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

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

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'}

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

schemas/interview_plan.py

@@ -5,12 +5,19 @@ from typing import Optional
 
 
 class Interview(BaseModel):
-    job_name: str =None  # 应聘职位
+    date: dict  # 要查询的条件
-    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
+    # 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

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()
+    result = hex(random.randint(0, 16 ** 16)).replace('0x', '').upper()
-    if(len(result)<16):
+    if (len(result) < 16):
-        result = '0'*(16-len(result))+result
+        result = '0' * (16 - len(result)) + result
-    return 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