script.py

import urllib.request
import json
import os
import ssl
import pandas as pd

def allowSelfSignedHttps(allowed):
    # bypass the server certificate verification on client side
    if allowed and not os.environ.get('PYTHONHTTPSVERIFY', '') and getattr(ssl, '_create_unverified_context', None):
        ssl._create_default_https_context = ssl._create_unverified_context

allowSelfSignedHttps(True) # this line is needed if you use self-signed certificate in your scoring service.

# Request data goes here
# The example below assumes JSON formatting which may be updated
# depending on the format your endpoint expects.
# More information can be found here:
# https://docs.microsoft.com/azure/machine-learning/how-to-deploy-advanced-entry-script

input_df = pd.read_csv('D:\OneDrive\OneDrive - BUET\OneDrive\MVH\DengueDashboard\\train_test_datasets\\test.csv')
# print(input_df.index)

all_data = []
for i, row in input_df.iterrows():
	all_data += [{"Date": row['Date'] ,"Temperature": row['Temperature'],"Precipitation": row['Precipitation'],"City": row['City']}]


data =  {
  "Inputs": {
    "data": all_data
  },
  "GlobalParameters": {
    "quantiles": [
      0.025,
      0.975
    ]
  }
}

forecasts = []

body = str.encode(json.dumps(data))

url = 'http://9fa22fe3-0e2f-44f9-9a3e-c0bfd6eccbfa.eastus2.azurecontainer.io/score'
api_key = 'w63iNYH0gR1eok8Kojx7bw6JqW4yYopo' # Replace this with the API key for the web service

# The azureml-model-deployment header will force the request to go to a specific deployment.
# Remove this header to have the request observe the endpoint traffic rules
headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key)}

req = urllib.request.Request(url, body, headers)

try:
	response = urllib.request.urlopen(req)

	result = response.read()
	# print(result.decode())
	# print(type(json.loads(result.decode('utf-8'))))
	# df = pd.DataFrame.from_dict(json.loads(result.decode('utf-8')))
	forecasts = (json.loads(result.decode('utf-8'))['Results']['forecast'])
	# dfs.append(df)
	# print(forecasts[-1])

except urllib.error.HTTPError as error:
	print("The request failed with status code: " + str(error.code))

	# Print the headers - they include the requert ID and the timestamp, which are useful for debugging the failure
	print(error.info())
	print(error.read().decode("utf8", 'ignore'))


input_df['Value'] = forecasts
input_df_2 = input_df
print(input_df_2)