from googleapiclient import sample_tools

from googleapiclient.http import build_http

import sys

import json

import pandas as pd

discovery_doc = "gmb_discovery.json"

def main (argv):

# use the discovery doc to build a service that we can use to

# make MyBusiness API calls and authenticate the user so we can access their account

service, flags = sample_tools.init(argv, "mybusiness", "v4", __doc__, __file__, scope="https://www.googleapis.com/auth/business.manage", discovery_filename=discovery_doc)

## STEP 1 - Get the List of all accounts for the authenticated user

# GMB API call -- Method:accounts.list

output = service.accounts().list().execute()

print("List of GMB Accounts: \n\n")

print(json.dumps(output, indent=2) + "\n")

# extract the account name which will be used for further API calls

gmbAccount = output["accounts"][1]["name"]

## STEP 2 - Get the list of all available locations for the specified account (gmbAccount)

# Limitation - 100 locations fetched per API Call

# we use 'pageToken' - to fetch all the available locations

try:

page_token = None

# Defining empty dataframe and columns names where extracted info will be stored

loc_df = pd.DataFrame()

column_names = ['locationId', 'StoreID', 'Street', 'PostalCode', 'City', 'CountryCode', 'PrimaryPhone']

while True:

print("Fetching list of locations for account " + gmbAccount, "\n\n")

# GMB API Call - Method:accounts.locations.list

loclist = service.accounts().locations().list(parent=gmbAccount,pageToken=page_token).execute()

print(json.dumps(loclist, indent=2))

# Extracting only the necessary information from the response and append it to dataframe

for i in loclist['locations']:

name = i['name']

storeCode = i['storeCode']

address = i['address']['addressLines'][0]

postalCode = i['address']['postalCode']

locality = i['address']['locality']

regionCode = i['address']['locality']

primaryPhone = i['primaryPhone']

loc_df = loc_df.append(pd.Series([name, storeCode, address, postalCode, locality, regionCode, primaryPhone]),ignore_index=True)

# Checking for the 'nextPageToken' in the response

# if not available then break the loop

page_token = loclist.get('nextPageToken')

if not page_token:

break

finally:

print("All locations fetched for the account")

print("Next Page Token"+str(page_token))

loc_df.columns = column_names

#loc_df.to_csv('temp/allinone.csv', index=False)

## STEP 3: Getting the review data for each locationId

# Defining an empty dataframe and column names for storing the extracted review data

review_df = pd.DataFrame()

columns1 = ['locationId', 'ReviewerName', 'StarRating', 'ReviewCreateTime', 'ReviewerComments']

# Loop over each locationId and generate request body for the API call

for x in loc_df['locationId']:

body = {

"locationNames": [

x

]

}

print("Getting reviews for locationId " +x)

# GMB API Call - Method:accounts.locations.batchGetReviews

revlist = service.accounts().locations().batchGetReviews(name=gmbAccount,body=body).execute()

# extracting necessary information from the response message

for j in revlist['locationReviews']:

locationId = j['name']

ReviewerName = j['review']['reviewer']['displayName']

ReviewerRating = j['review']['starRating']

ReviewUpdateTime = j['review']['createTime']

ReviewerComments = j['review'].get('comment','NONE')

# appending the extracted values into dataframe

review_df = review_df.append(pd.Series([locationId, ReviewerName, ReviewerRating, ReviewUpdateTime, ReviewerComments]), ignore_index=True)

# Appending all location data and review data into single dataframe

review_df.columns = columns1

combined = pd.merge(loc_df, review_df, on='locationId')

combined.to_csv('temp/allmerged.csv', index=False)

#print(pd.merge(loc_df, review_df, on='locationId'))

# Writing pandas Dataframe to Google BigQuery

# Option1: Upload Dataframe using 'pandas.DataFrame.to_gbq()' function

## Option2: Saving Dataframe as csv and then upload as a file to BigQuery using the Python API

### Option3: Saving Dataframe as csv and then upload the file to Google Cloud Storage and then reading it from BigQuery

if __name__ == "__main__":

main(sys.argv)