06_CodeGeneration/00_code_generatation_w_bedrock.ipynb

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   "source": [
    "# Invoke Bedrock model for code generation\n",
    "\n",
    "> *This notebook should work well with the **`Data Science 3.0`** kernel in SageMaker Studio*"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Introduction\n",
    "\n",
    "In this notebook we show you how to use a LLM to generate code based on the text prompt.\n",
    "\n",
    "We will use Bedrock's Claude v2 using the Boto3 API. \n",
    "\n",
    "The prompt used in this example is called a zero-shot prompt because we are not providing any examples of text other than the prompt.\n",
    "\n",
    "**Note:** *This notebook can be run within or outside of AWS environment.*\n",
    "\n",
    "#### Context\n",
    "To demonstrate the code generation capability of Amazon Bedrock, we will explore the use of Boto3 client to communicate with Amazon Bedrock API. We will demonstrate different configurations available as well as how simple input can lead to desired outputs.\n",
    "\n",
    "#### Pattern\n",
    "We will simply provide the Amazon Bedrock API with an input consisting of a task, an instruction and an input for the model under the hood to generate an output without providing any additional example. The purpose here is to demonstrate how the powerful LLMs easily understand the task at hand and generate compelling outputs.\n",
    "\n",
    "![](./images/bedrock-code-gen.png)\n",
    "\n",
    "#### Use case\n",
    "To demonstrate the generation capability of models in Amazon Bedrock, let's take the use case of code generation.\n",
    "\n",
    "#### Persona\n",
    "\n",
    "You are Moe, a Data Analyst, at AnyCompany. The company wants to understand its sales performance for different products for different products over the past year. You have been provided a dataset named sales.csv. The dataset contains the following columns:\n",
    "\n",
    "- Date (YYYY-MM-DD) format\n",
    "- Product_ID (unique identifer for each product)\n",
    "- Price (price at which each product was sold)\n",
    "\n",
    "#### Implementation\n",
    "To fulfill this use case, in this notebook we will show how to generate code for a given prompt. We will use the Anthropic Claude v2 using the Amazon Bedrock API with Boto3 client. "
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "## Setup\n",
    "\n",
    "⚠️ ⚠️ ⚠️ Before running this notebook, ensure you've run the [Bedrock boto3 setup notebook](../00_Intro/bedrock_boto3_setup.ipynb#Prerequisites) notebook. ⚠️ ⚠️ ⚠️\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "776fd083",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import os\n",
    "import sys\n",
    "\n",
    "import boto3\n",
    "\n",
    "module_path = \"..\"\n",
    "sys.path.append(os.path.abspath(module_path))\n",
    "from utils import bedrock, print_ww\n",
    "\n",
    "\n",
    "# ---- ⚠️ Un-comment and edit the below lines as needed for your AWS setup ⚠️ ----\n",
    "\n",
    "# os.environ[\"AWS_DEFAULT_REGION\"] = \"<REGION_NAME>\"  # E.g. \"us-east-1\"\n",
    "# os.environ[\"AWS_PROFILE\"] = \"<YOUR_PROFILE>\"\n",
    "# os.environ[\"BEDROCK_ASSUME_ROLE\"] = \"<YOUR_ROLE_ARN>\"  # E.g. \"arn:aws:...\"\n",
    "\n",
    "\n",
    "boto3_bedrock = bedrock.get_bedrock_client(\n",
    "    assumed_role=os.environ.get(\"BEDROCK_ASSUME_ROLE\", None),\n",
    "    region=os.environ.get(\"AWS_DEFAULT_REGION\", None),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f634211-3de1-4390-8c3f-367af5554c39",
   "metadata": {},
   "source": [
    "## Code Generation\n",
    "\n",
    "Following on the use case explained above, let's prepare an input for  the Amazon Bedrock service to generate python program for our use-case."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7656be8",
   "metadata": {},
   "source": [
    "#### Lab setup - create sample sales.csv data for this lab.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89a0ad24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create sales.csv file\n",
    "import csv\n",
    "\n",
    "data = [\n",
    "    [\"date\", \"product_id\", \"price\", \"units_sold\"],\n",
    "    [\"2023-01-01\", \"P001\", 50, 20],\n",
    "    [\"2023-01-02\", \"P002\", 60, 15],\n",
    "    [\"2023-01-03\", \"P001\", 50, 18],\n",
    "    [\"2023-01-04\", \"P003\", 70, 30],\n",
    "    [\"2023-01-05\", \"P001\", 50, 25],\n",
    "    [\"2023-01-06\", \"P002\", 60, 22],\n",
    "    [\"2023-01-07\", \"P003\", 70, 24],\n",
    "    [\"2023-01-08\", \"P001\", 50, 28],\n",
    "    [\"2023-01-09\", \"P002\", 60, 17],\n",
    "    [\"2023-01-10\", \"P003\", 70, 29],\n",
    "    [\"2023-02-11\", \"P001\", 50, 23],\n",
    "    [\"2023-02-12\", \"P002\", 60, 19],\n",
    "    [\"2023-02-13\", \"P001\", 50, 21],\n",
    "    [\"2023-02-14\", \"P003\", 70, 31],\n",
    "    [\"2023-03-15\", \"P001\", 50, 26],\n",
    "    [\"2023-03-16\", \"P002\", 60, 20],\n",
    "    [\"2023-03-17\", \"P003\", 70, 33],\n",
    "    [\"2023-04-18\", \"P001\", 50, 27],\n",
    "    [\"2023-04-19\", \"P002\", 60, 18],\n",
    "    [\"2023-04-20\", \"P003\", 70, 32],\n",
    "    [\"2023-04-21\", \"P001\", 50, 22],\n",
    "    [\"2023-04-22\", \"P002\", 60, 16],\n",
    "    [\"2023-04-23\", \"P003\", 70, 34],\n",
    "    [\"2023-05-24\", \"P001\", 50, 24],\n",
    "    [\"2023-05-25\", \"P002\", 60, 21]\n",
    "]\n",
    "\n",
    "# Write data to sales.csv\n",
    "with open('sales.csv', 'w', newline='') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "    writer.writerows(data)\n",
    "\n",
    "print(\"sales.csv has been created!\")"
   ]
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  {
   "cell_type": "markdown",
   "id": "d68e8af6",
   "metadata": {},
   "source": [
    "#### Analyzing sales with Amazon Bedrock generated Python program"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# Create the prompt\n",
    "# Analyzing sales\n",
    "\n",
    "prompt_data = \"\"\"\n",
    "\n",
    "Human: You have a CSV, sales.csv, with columns:\n",
    "- date (YYYY-MM-DD)\n",
    "- product_id\n",
    "- price\n",
    "- units_sold\n",
    "\n",
    "Create a python program to analyze the sales data from a CSV file. The program should be able to read the data, and determine below:\n",
    "\n",
    "- Total revenue for the year\n",
    "- The product with the highest revenue\n",
    "- The date with the highest revenue\n",
    "- Visualize monthly sales using a bar chart\n",
    "\n",
    "Ensure the code is syntactically correct, bug-free, optimized, not span multiple lines unnessarily, and prefer to use standard libraries. Return only python code without any surrounding text, explanation or context.\n",
    "\n",
    "Assistant:\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc9784e5-5e9d-472d-8ef1-34108ee4968b",
   "metadata": {},
   "source": [
    "Let's start by using the Anthropic Claude V2 model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af670eb-ad02-40df-a19c-3ed835fac8d9",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Claude - Body Syntex\n",
    "body = json.dumps({\n",
    "                    \"prompt\": prompt_data,\n",
    "                    \"max_tokens_to_sample\":4096,\n",
    "                    \"temperature\":0.5,\n",
    "                    \"top_k\":250,\n",
    "                    \"top_p\":0.5,\n",
    "                    \"stop_sequences\": [\"\\n\\nHuman:\"]\n",
    "                  }) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "088cf6bf-dd73-4710-a0cc-6c11d220c431",
   "metadata": {},
   "source": [
    "#### Invoke the Anthropic Claude v2 model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "016a118a",
   "metadata": {},
   "outputs": [],
   "source": [
    "modelId = 'anthropic.claude-v2' # change this to use a different version from the model provider\n",
    "accept = 'application/json'\n",
    "contentType = 'application/json'\n",
    "\n",
    "response = boto3_bedrock.invoke_model(body=body, modelId=modelId, accept=accept, contentType=contentType)\n",
    "response_body = json.loads(response.get('body').read())\n",
    "\n",
    "print_ww(response_body.get('completion'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddddd1ec",
   "metadata": {},
   "source": [
    "#### (Optional) Execute the Bedrock generated code for validation. Go to text editor to copy the generated code as printed output can be trucncated. Replace the code in below cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "77d9b428",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample Generated Python Code ( Generated with Amazon Bedrock in previous step)\n",
    "\n",
    "import csv\n",
    "from collections import defaultdict\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "revenue = 0\n",
    "monthly_revenue = defaultdict(int)\n",
    "product_revenue = defaultdict(int)\n",
    "max_revenue = 0\n",
    "max_revenue_date = ''\n",
    "max_revenue_product = ''\n",
    "\n",
    "with open('sales.csv') as f:\n",
    "    reader = csv.reader(f)\n",
    "    next(reader)\n",
    "    for row in reader:\n",
    "        date = row[0]\n",
    "        product = row[1]\n",
    "        price = float(row[2])\n",
    "        units = int(row[3])\n",
    "\n",
    "        revenue += price * units\n",
    "        product_revenue[product] += price * units\n",
    "        monthly_revenue[date[:7]] += price * units\n",
    "\n",
    "        if revenue > max_revenue:\n",
    "            max_revenue = revenue\n",
    "            max_revenue_date = date\n",
    "            max_revenue_product = product\n",
    "\n",
    "months = list(monthly_revenue.keys())\n",
    "values = list(monthly_revenue.values())\n",
    "\n",
    "plt.bar(months, values)\n",
    "plt.xlabel('Month')\n",
    "plt.ylabel('Revenue')\n",
    "plt.title('Monthly Revenue')\n",
    "plt.show()\n",
    "\n",
    "print('Total Revenue:', revenue)\n",
    "print('Product with max revenue:', max_revenue_product)\n",
    "print('Date with max revenue:', max_revenue_date)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64b08b3b",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "You have now experimented with using `boto3` SDK which provides a vanilla exposure to Amazon Bedrock API. Using this API you generate a python program to analyze and visualize given sales data.\n",
    "\n",
    "### Take aways\n",
    "- Adapt this notebook to experiment with different models available through Amazon Bedrock such as Amazon Titan and AI21 Labs Jurassic models.\n",
    "\n",
    "\n",
    "## Thank You"
   ]
  }
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