1.导出所有简历面试安排
2.获取职位名称 3.面试情况详情
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@ -17,6 +17,7 @@ import crud, schemas
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from common import *
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from api import deps
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from core.configuration import *
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from db import get_database
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from db.ckdb import get_ck_db, CKDrive, ckdb
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from db.redisdb import get_redis_pool, RedisDrive
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@ -24,7 +25,7 @@ from db.redisdb import get_redis_pool, RedisDrive
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from models.behavior_analysis import BehaviorAnalysis
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from models.user_analysis import UserAnalysis
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from models.interview_zsgc import InterviewDo
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from utils import DfToStream, get_bijiao
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from utils import DfToStream, get_bijiao, get_time, qujian_time, Download_xlsx
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router = APIRouter()
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@ -154,3 +155,113 @@ async def event_edit(
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"""新增职位"""
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await crud.jobs.insert_job(db, data_in)
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return schemas.Msg(code=200, msg='ok', data='')
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@router.post("/condition")
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async def interview_insert(
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request: Request,
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data_in: schemas.Interview,
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ckdb: CKDrive = Depends(get_ck_db)
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) -> schemas.Msg:
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""" 面试情况 """
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res = data_in.date
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strs = []
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for k, v in res.items():
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if v != '':
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if 'int' in str(type(v)):
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str_s = f"{k} = {v}"
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strs.append(str_s)
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else:
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str_s = f"{k} = '{v}'"
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strs.append(str_s)
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where = ' and '.join(strs)
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# 当前日期
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times = get_time()
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# 今天的面试
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if data_in.time_type == 'now':
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# 查询返回的数据一共多少条
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len_sql = f"""select uid from HR.resumes where {where} and toDate(star_time) == '{times}' ORDER BY event_time"""
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sql = f"""select interview_round,interview_type,star_time,end_time,name,phone,job_name,hr_name,
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feedback,interview_name from HR.resumes where {where} and toDate(star_time) == '{times}' ORDER BY event_time
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LIMIT 10 OFFSET {(data_in.pages - 1) * 10}"""
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# 明天及之后的面试
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elif data_in.time_type == 'tomorrow':
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len_sql = f"""select uid from HR.resumes where {where} and toDate(star_time) > '{times}' ORDER BY event_time"""
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sql = f"""select interview_round,interview_type,star_time,end_time,name,phone,job_name,hr_name,
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feedback,interview_name from HR.resumes where {where} and toDate(star_time) > '{times}' ORDER BY event_time
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LIMIT 10 OFFSET {(data_in.pages - 1) * 10}"""
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# 昨天及以前的面试
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else:
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len_sql = f"""select uid from HR.resumes where {where} and toDate(star_time) < '{times}' ORDER BY event_time"""
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sql = f"""select interview_round,interview_type,star_time,end_time,name,phone,job_name,hr_name,
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feedback,interview_name from HR.resumes where {where} and toDate(star_time) < '{times}' ORDER BY event_time
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LIMIT 10 OFFSET {(data_in.pages - 1) * 10}"""
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if where == '':
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len_sql = len_sql.replace('where and', 'where', 1)
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sql = sql.replace('where and', 'where', 1)
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# 返回数据条数
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df_len = await ckdb.query_dataframe(len_sql)
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len_date = len(df_len)
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df = await ckdb.query_dataframe(sql)
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if df.empty:
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return schemas.Msg(code=-9, msg='无数据', data='')
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data = {'lens': len_date,
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'columns': df.columns.tolist(),
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'values': df.values.tolist()
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}
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return schemas.Msg(code=200, msg='ok', data=data)
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@router.post("/get_job")
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async def event_edit(
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request: Request,
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data_in: schemas.Jobs = None,
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db: AsyncIOMotorDatabase = Depends(get_database),
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current_user: schemas.UserDB = Depends(deps.get_current_user)
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) -> schemas.Msg:
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"""获取职位名称"""
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# 获取对应条件的职位
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res = await crud.jobs.all_fields(db, data_in)
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# 获取职位数量
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num = len(res)
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job_list = []
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job_id = []
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for i in res:
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# 获取职位名称
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job_list.append(i['job_name'])
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job_id.append(i['job_id'])
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data = {
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'num': num,
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'job_list': job_list,
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'job_id': job_id
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}
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return schemas.Msg(code=200, msg='ok', data=data)
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@router.post("/download_interview")
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async def download_inter(
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request: Request,
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data_in: schemas.Timesinter,
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ckdb: CKDrive = Depends(get_ck_db)):
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"""导出所有面试安排"""
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sql = f"""SELECT interview_type,interview_stage,star_time,end_time,name,phone,job_name,hr_name,interview_name,
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interview_sign FROM HR.resumes WHERE toDate(star_time) >= '{data_in.start_time}' and toDate(star_time) <= '{data_in.end_time}'"""
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df = await ckdb.query_dataframe(sql)
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# xlsx表名
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xlsx_name =data_in.start_time + '~' + data_in.end_time + '(包含起止日)'
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datas = []
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for i in range(len(df)):
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one_data = []
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one_data.append(interview_type_dict[df['interview_type'][i]])
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one_data.append(interview_stage_dict[df['interview_stage'][i]])
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df_time = qujian_time(df['star_time'][i], df['end_time'][i])
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one_data.append(df_time)
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one_data.append(df['name'][i])
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one_data.append(df['phone'][i])
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one_data.append(df['job_name'][i])
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one_data.append(df['hr_name'][i])
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one_data.append(df['interview_name'][i])
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one_data.append(interview_sign_dict[df['interview_sign'][i]])
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datas.append(one_data)
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columns = ['面试类型', '面试阶段', '面试时间', '候选人', '联系方式', '应聘职位', '面试负责人', '面试官', '面试签到']
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dfmi = pd.DataFrame(data=datas, columns=columns)
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Download = Download_xlsx(dfmi, xlsx_name)
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return Download
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20
core/configuration.py
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20
core/configuration.py
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# 基础的配置文件，用来映射数据返回给前端
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# 面试类型
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interview_type_dict = {0: '线上面试', 1: '线下面试'}
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# 面试签到
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interview_sign_dict = {1: '已签到', 0: '未签到'}
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# 面试反馈
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feedback_dict = {1: '已反馈', 0: '未反馈'}
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# 面试轮次
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interview_round_idct = {1: '初试', 2: '复试', 3: '终试'}
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# 简历阶段
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interview_stage_dict = {1: '初筛', 2: '用人部门复筛', 3: '初试', 4: '复试', 5: '沟通offer', 6: '待入职', 7: '淘汰', 8: '在职', 9: '离职'}
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# 简历来源
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owner_name_dict = {1: '前程无忧', 2: '人才库', 3: '智联招聘', 4: 'Boss直聘', 5: '58同城'}
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# 学历
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education_dict = {1: '大专', 2: '本科', 3: '研究生', 4: '博士', 5: '硕士'}
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# 推荐状态
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mmended_state_dict = {0: '未推荐', 1: '已推荐'}
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# 当前面试状态
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interview_state_dict = {1: '待安排', 2: '面试中', 3: '已结束'}
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53
liwei_接口文档.md
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53
liwei_接口文档.md
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#面试情况
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api:/api/v1/itr/condition
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请求方式：post
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参数：选择全部时传空字符串
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job_name: str # 应聘职位
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hr_name: str # 面试负责人
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interview_name: str # 面试官
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interview_type: str # 面试类型
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interview_sign: int # 面试签到
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feedback: int # 面试反馈
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interview_round: int # 面试轮次
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pages: int = 1 # 分页的当前页
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time_type: str # 要查询的时间范围类型
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#获取职位名称
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api:/api/v1/itr/get_job
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请求方式:post
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参数:可不传
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#新增职位
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api:/api/v1/itr/add_job
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请求方式：post
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参数:
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job_id: str = int(time.time()) # 职位的唯一id
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job_name: str # 职位名称
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job_sector: str # 职位部门
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job_nature: str # 职位性质
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job_priority: str # 职位优先级
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owner_name: str # 渠道
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principal: str # 负责人
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patronn: str # 协助人
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start_time: datetime = datetime.now() # 开始招聘时间
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function_type: str # 职能类型
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filtering_rules: bool # 是否使用筛选规则
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hiring_needs: bool # 是否关联招聘需求
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auto_repeater: bool # 是否自动转发
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cacsi_count: int = 0 # 面试满意度反馈次数
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state: bool = True # 职位招聘状态
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job_num: int # 招聘人数
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education: str # 学历要求
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job_rank: str # 职位级别
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work_exp: str # 工作经验
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report_obj: str # 汇报对象
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min_money: int # 薪资范围min
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max_money: int # 薪资范围max
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requirement: str # 需求描述
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#导出面试安排
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api:/api/v1/itr/download_interview
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请求方式：post
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参数:
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start_time: str # 开始时间
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end_time: str # 结束时间
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@ -5,12 +5,19 @@ from typing import Optional
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class Interview(BaseModel):
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job_name: str =None # 应聘职位
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hr_name: str # 面试负责人
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interview_name: str # 面试官
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interview_type: str # 面试类型
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interview_sign: int # 面试签到
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feedback: int # 面试反馈
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interview_round: int # 面试轮次
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date: dict # 要查询的条件
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pages: int = 1 # 分页的当前页
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time_type: str # 要查询的时间范围类型
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# date包含如下参数,如选择全部的则传空字符串
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# job_name: str # 应聘职位
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# hr_name: str # 面试负责人
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# interview_name: str # 面试官
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# interview_type: int # 面试类型
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# interview_sign: int # 面试签到
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# feedback: int # 面试反馈
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# interview_round: int # 面试轮次
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class Timesinter(BaseModel):
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start_time: str # 开始时间
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end_time: str # 结束时间
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@ -2,6 +2,8 @@ import random
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import time
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import datetime
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import pandas as pd
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def get_uid():
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return hex(int(time.time() * 10 ** 7) + random.randint(0, 10000))[2:]
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@ -22,7 +24,9 @@ def estimate_data(data_type):
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return "Nullable(DateTime('UTC'))"
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else:
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return "Nullable(String)"
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#将字典变成字符串
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# 将字典变成字符串
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def dict_to_str(dic):
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c = str()
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b = 0
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@ -36,18 +40,21 @@ def dict_to_str(dic):
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c += "\"%s\":\"%s\"}" % (k, v)
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return c
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def getEveryDay(begin_date,end_date):
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def getEveryDay(begin_date, end_date):
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# 前闭后闭
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date_list = []
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begin_date = datetime.datetime.strptime(begin_date, "%Y-%m-%d")
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end_date = datetime.datetime.strptime(end_date,"%Y-%m-%d")
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end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d")
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while begin_date <= end_date:
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date_str = begin_date.strftime("%Y-%m-%d")
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date_list.append(date_str)
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begin_date += datetime.timedelta(days=1)
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return date_list
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#print(getEveryDay('2016-01-01','2017-05-11'))
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def Download_xlsx(df,name):
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# print(getEveryDay('2016-01-01','2017-05-11'))
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def Download_xlsx(df, name):
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"""
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下载功能
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name为文件名
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@ -56,14 +63,15 @@ def Download_xlsx(df,name):
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import mimetypes
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from utils import DfToStream
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from fastapi.responses import StreamingResponse
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file_name=quote(f'{name}.xlsx')
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file_name = quote(f'{name}.xlsx')
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mime = mimetypes.guess_type(file_name)[0]
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df_to_stream = DfToStream((df, name))
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with df_to_stream as d:
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export = d.to_stream()
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Download=StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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Download = StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'})
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return Download
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def jiange_insert(list_date):
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"""
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间隔1条插入一条数据插入数据
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@ -76,6 +84,7 @@ def jiange_insert(list_date):
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i += 2
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return list_date
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def create_df(resp):
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"""
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分布分析外部下载功能的df数据
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@ -113,7 +122,9 @@ def create_df(resp):
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columns.insert(0, '事件发生时间')
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df = pd.DataFrame(data=date, columns=columns)
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return df
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def create_neidf(resp,columnName):
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def create_neidf(resp, columnName):
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"""
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分布分析内部下载功能的df数据
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"""
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@ -151,12 +162,46 @@ def create_neidf(resp,columnName):
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df = pd.DataFrame(data=date, columns=columns)
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return df
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def random_hex():
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"""
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生成16位随机数
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:return: 随机数
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"""
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result = hex(random.randint(0,16**16)).replace('0x','').upper()
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if(len(result)<16):
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result = '0'*(16-len(result))+result
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return result
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result = hex(random.randint(0, 16 ** 16)).replace('0x', '').upper()
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if (len(result) < 16):
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result = '0' * (16 - len(result)) + result
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return result
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def get_time(fmt: str = '%Y-%m-%d') -> str:
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'''
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获取当前时间
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'''
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ts = time.time()
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ta = time.localtime(ts)
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t = time.strftime(fmt, ta)
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return t
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def import_excel(data, columns, name):
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"""
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导出数据到xlsx表里面
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:param data: 需要导出的数据
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:param columns: df的表名 例：['a','b','c']
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:param name: 文件名。例：'随机数'
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:return:
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"""
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zh = pd.DataFrame(data, columns=columns)
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zh.to_excel(f'{name}.xlsx', index=False, header=True)
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def qujian_time(start_time, end_time):
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"""
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把两个时间变成区间
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:param start_time: '2022-07-01 10:00:00'
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:param end_time: '2022-07-01 10:30:00'
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:return: '2022-07-01 10:00:00~10:30:00'
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"""
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timess = str(end_time).split(' ')[-1]
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return str(start_time) + '~' + timess
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