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# Activate Google Cloud Shell
gcloud auth list
gcloud config list project

# Launch DataLab (can take up to 5 min)
datalab create bdmlvm --zone us-central1-a

# Click on WebPreview (in shell window)
# Adjust port form 808 to 8081
# open ungit
# clone rep from github
# get API key
#  - APIs & Services >> Library
#  - enable APIs needed
#  - APIs & Services >> Credentials >> Create credentials >> API key
# Open *.ipynb in Google Datalab
# replace API key with yours

# DataLab Code (python)
APIKEY="AIzaSyDrK4iIjkGmuWTi3S-cvOcKwTuqrP-Tc9s"  # Replace with your API key
!pip install --upgrade google-api-python-client



# running Translate API
from googleapiclient.discovery import build
service = build('translate', 'v2', developerKey=APIKEY)

# use the service
inputs = ['is it really this easy?', 'amazing technology', 'wow']
outputs = service.translations().list(source='en', target='fr', q=inputs).execute()
# print outputs
for input, output in zip(inputs, outputs['translations']):
  print("{0} -> {1}".format(input, output['translatedText']))



# Running Vision API
import base64
IMAGE="gs://cloud-training-demos/vision/sign2.jpg"
vservice = build('vision', 'v1', developerKey=APIKEY)
request = vservice.images().annotate(body={
        'requests': [{
                'image': {
                    'source': {
                        'gcs_image_uri': IMAGE
                    }
                },
                'features': [{
                    'type': 'TEXT_DETECTION',
                    'maxResults': 3,
                }]
            }],
        })
responses = request.execute(num_retries=3)
print(responses)


foreigntext = responses['responses'][0]['textAnnotations'][0]['description']
foreignlang = responses['responses'][0]['textAnnotations'][0]['locale']
print(foreignlang, foreigntext)

# Translate sign
inputs=[foreigntext]
outputs = service.translations().list(source=foreignlang, target='en', q=inputs).execute()
# print(outputs)
for input, output in zip(inputs, outputs['translations']):
  print("{0} -> {1}".format(input, output['translatedText']))



# Sentiment analysis with NLP API
lservice = build('language', 'v1beta1', developerKey=APIKEY)
quotes = [
  'To succeed, you must have tremendous perseverance, tremendous will.',
  'It’s not that I’m so smart, it’s just that I stay with problems longer.',
  'Love is quivering happiness.',
  'Love is of all passions the strongest, for it attacks simultaneously the head, the heart, and the senses.',
  'What difference does it make to the dead, the orphans and the homeless, whether the mad destruction is wrought under the name of totalitarianism or in the holy name of liberty or democracy?',
  'When someone you love dies, and you’re not expecting it, you don’t lose her all at once; you lose her in pieces over a long time — the way the mail stops coming, and her scent fades from the pillows and even from the clothes in her closet and drawers. '
]
for quote in quotes:
  response = lservice.documents().analyzeSentiment(
    body={
      'document': {
         'type': 'PLAIN_TEXT',
         'content': quote
      }
    }).execute()
  polarity = response['documentSentiment']['polarity']
  magnitude = response['documentSentiment']['magnitude']
  print('POLARITY=%s MAGNITUDE=%s for %s' % (polarity, magnitude, quote))
  
  
  
# Speech API (with audio file from Cloud Storage)
sservice = build('speech', 'v1beta1', developerKey=APIKEY)
response = sservice.speech().syncrecognize(
    body={
        'config': {
            'encoding': 'LINEAR16',
            'sampleRate': 16000
        },
        'audio': {
            'uri': 'gs://cloud-training-demos/vision/audio.raw'
            }
        }).execute()
print(response)



# Cleanup
# 1. Delete API key in GCP console
# 2. commit notelbooks to git
# 3. Stop Compute Engine VM is Datalab is used on one, in order to avoid costs