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import dash
import dash_core_components as dcc
import dash_html_components as html

external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']

app = dash.Dash(__name__, external_stylesheets=external_stylesheets)

app.layout = html.Div(children=[
    html.H1(children='Hello Dash'),

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import pandas as pd

COUNTRY_COLUMN = "Country/Region"
LOCATION_COLUMN = "Location"
DATE_COLUMN = "Date"
TYPE_COLUMN = "Type"
INFECTED_COLUMN = "Infected"
RECOVERED_COLUMN = "Recovered"
DEATHS_COLUMN = "Deaths"
CURRENT_COLUMN = "Current"

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aws_region="<region>"
aws_profile="<profile>"
function_bucket_name="<bucket-for-lambda-function>"
function_version="<version>"
s3_bucket_name_1="<bucket-1>"
s3_bucket_name_2="<bucket-2>"

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terraform {
  backend "s3" {
    bucket = "<bucket-name>"
    key    = "fraud-detection/terraform.tfstate"
    region = "<region>"
  }
}

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# cloudwatch_event.tf
resource "aws_cloudwatch_event_rule" "fraud_detection_scheduled_rule" {
  name                = "fraud-detection-scheduled-rule"
  schedule_expression = "rate(1 minute)"
  ...
}
# iam_lambda.tf
resource "aws_iam_role" "fraud_detection_lambda_role" {
  name = "fraud-detection-lambda-role"
  ...

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linear_predictor = linear.deploy(initial_instance_count=1,
                                 endpoint_name="fraud-detection-endpoint",
                                 instance_type='ml.m4.xlarge')

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# i. Download sample data and extract features and label (fraud/nonfraud).
...

# ii. Convert the n-dimensional arrays into RecordIO format (a highly efficient data format).
import sagemaker.amazon.common as smac
buf = io.BytesIO()
smac.write_numpy_to_dense_tensor(buf, features, labels)
...

# iii. Store the RecordIO data into S3 bucket.

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cd /home/ec2-user/SageMaker
aws s3 cp s3://${function_bucket_name}-${aws_region}/fraud-detection-using-machine-learning/${function_version}/notebooks/sagemaker_fraud_detection.ipynb .
sed -i 's/fraud-detection-end-to-end-demo/${s3_bucket_name_1}/g' sagemaker_fraud_detection.ipynb

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resource "aws_sagemaker_notebook_instance" "basic" {
  name          = "FraudDetectionNotebookInstance"
  role_arn      = "${aws_iam_role.sm_notebook_instance_role.arn}"
  instance_type = "ml.t2.medium"
  lifecycle_config_name = "${aws_sagemaker_notebook_instance_lifecycle_configuration.basic_lifecycle.name}"
}
resource "aws_sagemaker_notebook_instance_lifecycle_configuration" "basic_lifecycle" {
  name     = "BasicNotebookInstanceLifecycleConfig"
  on_start = "${base64encode(data.template_file.instance_init.rendered)}"
}

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# iam_sagemaker.tf
resource "aws_iam_role" "sm_notebook_instance_role" {
  name = "sm-notebook-instance-role"
  ...
}
resource "aws_iam_policy" "sm_notebook_instance_policy" {
  name        = "sm-notebook-instance-policy"
  description = "Policy for the Notebook Instance to manage training jobs, models and endpoints"
  ...
}