# pip is the simplest way to install cognious
# for python3 users using pip
pip3 install cognious

# for python 2.x using pip
pip install cognious

          

# In your python code import cognious
from cognious import CogniousApp

# <client_id> and <client_secret> should be passed to the CogniousApp,
# which returns an instance of your application
# <client_id> and <client_secret> can be obtained by creating
# a new application at https://cognious.com/accounts/applications
# app = CogniousApp(<client_id>, <client_secret>)
app = CogniousApp('DKP3ADiCHVWO0NvuDg9XIpYgZAgKIXc73ZiPfBe2', 'i55x7kQWGc83u2fpKgMmyGCXaWDHYlk7kS986a6e')

# OAuth2 protocol
# If the <client_id> and <client_secret> is valid, a Bearer token would be returned
# Using the python cognious client, you wouldn't have to worry about
# the Bearer token, this would be handeled within the app for you.
print(app.api.token)
            

from cognious import CogniousApp

app = CogniousApp('DKP3ADiCHVWO0NvuDg9XIpYgZAgKIXc73ZiPfBe2', 'i55x7kQWGc83u2fpKgMmyGCXaWDHYlk7kS986a6e')

# Get the model you want to use
# models.get function returns an instance of the model
model = app.models.get('ImageRecognition')

# Use the available methods to make a prediction
model.predict_by_url('https://cognious.com/static/images/imagerecog/image2.jpg')

print(model.response)
# model.response contains the response
# {
#   "success": true,
#   "result": {
#     "prediction": [
#       {
#         "answer": "Labrador_retriever",
#         "confidence": "0.826396"
#       },
#       {
#         "answer": "golden_retriever",
#         "confidence": "0.128493"
#       },
#       {
#         "answer": "Chesapeake_Bay_retriever",
#         "confidence": "0.0122971"
#       }
#     ]
#  }
# }
#
# model.result contains the result portion of the response