sjj | Python Fiddle

import urllib.request
import json
import time
from azure.storage.blob import *
import socket
socket.getaddrinfo('127.0.0.1', 8080)

def printHttpError(httpError):
    print("The request failed with status code: " + str(httpError.code))
    print(httpError.info())
    print(json.loads(httpError.read().decode("utf8", 'ignore')))
    return

def saveBlobToFile(blobUrl, resultsLabel):
    output_file = "output1.csv"
    print("Reading the result from " + blobUrl)
    try:
        response = urllib.request.urlopen(blobUrl)
    except urllib.error.HTTPError as error:
        printHttpError(error)
        return

with open(output_file, "w+") as f:
        f.write(response.read().decode("utf8", 'ignore'))
    print(resultsLabel + " have been written to the file " + output_file)
    return

def processResults(result):
    first = True
    results = result["Results"]
    for outputName in results:
        result_blob_location = results[outputName]
        sas_token = result_blob_location["SasBlobToken"]
        base_url = result_blob_location["BaseLocation"]
        relative_url = result_blob_location["RelativeLocation"]
        print("The results for " + outputName + " are available at the following Azure Storage location:")
        print("BaseLocation: " + base_url)
        print("RelativeLocation: " + relative_url)
        print("SasBlobToken: " + sas_token)
        if (first):
            first = False
            url3 = base_url + relative_url + sas_token
            saveBlobToFile(url3, "The results for " + outputName)
    return

def uploadFileToBlob(input_file, input_blob_name, storage_container_name, storage_account_name, storage_account_key):
    blob_service = BlockBlobService(account_name=storage_account_name, account_key=storage_account_key)
    print("Uploading the input to blob storage...")
    blob_service.create_blob_from_path(storage_container_name, input_blob_name, input_file)

def invokeBatchExecutionService():
    storage_account_name = "dapsjj" # Replace this with your Azure Storage Account name
    storage_account_key = "00ZhycrfeKH5d/VSq7JP7RMsosliS/X85AKPno07lN9pTppbYVtY7/mzQufWq+YbjUiSoQ/o17EhT+17moGmow==" # Replace this with your Azure Storage Key
    storage_container_name = "container1" # Replace this with your Azure Storage Container name
    connection_string = "DefaultEndpointsProtocol=https;AccountName=" + storage_account_name + ";AccountKey=" + storage_account_key
    api_key = "00ZhycrfeKH5d/VSq7JP7RMsosliS/X85AKPno07lN9pTppbYVtY7/mzQufWq+YbjUiSoQ/o17EhT+17moGmow==" # Replace this with the API key for the web service
    url = "https://ussouthcentral.services.azureml.net/workspaces/e810bf8e16bb4dec9719b07f0205b0bb/services/bc8304cebe1a42bfb2518b302eba979d/jobs"
    uploadFileToBlob("top_201819.csv", # Replace this with the location of your input file, and valid file extension (usually .csv)
        "top1.csv", # Replace this with the name you would like to use for your Azure blob; this needs to have the same extension as the input file
        storage_container_name, storage_account_name, storage_account_key)

payload = {
            "Inputs": {
                    "input1":
                    {
                        "ConnectionString": connection_string,
                        "RelativeLocation": "/" + storage_container_name + "/top_201819.csv"
                    },
            },

"Outputs": {
                    "output2":
                    {
                        "ConnectionString": connection_string,
                        "RelativeLocation": "/" + storage_container_name + "/output1.csv" # Replace this with the location you would like to use for your output file, and valid file extension (usually .csv for scoring results, or .ilearner for trained models)
                    },
                    "output1":
                    {
                        "ConnectionString": connection_string,
                        "RelativeLocation": "/" + storage_container_name + "/output2.csv" # Replace this with the location you would like to use for your output file, and valid file extension (usually .csv for scoring results, or .ilearner for trained models)
                    },
            },

"GlobalParameters": {
        }
    }

body = str.encode(json.dumps(payload))
    headers = { "Content-Type":"application/json", "Authorization":("Bearer " + api_key)}
    print("Submitting the job...")
    req = urllib.request.Request(url + "?api-version=2.0", body, headers)
    try:
        response = urllib.request.urlopen(req)
    except urllib.error.HTTPError as error:
        printHttpError(error)
        return

result = response.read()
    job_id = result.decode("utf8", 'ignore')[1:-1]
    print("Job ID: " + job_id)
    print("Starting the job...")
    body = str.encode(json.dumps({}))
    req = urllib.request.Request(url + "/" + job_id + "/start?api-version=2.0", body, headers)
    try:
        response = urllib.request.urlopen(req)
    except urllib.error.HTTPError as error:
        printHttpError(error)
        return
    url2 = url + "/" + job_id + "?api-version=2.0"
    while True:
        print("Checking the job status...")
        req = urllib.request.Request(url2, headers = { "Authorization":("Bearer " + api_key) })
        try:
            response = urllib.request.urlopen(req)
        except urllib.error.HTTPError as error:
            printHttpError(error)
            return

result = json.loads(response.read().decode("utf8", 'ignore'))
        status = result["StatusCode"]
        if (status == 0 or status == "NotStarted"):
            print("Job " + job_id + " not yet started...")
        elif (status == 1 or status == "Running"):
            print("Job " + job_id + " running...")
        elif (status == 2 or status == "Failed"):
            print("Job " + job_id + " failed!")
            print("Error details: " + result["Details"])
            break
        elif (status == 3 or status == "Cancelled"):
            print("Job " + job_id + " cancelled!")
            break
        elif (status == 4 or status == "Finished"):
            print("Job " + job_id + " finished!")
            processResults(result)
            break
        time.sleep(1) # wait one second
    return

invokeBatchExecutionService()

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