Djangoセロリ仕事は、私に「属性誤り」を見せています?

私はマイクロソフトグラフAPIからデータを取得し、私のdjangoビューの中にデータを取得するJjangoのWebアプリケーションを構築する.Py私は私のタスク' AddRange apichenデータを呼び出す午前
タスク.パイ

from celery import shared_task
from django.http import JsonResponse
import requests
from django.shortcuts import render
import pandas as pd
from sqlalchemy import create_engine
from tutorial.models import demo,assignment_details
from tutorial.graph_helper import *
from tutorial.auth_helper import get_sign_in_flow, get_token_from_code, store_user, remove_user_and_token, get_token,load_cache,save_cache,get_msal_app
from graph_tutorial.celery import app
import yaml
import msal
import os
import time
from . graph_helper import *

@shared_task
def add_csv_data():
    file = 'data.csv'
    df = pd.read_csv(file)
    engine = create_engine('sqlite:///db.sqlite3')
    # engine = create_engine(':memory:')
    df1 = df.to_sql(demo._meta.db_table, if_exists='replace', con=engine, index=False)
    print(demo._meta.db_table)
    return df1


@shared_task(bind=True)
# @app.task(bind=True)
def add_api_data(request):
  import json
#   from tutorial.views import initialize_context
  from . import views
  views.initialize_context
  #graph api base url
  base_url = "https://graph.microsoft.com/v1.0/"
  print(base_url)
  # endpoint = base_url +'education/me/assignments'
  # endpoint = base_url +'education/classes/9289f463-badc-4722-8b51-e6f1326a7f02/assignments'
  # endpoint = base_url +'education/classes/9289f463-badc-4722-8b51-e6f1326a7f02/members'
  # endpoint = base_url+ 'education/classes/9289f463-badc-4722-8b51-e6f1326a7f02/assignments/assignmentsid{{}}/submissions/submissionid{{}}/outcomes'

  headers = {'Authorization':'Bearer'}
  from tutorial.auth_helper import get_token
  token = get_token(request)
  print(token)
#   print(hasattr(token,'request'))
#   print(dir(token))
  headers = {
    'Authorization': 'Bearer {0}'.format(token),
    'Content-Type': 'application/json'
  }

  # endpoint0 = base_url+ "education/classes/"
  # educatiom_data = json.loads(requests.get(endpoint0,headers=headers).text)


  # class_id = []
  # class_name = []
  # for i in range(len(educatiom_data['value'])):
  #     id_ = educatiom_data['value'][i]['id']
  #     name = educatiom_data['value'][i]['mailNickname']
  #     class_id.append(id_)
  #     class_name.append(name)

  class_id = ["9289f463-badc-4722-8b51-e6f1326a7f02","9cfdd9bf-3b7a-43da-bb6a-e19990d4252e","5c30e2ca-d0ec-4277-ad76-5559b04a7bed"]
  class_name = ["MarchFullstack2021","JanFrontendBatch2022","JanFullStackBatch2022"]

  import pandas as pd

  myDataFrame = pd.DataFrame(columns = ['Id','Name','Email','Assingments','duedate','submiteddate','unsubmiteddate','returndate','reassingndate','status','feedback','points','assingment_delay','assingment_check_delay'])

  for class_data_id,class_data_name in zip(class_id,class_name):
    #assingment endpoint graph api url
    endpoint1 = base_url+ "education/classes/"+class_data_id+"/assignments/"
    #saving assingment id in list from assingment end point url 
    data = json.loads(requests.get(endpoint1,headers=headers).text)
    assingment_id = []
    for i in range(len(data['value'])):
      assingment_id.append(data['value'][i]['id'])

    #creating muultiple assingment endpoint for grade url and saved into list

    outcomsurl = []
    for i in assingment_id:
      # endpoint2 = endpoint1 + i + "/submissions/?$filter=status"+" "+"eq"+" "+"'working'&?$expand=outcomes"
      # endpoint2 = endpoint1 + i + "/submissions/?$filter=status"+" "+"eq"+" "+"'submitted'&?$expand=outcomes"
      # endpoint2 = endpoint1 + i + '/susubmissions/?$filter=status eq "working"&?$expand=outcomes'
      endpoint2 = endpoint1 + i + "/submissions?$expand=outcomes"
      outcomsurl.append(endpoint2)


    #all student assingment json response

    data_upd_list = []
    for i in range(len(outcomsurl)):
        templist1 = []
        data2 = json.loads(requests.get(outcomsurl[i],headers=headers).text)
        value1 = data2['value']
        for j in value1:
            templist1.append(j)
        if '@odata.nextLink' in data2.keys():
          datalink = data2['@odata.nextLink']
          data_next = json.loads(requests.get(datalink,headers=headers).text)
          value2 = data_next['value']
          for k in value2:
              templist1.append(k)
        data_upd_list.append(templist1)


    def multi_date(x):
      import pandas as pd
      final_date = []
      for i in range(len(data_upd_list)):
          date = []
          temp_id = []
          final_points1 = []
          final_points2 = []
          for j in range(len(data_upd_list[i])):
              temp_id.append(data_upd_list[i][j]['submittedBy']['user']['id'])
              if data_upd_list[i][j][x]==None:
                  date.append("Not Available")
              else:
                  date.append(data_upd_list[i][j][x][:10])
          for id1,date1 in sorted(zip(temp_id,date),key = lambda x: x[0]):
              zip_data = list((id1,date1))
              final_points1.append(zip_data[0])
              final_points2.append(zip_data[1])
          final_date.append(final_points1)
          final_date.append(final_points2)
      df = pd.DataFrame(final_date).T
      return df

    submit_df = multi_date('submittedDateTime')
    unsubmit_df = multi_date('unsubmittedDateTime')
    return_df = multi_date('returnedDateTime')
    reassingn_df = multi_date('reassignedDateTime')
    status_df = multi_date('status')

    def feedback():
      import pandas as pd
      final_feedback = []
      for i in range(len(data_upd_list)):
          feedback_text = []
          temp_id = []
          final_points1 = []
          final_points2 = []
          for j in range(len(data_upd_list[i])):
              temp_id.append(data_upd_list[i][j]['submittedBy']['user']['id'])
              if data_upd_list[i][j]['outcomes'][0]['publishedFeedback']==None:
                  feedback_text.append("No feedback")
              else:
                  feedback_text.append(data_upd_list[i][j]['outcomes'][0]['publishedFeedback']['text']['content'])
          for id1,text in sorted(zip(temp_id,feedback_text),key = lambda x: x[0]):
              zip_data = list((id1,text))
              final_points1.append(zip_data[0])
              final_points2.append(zip_data[1])
          final_feedback.append(final_points1)
          final_feedback.append(final_points2)
      df = pd.DataFrame(final_feedback).T
      return df

    feedback_df = feedback()



    assingment_data = []
    assingment_list = json.loads(requests.get(endpoint1,headers=headers).text)
    assingment_data.append(assingment_list)

    # #all user include teacher and student dispaly name ,email,id data
    member_endpoint = base_url+ "education/classes/"+class_data_id+ "/members?$select=id,displayName,userPrincipalName"
    teacher_endpoint = base_url+ "education/classes/"+class_data_id+ "/teachers?$select=id,displayName,userPrincipalName"
    member_data = []
    import pandas as pd
    member_list = json.loads(requests.get(member_endpoint,headers=headers).text)
    member_data.append(member_list)
    df_member = pd.DataFrame(member_data[0]['value'])

    # #Only teacher dispaly name ,email,id data
    teacher_data = []
    teacher_list = json.loads(requests.get(teacher_endpoint,headers=headers).text)
    teacher_data.append(teacher_list)
    df_teacher = pd.DataFrame(teacher_data[0]['value'])

    # #to get only student data we remove techer data into  all memeber this exclude teacher data and return only student data
    student_df = pd.concat([df_teacher,df_member], axis=0, ignore_index=True).drop_duplicates(subset=["id","userPrincipalName"],keep=False, ignore_index=True)
    student_df = student_df.sort_values(by='id').reset_index().drop('index',axis=1)

    # #this is final grade list of student sorted for by user id beacuse all assingment grade point mismatch with corrsopond users.
    final_points = []
    for i in range(len(data_upd_list)):
        temp_points = []
        temp_id = []
        final_points1 = []
        final_points2 = []
        for j in range(len(data_upd_list[i])):
            temp_id.append(data_upd_list[i][j]['submittedBy']['user']['id'])
            if (data_upd_list[i][j]['outcomes'][1]['points'] is None):
                temp_points.append('No Points')
            else:
                temp_points.append(str(data_upd_list[i][j]['outcomes'][1]['points']['points']))

        for letter, number in sorted(zip(temp_points,temp_id),key = lambda x: x[1]):
            zip_data = list((number,letter))
            final_points1.append(zip_data[0])
            final_points2.append(zip_data[1])
        final_points.append(final_points1)
        final_points.append(final_points2)

    # #assingment name saved into list used with name and id for pandas dataframe
    assignment_name1 = []
    for i in range(len(assingment_data[0]['value'])):
        var = assingment_data[0]['value'][i]['displayName']
        assignment_name1.append(var)

    assignment_duedate = []
    for i in range(len(data['value'])):
        var = data['value'][i]['dueDateTime'][:10]
        assignment_duedate.append(var)

    asdue_df = pd.DataFrame(assignment_duedate)
    asdue_df.columns = ['duedate']

    # #assingment name saved into list used for final dataframe
    assignment_name = []
    for i in range(len(assingment_data[0]['value'])):
        var = assingment_data[0]['value'][i]['displayName']
        assignment_name.append(var)

    #insert id column name with respective to assingment column
    for i in range(0,len(final_points),2):
      assignment_name.insert(i,f'id')

    df = pd.DataFrame(final_points).T
    df.columns = assignment_name

    new = []
    for i in range(0,len(df.columns),2):
        df1 = pd.merge(student_df,df.iloc[:, i:i+2],on='id',how='outer').iloc[:,-1].values.tolist()
        new.append(df1)

    # #final dataframe of student with grade of all student with respective to submitted assingment
    new_df = pd.DataFrame(new).T
    new_df.columns = assignment_name1
    final_df = pd.concat([student_df,new_df],axis=1)
    final_df = final_df.iloc[:student_df.shape[0],:final_df.shape[1]]
    final_df

    def column_name(id1,y):
      a = [id1,y]
      date = []
      for k in range(0,len(assignment_name1)):
          date.append(a[0])
          date.append(a[-1])
      return date


    def new_df(x,y):
      new1 = []
      for i in range(0,len(x.columns),2):
          df1 = pd.merge(y,x.iloc[:, i:i+2],on='id',how='outer').iloc[:,-1].values.tolist()
          new1.append(df1)
      new_df1 = pd.DataFrame(new1).T
      final_df1 = pd.concat([y,new_df1],axis=1)
      final_df1 = final_df1.iloc[:y.shape[0],:final_df1.shape[1]]
      return final_df1

    sdf = []
    submiteddate = column_name('id','submiteddate')
    submit_df.columns = submiteddate
    df1 = new_df(submit_df,student_df)
    sdf.append(df1)
    unsubmiteddate = column_name('id','unsubmiteddate')
    unsubmit_df.columns = unsubmiteddate
    df2 = new_df(unsubmit_df,student_df)
    sdf.append(df2)
    returndate = column_name('id','return')
    return_df.columns = returndate
    df3 = new_df(return_df,student_df)
    sdf.append(df3)
    reassingndate = column_name('id','reassingndate')
    reassingn_df.columns = reassingndate
    df4 = new_df(reassingn_df,student_df)
    sdf.append(df4)
    status = column_name('id','status')
    status_df.columns = status
    df5 = new_df(status_df,student_df)
    sdf.append(df5)
    feedback = column_name('id','feedback')
    feedback_df.columns = feedback
    df6 = new_df(feedback_df,student_df)
    sdf.append(df6)
    sdf.append(final_df)

    kd =[]
    kd1 =[]
    for i in range(0,final_df.shape[0]):
        md = []
        md1 = []
        for j in sdf:
            sd = j.iloc[i,3:].values.tolist()
            sdinfo = j.iloc[i,:3].values.tolist()
            md.append(sd)
            md1.append(sdinfo)
        kd.append(md)
        kd1.append(md1)



    from datetime import datetime
    assingnment_delay = []
    for i in range(final_df.shape[0]):
        f_delay= []
        for j in range(0,final_df.iloc[:,3:].shape[1]):
            s = asdue_df['duedate'][j]
            fs = pd.DataFrame((kd[i])).T
            fs.columns = ['submiteddate','unsubmiteddate','returndate','reassingndate','status','feedback','points']
            p = fs['submiteddate'][j]
            r = datetime.now().strftime("%Y-%m-%d")
            if type(p) is float or p =='Not Available' or p is None:
                q = datetime.fromisoformat(r)-datetime.fromisoformat(s)
                f_delay.append(q.days)
            else:
                q = datetime.fromisoformat(p)-datetime.fromisoformat(s)
                f_delay.append(q.days)
        assingnment_delay.append(f_delay)

    from datetime import datetime
    assingnment_check_delay = []
    for i in range(final_df.shape[0]):
        c_delay= []
        for j in range(0,final_df.iloc[:,3:].shape[1]):
            fs = pd.DataFrame((kd[i])).T
            fs.columns = ['submiteddate','unsubmiteddate','returndate','reassingndate','status','feedback','points']
            p = fs['submiteddate'][j]
            u = fs['feedback'][j]
            v = fs['points'][j]
            r = datetime.now().strftime("%Y-%m-%d")
            if ((type(u) is None or u =='No feedback') and (type(v) is None or v =='No Points')) and (p !='Not Available' or p is None):
                c_data = datetime.fromisoformat(r)-datetime.fromisoformat(p)
                c_delay.append(c_data.days)
            else:
                c_delay.append('Not Available')
        assingnment_check_delay.append(c_delay)


    for i in range(0,final_df.shape[0]):
        fs = pd.DataFrame((kd[i])).T
        fs1 = pd.DataFrame(kd1[i][0]).T
        fs1.columns = ['Id','Name','Email']
        asgment = pd.DataFrame(assignment_name1)
        asgment.columns = ['Assingments']
        fs.columns = ['submiteddate','unsubmiteddate','returndate','reassingndate','status','feedback','points']
        assingment_delay_df = pd.DataFrame(assingnment_delay[i],columns =['assingment_delay'])
        assingment_check_delay = pd.DataFrame(assingnment_check_delay[i],columns =['assingment_check_delay'])
        newfs1 = pd.concat([fs1]*len(assignment_name1),ignore_index=True)
        newfs = pd.concat([newfs1,asgment,asdue_df,fs,assingment_delay_df,assingment_check_delay],axis=1)
        # print(newfs)
        myDataFrame = pd.concat([myDataFrame,newfs],axis=0,ignore_index=True)
  # print(myDataFrame.shape)

  import pandas as pd
  import sqlite3 as sq

#   table_name = 'student3batchinfo' # table and file name
#   conn = sq.connect('{}.sqlite3'.format(table_name)) 
#   final_df = myDataFrame.to_sql(table_name, conn, if_exists='replace')
#   conn.close()
  table_name = 'assignment_details'
  engine = create_engine('sqlite:///db.sqlite3')
  apidf = myDataFrame.to_sql(table_name, if_exists='replace', con=engine)
  return 

私の意見.パイ

from django.shortcuts import render
from django.http import HttpResponse, HttpResponseRedirect
from django.urls import reverse
from datetime import datetime, timedelta
from dateutil import tz, parser
from tutorial.auth_helper import get_sign_in_flow, get_token_from_code, store_user, remove_user_and_token, get_token
from tutorial.graph_helper import *
from celery import shared_task
import time
from tutorial import task
import pandas as pd
from tutorial.models import demo,assignment_details

def education(request): 
  add_api_data = task.add_api_data.delay()
  api_list = assignment_details.objects.all().values()
  return render(request, 'tutorial/education.html', {'api_list':api_list,})


def home1(request):
  context = initialize_context(request)
  add_data = task.add_csv_data.delay()
  title1 = demo.objects.all().values()
  return render(request, 'tutorial/demo1.html',{'title1':title1,'context':context})

このタスクをコールするたびに、次のように「属性エラー」を表示します

Traceback (most recent call last):
  File "C:\Users\Ajay\AppData\Local\Programs\Python\Python310\lib\site-packages\celery\app\trace.py", line 451, in trace_task
    R = retval = fun(*args, **kwargs)
  File "C:\Users\Ajay\AppData\Local\Programs\Python\Python310\lib\site-packages\celery\app\trace.py", line 734, in __protected_call__
    return self.run(*args, **kwargs)
  File "F:\django_template\grapapi\graph_tutorial\tutorial\task.py", line 58, in add_api_data
    token = get_token(request)
  File "F:\django_template\grapapi\graph_tutorial\tutorial\auth_helper.py", line 72, in get_token
    cache = load_cache(request)
  File "F:\django_template\grapapi\graph_tutorial\tutorial\auth_helper.py", line 17, in load_cache
    if request.session.get('token_cache'):
AttributeError: 'add_api_data' object has no attribute 'session'