def getMongoDB(**context):
    token = context.get("ti").xcom_pull(key="token")
    response = requests.get(
    url=f"{dRoW_api_end_url}/api/module/document-export/airflow/workflow/667a4df5af0b2f37bdf6e00d?export_type=0",
    headers={
        "x-access-token": f"Bearer {token}",
        "ICWPxAccessKey": "nd@201907ICWP_[1AG:4UdI){n=b~"
    })

    RISC_Data = json.loads(response.text)
    Mapping= {
        "A3. Date Time" : "a3_date_time",
        "A1. No. of Walk": "a1_no_of_walk",
    }
    saftey_cats=[
        "1. General",
        "2. Flammable Liquids / Gases",
        "3. Hazardous Substances",
        "4. Electricity",
        "5. Fire Precaution",
        "6. Working Area",
        "7. Lifting Operation",
        "8. Material Hoist",
        "9. Confined Spaces",
        "10. Noise",
        "11. Gas Welding and Cutting Equipment",
        "12. Electricity‐arc Welding",
        "13. Mechanical Plant and Equipment",
        "14. Tunnel",
        "15. Formwork",
        "16. Hoarding",
        "17. Working at Height",
        "18. Abrasive Wheels",
        "19. Excavations",
        "20. Slings and other Lifting Gears",
        "21. Compressed Air/ Pneumatic Air Tools",
        "22. Protection of the Public",
        "23. Prevention of Mosquito Breed",
        "24. Work Over Water",
        "25. Welfare Facilities",
        "26. Others"
    ]

    host                  = 'drowdatewarehouse.crlwwhgepgi7.ap-east-1.rds.amazonaws.com'  
    dbUserName            = 'dRowAdmin'  
    dbUserPassword        = 'drowsuper'  
    database              = 'drowDateWareHouse'
    charSet               = "utf8mb4"  
    port                  = "5432"


    conn_string = ('postgres://' +
                           dbUserName + ':' + 
                           dbUserPassword +
                           '@' + host + ':' + port +
                           '/' + database)
    
    db = create_engine(conn_string)
    conn = db.connect()
    df = pd.DataFrame()

    with conn:
        for x in RISC_Data:
            # Normalize the nested JSON data into a flat DataFrame
            df_nested_list = json_normalize(x['data'])
            df2 = df_nested_list.reindex(columns=Mapping.keys())
            df2.rename(columns=Mapping, inplace=True)

            if len(x['ApproveLogSummary']) > 0:
                # request_date = pd.to_datetime(df2["C1 - Inspect on Date Time"]) - pd.Timedelta(days=1)
                request_data = [data for data in x['ApproveLogSummary'] if data.get('statusName')=="B : RSS Check/Agree Report"]
                if len(request_data) > 0 and 'from' in request_data[-1]:
                    df2['sup_rep_signed_date'] = request_data[len(request_data)-1]['from']
                else:
                    df2['sup_rep_signed_date'] = None
                if len(request_data) > 0 and 'to' in request_data[-1]:
                    df2['contractor_rep_signed_date'] = request_data[len(request_data)-1]['to']
                else:
                    df2['contractor_rep_signed_date'] = None
            else:
                df2['sup_rep_signed_date'] = None
                df2['contractor_rep_signed_date'] = None

            if x['data']['A1. No. of Walk'] != None :
                df2["report_name"] = x['data']['A1. No. of Walk']
            else :
                df2["report_name"] = None

            if (len(x['data']['Summary']) > 0):
                total_late_retification = 0
                for summaryData in x['data']['Summary']:
                    if ("Agreed Due Date for Completion" in summaryData and not (summaryData["Agreed Due Date for Completion"]!='') and (not (summaryData["Date Completion"]!='')) and (summaryData["Agreed Due Date for Completion"].astype('datetime64[ns]') < summaryData["Date Completion"].astype('datetime64[ns]')).bool()):
                        total_late_retification += 1
                df2['total_late_retification'] = total_late_retification
            else:
                total_late_retification = 0
            
            if (not df2['contractor_rep_signed_date'].isnull().bool() and not df2['a3_date_time'].isnull().bool()):
                df2['days_complete'] = (((df2['contractor_rep_signed_date'].astype('datetime64[ns]') - 
                df2['a3_date_time'].astype('datetime64[ns]'))/ np.timedelta64(1, 'h'))/24).round(2)
                if df2['days_complete'].isnull().bool() or df2['days_complete'].lt(0).bool():
                    df2['days_complete'] = 0
            else:
                df2['days_complete'] = None
            

            df4=pd.DataFrame()
            for saftey_cat in saftey_cats:
                df3=df2.copy()
                complete = 0
                incomplete = 0
                key = str(saftey_cat)[0:3].strip()+' Checklist'
                if not df2['sup_rep_signed_date'].isnull().bool():
                    key = str(saftey_cat)[0:3].strip()+' Checklist'
                    if (len(x['data'][key]) > 0):
                        for record in x['data'][key]:
                            if record[str(saftey_cat)[0:3].strip()+' Result'] != 'N/A':
                                complete += 1
                else:
                    if (len(x['data'][key]) > 0):
                        for record in x['data'][key]:
                            if record[str(saftey_cat)[0:3].strip()+' Result'] != 'N/A':
                                incomplete += 1
                df3['saftey_cat'] = saftey_cat
                df3['saftey_cat' + '_' + 'complete'] = complete
                df3['saftey_cat' + '_' + 'incomplete'] = incomplete
                df4 = df4.append(df3)
            df2=df2.append(df4)
            df = df.append(df2)

        df['sup_rep_signed_date']=df['sup_rep_signed_date'].apply(pd.to_datetime)
        df['contractor_rep_signed_date']=df['contractor_rep_signed_date'].apply(pd.to_datetime)
        df['a3_date_time']=df['a3_date_time'].apply(pd.to_datetime)
        df.columns = df.columns.str.replace(' ', '_').str.replace('.', '').str.replace('(', '_').str.replace(')', '').str.replace('%', 'percent').str.replace('/', '_')
        df.to_sql('safety_walk_cv202303', con=conn, if_exists='replace', index= False)