import boto3
import os
import json

client = boto3.client('servicecatalog')
cwd = os.getcwd().split('/')
i= cwd.index('S3Downloads')
pp_name = cwd[i + 1]
pp = client.describe_provisioned_product(Name=pp_name)
record_id = pp['ProvisionedProductDetail']['LastSuccessfulProvisioningRecordId']
record = client.describe_record(Id=record_id)

keys = [ x['OutputKey'] for x in record['RecordOutputs'] if 'OutputKey' and 'OutputValue' in x]
values = [ x['OutputValue'] for x in record['RecordOutputs'] if 'OutputKey' and 'OutputValue' in x]
stack_output = dict(zip(keys, values))

with open(f'/root/S3Downloads/{pp_name}/stack_outputs.json', 'w') as f:
    json.dump(stack_output, f)

import boto3
import sagemaker
from sagemaker.pytorch import PyTorchModel
import sys

sys.path.insert(0, '../package')
from package import config, utils

current_folder = utils.get_current_folder(globals())
sagemaker_client = boto3.client('sagemaker')

model_name = "{}-entity-recognition".format(config.SOLUTION_PREFIX)

model = PyTorchModel(
    name=model_name,
    model_data=f'{config.SOURCE_S3_PATH}/models/empty.tar.gz',
    entry_point='entry_point.py',
    source_dir='../containers/entity_recognition',
    role=config.IAM_ROLE,
    framework_version='1.5.0',
    py_version='py3',
    code_location='s3://' + config.S3_BUCKET + '/code'
)

from sagemaker.serializers import JSONSerializer
from sagemaker.deserializers import JSONDeserializer

predictor = model.deploy(
    endpoint_name=model_name,
    instance_type=config.HOSTING_INSTANCE_TYPE,
    initial_instance_count=1,
    serializer=JSONSerializer(),
    deserializer=JSONDeserializer()
)

# sagemaker_client.delete_endpoint(EndpointName=model_name)
# sagemaker_client.delete_endpoint_config(EndpointConfigName=model_name)
# sagemaker_client.delete_model(ModelName=model_name)

data = {'text': 'Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly.'}
response = predictor.predict(data=data)

print(response['entities'])
print(response['noun_chunks'])

sagemaker_client.delete_endpoint(EndpointName=model_name)
sagemaker_client.delete_endpoint_config(EndpointConfigName=model_name)