Python - IndentationError: unexpected indent

Question

I don't know what mistakes I've done. Only tab, no space. I grab this code from this tutorial, http://cloudacademy.com/blog/google-prediction-api/. (I'm using PyCharm for development).

Error message

/Library/Frameworks/Python.framework/Versions/2.7/bin/python2.7 /Users/ZERO/GooglePredictionApi/google.py File "/Users/ZERO/GooglePredictionApi/google.py", line 72 api = get_prediction_api() ^ IndentationError: unexpected indent

Process finished with exit code 1

Sample code

import httplib2, argparse, os, sys, json
from oauth2client import tools, file, client
from googleapiclient import discovery
from googleapiclient.errors import HttpError

#Project and model configuration
project_id = '132567073760'
model_id = 'HAR-model'

#activity labels
labels = {
    '1': 'walking', '2': 'walking upstairs', 
    '3': 'walking downstairs', '4': 'sitting', 
    '5': 'standing', '6': 'laying'
}

def main():
    """ Simple logic: train and make prediction """
    try:
        make_prediction()
    except HttpError as e: 
        if e.resp.status == 404: #model does not exist
            print("Model does not exist yet.")
            train_model()
            make_prediction()
        else: #real error
            print(e)


def make_prediction():
    """ Use trained model to generate a new prediction """

    api = get_prediction_api() //error here

    print("Fetching model.")

    model = api.trainedmodels().get(project=project_id, id=model_id).execute()

    if model.get('trainingStatus') != 'DONE':
        print("Model is (still) training. \nPlease wait and run me again!") #no polling
        exit()

    print("Model is ready.")

    """
    #Optionally analyze model stats (big json!)
  analysis = api.trainedmodels().analyze(project=project_id, id=model_id).execute()
    print(analysis)
    exit()
    """

    #read new record from local file
    with open('record.csv') as f:
        record = f.readline().split(',') #csv

    #obtain new prediction
    prediction = api.trainedmodels().predict(project=project_id, id=model_id, body={
        'input': {
            'csvInstance': record
        },
    }).execute()

    #retrieve classified label and reliability measures for each class
    label = prediction.get('outputLabel')
    stats = prediction.get('outputMulti')

    #show results
    print("You are currently %s (class %s)." % (labels[label], label) ) 
    print(stats)


def train_model():
  """ Create new classification model """

    api = get_prediction_api()

    print("Creating new Model.")

    api.trainedmodels().insert(project=project_id, body={
        'id': model_id,
        'storageDataLocation': 'machine-learning-dataset/dataset.csv',
        'modelType': 'CLASSIFICATION'
    }).execute()


def get_prediction_api(service_account=True):
    scope = [
        'https://www.googleapis.com/auth/prediction',
        'https://www.googleapis.com/auth/devstorage.read_only'
    ]
    return get_api('prediction', scope, service_account)


def get_api(api, scope, service_account=True):
    """ Build API client based on oAuth2 authentication """
    STORAGE = file.Storage('oAuth2.json') #local storage of oAuth tokens
    credentials = STORAGE.get()
    if credentials is None or credentials.invalid: #check if new oAuth flow is needed
        if service_account: #server 2 server flow
            with open('service_account.json') as f:
                account = json.loads(f.read())
                email = account['client_email']
                key = account['private_key']
            credentials = client.SignedJwtAssertionCredentials(email, key, scope=scope)
            STORAGE.put(credentials)
        else: #normal oAuth2 flow
            CLIENT_SECRETS = os.path.join(os.path.dirname(__file__), 'client_secrets.json')
            FLOW = client.flow_from_clientsecrets(CLIENT_SECRETS, scope=scope)
            PARSER = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, parents=[tools.argparser])
            FLAGS = PARSER.parse_args(sys.argv[1:])
            credentials = tools.run_flow(FLOW, STORAGE, FLAGS)

  #wrap http with credentials
    http = credentials.authorize(httplib2.Http())
    return discovery.build(api, "v1.6", http=http)


if __name__ == '__main__':
    main()

` """ Create new classification model """` docstring is indented by 2 spaces, it should be indented by four. — Łukasz Rogalski, Nov 27 '15 at 09:00
Well- if there is an "unexpected indent in line 72" you may want to fix the indentation of line 72, don't you. Likely there's a tab where there should be spaces or (hopefully not) vice-versa. — decltype_auto, Nov 27 '15 at 09:00
In PyCharm should be function "convert tabs to spaces" - http://stackoverflow.com/questions/11816147/pycharm-convert-tabs-to-spaces-automatically — furas, Nov 27 '15 at 09:08

score 4 · Answer 1 · edited May 23 '17 at 11:44

Here is Alex from CloudAcademy.

You can find the updated gist here: https://gist.github.com/alexcasalboni/cf11cc076ad70a445612

As others pointed out, the error is due to an inconsistent indentation. This is a general Python problem, not related to Google Prediction API or Machine Learning.

Whenever you find yourself in such a situation, I would recommend to simply follow PEP8 conventions and convert every hard tab into spaces. As this answer correctly suggested, you can fix the problem with tabnanny or by properly configuring your code editor.

score 2 · Answer 2 · edited Jun 20 '20 at 09:12

import httplib2, argparse, os, sys, json
from oauth2client import tools, file, client
from googleapiclient import discovery
from googleapiclient.errors import HttpError

#Project and model configuration
project_id = '132567073760'
model_id = 'HAR-model'

#activity labels
labels = {
    '1': 'walking', '2': 'walking upstairs',
    '3': 'walking downstairs', '4': 'sitting',
    '5': 'standing', '6': 'laying'
}

def main():
    """ Simple logic: train and make prediction """
    try:
        make_prediction()
    except HttpError as e:
        if e.resp.status == 404: #model does not exist
            print("Model does not exist yet.")
            train_model()
            make_prediction()
        else: #real error
            print(e)


def make_prediction():
    """ Use trained model to generate a new prediction """

    api = get_prediction_api() //error here

    print("Fetching model.")

    model = api.trainedmodels().get(project=project_id, id=model_id).execute()

    if model.get('trainingStatus') != 'DONE':
        print("Model is (still) training. \nPlease wait and run me again!") #no polling
        exit()

    print("Model is ready.")

    """
    #Optionally analyze model stats (big json!)
  analysis = api.trainedmodels().analyze(project=project_id, id=model_id).execute()
    print(analysis)
    exit()
    """

    #read new record from local file
    with open('record.csv') as f:
        record = f.readline().split(',') #csv

    #obtain new prediction
    prediction = api.trainedmodels().predict(project=project_id, id=model_id, body={
        'input': {
            'csvInstance': record
        },
    }).execute()

    #retrieve classified label and reliability measures for each class
    label = prediction.get('outputLabel')
    stats = prediction.get('outputMulti')

    #show results
    print("You are currently %s (class %s)." % (labels[label], label) )
    print(stats)


def train_model():
    """ Create new classification model """

    api = get_prediction_api()

    print("Creating new Model.")

    api.trainedmodels().insert(project=project_id, body={
        'id': model_id,
        'storageDataLocation': 'machine-learning-dataset/dataset.csv',
        'modelType': 'CLASSIFICATION'
    }).execute()


def get_prediction_api(service_account=True):
    scope = [
        'https://www.googleapis.com/auth/prediction',
        'https://www.googleapis.com/auth/devstorage.read_only'
    ]
    return get_api('prediction', scope, service_account)


def get_api(api, scope, service_account=True):
    """ Build API client based on oAuth2 authentication """
    STORAGE = file.Storage('oAuth2.json') #local storage of oAuth tokens
    credentials = STORAGE.get()
    if credentials is None or credentials.invalid: #check if new oAuth flow is needed
        if service_account: #server 2 server flow
            with open('service_account.json') as f:
                account = json.loads(f.read())
                email = account['client_email']
                key = account['private_key']
            credentials = client.SignedJwtAssertionCredentials(email, key, scope=scope)
            STORAGE.put(credentials)
        else: #normal oAuth2 flow
            CLIENT_SECRETS = os.path.join(os.path.dirname(__file__), 'client_secrets.json')
            FLOW = client.flow_from_clientsecrets(CLIENT_SECRETS, scope=scope)
            PARSER = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, parents=[tools.argparser])
            FLAGS = PARSER.parse_args(sys.argv[1:])
            credentials = tools.run_flow(FLOW, STORAGE, FLAGS)

  #wrap http with credentials
    http = credentials.authorize(httplib2.Http())
    return discovery.build(api, "v1.6", http=http)


if __name__ == '__main__':
    main()

You had wrong indent on """ Create new classification model """ Just look here to know more about indent coding of python.

score 1 · Answer 3 · answered Nov 27 '15 at 09:03

1

Change

def train_model():
  """ Create new classification model """

    api = get_prediction_api()

to

def train_model():
    """ Create new classification model """

    api = get_prediction_api()

answered Nov 27 '15 at 09:03

elzell

2,228
1
16
26

1

I got print("Creating new Model.") ^ IndentationError: unindent does not match any outer indentation level. – Nurdin Nov 27 '15 at 09:04
1

Don't copy and paste what I wrote, because then spaces and tabs might be mixed. Fix the indentation in your code: If you use tab all the way, then also insert a tab before """ Create new classification model """. By the way: Good style is to consequently use four space indention instead of tabs. – elzell Nov 27 '15 at 09:06

score 1 · Answer 4 · answered Nov 27 '15 at 09:06

There were many indentation errors, try this:

import httplib2
import argparse
import os
import sys
import json
from oauth2client import tools, file, client
from googleapiclient import discovery
from googleapiclient.errors import HttpError

# Project and model configuration
project_id = '132567073760'
model_id = 'HAR-model'

# activity labels
labels = {
    '1': 'walking', '2': 'walking upstairs',
    '3': 'walking downstairs', '4': 'sitting',
    '5': 'standing', '6': 'laying'
}


def main():
    """ Simple logic: train and make prediction """
    try:
        make_prediction()
    except HttpError as e:
        if e.resp.status == 404:  # model does not exist
            print("Model does not exist yet.")
            train_model()
            make_prediction()
        else:  # real error
            print(e)


def make_prediction():
    """ Use trained model to generate a new prediction """

    api = get_prediction_api()

    print("Fetching model.")

    model = api.trainedmodels().get(project=project_id, id=model_id).execute()

    if model.get('trainingStatus') != 'DONE':
        # no polling
        print("Model is (still) training. \nPlease wait and run me again!")
        exit()

    print("Model is ready.")

    """
    #Optionally analyze model stats (big json!)
    analysis = api.trainedmodels().analyze(project=project_id, id=model_id).execute()
    print(analysis)
    exit()
    """

    # read new record from local file
    with open('record.csv') as f:
        record = f.readline().split(',')  # csv

    # obtain new prediction
    prediction = api.trainedmodels().predict(project=project_id, id=model_id, body={
        'input': {
            'csvInstance': record
        },
    }).execute()

    # retrieve classified label and reliability measures for each class
    label = prediction.get('outputLabel')
    stats = prediction.get('outputMulti')

    # show results
    print("You are currently %s (class %s)." % (labels[label], label))
    print(stats)


def train_model():
    """ Create new classification model """
    api = get_prediction_api()
    print("Creating new Model.")
    api.trainedmodels().insert(project=project_id, body={
        'id': model_id,
        'storageDataLocation': 'machine-learning-dataset/dataset.csv',
        'modelType': 'CLASSIFICATION'
    }).execute()


def get_prediction_api(service_account=True):
    scope = [
        'https://www.googleapis.com/auth/prediction',
        'https://www.googleapis.com/auth/devstorage.read_only'
    ]
    return get_api('prediction', scope, service_account)


def get_api(api, scope, service_account=True):
    """ Build API client based on oAuth2 authentication """
    STORAGE = file.Storage('oAuth2.json')  # local storage of oAuth tokens
    credentials = STORAGE.get()
    # check if new oAuth flow is needed
    if credentials is None or credentials.invalid:
        if service_account:  # server 2 server flow
            with open('service_account.json') as f:
                account = json.loads(f.read())
                email = account['client_email']
                key = account['private_key']
            credentials = client.SignedJwtAssertionCredentials(
                email, key, scope=scope)
            STORAGE.put(credentials)
        else:  # normal oAuth2 flow
            CLIENT_SECRETS = os.path.join(
                os.path.dirname(__file__), 'client_secrets.json')
            FLOW = client.flow_from_clientsecrets(CLIENT_SECRETS, scope=scope)
            PARSER = argparse.ArgumentParser(
                description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter, parents=[tools.argparser])
            FLAGS = PARSER.parse_args(sys.argv[1:])
            credentials = tools.run_flow(FLOW, STORAGE, FLAGS)

    # wrap http with credentials
    http = credentials.authorize(httplib2.Http())
    return discovery.build(api, "v1.6", http=http)


if __name__ == '__main__':
    main()

AbdulWahid · Answer 5 · 2015-11-27T09:16:14.320

1

maybe the fault is at this:

def train_model(): """ Create new classification model """

api = get_prediction_api()

print("Creating new Model.")

should be properly indented but others have pointed out other indentation errors, just check your indentations always as you code by otherwise it can be a mess to figure out where it is wrong.

edited Nov 27 '15 at 09:16

answered Nov 27 '15 at 09:12

AbdulWahid

11
3

What is the difference between both? – niyasc Nov 27 '15 at 09:14
niyasc lol, not much but the website is making it impossible for me to show the correct indentation anyway I edited to note that indentation there is incorrect and should be corrected, that's all – AbdulWahid Nov 27 '15 at 09:17

Python - IndentationError: unexpected indent

5 Answers5