacity='0.5' /%3E%3C/svg%3E",
|
||
"score": 94,
|
||
"evaluationData": [92, 85, 76, 89, 78, 82]
|
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}
|
||
]
|
||
|
||
# 模拟仪表盘数据
|
||
dashboard_data = {
|
||
"paperCount": 3500,
|
||
"patentCount": 2000,
|
||
"highImpactPapers": 100,
|
||
"keyProjects": 50,
|
||
"fundingAmount": "500万元",
|
||
"researcherStats": {
|
||
"professor": 120,
|
||
"associateProfessor": 180,
|
||
"assistantProfessor": 150,
|
||
"postdoc": 90,
|
||
"phd": 250,
|
||
"master": 400
|
||
},
|
||
"newsData": [
|
||
{"title": "北京理工大学获批国家重点研发计划项目", "date": "2023-09-15"},
|
||
{"title": "我校教授在Nature期刊发表重要研究成果", "date": "2023-08-30"},
|
||
{"title": "北京理工大学举办2023年学术科技节", "date": "2023-08-20"},
|
||
{"title": "我校研究团队在量子计算领域取得突破性进展", "date": "2023-08-10"},
|
||
{"title": "北京理工大学与华为公司签署战略合作协议", "date": "2023-07-25"},
|
||
{"title": "北京理工大学新增两个国家重点工程研究中心", "date": "2023-07-15"},
|
||
{"title": "我校研究生在国际大赛中获一等奖", "date": "2023-07-01"},
|
||
{"title": "北京理工大学成功研发新型纳米材料", "date": "2023-06-20"},
|
||
{"title": "我校科研团队获2023年度国家科技进步奖提名", "date": "2023-06-10"},
|
||
{"title": "北京理工大学举办人工智能与未来教育论坛", "date": "2023-05-25"}
|
||
]
|
||
}
|
||
|
||
# 工具函数
|
||
def verify_password(plain_password, hashed_password):
|
||
return pwd_context.verify(plain_password, hashed_password)
|
||
|
||
def get_password_hash(password):
|
||
return pwd_context.hash(password)
|
||
|
||
def get_user(db, username: str):
|
||
if username in db:
|
||
user_dict = db[username]
|
||
return UserInDB(**user_dict)
|
||
return None
|
||
|
||
def authenticate_user(fake_db, username: str, password: str):
|
||
user = get_user(fake_db, username)
|
||
if not user:
|
||
return False
|
||
if not verify_password(password, user.hashed_password):
|
||
return False
|
||
return user
|
||
|
||
def create_access_token(data: dict, expires_delta: timedelta = None):
|
||
to_encode = data.copy()
|
||
if expires_delta:
|
||
expire = datetime.utcnow() + expires_delta
|
||
else:
|
||
expire = datetime.utcnow() + timedelta(minutes=15)
|
||
to_encode.update({"exp": expire})
|
||
encoded_jwt = jwt.encode(to_encode, SECRET_KEY, algorithm=ALGORITHM)
|
||
return encoded_jwt
|
||
|
||
async def get_current_user(token: str = Depends(oauth2_scheme), db: Session = Depends(get_db)):
|
||
credentials_exception = HTTPException(
|
||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||
detail="Could not validate credentials",
|
||
headers={"WWW-Authenticate": "Bearer"},
|
||
)
|
||
try:
|
||
payload = jwt.decode(token, SECRET_KEY, algorithms=[ALGORITHM])
|
||
username: str = payload.get("sub")
|
||
if username is None:
|
||
raise credentials_exception
|
||
token_data = TokenData(username=username)
|
||
except JWTError:
|
||
raise credentials_exception
|
||
|
||
user = crud.get_user(db, username=token_data.username)
|
||
if user is None:
|
||
raise credentials_exception
|
||
return user
|
||
|
||
async def get_current_active_user(current_user: models.User = Depends(get_current_user)):
|
||
if current_user.disabled:
|
||
raise HTTPException(status_code=400, detail="Inactive user")
|
||
return current_user
|
||
|
||
# 下载并保存图片
|
||
def download_and_save_image(image_url, url_base=""):
|
||
try:
|
||
# 设置请求头，模拟浏览器访问
|
||
headers = {
|
||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
|
||
'Accept': 'image/webp,image/apng,image/*,*/*;q=0.8',
|
||
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
|
||
'Connection': 'keep-alive',
|
||
'Referer': url_base
|
||
}
|
||
|
||
# 如果URL是相对路径，则与基础URL合并
|
||
if image_url.startswith('../') or image_url.startswith('./'):
|
||
image_url = urllib.parse.urljoin(url_base, image_url)
|
||
|
||
# 发送请求获取图片
|
||
response = requests.get(image_url, stream=True, headers=headers, timeout=30)
|
||
response.raise_for_status()
|
||
|
||
# 获取图片扩展名
|
||
content_type = response.headers.get('Content-Type', '')
|
||
if 'image/jpeg' in content_type or image_url.lower().endswith('.jpg') or image_url.lower().endswith('.jpeg'):
|
||
ext = '.jpg'
|
||
elif 'image/png' in content_type or image_url.lower().endswith('.png'):
|
||
ext = '.png'
|
||
elif 'image/gif' in content_type or image_url.lower().endswith('.gif'):
|
||
ext = '.gif'
|
||
else:
|
||
ext = '.png' # 默认使用PNG格式
|
||
|
||
# 生成唯一文件名
|
||
image_filename = f"{uuid.uuid4().hex}{ext}"
|
||
image_path = os.path.join(IMAGES_DIR, image_filename)
|
||
|
||
# 保存图片到本地
|
||
with open(image_path, 'wb') as out_file:
|
||
shutil.copyfileobj(response.raw, out_file)
|
||
|
||
# 返回图片的静态访问地址
|
||
return f"/static/images/{image_filename}"
|
||
|
||
except Exception as e:
|
||
print(f"Error downloading image: {e}")
|
||
return None
|
||
|
||
# 路由
|
||
@app.post("/token", response_model=Token)
|
||
async def login_for_access_token(
|
||
form_data: OAuth2PasswordRequestForm = Depends(),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
user = crud.authenticate_user(db, form_data.username, form_data.password)
|
||
if not user:
|
||
raise HTTPException(
|
||
status_code=status.HTTP_401_UNAUTHORIZED,
|
||
detail="Incorrect username or password",
|
||
headers={"WWW-Authenticate": "Bearer"},
|
||
)
|
||
access_token_expires = timedelta(minutes=ACCESS_TOKEN_EXPIRE_MINUTES)
|
||
access_token = create_access_token(
|
||
data={"sub": user.username}, expires_delta=access_token_expires
|
||
)
|
||
return {"access_token": access_token, "token_type": "bearer"}
|
||
|
||
@app.get("/users/me", response_model=User)
|
||
async def read_users_me(current_user: User = Depends(get_current_active_user)):
|
||
return current_user
|
||
|
||
@app.get("/dashboard")
|
||
async def get_dashboard(db: Session = Depends(get_db)):
|
||
dashboard = crud.get_dashboard(db)
|
||
if dashboard:
|
||
# 获取相关新闻
|
||
news = db.query(models.News).filter(models.News.dashboard_id == dashboard.id).all()
|
||
|
||
# 构建响应
|
||
result = {
|
||
"paperCount": dashboard.paperCount,
|
||
"patentCount": dashboard.patentCount,
|
||
"highImpactPapers": dashboard.highImpactPapers,
|
||
"keyProjects": dashboard.keyProjects,
|
||
"fundingAmount": dashboard.fundingAmount,
|
||
"researcherStats": dashboard.researcherStats,
|
||
"newsData": [{"title": n.title, "date": n.date} for n in news]
|
||
}
|
||
return result
|
||
|
||
raise HTTPException(status_code=404, detail="Dashboard data not found")
|
||
|
||
@app.get("/talents", response_model=List[schemas.Talent])
|
||
async def get_talents(db: Session = Depends(get_db)):
|
||
talents = crud.get_talents(db)
|
||
return talents
|
||
|
||
@app.post("/talents", response_model=schemas.Talent)
|
||
async def create_talent(
|
||
talent: schemas.TalentCreate,
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
return crud.create_talent(db, talent)
|
||
|
||
@app.get("/labs", response_model=List[schemas.Lab])
|
||
async def get_labs(db: Session = Depends(get_db)):
|
||
labs = crud.get_labs(db)
|
||
return labs
|
||
|
||
@app.post("/labs", response_model=schemas.Lab)
|
||
async def create_lab(
|
||
lab: schemas.LabCreate,
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
return crud.create_lab(db, lab)
|
||
|
||
# 健康检查接口
|
||
@app.get("/health")
|
||
async def health_check():
|
||
return {"status": "healthy"}
|
||
|
||
# 调试接口 - 检查用户状态
|
||
@app.get("/debug/users")
|
||
async def debug_users(db: Session = Depends(get_db)):
|
||
try:
|
||
users = db.query(models.User).all()
|
||
user_list = []
|
||
for user in users:
|
||
user_list.append({
|
||
"username": user.username,
|
||
"email": user.email,
|
||
"full_name": user.full_name,
|
||
"disabled": user.disabled,
|
||
"has_password": bool(user.hashed_password)
|
||
})
|
||
return {
|
||
"total_users": len(users),
|
||
"users": user_list
|
||
}
|
||
except Exception as e:
|
||
return {"error": str(e)}
|
||
|
||
# 调试接口 - 重新创建默认用户
|
||
@app.post("/debug/create-admin")
|
||
async def debug_create_admin(db: Session = Depends(get_db)):
|
||
try:
|
||
# 检查是否已经存在admin用户
|
||
existing_user = db.query(models.User).filter(models.User.username == "admin").first()
|
||
|
||
if existing_user:
|
||
return {"message": "Admin用户已存在", "user": existing_user.username}
|
||
|
||
# 创建admin用户
|
||
hashed_password = get_password_hash("123456")
|
||
default_user = models.User(
|
||
username="admin",
|
||
email="admin@example.com",
|
||
full_name="系统管理员",
|
||
hashed_password=hashed_password,
|
||
disabled=False
|
||
)
|
||
db.add(default_user)
|
||
db.commit()
|
||
db.refresh(default_user)
|
||
|
||
return {"message": "Admin用户创建成功", "user": default_user.username}
|
||
except Exception as e:
|
||
db.rollback()
|
||
return {"error": str(e)}
|
||
|
||
# URL抓取接口 - 更新版本
|
||
@app.post("/api/scrape-url")
|
||
async def scrape_url(request: schemas.ScrapeRequest):
|
||
try:
|
||
# 设置请求头，模拟浏览器访问
|
||
headers = {
|
||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
|
||
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
|
||
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
|
||
'Connection': 'keep-alive',
|
||
'Upgrade-Insecure-Requests': '1',
|
||
'Cache-Control': 'max-age=0'
|
||
}
|
||
|
||
# 发送HTTP请求获取页面内容，增加超时时间
|
||
response = requests.get(request.url, headers=headers, timeout=30)
|
||
response.raise_for_status() # 如果请求失败，抛出异常
|
||
|
||
# 设置编码以正确处理中文字符
|
||
response.encoding = 'utf-8'
|
||
|
||
# 解析HTML
|
||
soup = BeautifulSoup(response.text, 'html.parser')
|
||
|
||
# 获取基础URL用于解析相对路径
|
||
url_parts = urllib.parse.urlparse(request.url)
|
||
base_url = f"{url_parts.scheme}://{url_parts.netloc}"
|
||
|
||
# 初始化数据字典
|
||
teacher_data = {
|
||
"id": f"BLG{random.randint(10000, 99999)}",
|
||
"photo": f"data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='100' height='120' viewBox='0 0 100 120'%3E%3Crect width='100' height='120' fill='%234986ff' opacity='0.3'/%3E%3Ccircle cx='50' cy='45' r='25' fill='%234986ff' opacity='0.6'/%3E%3Ccircle cx='50' cy='95' r='35' fill='%234986ff' opacity='0.6'/%3E%3C/svg%3E",
|
||
"evaluationData": [
|
||
round(min(100, max(60, 70 + 20 * (0.5 - random.random())))) for _ in range(6)
|
||
]
|
||
}
|
||
|
||
# 从教师信息表提取基本信息
|
||
info_table = soup.find('div', class_='wz_teacher')
|
||
if info_table:
|
||
table = info_table.find('table')
|
||
if table:
|
||
rows = table.find_all('tr')
|
||
|
||
# 提取姓名、性别、出生年月
|
||
if len(rows) > 0:
|
||
cells = rows[0].find_all('td')
|
||
if len(cells) >= 6:
|
||
teacher_data["name"] = cells[1].text.strip()
|
||
teacher_data["gender"] = cells[3].text.strip()
|
||
teacher_data["birthDate"] = cells[5].text.strip()
|
||
|
||
# 提取职称、职务、最高学历
|
||
if len(rows) > 1:
|
||
cells = rows[1].find_all('td')
|
||
if len(cells) >= 6:
|
||
teacher_data["title"] = cells[1].text.strip()
|
||
position = cells[3].text.strip()
|
||
teacher_data["position"] = position if position else ""
|
||
teacher_data["education"] = cells[5].text.strip()
|
||
|
||
# 提取学科方向
|
||
if len(rows) > 2:
|
||
cells = rows[2].find_all('td')
|
||
if len(cells) >= 2:
|
||
teacher_data["academicDirection"] = cells[1].text.strip()
|
||
|
||
# 提取人才计划和办公地点
|
||
if len(rows) > 3:
|
||
cells = rows[3].find_all('td')
|
||
if len(cells) >= 6:
|
||
talent_plan = cells[1].text.strip()
|
||
teacher_data["talentPlan"] = talent_plan if talent_plan else ""
|
||
teacher_data["officeLocation"] = cells[5].text.strip()
|
||
|
||
# 提取电子邮件和联系方式
|
||
if len(rows) > 4:
|
||
cells = rows[4].find_all('td')
|
||
if len(cells) >= 6:
|
||
email = cells[1].text.strip()
|
||
teacher_data["email"] = email if email else ""
|
||
phone = cells[5].text.strip()
|
||
teacher_data["phone"] = phone if phone else ""
|
||
|
||
# 提取通讯地址
|
||
if len(rows) > 5:
|
||
cells = rows[5].find_all('td')
|
||
if len(cells) >= 2:
|
||
teacher_data["address"] = cells[1].text.strip()
|
||
|
||
# 提取导师类型
|
||
if len(rows) > 6:
|
||
cells = rows[6].find_all('td')
|
||
if len(cells) >= 2:
|
||
teacher_data["tutorType"] = cells[1].text.strip()
|
||
|
||
# 提取照片
|
||
photo_element = soup.select_one('.teacherInfo .img img')
|
||
if photo_element and photo_element.get('src'):
|
||
img_src = photo_element['src']
|
||
|
||
# 处理相对路径，构建完整的图片URL
|
||
if img_src.startswith('../../../'):
|
||
# 移除 '../../../' 前缀，直接拼接到基础URL
|
||
img_relative = img_src[9:] # 移除 '../../../'
|
||
img_url = f"{base_url}/{img_relative}"
|
||
elif img_src.startswith('../../'):
|
||
# 处理 '../../' 相对路径
|
||
img_relative = img_src[6:] # 移除 '../../'
|
||
img_url = f"{base_url}/{img_relative}"
|
||
elif img_src.startswith('../'):
|
||
# 处理 '../' 相对路径
|
||
img_relative = img_src[3:] # 移除 '../'
|
||
img_url = f"{base_url}/{img_relative}"
|
||
else:
|
||
img_url = urllib.parse.urljoin(base_url, img_src)
|
||
|
||
# 直接保存完整的图片URL，不下载到本地
|
||
teacher_data["photo"] = img_url
|
||
|
||
# 提取详细信息部分
|
||
content_divs = soup.select('.con01_t')
|
||
for div in content_divs:
|
||
heading = div.find('h3')
|
||
if not heading:
|
||
continue
|
||
|
||
heading_text = heading.text.strip()
|
||
|
||
# 获取该部分的所有段落文本
|
||
paragraphs = [p.text.strip() for p in div.find_all('p') if p.text.strip()]
|
||
section_content = '\n'.join(paragraphs)
|
||
|
||
# 根据标题将内容映射到相应字段
|
||
if '教育与工作经历' in heading_text:
|
||
teacher_data["eduWorkHistory"] = section_content
|
||
elif '研究方向' in heading_text:
|
||
teacher_data["researchDirection"] = section_content
|
||
elif '近5年承担的科研项目' in heading_text or '近五年承担的科研项目' in heading_text:
|
||
teacher_data["recentProjects"] = section_content
|
||
# 计算项目数量
|
||
project_count = len([p for p in paragraphs if p.strip().startswith(str(len(paragraphs) - paragraphs.index(p))+".")])
|
||
if project_count > 0:
|
||
teacher_data["projects"] = f"{project_count}项"
|
||
else:
|
||
teacher_data["projects"] = f"{len(paragraphs)}项"
|
||
elif '代表性学术论文' in heading_text:
|
||
teacher_data["representativePapers"] = section_content
|
||
# 计算论文数量
|
||
paper_count = len([p for p in paragraphs if p.strip().startswith("[")])
|
||
if paper_count > 0:
|
||
teacher_data["papers"] = f"{paper_count}篇"
|
||
else:
|
||
teacher_data["papers"] = f"{len(paragraphs)}篇"
|
||
elif '授权国家发明专利' in heading_text or '专利' in heading_text:
|
||
teacher_data["patents"] = section_content
|
||
|
||
return teacher_data
|
||
|
||
except Exception as e:
|
||
print(f"抓取错误: {str(e)}")
|
||
return JSONResponse(
|
||
status_code=500,
|
||
content={"error": f"抓取网页失败: {str(e)}"},
|
||
)
|
||
|
||
# 保存评估数据接口
|
||
@app.post("/api/save-data")
|
||
async def save_evaluation_data(
|
||
request: schemas.SaveDataRequest,
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
try:
|
||
if request.data_type == "talent":
|
||
# 获取人才ID
|
||
talent_id = request.data.get("id")
|
||
|
||
# 如果id不存在、为空字符串或为null，执行新增操作
|
||
if not talent_id:
|
||
# 确保有必要的字段
|
||
if not request.data.get("name"):
|
||
return JSONResponse(
|
||
status_code=400,
|
||
content={"error": "缺少必要字段：name"},
|
||
)
|
||
|
||
# 生成新ID
|
||
new_id = f"BLG{random.randint(10000, 99999)}"
|
||
# 复制数据并添加ID
|
||
talent_data = {**request.data, "id": new_id}
|
||
|
||
# 创建Talent对象并保存到数据库
|
||
# 过滤掉数据库模型中不存在的字段
|
||
valid_fields = {
|
||
'id', 'idcode', 'name', 'gender', 'birthDate', 'title', 'position',
|
||
'education', 'educationBackground', 'address', 'academicDirection',
|
||
'talentPlan', 'officeLocation', 'email', 'phone', 'tutorType',
|
||
'papers', 'projects', 'photo', 'eduWorkHistory', 'researchDirection',
|
||
'recentProjects', 'representativePapers', 'patents', 'evaluationData'
|
||
}
|
||
filtered_talent_data = {k: v for k, v in talent_data.items() if k in valid_fields}
|
||
db_talent = models.Talent(**filtered_talent_data)
|
||
db.add(db_talent)
|
||
db.commit()
|
||
db.refresh(db_talent)
|
||
|
||
return {"success": True, "message": "人才评估数据已新增", "id": new_id}
|
||
else:
|
||
# 执行更新操作
|
||
talent = crud.update_talent(db, talent_id, request.data)
|
||
if not talent:
|
||
return JSONResponse(
|
||
status_code=404,
|
||
content={"error": f"未找到ID为 {talent_id} 的人才"},
|
||
)
|
||
|
||
return {"success": True, "message": "人才评估数据已更新"}
|
||
|
||
elif request.data_type == "lab":
|
||
# 获取工程研究中心ID
|
||
lab_id = request.data.get("id")
|
||
|
||
# 如果id不存在、为空字符串或为null，执行新增操作
|
||
if not lab_id:
|
||
# 确保有必要的字段
|
||
if not request.data.get("name"):
|
||
return JSONResponse(
|
||
status_code=400,
|
||
content={"error": "缺少必要字段：name"},
|
||
)
|
||
|
||
# 生成新ID
|
||
new_id = f"BLG{random.randint(10000, 99999)}"
|
||
# 复制数据并添加ID
|
||
lab_data = {**request.data, "id": new_id}
|
||
|
||
# 创建Lab对象并保存到数据库
|
||
db_lab = models.Lab(**lab_data)
|
||
db.add(db_lab)
|
||
db.commit()
|
||
db.refresh(db_lab)
|
||
|
||
return {"success": True, "message": "工程研究中心评估数据已新增", "id": new_id}
|
||
else:
|
||
# 执行更新操作
|
||
lab = crud.update_lab(db, lab_id, request.data)
|
||
if not lab:
|
||
return JSONResponse(
|
||
status_code=404,
|
||
content={"error": f"未找到ID为 {lab_id} 的工程研究中心"},
|
||
)
|
||
|
||
return {"success": True, "message": "工程研究中心评估数据已更新"}
|
||
|
||
else:
|
||
return JSONResponse(
|
||
status_code=400,
|
||
content={"error": f"不支持的数据类型: {request.data_type}"},
|
||
)
|
||
|
||
except Exception as e:
|
||
return JSONResponse(
|
||
status_code=500,
|
||
content={"error": f"保存数据失败: {str(e)}"},
|
||
)
|
||
|
||
# 添加抓取教师信息和头像的新接口
|
||
@app.post("/api/fetch-teacher-data")
|
||
async def fetch_teacher_data(db: Session = Depends(get_db)):
|
||
try:
|
||
# 抓取网页内容
|
||
url = "https://ac.bit.edu.cn/szdw/jsml/kzllykzgcyjs1/c6533e24f85749578699deca43c38b40.htm"
|
||
|
||
# 设置请求头，模拟浏览器访问
|
||
headers = {
|
||
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36',
|
||
'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
|
||
'Accept-Language': 'zh-CN,zh;q=0.9,en;q=0.8',
|
||
'Connection': 'keep-alive',
|
||
'Upgrade-Insecure-Requests': '1',
|
||
'Cache-Control': 'max-age=0'
|
||
}
|
||
|
||
response = requests.get(url, headers=headers, timeout=30)
|
||
response.raise_for_status()
|
||
|
||
# 设置编码以正确处理中文字符
|
||
response.encoding = 'utf-8'
|
||
|
||
# 解析HTML
|
||
soup = BeautifulSoup(response.text, 'html.parser')
|
||
|
||
# 获取基础URL用于解析相对路径
|
||
url_parts = urllib.parse.urlparse(url)
|
||
base_url = f"{url_parts.scheme}://{url_parts.netloc}"
|
||
|
||
# 初始化教师数据
|
||
teacher_data = {
|
||
"id": f"BLG{random.randint(10000, 99999)}",
|
||
"evaluationData": [
|
||
round(min(100, max(60, 70 + 20 * (0.5 - random.random())))) for _ in range(6)
|
||
]
|
||
}
|
||
|
||
# 从教师信息表提取基本信息
|
||
info_table = soup.find('div', class_='wz_teacher')
|
||
if info_table:
|
||
table = info_table.find('table')
|
||
if table:
|
||
rows = table.find_all('tr')
|
||
|
||
# 提取姓名、性别、出生年月
|
||
if len(rows) > 0:
|
||
cells = rows[0].find_all('td')
|
||
if len(cells) >= 6:
|
||
teacher_data["name"] = cells[1].text.strip()
|
||
teacher_data["gender"] = cells[3].text.strip()
|
||
teacher_data["birthDate"] = cells[5].text.strip()
|
||
|
||
# 提取职称、职务、最高学历
|
||
if len(rows) > 1:
|
||
cells = rows[1].find_all('td')
|
||
if len(cells) >= 6:
|
||
teacher_data["title"] = cells[1].text.strip()
|
||
position = cells[3].text.strip()
|
||
teacher_data["position"] = position if position else ""
|
||
teacher_data["education"] = cells[5].text.strip()
|
||
|
||
# 提取学科方向
|
||
if len(rows) > 2:
|
||
cells = rows[2].find_all('td')
|
||
if len(cells) >= 2:
|
||
teacher_data["academicDirection"] = cells[1].text.strip()
|
||
|
||
# 提取人才计划和办公地点
|
||
if len(rows) > 3:
|
||
cells = rows[3].find_all('td')
|
||
if len(cells) >= 6:
|
||
talent_plan = cells[1].text.strip()
|
||
teacher_data["talentPlan"] = talent_plan if talent_plan else ""
|
||
teacher_data["officeLocation"] = cells[5].text.strip()
|
||
|
||
# 提取电子邮件和联系方式
|
||
if len(rows) > 4:
|
||
cells = rows[4].find_all('td')
|
||
if len(cells) >= 6:
|
||
email = cells[1].text.strip()
|
||
teacher_data["email"] = email if email else ""
|
||
phone = cells[5].text.strip()
|
||
teacher_data["phone"] = phone if phone else ""
|
||
|
||
# 提取通讯地址
|
||
if len(rows) > 5:
|
||
cells = rows[5].find_all('td')
|
||
if len(cells) >= 2:
|
||
teacher_data["address"] = cells[1].text.strip()
|
||
|
||
# 提取导师类型
|
||
if len(rows) > 6:
|
||
cells = rows[6].find_all('td')
|
||
if len(cells) >= 2:
|
||
teacher_data["tutorType"] = cells[1].text.strip()
|
||
|
||
# 提取详细信息部分
|
||
content_divs = soup.select('.con01_t')
|
||
for div in content_divs:
|
||
heading = div.find('h3')
|
||
if not heading:
|
||
continue
|
||
|
||
heading_text = heading.text.strip()
|
||
|
||
# 获取该部分的所有段落文本
|
||
paragraphs = [p.text.strip() for p in div.find_all('p') if p.text.strip()]
|
||
section_content = '\n'.join(paragraphs)
|
||
|
||
# 根据标题将内容映射到相应字段
|
||
if '教育与工作经历' in heading_text:
|
||
teacher_data["eduWorkHistory"] = section_content
|
||
elif '研究方向' in heading_text:
|
||
teacher_data["researchDirection"] = section_content
|
||
elif '近5年承担的科研项目' in heading_text or '近五年承担的科研项目' in heading_text:
|
||
teacher_data["recentProjects"] = section_content
|
||
# 计算项目数量
|
||
project_count = len([p for p in paragraphs if p.strip().startswith(str(len(paragraphs) - paragraphs.index(p))+".")])
|
||
if project_count > 0:
|
||
teacher_data["projects"] = f"{project_count}项"
|
||
else:
|
||
teacher_data["projects"] = f"{len(paragraphs)}项"
|
||
elif '代表性学术论文' in heading_text:
|
||
teacher_data["representativePapers"] = section_content
|
||
# 计算论文数量
|
||
paper_count = len([p for p in paragraphs if p.strip().startswith("[")])
|
||
if paper_count > 0:
|
||
teacher_data["papers"] = f"{paper_count}篇"
|
||
else:
|
||
teacher_data["papers"] = f"{len(paragraphs)}篇"
|
||
elif '授权国家发明专利' in heading_text or '专利' in heading_text:
|
||
teacher_data["patents"] = section_content
|
||
|
||
# 提取照片
|
||
photo_element = soup.select_one('.teacherInfo .img img')
|
||
if photo_element and photo_element.get('src'):
|
||
img_src = photo_element['src']
|
||
|
||
# 处理相对路径，构建完整的图片URL
|
||
if img_src.startswith('../../../'):
|
||
# 从URL获取基础路径（移除文件名和最后两级目录）
|
||
url_parts = url.split('/')
|
||
if len(url_parts) >= 4:
|
||
base_path = '/'.join(url_parts[:-3])
|
||
img_url = f"{base_path}/{img_src[9:]}" # 移除 '../../../'
|
||
else:
|
||
img_url = urllib.parse.urljoin(base_url, img_src)
|
||
else:
|
||
img_url = urllib.parse.urljoin(base_url, img_src)
|
||
|
||
# 直接保存完整的图片URL，不下载到本地
|
||
teacher_data["photo"] = img_url
|
||
|
||
# 保存到数据库
|
||
db_talent = models.Talent(**teacher_data)
|
||
db.add(db_talent)
|
||
db.commit()
|
||
db.refresh(db_talent)
|
||
|
||
# 返回结果
|
||
return {"success": True, "message": "教师数据已成功抓取并保存", "data": teacher_data}
|
||
|
||
except Exception as e:
|
||
print(f"抓取教师数据错误: {str(e)}")
|
||
return JSONResponse(
|
||
status_code=500,
|
||
content={"error": f"抓取失败: {str(e)}"},
|
||
)
|
||
|
||
# 添加获取单个教师详情的接口
|
||
@app.get("/talents/{talent_id}", response_model=schemas.Talent)
|
||
async def get_talent_detail(talent_id: str, db: Session = Depends(get_db)):
|
||
talent = crud.get_talent(db, talent_id)
|
||
if talent is None:
|
||
raise HTTPException(status_code=404, detail="教师信息不存在")
|
||
return talent
|
||
|
||
# 删除单个人才
|
||
@app.delete("/talents/{talent_id}")
|
||
async def delete_talent(
|
||
talent_id: str,
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
# 查找要删除的人才
|
||
talent = crud.get_talent(db, talent_id)
|
||
if talent is None:
|
||
raise HTTPException(status_code=404, detail="教师信息不存在")
|
||
|
||
# 删除人才记录
|
||
success = crud.delete_talent(db, talent_id)
|
||
if not success:
|
||
raise HTTPException(status_code=500, detail="删除失败")
|
||
|
||
return {"success": True, "message": "删除成功"}
|
||
|
||
# 添加获取单个工程研究中心详情的接口
|
||
@app.get("/labs/{lab_id}", response_model=schemas.Lab)
|
||
async def get_lab_detail(lab_id: str, db: Session = Depends(get_db)):
|
||
lab = crud.get_lab(db, lab_id)
|
||
if lab is None:
|
||
raise HTTPException(status_code=404, detail="工程研究中心信息不存在")
|
||
return lab
|
||
|
||
# 获取所有维度
|
||
@app.get("/dimensions", response_model=List[schemas.Dimension])
|
||
async def get_dimensions(db: Session = Depends(get_db)):
|
||
dimensions = crud.get_all_dimensions(db)
|
||
return dimensions
|
||
|
||
# 获取特定类别的维度
|
||
@app.get("/dimensions/{category}", response_model=List[schemas.Dimension])
|
||
async def get_dimensions_by_category(category: str, db: Session = Depends(get_db)):
|
||
dimensions = db.query(models.Dimension).filter(models.Dimension.category == category).all()
|
||
|
||
# 处理返回数据，添加subDimensions字段
|
||
result = []
|
||
for dim in dimensions:
|
||
dim_dict = {
|
||
"id": dim.id,
|
||
"name": dim.name,
|
||
"weight": dim.weight,
|
||
"category": dim.category,
|
||
"description": dim.description,
|
||
"sub_dimensions": dim.sub_dimensions,
|
||
"subDimensions": dim.sub_dimensions # 添加subDimensions与sub_dimensions内容相同
|
||
}
|
||
result.append(dim_dict)
|
||
|
||
return result
|
||
|
||
# 创建新维度
|
||
@app.post("/dimensions", response_model=schemas.Dimension)
|
||
async def create_dimension(
|
||
dimension: schemas.DimensionCreate,
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
return crud.create_dimension(
|
||
db=db,
|
||
name=dimension.name,
|
||
weight=dimension.weight,
|
||
category=dimension.category,
|
||
description=dimension.description
|
||
)
|
||
|
||
@app.put("/dimensions/{dimension_id}", response_model=schemas.Dimension)
|
||
async def update_dimension(
|
||
dimension_id: int,
|
||
dimension: schemas.DimensionCreate,
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
db_dimension = crud.get_dimension(db, dimension_id)
|
||
if not db_dimension:
|
||
raise HTTPException(status_code=404, detail="Dimension not found")
|
||
|
||
dimension_data = dimension.dict(exclude_unset=True)
|
||
return crud.update_dimension(db, dimension_id, dimension_data)
|
||
|
||
@app.delete("/dimensions/{dimension_id}", response_model=dict)
|
||
async def delete_dimension(
|
||
dimension_id: int,
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
success = crud.delete_dimension(db, dimension_id)
|
||
if not success:
|
||
raise HTTPException(status_code=404, detail="Dimension not found")
|
||
return {"success": True}
|
||
|
||
# 添加新的API端点用于保存维度数据
|
||
@app.post("/api/save-dimensions")
|
||
async def save_dimensions(
|
||
request: dict = Body(...),
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
try:
|
||
dimensions = request.get("dimensions", [])
|
||
category = request.get("category", "")
|
||
|
||
if not dimensions:
|
||
return JSONResponse(
|
||
status_code=400,
|
||
content={"success": False, "message": "未提供维度数据"}
|
||
)
|
||
|
||
if not category:
|
||
return JSONResponse(
|
||
status_code=400,
|
||
content={"success": False, "message": "未提供分类信息"}
|
||
)
|
||
|
||
# 根据category删除现有维度并重新创建
|
||
# 首先删除该类别的所有现有维度
|
||
existing_dimensions = db.query(models.Dimension).filter(models.Dimension.category == category).all()
|
||
for dim in existing_dimensions:
|
||
db.delete(dim)
|
||
|
||
# 添加新的维度
|
||
for dim_data in dimensions:
|
||
new_dimension = models.Dimension(
|
||
name=dim_data.get("name", ""),
|
||
weight=dim_data.get("weight", 1.0),
|
||
category=category,
|
||
description=dim_data.get("description", "")
|
||
)
|
||
db.add(new_dimension)
|
||
|
||
db.commit()
|
||
|
||
return {"success": True, "message": "维度数据保存成功"}
|
||
except Exception as e:
|
||
db.rollback()
|
||
return JSONResponse(
|
||
status_code=500,
|
||
content={"success": False, "message": f"保存维度数据失败: {str(e)}"}
|
||
)
|
||
|
||
# 添加处理人才文档的新API
|
||
@app.post("/api/upload-talent-document")
|
||
async def upload_talent_document(
|
||
file: UploadFile = File(...),
|
||
current_user: models.User = Depends(get_current_active_user)
|
||
):
|
||
# 检查文件是否为支持的文档格式
|
||
if not file.filename.endswith(('.docx', '.pdf', '.doc')):
|
||
raise HTTPException(status_code=400, detail="只支持.docx、.pdf、.doc格式的文档")
|
||
|
||
try:
|
||
# 创建临时文件保存上传的文档
|
||
temp_file_path = f"temp_{uuid.uuid4()}.{file.filename.split('.')[-1]}"
|
||
with open(temp_file_path, "wb") as buffer:
|
||
shutil.copyfileobj(file.file, buffer)
|
||
|
||
# 根据文件类型解析文档
|
||
if file.filename.endswith('.docx'):
|
||
talent_data = extract_talent_info_from_docx(temp_file_path)
|
||
else:
|
||
# 对于PDF和DOC文件，暂时返回基础模板数据
|
||
talent_data = get_default_talent_data()
|
||
talent_data["name"] = file.filename.split('.')[0] # 使用文件名作为姓名
|
||
|
||
# 删除临时文件
|
||
if os.path.exists(temp_file_path):
|
||
os.remove(temp_file_path)
|
||
|
||
return JSONResponse(
|
||
status_code=200,
|
||
content={"success": True, "data": talent_data}
|
||
)
|
||
|
||
except Exception as e:
|
||
# 确保删除任何临时文件
|
||
if 'temp_file_path' in locals() and os.path.exists(temp_file_path):
|
||
os.remove(temp_file_path)
|
||
|
||
print(f"处理人才文档时发生错误: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"处理文档时发生错误: {str(e)}")
|
||
|
||
finally:
|
||
# 关闭文件
|
||
await file.close()
|
||
|
||
# 添加处理Word文档的新API
|
||
@app.post("/api/upload-lab-document")
|
||
async def upload_lab_document(
|
||
file: UploadFile = File(...),
|
||
current_user: models.User = Depends(get_current_active_user)
|
||
):
|
||
# 检查文件是否为Word文档
|
||
if not file.filename.endswith(('.docx')):
|
||
raise HTTPException(status_code=400, detail="只支持.docx格式的Word文档")
|
||
|
||
try:
|
||
# 创建临时文件保存上传的文档
|
||
temp_file_path = f"temp_{uuid.uuid4()}.docx"
|
||
with open(temp_file_path, "wb") as buffer:
|
||
shutil.copyfileobj(file.file, buffer)
|
||
|
||
# 解析Word文档
|
||
lab_data = extract_lab_info_from_docx(temp_file_path)
|
||
|
||
# 删除临时文件
|
||
if os.path.exists(temp_file_path):
|
||
os.remove(temp_file_path)
|
||
|
||
return JSONResponse(
|
||
status_code=200,
|
||
content={"success": True, "data": lab_data}
|
||
)
|
||
|
||
except Exception as e:
|
||
# 确保删除任何临时文件
|
||
if os.path.exists(temp_file_path):
|
||
os.remove(temp_file_path)
|
||
|
||
print(f"处理文档时发生错误: {str(e)}")
|
||
raise HTTPException(status_code=500, detail=f"处理文档时发生错误: {str(e)}")
|
||
|
||
finally:
|
||
# 关闭文件
|
||
await file.close()
|
||
|
||
def extract_talent_info_from_docx(file_path):
|
||
"""从Word文档中提取人才信息"""
|
||
doc = docx.Document(file_path)
|
||
full_text = []
|
||
|
||
# 提取所有段落文本
|
||
for para in doc.paragraphs:
|
||
if para.text.strip():
|
||
full_text.append(para.text.strip())
|
||
|
||
# 合并文本以便于处理
|
||
text_content = "\n".join(full_text)
|
||
|
||
# 初始化人才数据
|
||
talent_data = get_default_talent_data()
|
||
|
||
# 提取姓名（假设第一行或包含"姓名"的行）
|
||
if full_text:
|
||
# 尝试从第一行提取姓名
|
||
first_line = full_text[0]
|
||
if len(first_line) <= 10 and not any(char in first_line for char in ['：', ':', '简历', '履历']):
|
||
talent_data["name"] = first_line
|
||
|
||
# 使用正则表达式提取各种信息
|
||
name_pattern = re.compile(r'姓名[：:]\s*([^\n\r]+)')
|
||
name_match = name_pattern.search(text_content)
|
||
if name_match:
|
||
talent_data["name"] = name_match.group(1).strip()
|
||
|
||
# 提取编号
|
||
id_pattern = re.compile(r'编号[：:]\s*([A-Za-z0-9]+)')
|
||
id_match = id_pattern.search(text_content)
|
||
if id_match:
|
||
talent_data["idcode"] = id_match.group(1)
|
||
|
||
# 提取性别
|
||
gender_pattern = re.compile(r'性别[：:]\s*([男女])')
|
||
gender_match = gender_pattern.search(text_content)
|
||
if gender_match:
|
||
talent_data["gender"] = gender_match.group(1)
|
||
|
||
# 提取出生日期
|
||
birth_pattern = re.compile(r'出生[日期年月]*[：:]\s*(\d{4}[年.-]\d{1,2}[月.-]\d{1,2}|\d{4}[年.-]\d{1,2})')
|
||
birth_match = birth_pattern.search(text_content)
|
||
if birth_match:
|
||
talent_data["birthDate"] = birth_match.group(1)
|
||
|
||
# 提取职称
|
||
title_pattern = re.compile(r'职称[：:]\s*([^\n\r]+)')
|
||
title_match = title_pattern.search(text_content)
|
||
if title_match:
|
||
talent_data["title"] = title_match.group(1).strip()
|
||
|
||
# 提取学历
|
||
education_pattern = re.compile(r'学历[：:]\s*([^\n\r]+)')
|
||
education_match = education_pattern.search(text_content)
|
||
if education_match:
|
||
talent_data["education"] = education_match.group(1).strip()
|
||
|
||
# 提取邮箱
|
||
email_pattern = re.compile(r'邮箱[：:]?\s*([a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,})')
|
||
email_match = email_pattern.search(text_content)
|
||
if email_match:
|
||
talent_data["email"] = email_match.group(1)
|
||
|
||
# 提取电话
|
||
phone_pattern = re.compile(r'电话[：:]?\s*(\d{11}|\d{3,4}-\d{7,8})')
|
||
phone_match = phone_pattern.search(text_content)
|
||
if phone_match:
|
||
talent_data["phone"] = phone_match.group(1)
|
||
|
||
# 提取研究方向
|
||
research_pattern = re.compile(r'研究方向[：:]\s*([^\n\r]+)')
|
||
research_match = research_pattern.search(text_content)
|
||
if research_match:
|
||
talent_data["researchDirection"] = research_match.group(1).strip()
|
||
|
||
# 提取工作经历（查找包含"工作经历"、"教育经历"等关键词的段落）
|
||
work_keywords = ['工作经历', '教育经历', '学习经历', '履历']
|
||
for i, line in enumerate(full_text):
|
||
if any(keyword in line for keyword in work_keywords):
|
||
# 取该行及后续几行作为工作经历
|
||
work_history = []
|
||
for j in range(i, min(i + 5, len(full_text))):
|
||
if full_text[j].strip():
|
||
work_history.append(full_text[j].strip())
|
||
talent_data["eduWorkHistory"] = "\n".join(work_history)
|
||
break
|
||
|
||
# 提取论文信息
|
||
papers_pattern = re.compile(r'论文[数量]*[：:]\s*(\d+)')
|
||
papers_match = papers_pattern.search(text_content)
|
||
if papers_match:
|
||
talent_data["papers"] = f"{papers_match.group(1)}篇"
|
||
|
||
# 提取项目信息
|
||
projects_pattern = re.compile(r'项目[数量]*[：:]\s*(\d+)')
|
||
projects_match = projects_pattern.search(text_content)
|
||
if projects_match:
|
||
talent_data["projects"] = f"{projects_match.group(1)}项"
|
||
|
||
# 生成评估数据（基于提取到的信息）
|
||
try:
|
||
papers_num = int(re.search(r'\d+', talent_data["papers"] or "0").group(0) or 0)
|
||
projects_num = int(re.search(r'\d+', talent_data["projects"] or "0").group(0) or 0)
|
||
|
||
# 简单的评分算法
|
||
work_score = min(100, 60 + len(talent_data["eduWorkHistory"]) // 10)
|
||
research_score = min(100, 60 + papers_num * 2)
|
||
project_score = min(100, 60 + projects_num * 3)
|
||
paper_score = min(100, 60 + papers_num * 2.5)
|
||
patent_score = min(100, 60 + random.randint(0, 20)) # 随机生成专利分数
|
||
impact_score = min(100, (research_score + project_score + paper_score) // 3)
|
||
|
||
talent_data["evaluationData"] = [
|
||
work_score, # 工作经历
|
||
research_score, # 研究方向
|
||
project_score, # 科研项目
|
||
paper_score, # 学术论文
|
||
patent_score, # 专利专著
|
||
impact_score # 学术影响
|
||
]
|
||
except Exception as e:
|
||
print(f"计算人才评分时出错: {str(e)}")
|
||
# 出错时保留默认评分
|
||
|
||
return talent_data
|
||
|
||
def get_default_talent_data():
|
||
"""获取默认的人才数据模板"""
|
||
return {
|
||
"idcode": "",
|
||
"name": "",
|
||
"gender": "",
|
||
"birthDate": "",
|
||
"title": "",
|
||
"position": "",
|
||
"education": "",
|
||
"educationBackground": "",
|
||
"address": "",
|
||
"academicDirection": "",
|
||
"talentPlan": "",
|
||
"officeLocation": "",
|
||
"email": "",
|
||
"phone": "",
|
||
"tutorType": "",
|
||
"papers": "",
|
||
"projects": "",
|
||
"photo": "",
|
||
"eduWorkHistory": "",
|
||
"researchDirection": "",
|
||
"recentProjects": "",
|
||
"representativePapers": "",
|
||
"patents": "",
|
||
"evaluationData": [60, 60, 60, 60, 60, 60] # 默认评估数据
|
||
}
|
||
|
||
def extract_lab_info_from_docx(file_path):
|
||
"""从Word文档中提取工程研究中心信息"""
|
||
doc = docx.Document(file_path)
|
||
full_text = []
|
||
|
||
# 提取所有段落文本
|
||
for para in doc.paragraphs:
|
||
if para.text.strip():
|
||
full_text.append(para.text.strip())
|
||
|
||
# 合并文本以便于处理
|
||
text_content = "\n".join(full_text)
|
||
|
||
# 初始化工程研究中心数据
|
||
lab_data = {
|
||
"name": "",
|
||
"idcode": "",
|
||
"personnel": "",
|
||
"nationalProjects": "",
|
||
"otherProjects": "",
|
||
"achievements": "",
|
||
"labAchievements": "",
|
||
"evaluationData": [60, 60, 60, 60, 60, 60] # 默认评估数据
|
||
}
|
||
|
||
# 提取工程研究中心名称（假设第一行是工程研究中心名称）
|
||
if full_text:
|
||
lab_data["name"] = full_text[0]
|
||
|
||
# 提取工程研究中心编号（寻找带有"编号"的行）
|
||
id_pattern = re.compile(r'编号[：:]\s*([A-Za-z0-9]+)')
|
||
id_matches = id_pattern.search(text_content)
|
||
if id_matches:
|
||
lab_data["idcode"] = id_matches.group(1)
|
||
|
||
# 提取人员数量
|
||
personnel_pattern = re.compile(r'人员[数量]*[：:]\s*(\d+)')
|
||
personnel_matches = personnel_pattern.search(text_content)
|
||
if personnel_matches:
|
||
lab_data["personnel"] = f"{personnel_matches.group(1)}人"
|
||
|
||
# 提取国家级项目数量
|
||
national_projects_pattern = re.compile(r'国家级项目[：:]\s*(\d+)')
|
||
np_matches = national_projects_pattern.search(text_content)
|
||
if np_matches:
|
||
lab_data["nationalProjects"] = f"{np_matches.group(1)}项"
|
||
|
||
# 提取其他项目数量
|
||
other_projects_pattern = re.compile(r'(其他|其它)项目[：:]\s*(\d+)')
|
||
op_matches = other_projects_pattern.search(text_content)
|
||
if op_matches:
|
||
lab_data["otherProjects"] = f"{op_matches.group(2)}项"
|
||
|
||
# 提取成果数量
|
||
achievements_pattern = re.compile(r'成果[数量]*[：:]\s*(\d+)')
|
||
ach_matches = achievements_pattern.search(text_content)
|
||
if ach_matches:
|
||
lab_data["achievements"] = f"{ach_matches.group(1)}项"
|
||
|
||
# 提取工程研究中心成就信息（取文本的中间部分作为工程研究中心成就）
|
||
if len(full_text) > 2:
|
||
# 跳过第一行（标题）和最后一行，取中间的文本作为成就描述
|
||
lab_data["labAchievements"] = "\n".join(full_text[1:-1])
|
||
|
||
# 根据提取到的信息，给出一个评估评分
|
||
# 这里可以编写更复杂的评分算法，示例中使用简单方法
|
||
try:
|
||
# 解析数字
|
||
personnel_num = int(re.search(r'\d+', lab_data["personnel"] or "0").group(0) or 0)
|
||
national_num = int(re.search(r'\d+', lab_data["nationalProjects"] or "0").group(0) or 0)
|
||
other_num = int(re.search(r'\d+', lab_data["otherProjects"] or "0").group(0) or 0)
|
||
ach_num = int(re.search(r'\d+', lab_data["achievements"] or "0").group(0) or 0)
|
||
|
||
# 简单计算评分 (仅示例)
|
||
innovation_score = min(100, 50 + national_num * 5)
|
||
research_score = min(100, 50 + (national_num + other_num) * 2)
|
||
transform_score = min(100, 50 + ach_num * 2)
|
||
discipline_score = min(100, 50 + personnel_num * 2)
|
||
contribution_score = min(100, 50 + (national_num + ach_num) * 1.5)
|
||
potential_score = min(100, (innovation_score + research_score + transform_score) / 3)
|
||
|
||
lab_data["evaluationData"] = [
|
||
innovation_score, # 创新水平
|
||
research_score, # 研究能力
|
||
transform_score, # 成果转化
|
||
discipline_score, # 学科建设
|
||
contribution_score, # 行业贡献
|
||
potential_score # 发展潜力
|
||
]
|
||
except Exception as e:
|
||
print(f"计算评分时出错: {str(e)}")
|
||
# 出错时保留默认评分
|
||
|
||
return lab_data
|
||
|
||
# 添加新的API端点用于清空所有人才和工程研究中心信息
|
||
@app.post("/api/clear-all-data")
|
||
async def clear_all_data(
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
try:
|
||
# 删除所有人才数据
|
||
db.query(models.Talent).delete()
|
||
|
||
# 删除所有工程研究中心数据
|
||
db.query(models.Lab).delete()
|
||
|
||
# 提交事务
|
||
db.commit()
|
||
|
||
return {"success": True, "message": "所有数据已清空"}
|
||
except Exception as e:
|
||
db.rollback()
|
||
return JSONResponse(
|
||
status_code=500,
|
||
content={"success": False, "message": f"清空数据失败: {str(e)}"}
|
||
)
|
||
|
||
# 添加端点用于保存二级维度结构
|
||
@app.post("/dimensions/save")
|
||
async def save_dimensions_structure(
|
||
request: schemas.SaveDimensionsRequest,
|
||
current_user: models.User = Depends(get_current_active_user),
|
||
db: Session = Depends(get_db)
|
||
):
|
||
try:
|
||
category = request.category
|
||
new_dimensions = request.dimensions
|
||
|
||
# 先删除该类别的所有现有维度
|
||
existing_dimensions = db.query(models.Dimension).filter(models.Dimension.category == category).all()
|
||
for dim in existing_dimensions:
|
||
db.delete(dim)
|
||
|
||
# 添加新的维度结构
|
||
for dimension in new_dimensions:
|
||
sub_dimensions_data = None
|
||
# 检查是否有子维度数据，优先使用subDimensions字段（前端使用的字段名）
|
||
if hasattr(dimension, 'subDimensions') and dimension.subDimensions:
|
||
sub_dimensions_data = [
|
||
{"name": sub.name, "weight": sub.weight, "description": getattr(sub, 'description', None)}
|
||
for sub in dimension.subDimensions
|
||
]
|
||
# 兼容处理sub_dimensions字段
|
||
elif dimension.sub_dimensions:
|
||
sub_dimensions_data = [
|
||
{"name": sub.name, "weight": sub.weight, "description": getattr(sub, 'description', None)}
|
||
for sub in dimension.sub_dimensions
|
||
]
|
||
|
||
db_dimension = models.Dimension(
|
||
name=dimension.name,
|
||
category=category,
|
||
weight=0.0, # 一级维度不需要权重
|
||
description=dimension.description,
|
||
sub_dimensions=sub_dimensions_data
|
||
)
|
||
db.add(db_dimension)
|
||
|
||
db.commit()
|
||
return {"success": True, "message": f"已成功保存{len(new_dimensions)}个维度及其子维度"}
|
||
|
||
except Exception as e:
|
||
db.rollback()
|
||
return JSONResponse(
|
||
status_code=500,
|
||
content={"success": False, "message": f"保存维度失败: {str(e)}"}
|
||
)
|
||
|
||
# 启动时初始化数据库
|
||
@app.on_event("startup")
|
||
async def startup_db_client():
|
||
# 创建表（如果不存在）
|
||
models.Base.metadata.create_all(bind=engine)
|
||
|
||
# 运行数据库表结构修改脚本
|
||
try:
|
||
from alter_table import check_and_alter_table
|
||
check_and_alter_table()
|
||
print("数据库表结构检查完成")
|
||
except Exception as e:
|
||
print(f"运行表结构修改脚本时出错: {e}")
|
||
|
||
# 初始化维度数据
|
||
from init_dimensions import init_dimensions
|
||
init_dimensions()
|
||
|
||
# 初始化其他数据
|
||
from database import SessionLocal
|
||
db = SessionLocal()
|
||
try:
|
||
# 检查是否已经存在用户数据
|
||
existing_user = db.query(models.User).filter(models.User.username == "admin").first()
|
||
|
||
if not existing_user:
|
||
print("初始化默认用户...")
|
||
hashed_password = get_password_hash("123456")
|
||
default_user = models.User(
|
||
username="admin",
|
||
email="admin@example.com",
|
||
full_name="系统管理员",
|
||
hashed_password=hashed_password
|
||
)
|
||
db.add(default_user)
|
||
db.commit()
|
||
print("默认用户已创建: admin/123456")
|
||
finally:
|
||
db.close()
|
||
|
||
if __name__ == "__main__":
|
||
import uvicorn
|
||
uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True) |