diff --git a/api/api_v1/endpoints/interview.py b/api/api_v1/endpoints/interview.py index 6070ca4..e567328 100644 --- a/api/api_v1/endpoints/interview.py +++ b/api/api_v1/endpoints/interview.py @@ -17,6 +17,7 @@ import crud, schemas from common import * from api import deps +from core.configuration import * from db import get_database from db.ckdb import get_ck_db, CKDrive, ckdb from db.redisdb import get_redis_pool, RedisDrive @@ -24,7 +25,7 @@ from db.redisdb import get_redis_pool, RedisDrive from models.behavior_analysis import BehaviorAnalysis from models.user_analysis import UserAnalysis from models.interview_zsgc import InterviewDo -from utils import DfToStream, get_bijiao +from utils import DfToStream, get_bijiao, get_time, qujian_time, Download_xlsx router = APIRouter() @@ -154,3 +155,113 @@ async def event_edit( """新增职位""" await crud.jobs.insert_job(db, data_in) return schemas.Msg(code=200, msg='ok', data='') + +@router.post("/condition") +async def interview_insert( + request: Request, + data_in: schemas.Interview, + ckdb: CKDrive = Depends(get_ck_db) +) -> schemas.Msg: + """ 面试情况 """ + res = data_in.date + strs = [] + for k, v in res.items(): + if v != '': + if 'int' in str(type(v)): + str_s = f"{k} = {v}" + strs.append(str_s) + else: + str_s = f"{k} = '{v}'" + strs.append(str_s) + where = ' and '.join(strs) + # 当前日期 + times = get_time() + # 今天的面试 + if data_in.time_type == 'now': + # 查询返回的数据一共多少条 + len_sql = f"""select uid from HR.resumes where {where} and toDate(star_time) == '{times}' ORDER BY event_time""" + sql = f"""select interview_round,interview_type,star_time,end_time,name,phone,job_name,hr_name, + feedback,interview_name from HR.resumes where {where} and toDate(star_time) == '{times}' ORDER BY event_time + LIMIT 10 OFFSET {(data_in.pages - 1) * 10}""" + # 明天及之后的面试 + elif data_in.time_type == 'tomorrow': + len_sql = f"""select uid from HR.resumes where {where} and toDate(star_time) > '{times}' ORDER BY event_time""" + sql = f"""select interview_round,interview_type,star_time,end_time,name,phone,job_name,hr_name, + feedback,interview_name from HR.resumes where {where} and toDate(star_time) > '{times}' ORDER BY event_time + LIMIT 10 OFFSET {(data_in.pages - 1) * 10}""" + # 昨天及以前的面试 + else: + len_sql = f"""select uid from HR.resumes where {where} and toDate(star_time) < '{times}' ORDER BY event_time""" + sql = f"""select interview_round,interview_type,star_time,end_time,name,phone,job_name,hr_name, + feedback,interview_name from HR.resumes where {where} and toDate(star_time) < '{times}' ORDER BY event_time + LIMIT 10 OFFSET {(data_in.pages - 1) * 10}""" + if where == '': + len_sql = len_sql.replace('where and', 'where', 1) + sql = sql.replace('where and', 'where', 1) + # 返回数据条数 + df_len = await ckdb.query_dataframe(len_sql) + len_date = len(df_len) + df = await ckdb.query_dataframe(sql) + if df.empty: + return schemas.Msg(code=-9, msg='无数据', data='') + data = {'lens': len_date, + 'columns': df.columns.tolist(), + 'values': df.values.tolist() + } + return schemas.Msg(code=200, msg='ok', data=data) + +@router.post("/get_job") +async def event_edit( + request: Request, + data_in: schemas.Jobs = None, + db: AsyncIOMotorDatabase = Depends(get_database), + current_user: schemas.UserDB = Depends(deps.get_current_user) +) -> schemas.Msg: + """获取职位名称""" + # 获取对应条件的职位 + res = await crud.jobs.all_fields(db, data_in) + # 获取职位数量 + num = len(res) + job_list = [] + job_id = [] + for i in res: + # 获取职位名称 + job_list.append(i['job_name']) + job_id.append(i['job_id']) + data = { + 'num': num, + 'job_list': job_list, + 'job_id': job_id + } + return schemas.Msg(code=200, msg='ok', data=data) + + +@router.post("/download_interview") +async def download_inter( + request: Request, + data_in: schemas.Timesinter, + ckdb: CKDrive = Depends(get_ck_db)): + """导出所有面试安排""" + sql = f"""SELECT interview_type,interview_stage,star_time,end_time,name,phone,job_name,hr_name,interview_name, + interview_sign FROM HR.resumes WHERE toDate(star_time) >= '{data_in.start_time}' and toDate(star_time) <= '{data_in.end_time}'""" + df = await ckdb.query_dataframe(sql) + # xlsx表名 + xlsx_name =data_in.start_time + '~' + data_in.end_time + '(包含起止日)' + datas = [] + for i in range(len(df)): + one_data = [] + one_data.append(interview_type_dict[df['interview_type'][i]]) + one_data.append(interview_stage_dict[df['interview_stage'][i]]) + df_time = qujian_time(df['star_time'][i], df['end_time'][i]) + one_data.append(df_time) + one_data.append(df['name'][i]) + one_data.append(df['phone'][i]) + one_data.append(df['job_name'][i]) + one_data.append(df['hr_name'][i]) + one_data.append(df['interview_name'][i]) + one_data.append(interview_sign_dict[df['interview_sign'][i]]) + datas.append(one_data) + 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'] + dfmi = pd.DataFrame(data=datas, columns=columns) + Download = Download_xlsx(dfmi, xlsx_name) + return Download \ No newline at end of file diff --git a/core/configuration.py b/core/configuration.py new file mode 100644 index 0000000..241742e --- /dev/null +++ b/core/configuration.py @@ -0,0 +1,20 @@ +# 基础的配置文件，用来映射数据返回给前端 + +# 面试类型 +interview_type_dict = {0: '线上面试', 1: '线下面试'} +# 面试签到 +interview_sign_dict = {1: '已签到', 0: '未签到'} +# 面试反馈 +feedback_dict = {1: '已反馈', 0: '未反馈'} +# 面试轮次 +interview_round_idct = {1: '初试', 2: '复试', 3: '终试'} +# 简历阶段 +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'} +# 简历来源 +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'} +# 学历 +education_dict = {1: '大专', 2: '本科', 3: '研究生', 4: '博士', 5: '硕士'} +# 推荐状态 +mmended_state_dict = {0: '未推荐', 1: '已推荐'} +# 当前面试状态 +interview_state_dict = {1: '待安排', 2: '面试中', 3: '已结束'} \ No newline at end of file diff --git a/liwei_接口文档.md b/liwei_接口文档.md new file mode 100644 index 0000000..f783c67 --- /dev/null +++ b/liwei_接口文档.md @@ -0,0 +1,53 @@ +#面试情况 +api:/api/v1/itr/condition +请求方式：post +参数：选择全部时传空字符串 + job_name: str # 应聘职位 + hr_name: str # 面试负责人 + interview_name: str # 面试官 + interview_type: str # 面试类型 + interview_sign: int # 面试签到 + feedback: int # 面试反馈 + interview_round: int # 面试轮次 + pages: int = 1 # 分页的当前页 + time_type: str # 要查询的时间范围类型 + +#获取职位名称 +api:/api/v1/itr/get_job +请求方式:post +参数:可不传 + +#新增职位 +api:/api/v1/itr/add_job +请求方式：post +参数: + job_id: str = int(time.time()) # 职位的唯一id + job_name: str # 职位名称 + job_sector: str # 职位部门 + job_nature: str # 职位性质 + job_priority: str # 职位优先级 + owner_name: str # 渠道 + principal: str # 负责人 + patronn: str # 协助人 + start_time: datetime = datetime.now() # 开始招聘时间 + function_type: str # 职能类型 + filtering_rules: bool # 是否使用筛选规则 + hiring_needs: bool # 是否关联招聘需求 + auto_repeater: bool # 是否自动转发 + cacsi_count: int = 0 # 面试满意度反馈次数 + state: bool = True # 职位招聘状态 + job_num: int # 招聘人数 + education: str # 学历要求 + job_rank: str # 职位级别 + work_exp: str # 工作经验 + report_obj: str # 汇报对象 + min_money: int # 薪资范围min + max_money: int # 薪资范围max + requirement: str # 需求描述 + +#导出面试安排 +api:/api/v1/itr/download_interview +请求方式：post +参数: + start_time: str # 开始时间 + end_time: str # 结束时间 diff --git a/schemas/interview_plan.py b/schemas/interview_plan.py index ebca0d3..80c48a7 100644 --- a/schemas/interview_plan.py +++ b/schemas/interview_plan.py @@ -5,12 +5,19 @@ from typing import Optional class Interview(BaseModel): - job_name: str =None # 应聘职位 - hr_name: str # 面试负责人 - interview_name: str # 面试官 - interview_type: str # 面试类型 - interview_sign: int # 面试签到 - feedback: int # 面试反馈 - interview_round: int # 面试轮次 + date: dict # 要查询的条件 pages: int = 1 # 分页的当前页 time_type: str # 要查询的时间范围类型 + # date包含如下参数,如选择全部的则传空字符串 + # job_name: str # 应聘职位 + # hr_name: str # 面试负责人 + # interview_name: str # 面试官 + # interview_type: int # 面试类型 + # interview_sign: int # 面试签到 + # feedback: int # 面试反馈 + # interview_round: int # 面试轮次 + + +class Timesinter(BaseModel): + start_time: str # 开始时间 + end_time: str # 结束时间 diff --git a/utils/func.py b/utils/func.py index d164861..cdbe259 100644 --- a/utils/func.py +++ b/utils/func.py @@ -2,6 +2,8 @@ import random import time import datetime import pandas as pd + + def get_uid(): return hex(int(time.time() * 10 ** 7) + random.randint(0, 10000))[2:] @@ -22,7 +24,9 @@ def estimate_data(data_type): return "Nullable(DateTime('UTC'))" else: return "Nullable(String)" -#将字典变成字符串 + + +# 将字典变成字符串 def dict_to_str(dic): c = str() b = 0 @@ -36,18 +40,21 @@ def dict_to_str(dic): c += "\"%s\":\"%s\"}" % (k, v) return c -def getEveryDay(begin_date,end_date): + +def getEveryDay(begin_date, end_date): # 前闭后闭 date_list = [] begin_date = datetime.datetime.strptime(begin_date, "%Y-%m-%d") - end_date = datetime.datetime.strptime(end_date,"%Y-%m-%d") + end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d") while begin_date <= end_date: date_str = begin_date.strftime("%Y-%m-%d") date_list.append(date_str) begin_date += datetime.timedelta(days=1) return date_list -#print(getEveryDay('2016-01-01','2017-05-11')) -def Download_xlsx(df,name): + + +# print(getEveryDay('2016-01-01','2017-05-11')) +def Download_xlsx(df, name): """ 下载功能 name为文件名 @@ -56,14 +63,15 @@ def Download_xlsx(df,name): import mimetypes from utils import DfToStream from fastapi.responses import StreamingResponse - file_name=quote(f'{name}.xlsx') + file_name = quote(f'{name}.xlsx') mime = mimetypes.guess_type(file_name)[0] df_to_stream = DfToStream((df, name)) with df_to_stream as d: export = d.to_stream() - Download=StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'}) + Download = StreamingResponse(export, media_type=mime, headers={'Content-Disposition': f'filename="{file_name}"'}) return Download + def jiange_insert(list_date): """ 间隔1条插入一条数据插入数据 @@ -76,6 +84,7 @@ def jiange_insert(list_date): i += 2 return list_date + def create_df(resp): """ 分布分析外部下载功能的df数据 @@ -113,7 +122,9 @@ def create_df(resp): columns.insert(0, '事件发生时间') df = pd.DataFrame(data=date, columns=columns) return df -def create_neidf(resp,columnName): + + +def create_neidf(resp, columnName): """ 分布分析内部下载功能的df数据 """ @@ -151,12 +162,46 @@ def create_neidf(resp,columnName): df = pd.DataFrame(data=date, columns=columns) return df + def random_hex(): """ 生成16位随机数 :return: 随机数 """ - result = hex(random.randint(0,16**16)).replace('0x','').upper() - if(len(result)<16): - result = '0'*(16-len(result))+result - return result \ No newline at end of file + result = hex(random.randint(0, 16 ** 16)).replace('0x', '').upper() + if (len(result) < 16): + result = '0' * (16 - len(result)) + result + return result + + +def get_time(fmt: str = '%Y-%m-%d') -> str: + ''' + 获取当前时间 + ''' + ts = time.time() + ta = time.localtime(ts) + t = time.strftime(fmt, ta) + return t + + +def import_excel(data, columns, name): + """ + 导出数据到xlsx表里面 + :param data: 需要导出的数据 + :param columns: df的表名 例：['a','b','c'] + :param name: 文件名。例：'随机数' + :return: + """ + zh = pd.DataFrame(data, columns=columns) + zh.to_excel(f'{name}.xlsx', index=False, header=True) + + +def qujian_time(start_time, end_time): + """ + 把两个时间变成区间 + :param start_time: '2022-07-01 10:00:00' + :param end_time: '2022-07-01 10:30:00' + :return: '2022-07-01 10:00:00~10:30:00' + """ + timess = str(end_time).split(' ')[-1] + return str(start_time) + '~' + timess