{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleanup\n",
    "\n",
    "After building your model you may want to delete your campaign, solutions, and datasets. The following cells will ensure that you have successfully cleaned up all of the resources you created in this lab.\n",
    "\n",
    "## Imports and Connections to AWS\n",
    "\n",
    "The following lines import all the necessary libraries and then connect you to Amazon Personalize.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import time\n",
    "import os\n",
    "import boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Configure the SDK to Personalize:\n",
    "personalize = boto3.client('personalize')\n",
    "personalize_runtime = boto3.client('personalize-runtime')\n",
    "s3_client = boto3.client('s3')\n",
    "iam = boto3.client(\"iam\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining the Things to Cleanup\n",
    "\n",
    "Using the store command we will retrieve all the values needed to cleanup our work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%store -r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Delete Personalize Constructs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete the filter:\n",
    "delete_filter_response = personalize.delete_filter(\n",
    "    filterArn=filter_arn\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_filter_response, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    time.sleep(5)\n",
    "    try:\n",
    "        describe_filter_response = personalize.describe_filter(\n",
    "            filterArn=filter_arn\n",
    "            )\n",
    "        status = describe_filter_response['filter']['status']\n",
    "    except:\n",
    "        status = \"DELETED\"\n",
    "    print(\"CampaignStatus: {}\".format(status))\n",
    "    if status == \"DELETE IN_PROGRESS\" or status == \"DELETE PENDING\" :\n",
    "        continue\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete the campaign:\n",
    "delete_campaign_response = personalize.delete_campaign(\n",
    "    campaignArn=campaign_arn\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_campaign_response, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    time.sleep(15)\n",
    "    try:\n",
    "        describe_campaign_response = personalize.describe_campaign(\n",
    "            campaignArn=campaign_arn\n",
    "            )\n",
    "        status = describe_campaign_response['campaign']['status']\n",
    "    except:\n",
    "        status = \"DELETED\"\n",
    "    print(\"CampaignStatus: {}\".format(status))\n",
    "    if status == \"DELETE IN_PROGRESS\" or status == \"DELETE PENDING\" :\n",
    "        continue\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete the solution\n",
    "delete_solution_response = personalize.delete_solution(\n",
    "    solutionArn=solution_arn\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_solution_response, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    time.sleep(15)\n",
    "    try:\n",
    "        describe_solution_response = personalize.describe_solution(\n",
    "            solutionArn=solution_arn\n",
    "        )\n",
    "        status = describe_solution_response['solution']['status']\n",
    "    except:\n",
    "        status = \"DELETED\"\n",
    "    print(\"SolutionStatus: {}\".format(status))\n",
    "    if status == \"DELETE IN_PROGRESS\" or status == \"DELETE PENDING\":\n",
    "        continue\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete the interaction dataset\n",
    "delete_interactions_dataset_response = personalize.delete_dataset(\n",
    "    datasetArn=interactions_dataset_arn\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_interactions_dataset_response, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    time.sleep(15)\n",
    "    try:\n",
    "        describe_interactions_dataset_response = personalize.describe_dataset(\n",
    "            datasetArn=interactions_dataset_arn\n",
    "        )\n",
    "        status = describe_interactions_dataset_response['dataset']['status']\n",
    "    except:\n",
    "        status = \"DELETED\"\n",
    "    print(\"DataSetStatus: {}\".format(status))\n",
    "    if status == \"DELETE IN_PROGRESS\" or status == \"DELETE PENDING\":\n",
    "        continue\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete the items dataset\n",
    "delete_items_dataset_response = personalize.delete_dataset(\n",
    "    datasetArn=items_dataset_arn\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_items_dataset_response, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    time.sleep(15)\n",
    "    try:\n",
    "        describe_items_dataset_response = personalize.describe_dataset(\n",
    "            datasetArn=items_dataset_arn\n",
    "        )\n",
    "        status = describe_items_dataset_response['dataset']['status']\n",
    "    except:\n",
    "        status = \"DELETED\"\n",
    "    print(\"DataSetStatus: {}\".format(status))\n",
    "    if status == \"DELETE IN_PROGRESS\" or status == \"DELETE PENDING\":\n",
    "        continue\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete the interactions schema\n",
    "delete_interactions_schema_response = personalize.delete_schema(\n",
    "    schemaArn=interactions_schema_arn\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_interactions_schema_response, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete the items schema\n",
    "delete_items_schema_response = personalize.delete_schema(\n",
    "    schemaArn=items_schema_arn\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_items_schema_response, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Delete the Dataset Group\n",
    "\n",
    "delete_dataset_group_response = personalize.delete_dataset_group(\n",
    "    datasetGroupArn=dataset_group_arn\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_dataset_group_response, indent=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "%%time\n",
    "\n",
    "max_time = time.time() + 3*60*60 # 3 hours\n",
    "while time.time() < max_time:\n",
    "    time.sleep(15)\n",
    "    try:\n",
    "        describe_dataset_group_response = personalize.describe_dataset_group(\n",
    "            datasetGroupArn=dataset_group_arn\n",
    "        )\n",
    "        status = describe_dataset_group_response['datasetGroup']['status']\n",
    "    except:\n",
    "        status = \"DELETED\"\n",
    "    print(\"DataSetStatus: {}\".format(status))\n",
    "    if status == \"DELETE PENDING\" or status == \"DELETE IN_PROGRESS\":\n",
    "        continue\n",
    "    else:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "iam.detach_role_policy(PolicyArn=\"arn:aws:iam::aws:policy/AmazonS3FullAccess\", RoleName=role_name)\n",
    "iam.detach_role_policy(PolicyArn=\"arn:aws:iam::aws:policy/service-role/AmazonPersonalizeFullAccess\",RoleName=role_name)\n",
    "\n",
    "delete_role_response = iam.delete_role(\n",
    "    RoleName=role_name\n",
    ")\n",
    "\n",
    "print(json.dumps(delete_role_response, indent=2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Empty Your S3 Bucket\n",
    "\n",
    "Next empty your S3 bucket, you uploaded a movie file to it in the first notebook.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "boto3.Session().resource('s3').Bucket(bucket).Object(interactions_filename).delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "boto3.Session().resource('s3').Bucket(bucket).Object(items_filename).delete()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if os.path.exists(interactions_filename):\n",
    "  os.remove(interactions_filename)\n",
    "else:\n",
    "  print(\"The file does not exist\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "if os.path.exists(items_filename):\n",
    "  os.remove(items_filename)\n",
    "else:\n",
    "  print(\"The file does not exist\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Last Step\n",
    "\n",
    "After cleaning up all of the resources you can now close this window and go back to the github page you stareted on. At the bottom of the Readme file are steps to delete the CloudFormation stack you created earlier. Once that is done you are 100% done with the lab.\n",
    "\n",
    "Deleting CloudFormation Template (Manual)\n",
    "\n",
    "1. After executing above cells, ensure the SageMaker kernel is brought down (by navigating to the circle button on the left menu).\n",
    "2. Close the SageMaker Studio domain browser window.\n",
    "3. Delete the CloudFormation template.\n",
    "4. Due to a known issue, the EFS file share auto-created by SageMaker Studio will cause issues when executing above step. If that happens, the CloudFormation stack delete may fail after executing for a long time.\n",
    "5. To successfully delete the stack, manually remove the auto-created EFS volume related to the SageMaker domain by navigating to the console. Also, recommend deleting the auto-created VPC manually so it removes all related ENIs and security groups.\n",
    "\n",
    "Thanks for working through this content, reach out to us if you have any questions.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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