我不知道我犯了什么错误。只有制表符，没有空格。我从这个教程中获取了这段代码：http://cloudacademy.com/blog/google-prediction-api/。（我使用PyCharm进行开发）。
错误信息：
错误信息：
示例代码：/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
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()