Blog1-TitanEmbeddings-LVM/notebooks/3_rag_inference.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieval Augumented Generation (RAG) inference\n",
    "\n",
    "***This notebook works best with the `conda_python3` on the `ml.t3.large` instance***.\n",
    "\n",
    "---\n",
    "\n",
    "At this point our slide deck data is ingested into Amazon OpenSearch Service Serverless collection. We are now ready to talk to our slide deck using a large multimodal model. We are using the [LLaVA 1.5-7b](https://huggingface.co/anymodality/llava-v1.5-7b) for this purpose. LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Step 1. Setup\n",
    "\n",
    "Install the required Python packages and import the relevant files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "import sys\n",
    "!{sys.executable} -m pip install -r requirements.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import io\n",
    "import sys\n",
    "import json\n",
    "import glob\n",
    "import boto3\n",
    "import base64\n",
    "import logging\n",
    "import requests\n",
    "import botocore\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import globals as g\n",
    "from PIL import Image\n",
    "from pathlib import Path\n",
    "from typing import List, Dict\n",
    "from IPython.display import Image\n",
    "from utils import get_cfn_outputs\n",
    "from urllib.parse import urlparse\n",
    "from botocore.auth import SigV4Auth\n",
    "from pandas.core.series import Series\n",
    "from sagemaker import get_execution_role\n",
    "from botocore.awsrequest import AWSRequest\n",
    "from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth\n",
    "from sagemaker.huggingface.model import HuggingFaceModel, HuggingFacePredictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33m\"\"\"\u001b[39;49;00m\n",
      "\u001b[33mGlobal variables used throughout the code.\u001b[39;49;00m\n",
      "\u001b[33m\"\"\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mboto3\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36msagemaker\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# model deployment\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "HF_MODEL_ID = \u001b[33m\"\u001b[39;49;00m\u001b[33manymodality/llava-v1.5-13b\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "HF_MODEL_ID: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33manymodality/llava-v1.5-7b\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "HF_TASK: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mquestion-answering\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "TRANSFORMERS_VERSION: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33m4.28.1\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "PYTORCH_VERSION: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33m2.0.0\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "PYTHON_VERSION: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mpy310\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# S3 bucket strucutre, we use the default sagemaker bucket in the current region\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "BUCKET_NAME: \u001b[36mstr\u001b[39;49;00m = sagemaker.Session().default_bucket()\u001b[37m\u001b[39;49;00m\n",
      "BUCKET_PREFIX: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mmultimodal\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "BUCKET_EMB_PREFIX: \u001b[36mstr\u001b[39;49;00m = \u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mBUCKET_PREFIX\u001b[33m}\u001b[39;49;00m\u001b[33m/osi-embeddings-json\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "BUCKET_IMG_PREFIX: \u001b[36mstr\u001b[39;49;00m = \u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mBUCKET_PREFIX\u001b[33m}\u001b[39;49;00m\u001b[33m/img\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# Amazon Titan multimodal model\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "AWS_REGION: \u001b[36mstr\u001b[39;49;00m = boto3.Session().region_name\u001b[37m\u001b[39;49;00m\n",
      "FMC_URL: \u001b[36mstr\u001b[39;49;00m = \u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mhttps://bedrock-runtime.\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mAWS_REGION\u001b[33m}\u001b[39;49;00m\u001b[33m.amazonaws.com\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "FMC_MODEL_ID: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mamazon.titan-embed-image-v1\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "ACCEPT_ENCODING: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mapplication/json\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "CONTENT_ENCODING: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mapplication/json\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# model.tar.gz path in S3\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "S3_MODEL_URI: \u001b[36mstr\u001b[39;49;00m = os.path.join(\u001b[33m\"\u001b[39;49;00m\u001b[33ms3://\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, BUCKET_NAME, BUCKET_PREFIX, os.path.basename(HF_MODEL_ID))\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# Amazon OpenSearch Service Serverless\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "OS_SERVICE: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33maoss\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# local files and folder structure\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "IMAGE_DIR: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mimg\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "IMAGE_FILE_EXTN: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33m.jpg\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "B64_ENCODED_IMAGES_DIR: \u001b[36mstr\u001b[39;49;00m = os.path.join(IMAGE_DIR, \u001b[33m\"\u001b[39;49;00m\u001b[33mb64_images\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "ENDPOINT_FILENAME: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mendpoint.txt\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# this is the slide deck to which we will be talking to. Replace with your slide deck's URL to analyze a different deck\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "SLIDE_DECK: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mhttps://d1.awsstatic.com/events/Summits/torsummit2023/CMP301_TrainDeploy_E1_20230607_SPEdited.pdf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# AWS CloudFormation stack that created the resources for this blog post including this notebook\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m# if a different name is used while creating the CloudFormation stack then change this to match the name you used\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "CFN_STACK_NAME: \u001b[36mstr\u001b[39;49;00m = \u001b[33m\"\u001b[39;49;00m\u001b[33mmultimodal-stack\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n"
     ]
    }
   ],
   "source": [
    "!pygmentize globals.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "logging.basicConfig(format='[%(asctime)s] p%(process)s {%(filename)s:%(lineno)d} %(levelname)s - %(message)s', level=logging.INFO)\n",
    "logger = logging.getLogger(__name__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2. Create an OpenSearch client and SageMaker Predictor object\n",
    "\n",
    "We create an OpenSearch client so that we can query the vector database for embeddings (slides) similar to the questions that we might want to ask of our slide deck and then we create a SageMaker [`Predictor`](https://sagemaker.readthedocs.io/en/stable/api/inference/predictors.html) to run inference using the LLaVA model given the slide we retrieved from OpenSearch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the name of the OpenSearch Service Serverless collection endpoint and index name from the CloudFormation stack outputs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:05,819] p24755 {2210621458.py:4} INFO - opensearchhost=nuqahxflpijquaxm4mbc.us-west-2.aoss.amazonaws.com, index=slides\n"
     ]
    }
   ],
   "source": [
    "outputs = get_cfn_outputs(g.CFN_STACK_NAME)\n",
    "host = outputs['MultimodalCollectionEndpoint'].split('//')[1]\n",
    "index_name = outputs['OpenSearchIndexName']\n",
    "logger.info(f\"opensearchhost={host}, index={index_name}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use the OpenSearch client to create an index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:05,862] p24755 {credentials.py:1075} INFO - Found credentials from IAM Role: BaseNotebookInstanceEc2InstanceRole\n"
     ]
    }
   ],
   "source": [
    "session = boto3.Session()\n",
    "credentials = session.get_credentials()\n",
    "auth = AWSV4SignerAuth(credentials, g.AWS_REGION, g.OS_SERVICE)\n",
    "\n",
    "os_client = OpenSearch(\n",
    "    hosts = [{'host': host, 'port': 443}],\n",
    "    http_auth = auth,\n",
    "    use_ssl = True,\n",
    "    verify_certs = True,\n",
    "    connection_class = RequestsHttpConnection,\n",
    "    pool_maxsize = 20\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now create the SageMaker Predictor for the LLaVA endpoint we deployed in the [`0_deploy_llava.ipynb`](./0_deploy_llava.ipynb) notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker.config INFO - Not applying SDK defaults from location: /etc/xdg/sagemaker/config.yaml\n",
      "sagemaker.config INFO - Not applying SDK defaults from location: /home/ec2-user/.config/sagemaker/config.yaml\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:06,133] p24755 {1280433889.py:3} INFO - created predictor for the huggingface-pytorch-inference-2024-01-08-23-24-45-284 endpoint\n"
     ]
    }
   ],
   "source": [
    "endpoint_name = Path(g.ENDPOINT_FILENAME).read_text()\n",
    "predictor = HuggingFacePredictor(endpoint_name)\n",
    "logger.info(f\"created predictor for the {endpoint_name} endpoint\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Read for RAG\n",
    "\n",
    "We now have all the pieces for RAG. Here is how we _talk to our slide deck_.\n",
    "\n",
    "1. Convert the user question into embeddings using the Titan Multimodal Embeddings model.\n",
    "\n",
    "1. Find the most similar slide (image) corresponding to the the embeddings (for the user question) from the vector database (OpenSearch Serverless).\n",
    "\n",
    "1. Now ask LLaVA (via the SageMaker Endpoint) to answer the user question using the retrieved image for the most similar slide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def get_text_embeddings(bedrock: botocore.client, image: str) -> np.ndarray:\n",
    "    body = json.dumps(dict(inputText=image))\n",
    "    try:\n",
    "        response = bedrock.invoke_model(\n",
    "            body=body, modelId=g.FMC_MODEL_ID, accept=g.ACCEPT_ENCODING, contentType=g.CONTENT_ENCODING\n",
    "        )\n",
    "        response_body = json.loads(response.get(\"body\").read())\n",
    "        embeddings = np.array([response_body.get(\"embedding\")]).astype(np.float32)\n",
    "    except Exception as e:\n",
    "        logger.error(f\"exception while text={text}, exception={e}\")\n",
    "        embeddings = None\n",
    "\n",
    "    return embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "bedrock = boto3.client(service_name=\"bedrock-runtime\", endpoint_url=g.FMC_URL)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the following prompt template to make sure that the model only answers from the slides (images)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "prompt_template: str = \"\"\"Pretend that you are a helpful assistant that answers questions about content in a slide deck. \n",
    "Using only the information in the provided slide image answer the following question.\n",
    "If you do not find the answer in the image then say I did not find the answer to this question in the slide deck.\n",
    "\n",
    "{question}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A handy function for similarity search in the vector db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def find_similar_data(text_embeddings: np.ndarray) -> Dict:\n",
    "    query = {\n",
    "        \"size\": 1,\n",
    "        \"query\": {\n",
    "            \"knn\": {\n",
    "                \"vector_embedding\": {\n",
    "                    \"vector\": text_embeddings[0].tolist(),\n",
    "                    \"k\": 1\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "    try:\n",
    "        image_based_search_response = os_client.search(body=query, index=index_name)\n",
    "        # remove the vector_embedding field for readability purposes, it was needed during\n",
    "        # the similarity search (by the vector db), we do not need it any more.\n",
    "        source = image_based_search_response['hits']['hits'][0]['_source'].pop('vector_embedding')\n",
    "        logger.info(f\"received response from OpenSearch, response={json.dumps(image_based_search_response, indent=2)}\")\n",
    "    except Exception as e:\n",
    "        logger.error(f\"error occured while querying OpenSearch index={index_name}, exception={e}\")\n",
    "        image_based_search_response = None\n",
    "    return image_based_search_response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "\n",
    "Create a prompt and convert it to embeddings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "question: str = \"How does Inf2 compare in performance to comparable EC2 instances? I need numbers.\"\n",
    "prompt = prompt_template.format(question=question)\n",
    "text_embeddings = get_text_embeddings(bedrock, question)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find the most similar slide from the vector db."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:06,899] p24755 {base.py:259} INFO - POST https://nuqahxflpijquaxm4mbc.us-west-2.aoss.amazonaws.com:443/slides/_search [status:200 request:0.488s]\n",
      "[2024-01-09 04:18:06,901] p24755 {2807163302.py:18} INFO - received response from OpenSearch, response={\n",
      "  \"took\": 315,\n",
      "  \"timed_out\": false,\n",
      "  \"_shards\": {\n",
      "    \"total\": 0,\n",
      "    \"successful\": 0,\n",
      "    \"skipped\": 0,\n",
      "    \"failed\": 0\n",
      "  },\n",
      "  \"hits\": {\n",
      "    \"total\": {\n",
      "      \"value\": 2,\n",
      "      \"relation\": \"eq\"\n",
      "    },\n",
      "    \"max_score\": 0.51882845,\n",
      "    \"hits\": [\n",
      "      {\n",
      "        \"_index\": \"slides\",\n",
      "        \"_id\": \"1%3A0%3Axv1j7IwBHSUkt53bUFuR\",\n",
      "        \"_score\": 0.51882845,\n",
      "        \"_source\": {\n",
      "          \"image_path\": \"s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_25.jpg\",\n",
      "          \"metadata\": {\n",
      "            \"model_id\": \"amazon.titan-embed-image-v1\",\n",
      "            \"slide_description\": \"\",\n",
      "            \"slide_filename\": \"https://d1.awsstatic.com/events/Summits/torsummit2023/CMP301_TrainDeploy_E1_20230607_SPEdited.pdf\"\n",
      "          },\n",
      "          \"s3\": {\n",
      "            \"bucket\": \"sagemaker-us-west-2-597703351594\",\n",
      "            \"key\": \"multimodal/osi-embeddings-json/CMP301_TrainDeploy_E1_20230607_SPEdited.json\"\n",
      "          },\n",
      "          \"@timestamp\": \"2024-01-09T04:01:58.462Z\"\n",
      "        }\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "vector_db_response: Dict = find_similar_data(text_embeddings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve the image path from the search results and provide it to LLaVA along with the user question."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:06,909] p24755 {3260149686.py:2} INFO - going to answer the question=\"How does Inf2 compare in performance to comparable EC2 instances? I need numbers.\" using the image \"s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_25.jpg\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_25.jpg to ./CMP301_TrainDeploy_E1_20230607_SPEdited_image_25.jpg\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3_img_path = vector_db_response.get('hits', {}).get('hits')[0].get('_source').get('image_path')\n",
    "logger.info(f\"going to answer the question=\\\"{question}\\\" using the image \\\"{s3_img_path}\\\"\")\n",
    "\n",
    "!aws s3 cp {s3_img_path} .\n",
    "local_img_path = os.path.basename(s3_img_path)\n",
    "Image(filename=local_img_path) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:16,736] p24755 {550040888.py:7} INFO - Image=s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_25.jpg\n",
      "Question: How does Inf2 compare in performance to comparable EC2 instances? I need numbers.\n",
      "Answer: According to the slide deck, Inf2 instances by AWS Inferentia2 offer up to 4x higher throughput and 10x lower latency compared to comparable EC2 instances.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "data = {\n",
    "    \"image\" : s3_img_path,\n",
    "    \"question\" : prompt,\n",
    "    \"temperature\" : 0.1,\n",
    "}\n",
    "output = predictor.predict(data)\n",
    "logger.info(f\"Image={s3_img_path}\\nQuestion: {question}\\nAnswer: {output}\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Question 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:18,253] p24755 {base.py:259} INFO - POST https://nuqahxflpijquaxm4mbc.us-west-2.aoss.amazonaws.com:443/slides/_search [status:200 request:1.334s]\n",
      "[2024-01-09 04:18:18,255] p24755 {2807163302.py:18} INFO - received response from OpenSearch, response={\n",
      "  \"took\": 1180,\n",
      "  \"timed_out\": false,\n",
      "  \"_shards\": {\n",
      "    \"total\": 0,\n",
      "    \"successful\": 0,\n",
      "    \"skipped\": 0,\n",
      "    \"failed\": 0\n",
      "  },\n",
      "  \"hits\": {\n",
      "    \"total\": {\n",
      "      \"value\": 2,\n",
      "      \"relation\": \"eq\"\n",
      "    },\n",
      "    \"max_score\": 0.52241534,\n",
      "    \"hits\": [\n",
      "      {\n",
      "        \"_index\": \"slides\",\n",
      "        \"_id\": \"1%3A0%3Auf1j7IwBHSUkt53bUFuR\",\n",
      "        \"_score\": 0.52241534,\n",
      "        \"_source\": {\n",
      "          \"image_path\": \"s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_12.jpg\",\n",
      "          \"metadata\": {\n",
      "            \"model_id\": \"amazon.titan-embed-image-v1\",\n",
      "            \"slide_description\": \"\",\n",
      "            \"slide_filename\": \"https://d1.awsstatic.com/events/Summits/torsummit2023/CMP301_TrainDeploy_E1_20230607_SPEdited.pdf\"\n",
      "          },\n",
      "          \"s3\": {\n",
      "            \"bucket\": \"sagemaker-us-west-2-597703351594\",\n",
      "            \"key\": \"multimodal/osi-embeddings-json/CMP301_TrainDeploy_E1_20230607_SPEdited.json\"\n",
      "          },\n",
      "          \"@timestamp\": \"2024-01-09T04:01:58.419Z\"\n",
      "        }\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n",
      "[2024-01-09 04:18:18,256] p24755 {2163136891.py:11} INFO - going to answer the question=\"As per the AI/ML flywheel, what do the AWS AI/ML services provide?\" using the image \"s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_12.jpg\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_12.jpg to ./CMP301_TrainDeploy_E1_20230607_SPEdited_image_12.jpg\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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S/wCyTyjfUMBWc9FzLoNdjn6KnvbSWwvZrSYYkhco34f0qCrvfYAooooAK9F0n4Na/qlnb3gvtLigngE4LTksikZGQF+nevOq9A8ASNZeFfGepbgPL09YFz6u2BWGIc4wvB2/4JUbX1MPxX4RPhU2qtrGm6g84JZbObeYyPX29DXN1u+DdCi8S+K7DSJ5JY4rhiHeLG5QATnn6V6HD8LNCt7W60e+1Jz4oeCW5t4YzlY0X7obsSR71Mq8aXuzd36DUb6o8for0zw78MrTVPDOneIL2/e0svMlbUGcgBI0OBt75PSpb/4aabqXivRbfw9fumkavbNPHNMpZkC/eGOCT9cUfWqd2r7X/AOVnl1Fen6hpPwttdQfRv7R1RZ0PlvqC4eJX75GORn0qfTvB9vp1r4t8MXtpBc6olmL6wvAuS8S85X0/wD1ik8VG17MfKzymkrtvh7oFld3N3r2uRA6HpcRkmD9JXx8sY9SfT6etTweE9Fg0v8A4SDxPeT6ZZ37s+n2NmgeV0JznnooBHWqlXipOP8AXoLlZwVdN4K8J/8ACX6heWguHhaC0edSqbtzDGF/HNX/ABp4FHhzTtN1qwknn0m/RWjNwoEiMRkBgOORyK9A+H3xHn1vXv7JttF07T4hZSMGt0w25F4OfT2rKtXk6XPS1/QpR1szxubQNZt4RNPpN9FESF8yS3dVyTgckY60/XvDmq+Gr1LTVrRreV0EiZIIZT6EcGr2t+MfFGvxTQanq11cW4I8yMfLHweMqoA6gYzW+nhDX9d17w7oep620kN9Zi6tpJGaRYUIJKgHvhfp0q3UlGzm0txWvsefUleoalpHwtsdRfRW1DVftEf7t9QTDRq/fIxyAeuKNP8Ag9c3eoa3pxui1xaxRS2MqYEc6uTgtnkDAPSl9agleV16hys8wor0vxZ4I8N2PhCbUPD+pSXl3plwltqDH7rMwxlR25+vevNK0p1I1FdA1YQ0UUVYgooooGFFFFABSUUUhhRRRSAKKKKBhRRRSAKSlpKQwooooAKKKKQ0FFFFAwpKWkpAFFFFIAooooGFJRRSGJS0lLVHKFFFFMApaKKACiiimAUUUtMAooooAKKKWqQgooooAKKKKYC0UUUwClpKWgQUUUUwCiiimAUUUUALRRRQBreFwG8V6SCcA3kXfH8Qrr/GPiPWfCvxU1q4026kgLThjGeUkUqPvL0IrF8IWvhJ45LrxBrl5p93DKrW6W8BfcBznO0jr24rsPEfi34b6xrLatdabqmo3exUIOIY5Noxk4Oa5KjvV+FtWtt6Fr4dzD8ZaVY6x4dsvGuj2otUu5jb3tog+WOcd19j/Ue9bOn/AA00rSNMsJvE8OtXV7fgFLXTICwhB/vtjr7fzrA1P4htqt/pEC6bb2Og6ddRzLp8A4YKwJ3HucZ/OvQNej8e3mpzaz4H1aS+0fUcSKscsZ8k7QCuH6dO341jN1YRjBvlWu7+5XKXK3fc8/1v4c3Fv42v9A0i4S5+z2pvAZGw2wAHacD73P8AKpx8IvEBt9FkBi36mfmjIINsuM7n46Y/XitvSIdV+GfiEeJ/FsqXE95FJCYEuBJPuYbgzDpj5cde4rO1f4v6rqfhOXS1R4by4mkae5V/+WTEkIvpxgfQe9Wp4iVlTaa01FaK3MfVvhzq1j41j8NWhW8mlQSxTKCqmM9WPoBg5rYh+GmlanNcaXovieO/1q3iaR4UgIiO0gEb/qQKsaf8SdLGo6TcX9tcsBpB0u/dMbsD7roc9fXpULeOdA8H2rweBLWf7TM6me/vQCxQHOxR6GhyxLsuv4fMLQOR8M+HH13xfa6BcSPavNI0bttyUKqSePwruYPhDAml3dte6qqeIvKkuLWyQg5iQ9WHXJ/T86im8b+FoPFGm+MrKyuBqpLfbrELiPcVI3q3rz+PPTvz+jeO7i3+IyeKdS8ycPI/nRqekbArtHsAeB7Vc3iJ6x0svx7eglyrck0LwDFe6Cuua5rdtothM5jtmmUs0zeoHp711d5BP4e+A97Zq8dxFdakY1uIjlJIwwIdT6EpVP8At3wBregaRFrNxqcL6SHjjs4kyJ03ZGTjAyMdxVW5+JVtrVnrWkajatbaNcWqx6fBCoP2Z4+U/M9fwrOXtqkldOyd/ufTv3GuVHmtFFFemZBRRRQBZ06zfUdUtLKP79xMkS/VmA/rWj4sukuvE175XEEL/Z4R6JGNi/oKs+BY1k8Y2BfpGWfPoQpwfwODXPzSGaaSVvvOxY/Ums96novz/wCGH0GUUUVoxBRRRSAkggluZ44II2klkYKiIMliegAr2bw/puhfDvRLpda1yOx8SXsIBMMfmyWsZ/hAHRj3/D0rx2xvrnTb2G9s5mhuYW3xyL1U+tMuLme8uZLi5meaaRizySMSzH1JrCtSdX3b2RUZW1PX/ElpoMnwY07+yf7TvoU1Bo7aRgFYzNuyWXByOoAHOSKr6T4K8Sn4TX1jDpdwl7fahG5glXy28pR1IbGBmvNovEOrQaSmlxX8yWUc4uEiU4CyDowPUev1q3eeNvFGoIUute1CRCclfPYD8hWH1eqlyxa3vqVzLcnTwJrv/CVWvh6e1EN7cAOMsGCpzliR2GD+VX/Hmt2RFr4Y0Ns6NpeQHBz5838Uh9e4FYWm+JdS0qG/S1lAlvohDJO3MipnkK3bPesit1Tk5qU+n59xXVtD1XwbCng74cat4vmeM3moIbSwUNkjkgn8xn6L70zS/jFd2nhbTNHeFxcW08SyXQb71urA4x1zgY+leZNcTvbpbtNIYUJZIyx2qT1IHaoqh4WE23U1bd/8h87Wx7fb+GI3+P13JKFeNYzqdspIxKxA24+jE/8AfNcBqXgjxrqWr3d1P4fvzPPK0rny+Mk569O9Zl94w1m/GmNJdFJ9Oj8qC4h+SUL2BYcnFTt8QPF7x7D4j1LHqJ2B/Mc1EKVaFmmtktfL/MG4sy9X0LVNAuVt9VsJ7SVl3Ksq43D1B6Gs+tLVtf1bXmhbVdQnvGhUrGZm3FQetZtdUea3vbkO3QKQ0tJVAFOjdopFkRirqQykdiKbRQM6bxoiTaha6lGoVL23R2A7OAMj8iv51zNdPrX73wjosp6gEZ/Nf/aYrmKypfDbsN7hRRRWggHXnpXqun+M/h5ovhu60m20HVbxbwRNcLcyKokZTkZYNwAfQV5VRWVWlGpZSuVF2O3h8c6Zpfiuw1rRPDUFilqrK0XnM3mbhjJPbH0rO0fxneWHjpPE90DczNKzzpnG9WBBUenB4+grmaKXsYWem6t8h8zOs1nxzc6l4UtvDkEAtrCCeSUhWyZAWJUN9Mn68Vb0n4iz6Tp3h+KO0D3WjzSlZGf5ZIZOqEevv7VxFFJ0KduWwczPQX8WeBkum1GDwbM9+zmTy57wtCGJznb3HtWRB8QNYTxuniiZlkuQdrRDhDFjHlgdhj/GuVpKSoQXn66hzM7Lxn45j8Q20GmaTpyaVo0LGQWsePnkPVmxx9K2Lbx54Vu9H0v/AISHw5NfappduLeBkk2xSKv3d4z/AENea0Unh4cqj2Dme56Va/FU6pNqFn4tsje6Le/dt7cANbEfd2ZI6fX3rB8E+IdO8M+OotRZJv7My8R34MixsMZOOCRxnFcnRQqEEnFKyY+ZnoXiDxB4V0zw7qeheFVu7k6nKj3V3dAABUbcFQYHfvj/AOtAfiNJbzeFLq0tj9q0W3aCUyYxKDxgHrjbXCUlCw8LWev/AA1g5mehSeLPAv2t9Sj8GzPfu5kMc14TCGJznHcZ7YqFPiprYGvSME+06rGkSyJlfsyLkbUHphiPrzXCUUfV6fVX9dR8zOg0TxMNL8P65o81uZodSiUKQ2DHIpyG9x1rnqDRWiik211EFFFFMAooopAFFFJTGFFFFSwCigUUDCiiikAUUUlIAooooGFFFFIYUUUUDQUUUlIAooopAFFFJQMKU0UlIYUUUUhhRRRVnIFLSUtMAooopgFFFFMApaKKACiiimhMBS0UUwCiiimAUtFFMAooooELRRRTAKKKKACiiimAUtIKWgAooopiCiuu1CH+z/A2h21pbRtJrBkmnuDGC7FZNqxhuwGMkD+8KxZ/Duq22vLoktm41BnVBCCCcnkcjjvURqJ/j+A2jLqxbX95Zbvsl3PBu+95UhXP1xXSar4Hvk1m7s9Ftb29itEUzu8YG1iOQD0YdwR1FafjPw9LqPjRNL0GwDPb2EAlWNQgUhBkseAOo5NR7aDaXfUfKzgnd5HLuxZ2OSzHJJpK6a28B65PNexzxQWX2NlSV7ydYk3MMqoJ4JI5FYN9ZXGnXstndJsniba65BwfqODWkZxk7RYmmivRWu3hfW00cas2nTCxK7/Nx/D/AHsddvv0rd8B+E9Qv/EGjahcabK+ktdLulZMowBz07jIx6UpVYRi5X2BRbdji6K6278Iazrd/qOpaRpTtp7XUohK7VDgMeEBI3Yx2qhbeDPEF1pb6lHpkws0co8rDG3Bwxx1wO5xxQqsLatByswaK73VvBmh6X4T0+9m1+2+2SSTI72yvPHKVxhVIAAIzyT61kDwhd6rdlfD1rez26QJJK93GIfLLDoSTgg9Qe4ojXg1foNxZzNFb9v4J8SXV81lFo9yZ0kETArgKxG4ZPToM56Vprapqngi+s5rSODV9Ak8wkRhXeBm2urepViOT61TqxWzv/wRcrONooorQR0ngT5vFltF/wA9UkT/AMcJ/pXN1f0S9/s7XrC8P3YbhHYZ6qGGR+WabrFobHWb21IA8qd0GOmATis9pv0H0KVFFFWIKKKKAClAPOAeK6rwf4JuPExmvLidbHR7Xm5vJOgHovq1dfrXibRfCGj3eh6H4Tmia5iMf2/UYsNKpGNwBGT7dPpXPOvaXJBXZSjpdnktFOjjeaVIokZ5HIVVUZJJ6ACutu/hf4xstPa9m0aTylXewSRHcD/dBz+layqQhZSdrgk3schRWnpfhzWda8z+zdMuroRnDmKMkKfQn1qrdade2V59jurSeG5yB5UkZDEnpxT5o3tfUVitRUk9vNaztBcQyQyocNHIpVl+oNdD4N8G3fjO/uLa1uoLZbeLzZJJicAZx2pSnGEeaT0BJt2OcaN1VWZGCtypI4P0pte6+JvDfhO28D+G7fXfEOyK1WRIp7GPzPtByM4wD0/rXEaH4e8O6zeeJLfTHubm3t9Mae1mulCOsikHOFOMfXsa54YuMouVnp/nYtwadjgaKXBxnBx60hrqIEooooGFFFFIDp9V+XwVpKn8P++5j/7MK5iun8UD7Lpul2J4ZY95HoAqr/6EslcxWdL4bjYVs6JoaalbX99dXX2WxsYw8smzczMThUUZGWJ9+1Y1dZ4Tu7C80zUfDOo3AtE1Bkkt7pvuxzJnaH/2TkjPaiq2o3QJXZydFeoW+m+D9ItrDQ9Tgt5tauIJ4by48/5LVwXMbA9Nx+UZ6YHvVq80Dw7pHh+PTE0W71LUrqyWSK5htmfzZHAIdJA2Ai8jG3JrF4mN7Wf9dSuQ8lra8LaGuv62ltNKYbKNTNdTgf6qJRkn69h7kV6B4l0DwFZ6tLqkeqW7JbyAtpNryJAFGxAfUkZY9snvWJruqw6RiwEcEN7qE8dzqq26AJAgIKW6geg5b3x6UvbuorQTu/6/r5D5bbnK+JNIGg+Ir3SxN5y28m1XxjIIyM++DWVXsXiPQvCviDVb29a+t7RryaL7JdR36ymcuQCDEBlAB64qDX5NN8ORXEXhrw7dQ6nYuR9olsSRFEAQ7OzFlfPUHAxUwxN0lbUHE8z1XRdQ0a5aC9t2RlVWLAZXDKGHPToRS6toWo6LcmC+tmRgqvuHK4YAjnp3r03xnq+v2k0XiKLWoxZtbW4tLaRxIkzNH87LHyAVPcjvUniTT9Q8V6ppc194g8jRb9LNIoDPxKzKN5VAcAg55I60o4iWjlawcp47RXskmmeFJNc0+yj8Oz2sVnd7rqa5tmjjjiVT8srliHy+OeBg15t4g19tak2yafpsDRSNtmtLcRFl7A7eCB24z71rTre0eiBqxiVs+HNKt9WuL9Ll3VbfT7i5Uof4kQsM+2a6DwVpeh3Ok3N5fS6a12lwqNHqEzIkUGMtIqqQXbPAANaa6doNnqusX2iahA2mX+kXQtI3lAljfbhkZScjvjPUVM6y1ir3BI8yoFeqHw74KutCmuoLmE2FhbRzTXEMzG7eRiF2uhG1RuPbmrN1p3hGS9mt7q20a00Rdi2V5a3e+5lYlfvjceCM53AYpfWV2Y+U8ss7C81BpVs7aWdoozK4jUttQdScdqnfRdQTS7XUvs7Na3JcRsg3coQGzjp1rvX17T7rxnfaZ5WmWNjbtMLK9skELJsVtg3rjcpIGQc5q82qa74q+H1ncLraWEcM9yL+SOQQoqBQUUquM56Ae9KVeaadrILI8purWeyuXt7qF4ZkOGRxgioa9e1ey8Fa94guLee+J1CW0iuXvVnCwoVVA0Q7Fiu459Rior3w78MrXNpbarNc3ckc8cbmYbEkXJVmPTHG0evWhYpWV07+g+U8nQKXUOSqk8kDOB9K2fEmgHQL6FI7lbuzuYVuLa5VdokQ+3Yg5BFei32gwa54JRtPstM0iJbVZ3kAilSVgN2PPz5iN22n6Zrk/FEMmm+DdB0jUHiOpwSzyeWsgdoYX2lVYjOCTuOPQ0Rrc8lb7gtY4yiiiukkSiiikMKKKKQBRRRQMKKKKQBSUUUhhRRRSAKKKKCkFFFFIApKKKACiiikMKKSloASiiikMKKKKQwoooFaHILRRRQAUUUUwClpKWmAUUUUwClpKWmIKKKKYBRRS0AFFFFMQUtAooAKKKKYBRRRTAKKKWgAooopgFFFFAjptA8Vrp2my6VqdmL/AE4t50Cbtr28w6Oh+uMjoa3dZ+LWoanbTxW+l2Vm77vLuEBaWPeAHwx7t6155RWUsPTlLmaK5mlY75fiWsmlWtneaFb3LWgiaB2mYL5iLtDOo+/24PHFaGnfEdL+C9j1GUabqN1HCsmox2y3CzFMjEkbDGCCOncZrzKipeFpNbBzs7TxR4pgvtFfSo72bVJZrxbya+lhEPzBCgRUH8OMen0riwcHNFFa04KCshN3O/PxQkS48+DRLRZbiNItQaR2f7TGq7dgB4jXHOF701viUtt9kn0rRIbS+tIRbQXDzNL5cIYnaFIwCc4LdSK4Kio+rUuw+dnay+OrO6FobrQVkewmlls1W7dEj3yGTDKPvYY9iK63W/iFJYaf4bv5dLS5uZLV7uGcStGizOzrKCo4YdOK8dqR7ieWGKGSaR4os+WjMSqZ64HbNS8LTk1pt6jU2dVpfivTLXwxFp2oaS17c2k80tqWkAiHmKAd64ycEZAqUePEudJOl6lpf2mzaC3jYR3BjYtECAxIBzkEZHtXGUVq6EG7tC5mdpe/EvWJtJbS7OOKxtd0QjEJJZI4x8qbjyRnkk8/hUWo+MbS5ttYltdMaDU9YYi6mabciRFg2xFx3KjJPpXIUULD01sg5mFFFFakhXReI4vtmn6Vric/aYBBcH0miAU5+qhT+dc7XT+F3TVLG98NzMAbr99aMf4J1HT/AIEBisqrtaXb8hrscxRTpI3ileORSroSrKRyCO1NrQQUUUtAHrsWmXniv4R6LZeFZEa40+Z2vrJZAru5YkNyefUfX2rqrfxL4n0LwbqVz44g09JEiAsYpdhkmfpgovBHTmvn22vLmyl820uJoJP78TlT+Youbu5vJPMuriWeQ/xSuWP5muGWD5nZtWvfbX7zRTsfQPh630zW28I6/d6Naafq1xdSlEt02K8So53EfUKfxryHW/E3iC08Z6jdLql3FdR3cgBWU4XDEAY6Yx2qHV/Gmqaq+jvG5s20q2WCAwOQcjq2fU8Vzzu8sjSSMWdiSzMckk9zToYZxblPr07asJSvse769qPi7WfA/h6TwiryJc24fUJbIKsn2jA3ZxjGW3E4quJ72007w/D40uIZPEK6vbGxUlWnii3ru8wjtjPX2/Dxi01K/sM/Y725t89fJlZM/kagaaV5vNeV2kzneWJOfXNJYOy5dLemv3j5z3/xH8MB4m1jxDqN5cCPVLgk6ZAsiqCiKFDOMZIJx9K4XxHqFr4H8Pv4Q0adJdQuADq97Ge//PJT6Dv/APXNcTceINZu74X0+qXj3YTYJjM24L0wDnpWczFmLMSSTkk96dLDTjZVJXS6Cc10R6Ze6Ve6z8GPCy6dZy3U0N7cRssKFmXc7Ht0HT9K2/AvhFdD8dapokt8sofR/wDSmA2+Uz4yp57V5po3jLxB4f0+ex0vUpba3mbcyrjg+oJ6H6Vl/wBo3ouZrkXlwJ5gRLKJTucHruOcnNJ0KjUoXsnf8Xf8A5loz2bw94h8O6tq9x4AsNPgj0Ka3khiuH5knnA/1hPvg4+g+leJzwyW9xJBKu2SNijD0IODSwXE1pcR3FvK8U0bBkkRsMpHcGmSSPNK8sjs8jsWZmOSSepNbUqPs5Np6P8APuJyuNooorcQVp+H7E6jrlrAULpu3uvqq8kfjjH41mV1emAaB4Wn1WTi7v8AMNovooPzP+eMe4FZVJWjpuxoyvEt79u1ydxIJFj/AHYcdGx1I+rZP41k0UVcVyqwgooooGgrRt9f1i0sjZW2qXkNsf8AllHMyr+QNZ1FJpPcYdaKKKYgrRn8QazdWS2U+qXklsowImmYrj0xms2ipaT3GFA45FFFMDRu/EGsX9qtrd6peTwKMCOSZiv5ZrOqa5tZ7OQR3ETxuyhwGGMqRkGoalJJaDCkpaSmB1l34o0x/BzaTY6OLK9uPJW7mSTKSrHkg4PIYk5P0rlKKKiMFHYdwpKKKoQUUUUDCiiikAUlLSUDCiiikAUUUUDCiiikAUUUlIYUUUUAFFFFIYUUUUDCg0UlIAooopAFJS0UDCkpaSkMKKKKQwooooAKWiitDkCiiimAUUUUwFooooAKKKBTQhaKKKYBRRRTAWiiimAUUUtAgooopgFFFFABRRRTABS0UUAFFFFMQUUUUwCiiloAKKKKACiiimBK1rOlqly0LiCRiiSFflZhjIB9RkfnUVeqWNnZ3fwxttFugqSyAXkU548pnkMak+xO1T7HPavL54JbW4kt542jljYo6MMFSOCDWVKrztrsxtWI6KKK2EFFFFABRRRQAUUUUMAp8UrwSpLE5SRCGVh1BHemUUgOn11I9f05fEVoqi4XbHqMK9VftKB/db9D9a5irmmanPpV4LiDawKlJI3GUkQ9VYdwavalpMT2n9q6Tvk09jiRCdz2rf3X9vRu/wBeKyj7nuvbp/kN66mKKKWkrUQUUUUDCiiigAooooAKKKKQBQaKSgAooooGFFFa2i6DPq7vK8i2thCN093KPkQe3q3oKmUlFXYC6Bo41O6eW5cw6bajzLufH3E9B6segFM17WG1nUPNWMQ2sSiK2gHSKMdB9fX3qfWdZhnto9K0tHh0qBtyhvvzP3kfHU+g7CsSoim3zMb7BRRRWggooopDCiiigAoopKQBRRRQMKsWFq19qFtaJ96eVYx+JxVeug8F2ouvFFsHOEjDOzf3QB1/MionLli2NCeNLpbvxXeNHxFHtijHoqqBj9KwKsX10b3ULm6YYM0rSEemTmq1EFyxSBhS0CiqAKSlpKQBRRRQMKKKKQBRRSUwCiiikxhQKKKQBRRRSGFFFFIBKKKKBhRRRSGFFFFA0FFFFIBKKKKQBRRRQMSlopKBhRRRSAKKKKQwpKWkpDHUUUVscYUUUUAFLRRTAKKKKYMKWiimIKKKKYBS0UUAFFFFMQUtFFMAooooAKKKKYBQKKWgAooopiCiiigAooopgLRRRQAUUUUwCiiigD0abDeI7fQTwsui/YsekgQsv471H51z+rqNe0WPXY8G9t9sGooOp7JL+PQ+496bq3iKObxuuuWQcRxyxSRgjB+ULn9QaZrRl8O+KtRSxcLDLuCArlXhkGQMHqM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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:20,822] p24755 {2163136891.py:24} INFO - Image=s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_12.jpg\n",
      "Question: As per the AI/ML flywheel, what do the AWS AI/ML services provide?\n",
      "Answer: According to the AI/ML flywheel, AWS AI/ML services provide better $/perfer capabilities, new capabilities, and investment in innovation.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create prompt and convert to embeddings\n",
    "question: str = \"As per the AI/ML flywheel, what do the AWS AI/ML services provide?\"\n",
    "prompt = prompt_template.format(question=question)\n",
    "text_embeddings = get_text_embeddings(bedrock, question)\n",
    "\n",
    "# vector db search\n",
    "vector_db_response: Dict = find_similar_data(text_embeddings)\n",
    "\n",
    "# download image for local notebook display\n",
    "s3_img_path = vector_db_response.get('hits', {}).get('hits')[0].get('_source').get('image_path')\n",
    "logger.info(f\"going to answer the question=\\\"{question}\\\" using the image \\\"{s3_img_path}\\\"\")\n",
    "\n",
    "!aws s3 cp {s3_img_path} .\n",
    "local_img_path = os.path.basename(s3_img_path)\n",
    "display(Image(filename=local_img_path))\n",
    "\n",
    "# Ask LLaVA\n",
    "data = {\n",
    "    \"image\" : s3_img_path,\n",
    "    \"question\" : prompt,\n",
    "    \"temperature\" : 0.1,\n",
    "}\n",
    "output = predictor.predict(data)\n",
    "logger.info(f\"Image={s3_img_path}\\nQuestion: {question}\\nAnswer: {output}\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "What about slides that contain charts and graphs? We want to see if the LLaVA model can correcly analyze a graph and pull appropriate metrics from the slide. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 22:48:48,074] p24755 {base.py:259} INFO - POST https://nuqahxflpijquaxm4mbc.us-west-2.aoss.amazonaws.com:443/slides/_search [status:200 request:0.231s]\n",
      "[2024-01-09 22:48:48,075] p24755 {2807163302.py:18} INFO - received response from OpenSearch, response={\n",
      "  \"took\": 88,\n",
      "  \"timed_out\": false,\n",
      "  \"_shards\": {\n",
      "    \"total\": 0,\n",
      "    \"successful\": 0,\n",
      "    \"skipped\": 0,\n",
      "    \"failed\": 0\n",
      "  },\n",
      "  \"hits\": {\n",
      "    \"total\": {\n",
      "      \"value\": 2,\n",
      "      \"relation\": \"eq\"\n",
      "    },\n",
      "    \"max_score\": 0.47374642,\n",
      "    \"hits\": [\n",
      "      {\n",
      "        \"_index\": \"slides\",\n",
      "        \"_id\": \"1%3A0%3Arf1i7IwBHSUkt53bXlub\",\n",
      "        \"_score\": 0.47374642,\n",
      "        \"_source\": {\n",
      "          \"image_path\": \"s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_13.jpg\",\n",
      "          \"metadata\": {\n",
      "            \"model_id\": \"amazon.titan-embed-image-v1\",\n",
      "            \"slide_description\": \"\",\n",
      "            \"slide_filename\": \"https://d1.awsstatic.com/events/Summits/torsummit2023/CMP301_TrainDeploy_E1_20230607_SPEdited.pdf\"\n",
      "          },\n",
      "          \"s3\": {\n",
      "            \"bucket\": \"sagemaker-us-west-2-597703351594\",\n",
      "            \"key\": \"multimodal/osi-embeddings-json/CMP301_TrainDeploy_E1_20230607_SPEdited.json\"\n",
      "          },\n",
      "          \"@timestamp\": \"2024-01-09T04:01:58.369Z\"\n",
      "        }\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n",
      "[2024-01-09 22:48:48,076] p24755 {990086659.py:12} INFO - going to answer the question=\"Compared to GPT-2, how many more parameters does GPT-3 have? What is the numerical difference between the parameter size of GPT-2 and GPT-3?\" using the image \"s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_13.jpg\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_13.jpg to ./CMP301_TrainDeploy_E1_20230607_SPEdited_image_13.jpg\n"
     ]
    },
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 22:48:51,454] p24755 {990086659.py:25} INFO - Image=s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_13.jpg\n",
      "Question: Compared to GPT-2, how many more parameters does GPT-3 have? What is the numerical difference between the parameter size of GPT-2 and GPT-3?\n",
      "Answer: According to the slide, GPT-3 has 175 billion parameters, while GPT-2 has 1.5 billion parameters. The numerical difference between the parameter size of GPT-2 and GPT-3 is 173.5 billion.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create prompt and convert to embeddings\n",
    "question: str = \"Compared to GPT-2, how many more parameters does GPT-3 have? What is the numerical difference between the parameter size of GPT-2 and GPT-3?\"\n",
    "\n",
    "prompt = prompt_template.format(question=question)\n",
    "text_embeddings = get_text_embeddings(bedrock, question)\n",
    "\n",
    "# vector db search\n",
    "vector_db_response: Dict = find_similar_data(text_embeddings)\n",
    "\n",
    "# download image for local notebook display\n",
    "s3_img_path = vector_db_response.get('hits', {}).get('hits')[0].get('_source').get('image_path')\n",
    "logger.info(f\"going to answer the question=\\\"{question}\\\" using the image \\\"{s3_img_path}\\\"\")\n",
    "\n",
    "!aws s3 cp {s3_img_path} .\n",
    "local_img_path = os.path.basename(s3_img_path)\n",
    "display(Image(filename=local_img_path))\n",
    "\n",
    "# Ask LLaVA\n",
    "data = {\n",
    "    \"image\" : s3_img_path,\n",
    "    \"question\" : prompt,\n",
    "    \"temperature\" : 0.2,\n",
    "}\n",
    "output = predictor.predict(data)\n",
    "logger.info(f\"Image={s3_img_path}\\nQuestion: {question}\\nAnswer: {output}\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "How about a question that cannot be answered based on this slide deck? We want to confirm that while some slide image will be retrieved but the LLaVA model does not hallucinate and correctly says  \"I do not know\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:21,238] p24755 {base.py:259} INFO - POST https://nuqahxflpijquaxm4mbc.us-west-2.aoss.amazonaws.com:443/slides/_search [status:200 request:0.089s]\n",
      "[2024-01-09 04:18:21,240] p24755 {2807163302.py:18} INFO - received response from OpenSearch, response={\n",
      "  \"took\": 66,\n",
      "  \"timed_out\": false,\n",
      "  \"_shards\": {\n",
      "    \"total\": 0,\n",
      "    \"successful\": 0,\n",
      "    \"skipped\": 0,\n",
      "    \"failed\": 0\n",
      "  },\n",
      "  \"hits\": {\n",
      "    \"total\": {\n",
      "      \"value\": 2,\n",
      "      \"relation\": \"eq\"\n",
      "    },\n",
      "    \"max_score\": 0.4561107,\n",
      "    \"hits\": [\n",
      "      {\n",
      "        \"_index\": \"slides\",\n",
      "        \"_id\": \"1%3A0%3AvP1j7IwBHSUkt53bUFuR\",\n",
      "        \"_score\": 0.4561107,\n",
      "        \"_source\": {\n",
      "          \"image_path\": \"s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_9.jpg\",\n",
      "          \"metadata\": {\n",
      "            \"model_id\": \"amazon.titan-embed-image-v1\",\n",
      "            \"slide_description\": \"\",\n",
      "            \"slide_filename\": \"https://d1.awsstatic.com/events/Summits/torsummit2023/CMP301_TrainDeploy_E1_20230607_SPEdited.pdf\"\n",
      "          },\n",
      "          \"s3\": {\n",
      "            \"bucket\": \"sagemaker-us-west-2-597703351594\",\n",
      "            \"key\": \"multimodal/osi-embeddings-json/CMP301_TrainDeploy_E1_20230607_SPEdited.json\"\n",
      "          },\n",
      "          \"@timestamp\": \"2024-01-09T04:01:58.429Z\"\n",
      "        }\n",
      "      }\n",
      "    ]\n",
      "  }\n",
      "}\n",
      "[2024-01-09 04:18:21,241] p24755 {3951696921.py:11} INFO - going to answer the question=\"What are quarks in particle physics?\" using the image \"s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_9.jpg\"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "download: s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_9.jpg to ./CMP301_TrainDeploy_E1_20230607_SPEdited_image_9.jpg\n"
     ]
    },
    {
     "data": {
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jyF5sk0zTUaD7defLbLnHcMR2PpVXUNSkvpMcxwrgLEDkCnalqLXcpjjG2EHgDjd7kDirVhaQ2du17eAkDK+UyZ57daW+g/NkltYxadai7vEBkVsquevTHSsu7vJby4aV8AE8KDwKbe3sl9OZHwowAFHQCp9NsftUhdiBFHhmLcZHcUX6INtWW9J07zALq5KrbjkBjgN/hUeqavJeHyYjiFeOerUmqamLgm2tyRarwFHAb3xVWxs5LuYKowo+8T/Ki/RB5ss6VpbX8u5htiX7xz19vrVzVtSSFBZWZCoowzLRqOorawCxtOAow7ent/8AXrNsLGS9n2qSBglmAzijbRBbqxbGwlv5dqfdXG4+1bd9fQ6Tb/Y7LHnYAdwOD9feo7y8h0q3NpaHEh6kduBzWVp+n3GpzkRqSByzmnsLfVi6dYSaldbQSE6s/pWrqWoRadCLO0G1l4z1x/8AXpdQv49Ks1sLQ5bby3H51zmWd9zEknrnmk3YaVyVbaaRBIEOxjjd71d07SJrx1Z1KwD7xzz+FWtGhu5gA3Nso5Rh1B9Kl1PVo4IRa2bggfxD+H6GnZbib6ITVNVECLZ2ZwEG0sGzWRZWsl9dLEgLEnn6Uy2t3vLhIkI3OcZNdMBDoFoHODMRjjuaNwemiOp0zTV0z4V+N41lDhjYnHcfvhXmtd54buJLr4UePJZWLMXseT/12FcFXpYD+G/X9ED8woooruEQ0UUVzmYV9fC+jQcuM18hV9HxXYjGSGJ968jNnbk+f6HTh1e51hvGk/1ak+9NKzyH5n2j0Fc+muMnHQVMuur/ABGvHtc6djfEUScn5j71R81TfYUAfSs59diY4Q7j7VDZ3TSagS3HtSlHQcWW9au1iuk3N2rJn1Nn+WMGp9YQS3JuGIEacZJ71kGeOXIBKgd8USq046NlRpTlsh7zSsSXfArImnWSRypzzitK40i/uYM2jq5I6dKwmtbqx+W4hkQqedy4rNzjNWiVySj8SOgs75beEKBlgKj1DxAbeBmkkCDHAHWuZutbFuhbFc9c3sl7L5kjEjsK6ad2Yysixf6pPfOxZiEz09awNXH+iKf+mg/ka0iMkCqGtYFmgH/PQfyNd2EVq0fUxqP3WYVdhpEuNDt1HUBuD3+Y1x4rrtNTd4ft+zDdhvT5jXsY7+GvU56XxDLl0xuBOM9u1UtQmluNMntmYHcvB7nkH+YFLczCN8vwCcMD61ATktE/K4+U15LaZ0HKAFTg9RVlJJfIEYJCA84781cuLIGQluCO471E8SJEskZJVgCV9D/+sVDelilFtXI1D8OvT2rrPBvim68Ma5bX8TEqDtljzjencVzELYcoehA/lUm7DqM9CTU2ugufZGj31rrunxahaT7reVdysOSPUEnOCPoK+efEPgLVrLxvFqKxi4tbu/aYMhwUAlJIYHnpznoa0PhN4x/sjWV0u9l/0C9O35jwkhGAfx6H8K6i+8PLfa7/AKQrpGmo/afMyOYzzj15IA/CuefuBFvmSXUzV8G6rJGt2GgiinJMYdsnGfYHFWNP0xbOdkmmLGMgyyIm6PcGPy5HTGO/vTtG8QXc2qGwmt9kEKuGcZYBhwuMn1HSo7HVvsuqpaWUdxJbK374Oozvc9+w6mpeto3NaUIR1aJLvwt5nhiKyR7eWZA5VnBX5mYMMHp0yKw9R8AXen+G7+7nurUvHbvJ5a5bbhSSN3rgVY0jxBLa50yYPJF5zCOTqQozgVNqOuXlz4U1WHYpYWrhiGz8pGCa56der7SK80d08PR5HJHjlLRRX3584FFFLQIKKKKYBRRRQAUUUUwCloooAKKKKYgooopgFFFLQAUUUUAFFFFMAooooAKKKKYBRRRQAUUUUAFFFFABRRRSAKKKKACilpKACiiigYUUUUAFFFFABRRRSAKKKQ0AFFFFAzc8F/8AI9eHv+wnbf8Ao1a+vXGXPpXyF4L/AOR68Pf9hO2/9GrX16/3zXz2dfFE6aHU4b4qD/ikW/66ivnHVzJ5EKxbtzK33evWvo/4pn/ikH/66LXzlqNwtusDupI2v0+tefhtmaVOhhEagpz+/wDzNJnUOuZ/TqatHV4+f3b/AJ0n9rR5/wBU3512Wh/MIrFtQOSTNz9aTzL9c/NOPXrVn+1Ux/qm/Ok/tVP+eTfnStT/AJgsOg1vWrSPbFfXUaA5wGIFSRa9rlyRbLqNy+8kBfMPJNV5tRWW3aIRsCRjOabpLMmqW8ixGUrIG2dM1ErJ6O5LiuxsXvh3X9PtDdzh/KHUrJkj61JZeGtfvbfzogwUruUPJgtXU654303+zLi0t98lw4KlXT5QT159qZo/j/TbPS40uY5WuQgV9qDBxwKRz81S2x55ctePlZzKyxnB3ZwDWzokN1pEy6hKY4omXGJOjqewqrd+ILuZrtTsaO4k3kMvTnIqrqGr3WprEk5XbH02jGc0tFqbWbVhJbmW7nKQgpGx4UE4HuauTuujwGCNke6b75xnb9DWda3c1mxaBtrEEE46io44pbu4Ea/NI54yaLlWH2lrJe3GBuxnLtjO0epq7qF1Fbo1ja4MX8TBs5PXGfSnvdjS7X7NbZFw4xMxHI9gap2VmblvMYlIl++/oKfkIl03TzdsZJflt4+Xk7Cl1HUvNU21udtuvHH8VSXurBrUWdqgjQfK8iceYO2RUWl2kTyrcXg/0VWwwHU8ccUm+iC3VjrCxRLd765zsiYfIy8PmtS38NeIfFES3tnYO9qMqh3AAAfU81v+FvC934g1iKbU7R49MtwCEkUgSDtz3r2DTrKx0q18mzgWCEsTsXOMmvNxmYKi+SGrO3D4R1PelseVx/BudVDTasigjJ2xEkH061dh+FkUcTxHV5tj/fVIgM/rXpck/mrtQVAAyb/cV5TzHEP7R6UcFRtqjhtM+EOkeU63V7cSSZyGTCgD9eaq6r8L7uzhun0e83RFd0cDrl2Ppur0mDMTKzA4Iq0blQoyg68YNKGPxEZX5iZ4Sm9Ej55Hw68S7TJJp8qqOWJxwPXrVGa6XS4WsrUKZCMSS4wfpX0kzpPA8ciAxupUjPUelZtv4L0Kecsuj2xJGcsua7qWav7a+45KmBtqmfNlvaS3c6ooPJ5Y9q3Wu4tNtvslhGXmbIZsHJPqK+ik8BaILZoo7KCJZDlti96jg+HOmadDPNY28ZvZRtWVhgRj2rqhmCk/hZyzoJdT5ZlhlWTMqkMeeetXtJ0pr2Xe4KQg4ZhivQPGHhSwstSexs1mujbqHuZwPlRjycn6VzGp3UlvZCKyjYxbdplC8Aeld8WpK6OeTa0INY1OK1tf7PtThhwzLxiubVWd8KCSfxq1DY3l0cxW8jj1Vc1r6dbw6dZm/uX+foi9wfTFVZsV0izbRWmj2aTXQUTYOxhnnvXP3V3c6pdbm3OxPyqKjvbyS+nMj8Dso6CltTcwN59uHBHG4ChvoNK2p3vheN4fhN48jkUq6vY5B/67CuErvPDMjy/CXx3JISXMllkn/rsK4OvTwH8N+v6IUgooortEQ0CilrEzCvpV9Tt2QYhVifQV81V9IWKwpDjaMjua8nNYqXJfz/Q6MPK1yNpkkbJtgq+tQyfZmP3cU+7u4kyCw+lYc0l1PJiBDj1NeK4RR1qTZux29oFyG2mqs9xBZs08MrM68Y9TUVrp8yxl55CTjO3NZLSG4uo1bhTLkj2HNc9efKrLqdFCnzO76BfeKU/taDTmSSV0IMjY+UE9q2TJDOI5UQJ5hwB2rCfQreK6a7iRvNbrj1rWsLFyyPKeE6ITiuWXLpynZBPqas2sWWl2sX+lLGxbGWPvXTyTWt9oavcxrNGcc/1zXBap4c/tPVojHFGwjHMbLkH/ADmulh0q5sfC91pwYKVUmEoT8uOcc/lWiUYxUovUzmnKVmtDyrxrCLXVzFGgSA/MmO4rmXuhGBhlwPWu98c6Xc6losVxCjNNZjDKoySprlvCPhSLxDHJNcsBGpxtJ5r0cNUi6XMzhrUZe15EYjavGo+aVfwqpc6lHeRiNGJIbdXrMfwr0aS33CPJ9a4vxh4Jg8NW8d5CSBJKItueOQT/AErtwdaEq8UkZVsPOEG2chXW6O4TRoA/3G3c+h3GuSrptODR6PA65IYNlfX5j0r2cd/DXr/mcdH4ivrUX7lmHUDPHcVb8KaZFr17FDO5WLZuchgDx6VDcrHPaSEnlQSpFdH4Qgi0/wALpNBzcXDBWbHI56V4GJq+zi2tz0MPS9pO3Q6iH4b6LHJ9plaaeLHEbtx+mCann8N+H4rcxjSrT6GIEn8TzWzqt9HpejRebMu8AD5m6nHP1rCmlkYq+SwPQ+nGa8v6xUb1Z6SoQS0RhN4Y0G5mO+yghPTILKP0OP0puueD9MbTHSxtVS6RcqyZO78e9U9b17S4rloZJZVdVG0p0zjvWMNdupLtdk7lF5AzjOK64VJWTOSdON2jntrwysjgq6nBB4Ir2fwXrv8AbGhgzSbry3/dysxyzdSGJ98/oa8g1i8W61m6mHQuR+XWrnhi9eLWY4/tBgjnZY2fJAGTwT7ZrrqU1VjZuxz4bk9sozdl37HU6MdSnvbiO4k32yMQm5R2Yr/Q1ctNLkTxZHetKggF1gKD1JXIB/EV31n4GkUx/arRpD5bB907AFt5K9D2U4q3b+ChHbCGeGNoxdm5baCMjaRsyWJxz1ya5PrEYyYOmk9Gc34e8N3GoQ6fcWqJtkmnnZjj1AwPwP6VR8T+G9Q8O6ReSXN3ax+bayxDdPyw2E4AI5PGB7muyv8AVNN0y0S1tlknkjb5Vhm8tYx3GUA/IfjXnfxJvW8Q6PbItqIGs2aYO8jSMRxkZY5FOhFTnH1RU5yV/Q8oooor7o8YKWiigAooopgFFFFMAoopaACiiimAUUUUCCiiimACloooAKKKKYBRRRQAUUUU0AUUVZs9Pu9Qk8u1geVu+0cD6noKYm0ldlaiuxsfAzsA1/c7P+mcQyfzP+Fb9r4a0m0A22aSN/el+cn8+KpQZxVMwox0Wp5gqM5wqlj6AZqyum3zjKWVyw9REx/pXrUcccS7Y0VF9FGBTqr2ZzPNH0j+J5G2mX6DLWNyo94mH9KrvG8Zw6Mp9CMV7JTXRZFKuoZT1BGRSdMSzR9Y/ieN0V6nc+HtJugd9lEpP8UY2H9KwL7wKMFrG6OeyTD+o/wqXTZ008wpS0ehxdFXL7S73TX23Vu8YJwG6qfoelVKg7oyUldMSiiigoKKKKACiiigAooopAFFFFABSUUUAFFFFIZueC/+R68Pf9hO2/8ARq19ev8AeNfIXgv/AJHrw9/2E7b/ANGrX15IcOa+ezx2cTpw/U4n4ojPg+T/AK6LXzXrQBtIfo/86+k/ic2fCEv++tfNurqWtIsdg/8AOvOwzvFmlTdFaDw1PcWiXCzIFZdwGDmrP/CHT5/4+o8EjHB5rFhnuwu2KSXaP7pPFXms9aFobsrcCEANuz29a6NDJ83c1YPAs8rLuu0CnOcKciuUmj8md4852sVz9KtJqt/EMJeTDvw5p9rpVzeuGI2K4yHkyAx9M+tS0nsOPMviZBZWU15IAinYCA744X3NbF5dWumwGzs0U3KjZJcLyHHXI9DVO6vYbdWi00yRo4xIr4Jz04PpVewsZL6VljxhRubJxxVbaIb7sLO2N3cgNnbnLnPapkigbV0iQ/uDKFyT2z61Jezqf9Es9xhTknGSfX8Kfd2Mdhp484r9qfDDackD/wDVRYTZ6Tr2i6RbeHLxpbWNQkeY3A53dsVwvgm1tbrX1jukR0CMQjfxGsyHUL++mjtprp5FfEYEjEgV2tx4BjsdKFxHcyC7TBYjp+FU3d3MLezjyye5N4vsdAsxbPcQ7Nz8+SMFh3/nXIX99pltLONIjOxyNpkBLIMc4P1rJvJ5pZ2SaeSXyyVUsSafY6ddX8m2CB37kqM4FJyu9DSEOSOrHWWm3epO4t4mlKYLBeuM13mmfD7VNXkj08TRxBULYkUqT7e5qLw/4a0+S9Mb3F1DOoUrJEpYxt33L1xX0J4eimOk2yXpjumRcC5C43Y6HBGQa5a1Z03ZGsY8+vQ8L0j4babaaksPiOa5gBJQhVwqtngk91PHNen2Hw/8OaDei/hjE0bJtVZcOo9xXZXUVuykSwRsp4O5c1z+pyhIA7IIo0JCKvQj6V5eJxknGy3O3D4dSlqVZHhUAR5WNeiheg9BVO6u0SPAJ4PSs641Eg4j53c59KznmaSRi75J615dm3dnuQpKKsa0V8BMBjjPrWi90m0/NhtvGK5lXx0Per1w7KRhv4RnFJx1LcU2b8N0hgVmfnbk5FULi5PnsFbjrWXHdSBAofjGCKGmLuGz2xRyijTs7mxbz/u1U4OT1zWxYS/vQueDxkVzaArb+YenStPTbseWUXk5zn0pbGVWF46HdWoRYVCnI96mZlxg9Kx9OvTJnzCABwMCrGoXsVhYvdT5EUY3MfavRhUvH3TxZ02pWZX1TSbLUIhb3ARbUnMkY48z0B9q8x8f6TJqEkdtblf3YJEMWFit4h3Y/wB41b1zx3ZXN4fs+9owuCemavWuoaVq+kx2stuUgnwXUHGee5FbQxTou7Whs8E5R8zjNFvdMtdI8tp4EZFO5SwAry/xBdR3Os3DwFDFuwrIMA+9eleJfBum39+h06zktLdWKvJv5f3CntWavw4sMlTeXGR04HWvcp1PbQ5o7HjTjHDVGpPU8zXG4Z6Zr1fS7zR38LuBcW0SIB8rHDE/SsDxB4KttJ04XEM8zOX24bBHQnt9Ko+H/C39rWxuJZii5wqjqatJxJnKFSPNc6XRpUm+GXxBljxsaWyIwMD/AFwrzuvS7HTP7J+Gvj20D71VrEqSOcGYda80r0cD8D9f0Rt0QUUUV2gRUUUViYhX0BLDcx6jJBu2oDwa+f6+h/EBmHl3cI+91ryM2lyqHz/Q6sMrtjhp1umHkbcfU0kt1DEu2JNxHpWaLlFiD3U+P9kGqlxraBSlnFubscV4LbkdySRrIk8/zyfKvYVUj04LqcedxADMMdiRV/RZnmsgLpdso5xWtp9unMrDLNwp9K5Zp82p1U2lHQ8n8c6new65BYxyyQwKmQy8ZbuSateBbu8uNdS3aeaeMuUljdSccZBBNaXi6GK81Fo57SYbTtEgQEcfewc+mOPeuh8GvollbxzyzpAijIeQncfoDzXoxhH2NrHNafPz3Oyt7DytS3LnOwZP4Uar5ixxwA4OSZMd/aiLVBqweexif7FHwsrDBmI9B/d/nXG+K/Fv9lxsUZZb2Q8KT90e9cMqTuoLc6I1F8ctkM8S6ha6FpE087YDAqFB5YmvO/CHiePS7mSNwoglYneR0qjrGoSXshu9WufNbqsQ6D8K5b7fvvCVi2xk8CvRwuHVOLjLqcWIxUpzUo9D6l8L6rZarZMY50cA/SvO/jRPF5dpbQkEJKGOD32tXnVvf3dmmba5mhz/AHWxVG+vLq8ffc3EkzZ/jOa9DCYflrRZhVxXPBqxTrr7KPPhe0lHVN//AKGa5Cu40RPN8LpH/v8A/oRr1Mw/hL1/zOej8Ri3JwjMvRh8w/rW14P1BJNFihkYARXsaNk9s/yxWG6/fjPUGqWmXCWyahbFsFnVxz9f8a+exMOaB6OHnyTPedd0Kz1ZfMkXA2gFg3Ue9VbizS2giiQcKvGep4q/pDjU9AsJ0cukkahs9VYda4vx1r9zo+onYrhFGUbbuB+v+FeZSpzlP2b6HpTqwjDn7nJeIbS3XW5sRqZCB8zDAzVERudQtYUTMjAgY+lW21G5163gubuNInycFVwCO1PNsY5LcxuRJvBDYzjFdVmnZnLdNXRzN7p2oWjtLcW8gQsSzYyAT7ioYpcY/CvTfPjS5TzUBEnGR60658N6Te5WW2RWbkSw4VgfcDrXXCstmckqFtUd34D+J0GpeHYbS9RjqVtGEx/z2AGN2ex9f84frGvX+pZRpfLiJ/1SHAx7+teRnRtT8M3cd3ZA3FtGwPnIOQB2Ze1ei2t1HqGnw3UPCSLn1xzyPzzWc6EL80eooOzs1qVpCynhKx9dYtpV4fLP+ofkjp8prckD7toBxj72RisnXFYaLe4Of3D/APoJq6MbTXqVOV0zyeiilr7M8gKKKKYBRRRTAKKKKAFooooAKKKKYgooopgFLSUtABRRRQAUUUUwCiiigApURpHCIpZmOAAMkmpLe3lu7hIIELyOcKor0XQfDkGkoJZMS3ZHL9l9l/xq4xuc2IxMaC137GLo3gwuFn1PKjqIFPP/AAI9voK7KC3htYRFBEkcY6KowKkorVJI8KtiKlZ3kwooopmAUUUUAFFFFABRRRQAyWKOaNo5UV0YYKsMg1yWs+DEcNPpnyt1MDHg/Q9vxrsKKTSe5tRrzpO8GeOSxSQStFKjJIpwysMEUyvUNa0G21iHLYjuFHySgfofUV5ve2U+n3T21yhSRfyI9R7VjKLR72GxUa67PsV6KKKk6gooooAKKKKQBSGlpKACiiigYUUUUgNzwX/yPXh7/sJ23/o1a+uZziQ18jeC/wDkevD3/YTtv/Rq19a3TYlYV85nztynVhVds434ln/ikZf99a+c76VEjhEgO1g44+tfRHxHbd4SlH+2K+db+Hz3s4d20O7Ln8RXnYN3gzSurNFSx1O302RvKhZ0Jz85Ga27rxfaXOlSQG3YSMmwJ/DWzN4EsI9Kl3rJHOoO2Xdndjvj0rgrS1tGdGubgKm8hlAOcetdeq0OZck9ewabp32lmlmQ+QoOSD3qxfahC5+zq0xhj4AV+D6Ul9fmfbBaqUiQbQR1Nadp4TimuLRZLhysv+sCrytC20LcktWc9usP+ec3/fQqYX223NpbphGbhj97ntmu81rwNpsGnSyRMbd4huLsSwxXGWbWWnvcvJKszL8sXy5V/wCo7UapkxnGauiWHydHh86VM3ZXAjY+/t7Vku819cM3zO557nApJZZ725LMWeRuAOv4V0MEcGh6ct3s82aWLDcnAJPT2o3KenqQ3Bg0vSLcxpE004O5sEng9Qe1XJ/iDqdzpP2GREPybfMBw31Nco8jyY3MSB0BPSui0zwdd6lpovVmjRSCVUg84pXb2FJQSvMxXuxJAsZgjDg/6wfeNd/4W8QWUtutokEkcka5O1c54/xo0X4d209nJJeTM77c5TgL9PWpPB2ito3iq5VpFeMREL64z3rOrN0qbn2HThDEzVJM63wlJps8j6jcwXVldCTIyOGHtmvVNK1KO8DRxOGRTgeprwbxbqN9a6uPJnKQugKYx+NdJ8LfEF8NUNrdJNLHPn963AXA/lXkSdSs/adz150KVGnyrdHql80scUnmFeOVANcVqszvbjdknJwc9BXZai4uI5PJG5wCPqa871q5uoGCSRspIPDLjrXm1ItzsjqwaVvMoOzFVY9D096QNzzV7w/YR3zusyMU7HPQ1r6p4fRLbfZx4ZcliT2pN2djsdRJ2ZzrsVb2PIqWWfL4OOBjiqTNycHP4UK2T1p2NC0knzJjgA85rXtdJmu4y8RXg4rBV/mGPWvQdA2CwG4EEk9qiehnWm4RujlNXD2DrCrFvl5z61HpF273YDOdvfFWvHFyUuYYkKMAN2R1pPCTWSstxeFQc4GTxVcvu6kqf7vmaO0sFwVcgYJzzXBfFq8nltWihguEiBBkmeYqjY42qufm/KvVbeKCWFZEA2kZGK8n+KcOoqTcRR28tqBzuhDPH+J7V2YWMab957nkSk6k7pbHi+SX4Jr1PwwwGh2CkEnaf5mvLWYmYk9T7V6X4WJGiW2Tzzj8zW+M+A7sHrNmxrGr2OnrGLqdId3Khu+KzYfE+jupYahDtzyDkHJrN8a2sdybJ3txPgsuC5XA4rlLfw1FIRJJKdjnKovb2zXpZb7uHVjxM2pxliG5PsdF4q8Q6Xd6akNtc+Y4kDHaD0wRXJ6R4ru9ItjbrFHKhPG/PFX38P2bQx7POBZ2XefbrXSweEtKOkwB4lcyEDzMc59c9q7WmzgThCPKVtEvpdS+F/j67m+/JJYk+374cV57Xo+m2kVh8OfiJawkmOOeyVSfTzhXnFehgf4b9f0R0dFYKKKK7RkVFFFZowCvoGwuRrlhNBGTlAQM18/V7u+radoaMlmN8x4O2vEzmDlyJef6HXhZqN2zMg8PXRlc3z7Y1PGT2pLvVdM0hClsgmlHeqF9rF1fh/NkKjsorDnRdpZ22/zNeZCj3LniOkTvNN89zb3k8y7Zf4QeBXfWGnEouBlTzXgujrfahrNrbpNIkCvnBPWvVfFep6jpHg15LOb98oGCOorlq0l7RROylVbpuS6EHiHw1rEWp3TxWkd7p9w3meXyGRuhwQQa19L8L/ao7eOfTUt7KIHK5O584z1+g/KuS+Hfi/W9VuJIbuVpCp6tWz8QfF+saRDDFZpsV+GlHauhNr931J9pLk5+h2us31joOhyMPLiWOMiONeO3AFfNF/dz3d9cTytu8xjjPar11qWpaq+bi4llJ7EmpbbSDuDXIKoOcetbU6fKcNStc5wafLcSZCk+5q/aaIpmHyb5D6CumS1a6YRWsW2Md8Vv6XpcVoASMyHqTWjdiIpy2MVfBbXOnFs7ZsZUVwmqW8lqTDMmyRHwR+de6RkIBzjFcH8SdOiGnQaioAdpxGcdwVY/0rpwdT97GLHUp6XPNa7vwzzoKD/e/wDQjXCV3XhXnRSPTP8AM16mYfwl6/5k0PiMu/Tbcs6jk9feuVvWEeog9mAJPr7V2eoRl5Mp69q5bWdPdZopiNu9goUnr9K8Vo6We6+Cg8Hhe1Vs+XIvzD685+tUNf0R9UB+2lJPLl2gleq8YP15rV0u/trGxtoWdY/kVdp7HFSzs13GDkbWcInPXP8A+qvMUv3jbPXoqHKoyPOI9Ku7rVri3C/ubRQVwMDBAx9e9V/7NnMpaTO4twMdK9NtdKBgmZSu5mBYj26Vk6lBDbuZrhlRV7nuaxlWftLLYbopJ2OaktkkMaMrMcfOR/Cexq88ciW6g/NgY3VKmpWNpGWSVGkc8KpyTTTrj26/vraPy26+tdUJLqc0otFnSZnTefmyPfNXtLi+zXzRRQxtZ3LM77T80Mm3pj+6ccelYzXcUkTtCCA3bNJpmpf2dOFGTNJlip6H0/lWqdtDGSvqdVNGgUnHSsLxCEGh3xAJzbyYP/ATW9JPFdW6TRjMbKGBz61z/iDcdEvSGXb5EnH/AAE1dG/Or9yZ/CzyGiiivtTxgooooAKKKKYBS0UUAFFFFMQUUUUAFFFLTAKKKKACiiimAUUUUAFORGkkVEUs7HCqByTTa7TwbovH9qTr6iAH9W/p+dVFX0Ma9ZUYObNfw7oKaRa75QGu5B87f3R/dFbdFFbpWPm6lSVSTlLcKKKKCAooooAKKKKACiiigAooooAKKKKACsvXNFi1izKEBZ0GYpPQ+h9jWpRQ1cuE5QkpR3PHZ4JbWd4JkKSIcMp7Go673xhov2m2/tCBf30I/eAfxJ6/h/KuCrnkrM+kw9dVocyCiiipNwoopDQAUUUUDCiiikAUUUUAbngv/kevD3/YTtv/AEatfWN4cTNXyd4L/wCR68Pf9hO2/wDRq19W3zYuGFfNcQP4Tswa1Zx/xCOfCkw/2hXznqrvHFbNGSGBfp9a+ifH5z4Wn/3hXz3ekBrUkgAb8k/WvOwOsGa4lWaA+L9YubL+zzImG+TcR82D2zWnpngWa/snllm8qVSNoxkEVyUrg37OnI35GK9O0LxNp8Nrtu50hl/unI7da74JN6nBV5oL3EP0/wACW1qWCzyOzLgkqMe9cZeanLpeqpbQzSwQwvyzfNjnPHtXpll4n0mUGQ3JMMY5JQ15vri2mo312lu6yOXZ48HOBnNVLTYzouTb5w1TxjqevRCwjWMBjtJiUgyD8ak0TwNJqDyJeyPAwUMgTBzmsnTdNW3iN/dyPEsMg+Tbyw9jXfaD4p0+7kERuAkhIHznH5VMUn8RpUbgrUyKz+G8VreR3MF9LuiIZSVHWsLx9aS2F0tusjSJKfMYYwM4r1KK9tUADXMYzzywHFec/Ee9CalBJbyLuXK4GDxgVUkktDGjOcp+8Y/hTRIrhH1SZ4JEtiS9vLxvGPWrkHia8FrNHp+ngWwYgDkhQTwK5Nrie7lHXc2BtUYH5V0VsZtG0C5ZinmGRNqkjK/hWafY6Zxu9dTU0nxLrelRNDJaNKs/CFs5zW34dhlhuJr29J+0TEhlI+6B0qjpC3F5apcXqBWUlowO+e9bUK5BYnjHr0rycdiea9OOx9BluXqC9tNWZjeKJmTU7eZdpAjO3IzWLHqd4tzb7Z5SxcKoDkcelP1q78/VJc5KodoGaq6NcmLxDZyrGG2SAAN0JNTQg1DTogxE4qXzPpTwvbLb6XGh3MVHJbrmsXxzdWiWxSR42Yj5QPvCq1/4uGgeHWn8vLEALz/Ea8O13xRPeF2MzmZz19BXPh6E8QlFCSVKbqT2PRfD2qQwamomnSKPBOWPBNekieG908spDoykEg182QTXKPbvDqMWoO55hiVtyY9cqK9D8N+LJ4baZJ5G2bNqxjjDU8bl9ShqzSFaGJ1juiW5QRXDqDkKccHNNU/IPrUTSCdi4zzUgyFFcR3j+MA811ekawkVhMJiQYxu+Udq5N1CRnkE9eKqXstzbB1wyB19O1Lk59CZpSVmZvifxGMzkZPmnCnHP4Vy9r4ou1cTfvjEvyhwOBTtWvGsr2KQoZBsfABxgkYz+tc39vuEsPsaPMqb9xHmHaf+A9Pxr6XC4Oh7BOW7PIxWLqwqcsNke2+FfiPcWskdtdylkYgBX+8K7fXZor6w42uJOoB7Gvnjw5JPqviOKW6cvIx646kDivftI0pX08s8zMRwBivGx1GNKolA6KE4zj7WSszw/wAQaadL1iRCMAncnPauv8K86LB2G5u/uaPiXok9rJBeuVCYEeO9N8McaBCvGTub9adSXPRTN8NZVXYi8cKkmmQAl8rLgMjYxxVDT/B1vNYJLJf3KyEZDeZxn2FafiCJZ9HmMhAEY35+lZlr4u0mPTolkD+agGFK55x1r1Msa9jZnj5zGarJxOTnu78SvYiZnZZSeOpNa76x4i0zTFt22GOXIBxllqtBbW8rSXxnERLbkcnjd6VWvNcNzqMEbyKIoztdxyG967tjz7X6HU+H1lX4V+PPO5kL2JJznP74V5/XoWhnPwv8fMJVkUyWOGU5484V57Xo4H4H6/ojQKKKK7AIqKKKgwFFehzyLESWOK88r0WLSVkiM99N8/aOvMzH7Pz/AEKSuZ73ckzYgX/gR6ULBg75GLv71YChX2oBjPQVpWenSXRyqfL3Y9BXm7gvIp2P2lLiOWAYMZzXRax4hnv9OFvdokUOPmHdqpTXNrpqtHARLcHgt2FYNwZbqXc5LsT+VTypu5spuKtc09C8QR6RqKyRW+LfGDjrW74n1iPxPZw2thG/ynLlhWPY+GZnCy3R8uLr7mttYlhTybZNq+vc0lTTlzEyryjHkZh2+npp4y2GkH6VctbCW/mDSHCVoLpu5dznJ9KfGXtjjHFXJ22Jp0nLWZrR2Vvb2oWNQuPSktIFklOfwqBLndH8x4qxa3Kow2jNRudKVtC3dwBEAA7VwnxHGPC9vj/n8X/0B69CmnR7YtJgYHNeP+OfEMeogafb8wxTby3qwBH9TXRglevEVR2iziq7nwlk6bt9VbH5muGruvCYK6aje5x+Zr18x/hL1/zMaHxETWz3E7AAhc9an8K2cGq+MoQI99tZRtIGPRnHGfwJ/Sp9Sl+yb9q5klJWNB1Yn+grX+Hdki3c8YI8xYcAjoTuGfw6D8D614Nd2pto7qKvUSZd8Qac72s0seTsUng98cYrJ0bxnutkSaNRtGM4OD7+1dvqFu4tJo+pkQ4xXidpcNDNNDIu145GBX05rlw1NTVmdOJm4O6PQofFcgv47ayTzJLh8BTnavuf1pdXE11u8w7iM43dhVLwJYC+1aS7ZvltEBA9WbIH9a0PFpMWn3EsfREJyBWNanGNVRia0ZylScmcjFcC0kb5gQTVbUdQnv7gIuFUDnHWsWymZk3uxztB6+orUto2K5I+Zzn6Cu+lQS1kcNSu3ojZ0SaOa4W1uHYDqpBxk+lJ4puJtNg32aBYyMmQnJ3dge9Y8UxW++Q8oc5+lb8zxzRvBcKHRhyGp1Uk1YdKTcWavw4uGuvDEts8xkeG4Y7uTwwzj891XvEduq6NqBzwLeTgf7prP8DHT7Ca5s7eWUyzHcFbhcD6d+vNbniaJR4fvyX25tpcDIGfkJx+lRG3tVbuh2ahZniFFFFfanjBRRRTAKKKWgAooopgFFFFAgooopgdT4c0e31jRruKUbZFkBjkA5U4/l7VgX9hcaddvbXKbXXv2Yeo9q7DwH/x43f/AF0H8q3NY0e31i08qUbZF5jkA5U/4e1a8l43PKeLdHESjL4fyPKqKs39hcaddvbXKbXXv2Yeo9qrVmeommroKKKKBhRRRTQFzS7B9T1KG0Xje3zH0Uck/lXq8MSQQpFGoVEUKoHYCuS8DWAWKe/YfMx8pPp1P9PyrsK2gtDwswrc9TkWyCiiiqPPCiiigAopskixRPI5wiKWY+gFZFjrd1f+VNDpE/2OU/LOZEB2/wB7bnOKiVSMWk92XGnKSbWxs0VWivoJr6ezQkzQKrOMcDd0/lU+9N+zcu70zzVKSewnFrcdRWbaaqLjVNQtHRY1tGQBy33twz+FO1HUzY3enwCIOLubyid2NvBOfep9rHl5r6bfjb8yvZS5uW2u/wCF/wAjQopGdVxuYDPAyetLVkBRRRQIKKKKAEIDKVYAgjBB715br2mf2Vq0sCj903zxn/ZP+HT8K9TrmPGtgJ9MS8UfPbtg/wC6eP54qJq6O7AVvZ1eV7M4CiiisD6AKSiigAooooGFFFFIAooooA3PBf8AyPXh7/sJ23/o1a+q78/6U/4fyr5U8F/8j14e/wCwnbf+jVr6rv8A/j5f8P5V8zxD9k7sFuzi/Hx/4pef6ivnfViPKgyCR83A+tfRPj/jwtcfUV4FLaLdvbIzbRl8n2zXnYD4GaYp2aZjW1zBbSK/2UOV6bmpL66+3XRmEewkAEZz0r1HR9KsRp0KvawMOm4qCTzWmmi6acf6DByP+eYr0vZs8x4mKexzmk/2emixFLuNcryjEZz6H8TXG2McWnyNqF2doBIVAOWz6V6ydD01oyDaRfQIBXjnicNH4gvIMny4ZCiL2UegpTXKh0JqbaRBqeqSalNnGyMfdQf196elrBZwpLe7t0nRMYIx61NYWkNlANQvgcD7sLAfOD3FZt/etfXTTMMDoq5zgVle2rOpLoi4byxwQIX/ADp9vNZ+axmtpHRlwPUH1rOs4xLdxIxIBcAkV7RZ2tu0K/IhGzuoya0gnIwrVFT6HnOiXtjZXly0sTeU/wDq125xUUQj1DxHGrgvFLLzu4yK9YXTrQgn7PF7HaOa5LxLaW+n+JdPuYIgGZGMiqOg9f1pVotU3YrCVozrq5rI3ljaFAVRgCmgkqW6KOSaaMtz61JJkWjqDkFTXzHU+56aHD300b3szo4KsxINXPC0lhd65ZWV2jFXn5IbG729qy7aLzb2OMjgtgjFdTpWhvo/irTdQvbaCS1aUKqyfdJxxnFe1Rham2fLYqreoos9O8e6LaQaJCscGU3AEFs9BxXgniGxaO8eRcCN8YAr6Q8Xo+oaOI2Kx7R5jZHHTpXl1voS6qkytsATB+YZya83CYpUJuXQ9H2Tq0UnueUW811biRIZXjDjD7TjIznFdfoO5NOjYk5JJ5+tas2gR20zRtAgZTyMVe0rSUuZ9j7ooVGSyrkCurE42NWFh4fBulLmbN/w1Ck8DSum5lOOelW76zCvsiU7QMkU+yu4DdiC32rGPQYBrQuoPNgLqDu9q8OUveuektjOtLQLd4bkAD9auXtlb3ahZAWQHPXFWoYEQE7TuAHU1V1CGT7E8scm0pyRnqKnmbkJtHm+uaBNLKf3TIAxCH1rJi8B39wwPyjJr0O1vzE2WUSN0Usc49a7HSNJS6CyQAKVwTu716EcdVpx5UclahRfvTOK8MeCG0G3a6uNjSYwCR9016PoMrNY5O3JPI/GqXifMVvGEY4wc/WqPhfVMSrbyABGJG6uSVSdWXPLcnlTo+6il8W4Wfwi8qw79kikuGxs56+/pXnXhIu2l9T99q9C+Mdy9v4bgtLa6CNdS7Wi25LqOevbnFee+D8LpZByGEhBBr0FTawt33McHNPEKPkberp5mhXigdYm/lXnum6WUH2q6CiIJuAJ/nXp7wtLbyRDAZlIBbp0ryDVtQnupvJcBVjJXaD3rqyyS5ZIzziL54tFrU/EBu7JtPhiRLYSbwwHJpNB0CXW52QOYkAyHZcgn0qtoukS6rdBVBESkb3A+7XXalrlp4Y0/wCwWQ3zY+XnlT716nmzxJNr3Y7mtpem/wBlfC/x3aGRJHVrEkr2zMK83rt/Cc8t18KPH80zl5HksSzHqf31cRXo4B3pv1/RGlrBSGlpK7REdLSUtQYBXoc0zuS0knFeeV65qculx2gs7OHeQeZj1JrzMy+z8/0KST3KejSaYGkkvshU5Vf71S6jrb3a+VboILcdFXqfrWU+M/Ko+laFho1zdEO4Mcf9415auNy6LYz4oJbmUJEhZjXR6ZpEVkRNOBJKOQvYVpW9rFaR7LePB6FscmtKy0p5WDS5C1XqSrydolMJNdvnB2j8hVtLQKvOM10cFpCkRCqMAVSlhBbCrScjop0ktXuZHlkHAqOWDOc9K1Hs3I3Y4qpPA23ipZtcy3j2RtzwKLaYqwOOKp6pdyWkDnGcVzx8XMkLIluS2MZNTF6g9i5418Ui2szZWkn72QYJB6CvMCW2cnOTnNWrhJ7m8kllyWds1Ld6dNb2KXDoVQuFBPrgn+lelgoWqJo5qku5nV6doFisXhjR7hGybiGQuPQiaRR/6DXmNesaNJDD4L8PEvukktp1MY68XEpBHr16V25n/CXr+jHh375iTFpNQuJyMy5KJ6RoP6nrV3RdTXQ9St5nGIzkOT12kYyB/T2qG6mgt5fLQqe4bv8AkapN5bOWLEuepPWvEkuZWZ2LR3R6Zr19b/2d9qgbzEb5ldem08V4tMnn6vcXH3Y2b5QO4rakv3jgNuss4hOcoucVQJUfd2p7k81lRpOmaVaqmehfDQxeVqUeMSHYdv8AsjPJP1NR+OniGkXcSEIVTv3Jz/hXJ6Hr0Xh/URefak2ldki5zlT1rM8V+LjrF25gjcwvjO47fwFZToydbm6GkK0VS5TFtB+9RWOFEak59q3YWZ1LEFIQOT3b2rM0GxvNVuJ7kBUhj2qABnB9v8966TTdBu9Wv2hZ9kUS7pmHG0A8Ae55/Kup1knY540W0mjMs4GeSVgpaaUHaijOM1sXdu1sg80YOMnNSx339l3ZfT0zsYqRjO8eldFd6NceIbuMxtGlpEQZSc5JPYccnH8xWE6jlI6IUlCO5d8LJp8miwXUVpElxgxyMFG4kHrn34p/iI/8SHUieP8ARJen+4a1IbG0sLQW1mgiROQCc/jk81j69IRoOpJuX/j1l/8AQDVUtZx9UZzejPEqKKK+3PFCnIu+RUHBYgU2pLf/AI+Yv98fzoE9jqf+EDu/+fyD8jR/wgl3/wA/kH5Gu7orp9nE+f8A7Qr9/wADhP8AhA7v/n8g/I0f8IJd/wDP5B+Rru6KPZxD+0K/f8DhP+EEu/8An8g/I0f8IJd/8/kH5Gu7oo9nEP7Qr9/wOE/4QS7/AOfyD8jR/wAIJd/8/kH5Gu7oo9nEP7Qr9/wMbw7osui280csqSGRwwKg8cVs0UVaVtDlqTlUk5S3Znaxo9vrFp5Uo2yLzHIByp/w9q8zv7C4067e2uU2uvfsw9R7V67WdrGj2+sWnlSjbIvMcgHKn/D2qJRudeExbovll8P5HlVFWb+wuNOu3trlNrr37MPUe1Vqxse8mmroKKKns4ftF9bwHpJIqfmcUwbsrnqGiWostGtIMYIjDN9Tyf1NX6OlFdB8rKTlJyfUKKKKCQooooAbIqNE6yYMZUhs9Md65O58zw1HbyabqP2izkmWNbKUh+GP8DDnj05rrSAylWAIIwQe9UoNF0u1uPPt9PtopezpEAR9PSsK1OU7cuj79v8AP0N6NSML82q7d/8AL1OesNP0uLxnqZkggV4/Kkh3cEMQSSPfNZF9NDcW0epWum2VqGvF8uYTf6QSHwTjHfnjNd/JY2k1wlxLawPOn3ZGjBZfoeoqAaLpYmeYada+Y5yzeUuSc59PWueeEk48sbJXfTv/AJHTDFxUuaV27L8P8znobLS73xPro1GOCTaYyolPQbOSB/WqFnK32Hw80shMKahIsTuf+WY3BeTXY3OkabeMWubC2lZjks8QJJ9zU0llazQJBLbQvCmNsbRgquOmBT+qyu2rdfnd31F9ajZJ36fKytocdNaPqmvavHcQ2MzoQkYu5GBjTbwUAB7966zSoZYNJtYZplnkSMK0inIbHfNF3pOn37q93ZW87LwGkjBIHpVqONIo1jjRURRhVUYAHsK1o0HTm5Pr/mY1q6qQUV0/yHUUUV0nMFFFFABUF7bLeWU9s3SVCv0yKnooGm07o8aZSrFSMEHBFJV/XIRb65exjgCViB7Hn+tZ9crPq4S5oqXcKKKKCwooopAFFFFABRRRQBueC/8AkevD3/YTtv8A0atfVd/n7U/4fyr5U8Gf8j14e/7Cdt/6NWvqu/bFy/4fyr5niD7J3YPdnGePRnwxcD3FfP11OYPssgJADMTjqeRXv/jtv+KZn9yK+d9VkaK3gdDhhuwfxFedgH7jZpilqkdhY+LrGGzEckNyrAEkqox1+tX08daUrZaO75BwfL6H868pOoXRH+uNN/tC6/57NXo+2PPeFiz1gfEHTFBZre63Dodg7dutcHfvBd6xd6vdRFbeVyyRNyT7GsE31z3mb86bNdz3AUTSFgvQGolUuaQoqGw+8vZbyXLsfLXhF/uj0q1o1mtzdq0io0SFSwc4BGabpelnUGctII44xljjnHqBUy6qkOlmwWBWO8nzc4J9DUpa3Zo9rI1dcTRrC5uVtVdbxJMhdvyD2HtWjp/j97a0WF7ESlRlnL9f04rmLHSNR1VvOitpZ03Ydx/jXban4Ys9G0gz20LvIytvLnO0bT+FaRct0c9T2ekZashPxM2LhNPGR93L1QHiL+2tWE7RBJXhMbgcgemK4omtmzhbTLVb+4A2yqQidyD3FTzuW5oqMIaxO/065W50+CVCDuGDj1FS3jtHp9w0ZwwQkGuQ8EXzNe/YCGIkJbOeAAK63UiI7K4J6BSOBXz9WnyVeU+toVva0ObyOO025jt9VguLjJjR8tgZNdh4p1lbnSrY28Uo2ShxLtwBiuBaYRkOBu2nOD0rorrxT9o8OTQfYtpkwhc9Afavbw9uRo+ZxcW6kZI9P1W7uNU8PWeoo7vBLEuT79Dn8a5/TZZYr1PKyST93sfrXV/D6KPWvhnBb7BvQPGd59ycismwt/7D1gTXWdqEhdozur52rD2c5RPewtTnp26o0B4SllWS7v5NszHeoUgqAPWsC+1KLdJFbKVZ+GYHg49K7vVdUS68OTzRK2xkYDnBrzC2eVblTF/rCcD8ayh725tTlJpuRoaQ4F8u7IzwMDvXcwwOUVAG6ZyRVDQ9Cg0w/wBpatMiyDlELDHTv71sx+ItHmT95exoQem8VFSLk7oiVe2iKxtpAxzG1Y2uFoI2h8kncoy/OBXRyeINFWPH2+InPUvVC41vRdST7C99GPMOBtPNSoST2CNdvdHEJBN5ZlCN5a9WxwK67wbqoSUWZY7jlsmqWraXNpVhIIbtmgduU9azPDrhNbgJfYATk+vFaN8yuaSSnBnbeJWSa2VC6o/8K+tZPhnSbiK5aS4YbQflC1ZkeTU9XVEQMitgHHSt7xA1p4f8M3l2ZI4ikZ2mUnBbHA496dGnKeiOepUVGmovdni3jrxJB4i8XW9rbSN5NoTGAy9WzyR+VV/D5CRuOgMzZ7968+n1W6e7adWCOWLfKOhrsPB1xLcWUzysCRJxx7V7uJjGGFUF0ODL5N4rmZ3u9MYDAivHb+wMutXcNuC0aStz7Zr1G3bc2GYDjqa8+8R3k2matcxxxBTKdyv6g965sssqjTO3N4v2cWu5L/wkVvodgbK1t1aYoQ0gbv6muRd5bucs7NJK55JOSaYSXYk8kmtGOJLG2E0g/ekcA/0r2Pi9DwElE7rwnC0Hwl8eI2M7rA8f9dhXEV2ng+RpfhL4+dupex/9HVxZr0sDb2bt3/RDYlFFFdohlFFFSYBWkde1I9bn/wAcX/Cs6iplTjP4lcDTi8QanDIHS4XcDkZiQ/zFXm8b+InxnUenQCCMD/0GueoFT7Cl/KvuA6KPxz4jjbcuogH3gjP/ALLVkfEfxYBgarx/17xf/E1ytFP2FL+VfcgTa2OrHxK8Wqu0atgf9e0X/wATSf8ACxvFZ66r/wCS0X/xNcrS0fV6P8i+5D55dzqh8SPFoGBqvH/XtF/8TUZ+IPihhzqY/wDAeL/4muZop/V6X8q+5Bzy7mzc+K9bu1Kz3u8Hr+6QfyFUBqV2DnzR/wB8D/CqtFP6vR/kX3IOeXctnUrs4zIOP9hf8KkvNYv9QgSC6n3xocqoRVwenYe9UaKuNOEfhSQnruFaSa9qSWENktyPs8AIiXy1OzJLHBxnqxrNoqpQjPSSuLma2L0usX84AluC2OhKjP54po1O7AwJR/3wv+FVKKn2FL+Vfch88u5bOpXbdZQf+AD/AAphvbg/8tP0FV6KPq9L+Vfcg55dySSeSUYdgR9BRBI1vMssYXevI3KGH5HIpgop/V6X8q+5ApyXU0Y9d1CDf5MyQ+Z94RQomfyHWp4fFOsW9tJBBdJFHJ9/ZBGC31IXNY9FL6rQ/kX3Ir29X+Z/eath4j1XTZ/OtblUkAIDGJGIz16g1pf8LA8T4wNTAHtbxf8AxNczRR9VofyL7kL21T+Z/edEfHXiRjk6jz/1wj/+JqC48Xa5dQSwTX2+OVCjjyUGQRg/w1iUVSw1BPSC+5C9pPuwooorcgKlt/8Aj4i/3x/Ooqlg/wCPiL/fH86EJ7HsdFFFdh8kFFFFABRRRQAUUUUAFFFFABRRRQBnaxo9vrFp5Uo2yLzHIByp/wAPavM7+wuNOu3trlNrr37MPUe1esXNzDZ27zzuEjQZLGvNNe1p9ZvA+3ZBHkRr3x6n3qJpHrZbOpdx+yZNaWgIH1+xB/56g/lzWbWn4dIXxBZE/wDPTFZrc9Ot/Dl6M9Tooorc+WCiiigArG8QX15aHT4rKSOJ7q5EJd03gAg9sitmuf8AE6yh9Knjt55lgvFkkEMZchQDzgVjiG1TbX9am+HSdRJ/1oOTUNS03VbWz1J4LiG7JSKeKMoVcDOGGT1qjquvSWWiX8keo+dcfaWiiZbcjyuRlemCQD1PWrEpuNe1nT3Syube0s5DM8lzGYyzYwAoPNZ1xYXh8O61ELWcySaiXRBGcsu9eQO49646k58slBu2tnr2/wA9jspwhzRc0r6XWnf/AC3HajrSWml2cFtqd8xuLgLJcSQN5ip/Fj5B+HGa6LTb+0kEdnFdzTzCLzczIVcpnGT8o71U8QW80x0jyopJPLvY2fapO0AHJPoKr3czad4w+2S21y8EtmIleGFpPmDZwcdK0TnTm3J6aLr/AJmbUKkEktdX0/yNKTXtOjtnuDOSizGDCxsWMg6qBjJP0qC41WOeGzlt7xrVXuViYS2zZc/3MEZGfWsS3tYxo87ahbajbsdQkmieGJjJET0bgHj8xSn+1L+0sDPFcTLFqaGOV4CjtEB95l7fXik8RUa1W6/rrdDWHpp6PZ/10szfvPEGnWNw8E0rmSMAyeXEziMerEA4qS41zTbVbdpbkYuFLQ7VLbwPTAPrWLBO+i3mrxXNldSm6mMsLwwtIJARjbkdCPeodM0u8s7nw4k0Mn7mKbzSASI93IBPQelV7eq3Zfltqlrr21J+r00rv899G9PnodHp2rWmqCX7M77oWCyJJGUZSemQRV2sbTIJY/EetSvE6xyeTscqQGwpzg962a6aUpSjeW+v4M5qsYxlaO2n4oKKKK0MgooooA8z8WoE8SXOP4gh/wDHRWJW74vIPiS4HoqD/wAdFYVc0t2fUYb+DH0QUUUVJuFFFFABRRRQAUUUUhm54L/5Hrw9/wBhO2/9GrX1Vf8A/H2/4fyr5V8Gf8j14e/7Cdt/6NWvqjUB/pb/AIfyr5niH7J3YL4mcb4948Mzn3FfO+rAGC3UkAEnJ9ORX0P4+z/wi8/1FfOusjNvCP8Ae/nXn4D+GzTFfEit9l0zO03L57kCrR0zTYLQSzySkq207RjOeRVa38P6tOokis5GHUECtkad4m8sI9kWXH8SA13peRxOSXUxcaSsEgKzNJg7T/Kmabpgn/f3BKQLyMj72OorQ1LR9QhtDd38Bi2MBt2YBH1FZt9qTXQEUSCKBeiCk0k9S07rQfqOoiciK3XZAhOzHBxXTeEvCtvewPNqVlIQ2DGWJAI/Cuc0iyjkvVa9ikNsMhivGDjivSNP1vTbG3S3Es7BFwCy5q6cbu7OevUcVyw3NrTNKstKh8uygMasckZJyffNZ/i8r/YEg3OMFsAd+D19qd/wlOmhV/fSj1+SsXxH4s02TTZLfLzs4+VNpXB+vpzWrskclOM3NNnDQWsFlYxajLOonPzxwsm5XAOKz7zUbm9ws0hMYJKoOi/SrAhv9au1jhgaRwMJHEnAHsK6zRvhffXsBnv7hbNFbBWRTk159StCG7sezToznsjlPDszQa7aMrlMyAEg44r06/G/T7g8HKHpTYfA/h7ToFl+0tNcghkw3f3FLeFUsLhhhgUJwD7V5derGrUTie7g6UqVKSkcHbGJZ0knRnhU5ZR3FR6lfXeq3G2K38uHgLCg4OOmferWlwRXF/BDOxWKSQB2BxgZru7y70zQrj7JodnC7of+PhxvbPsa6/rDprlSPOlh1Ud2b/ws1bU9GWHSdT094rW4bMTkYVOP6mu48V6FHeQm6hGXRegrxxLPVdQ/0q8uHt4t3Ek2evoB1/KvZfB2sRXmlwae/myyRR4adx8r4Pr/AI1yVU6ur3KX7lqUDjbXL6XeWsrOcKCq+lUNE8PyXqyXS+YTC2cIOmO9d94os7fTrOW6SPLyHnbxWb4YSYeHNSmiVjlG245OcHtXJTg3PlZ6Lrp0nUieLeLfFt/rF2bUyBIYCVATjd7muX3SZ++fzo1JpLe7n3rtkDcqwxg1nG+lP938q+lhCnSiopHg1JylJtmhvkIAMjfnUkU81u4kjkIYHIINZP22bPUflUkd47Eh8YPtVKUHpYi7PZ/hrrR1++m07VJhKzLmMSHhvau/n8NLZ6qLiGHywRtC44FeJfDU48T2cvyYFwg+Y8V9N6kokaIqcgHPFeTmGDjTj7SPU76GLndRZV0nTxYrNJJsBdskjsK8w+LnjeW3UaRaLujb5meNgwceh9MGux8e69babor23mxCSdSpDybcD1FfNxsLmW5eS1uEeRWyAkw3HntzVYDD2XOzmr1XJ6lE6gXctNawSeoK4/lXVeFJInhu2giMSGQEIWzjj1rnbmRyxj1G3YSdBJt2t9T/AHq6HwmiRfaUSQSKdrKR/L2NdWNX7lm2XWWIR00YJP0rifHDiPVbd9uf3ffvzXbr1IrifHm0XtpuHSM5/OvOwT/fI9fMlegzmRqTL0hjH4VHJetLOkjoCF/hPSklltmkQpGQoHzD1NNR4BNlkJT0r2m3tc+asux6P4PkEvwm8fMFCgvY8DoP3wriq7TweVPwl8fFF2qZLHA9P31cXXpYB/u36/5AwooortJGUtJS0jAK7Y6Bpg622B/10b/GuJrvpI5ZDlzx6ngCvIzarOHJySa32+R14SMZXuijLo2lr9y23H/ro2P51GmiWW7c1v8AKO25sfzrRVQMhfnb9BUMl1gbWbcRwAp4/OvJWJr/AM7+9nW6cOyKx0XT9wVbcknoA7f401tH02LmVAD/AHFdif511GlwQSlLYwGR3G5iDg496NT8LKg3RI8fHRuV/Opjjar+2/vY5UYrojjpbOwbiK0CD13sT/Ool0627xf+PH/GtiPTbl7jyEhYyc4XHXAzVN5VjJ38EHkGt1ia3Sb+9mfs4dkQppVoesX/AI8f8acNLse8X4lz/jT1nWT7hp4ySBT+sV/5397FyQ7EJ0yxGcQZ/wCBN/jSDSrM/wDLH/x4/wCNXFRmOFGT6CrkNk5AMjbR6Dk0/rNZfbf3sOSHYxzpdpkAQ/8Ajx/xqvq2lrZ2Mc6w+XucLksSTwT/AErrYoYYRnaoPqTzWL4puRNp0aKvyiYc/g1bYTEVZV4pydr9yKsIqDdjkq93+HXw+8Ka54G03UdT0oT3c3m75PtEq7sSuo4VgOgHavCK9n8D67f2vhjRIIJDBBAs5ZmAMbZkcndnHTPYn6da9jMakqdJOLtr/mcEFdnbzfC34fW6gz6RHGD3e9mH83qWL4UeAZgTHoquASpK3sxwR1H365rxTeanqtrBrVvZKVQiONWIfYA2RIBtx8xAHJPBqv4a8eXumavJZ3+n7GJ2+SrMgU5x905AORjPAFeI8XVTs5P72bKCOo1D4NeDrizdLLTPs1x1V/tMrDPoQWPFeZan4E07Tbx7a4tDHIhwR5jEfgc16Z/wnR1Wy1GGyvIbSZCBE7ruYAn5iOcHAzXF3F1c6hYRPqDlr0ZEpJ5Jyeaxr4qta8Zv72dmGhFv3or7jmV8NaKrYe3yP+ujf402XRvDy8C0HHLMZXwB+dT3MLhiAxrntQkuIZPmt3e0wQ7AjnPHvWVPE4iTt7R/ezqqxpKN1BfcLdLodxIsFhYBFB5mMjkt9AWxivRfAfgfwvrFiE1TS4pLhnLI63UqlkGN3AYDqQPXrXma6xbreIi6ZmBQFI3AEDHY4/GvXPCMGljQZbifV0ilC4RXAXy07Bsj5jz2z2rueIqxj8b+9nnckZPY6L/hVfgNjhNERj7Xsx/9nqE/DT4fIzq+kpuQbmVbuc7R74es3+0tQvNHjOmXQiCHy92NjTr7Mw+Uc9eoxxV3zXjtvLtzLhRhZCSzEnILFe544z69KhY2o/tv72P6tqZWqfD/AME+QGjsPsChlPmNcSMzDPIClz1Fc9rHh/wRb6HqElnps/2iO2kaKVp3IDhTg43Y6/Wr3iKE2FsJbmd5bubKrvA45yenU89f8K5XVLj/AIldxECf9S+eevBreliKkpJ87+9mcoKOjRwFFFFfTnILUkH/AB8Rf74/nUdPiYJMjHoGBP50xPY9korn/wDhM9I/vzf9+6P+Ez0j+/N/37rq5o9z5n6tW/lZ0FFc/wD8JnpH9+b/AL90f8JnpH9+b/v3RzLuH1at/KzoKK5//hMtI/vzf9+6P+Ey0j+/N/37o5l3D6tW/lZ0FFc//wAJlpH9+b/v3R/wmWkf35v+/dHMu4fVq38rOgoqjpmrWurRvJaliqHady45q9TMpRcXaS1Corm5hs7d553CRoMljRc3MNnbvPO4SNBksa8217XptZuMDKWyH93H/U+9KUrHRhsNKvLyDXtem1m4wMpbIf3cf9T71j0UVi3c+ghCMI8sVoFWtMlEOq2kpOAkyE/TIqrRQVJXTR7NRVbT7kXenW9wDnzI1Y/XHNWa3PlGmnZhRRRQIKKKKACiuasdS1aXxTf2zWqGCPywVNx/qwc/MBt5J9OKt6drV3qfmPDpuIY5JI2dpxyykgYGO/HXGM96wjiIS79ej6G8sPOPbp1XU2qKwotbvI9UtrS+s7dFuWZUaC48wowGcMNoqla6zDp2kvPa6e5EmoNAY/PySxbG7JH6UfWYf1fy8vMf1af9W/z8jqqKxINXuzqx02/sY4WkgaaNo5t+QDgg8Dms/TtbW10rSorDTWIvGlVIjcfdKknliOf89aPrML/8Pfp0t5h9Wnb/AIa3XrfyOrorBh8QyQvqMepWqwSWUQmbypN4ZT0xkDmmLr2oRmxa70uOOK9lSOMrcZZN394beuPSj6zT7/g/T8xfVqnb8V6nQ0UUVuYBRRRQAUUUyWRYYXlc4RFLMfQCgZ5f4ilE3iG9YHOJNv5DH9Ky6fNK008krfedix+pOaZXK3qfV048sFHsFFFFIsKKKKACiiikNBRRRQBueDP+R68Pf9hO2/8ARq19V34/0lvw/lXyp4L/AOR68Pf9hO2/9GrX1Zf/APHy/wCH8q+a4g+yduD3ZxPxAGPDEv8AvCvnPVdzLF6DP86+jfiCP+KXl/3hXzhqzbVibHr/ADrz8B/DZpifiR6R4ewkHTGUQcniugCAcHnAOQa8usPHL2UOz7GH+UL9/wBKvf8ACypu2nx++XPSvTU4njzw9RyukdH44Rf+EUuiTu5TH1yK8g2lXGVIPXmu2u/GM/iGM2clukUfDcHJJFbdr4Y0+9toZbm3DStGMnJqJLmd0bU5OjG0jk9OObSU/wC2P5VZJ6ADk1j3N4+mzzWsWNgbPI5zWjodnrPiGV1sokKRj53c7VWm60IL3mWqM5v3ULIJ5MQ2sLyTMQAoFdHo3wnvrkNc65dJYoCPlJDFh9c4FdzYz2Wj6FDY2NlElwyr58vmZ3OOp5rI1nxDLMotDtRFO5gTuye3NePiMfOo7U9Eevh8CoK8tzTsdC0rw3u/slkZzj95v3GqGreJpppBZu6SRo+S2Oc1iLfvgiNl5HQDrWfK/wA3z7Q+7PTHFcCg27ydz0kkkrI1ywdZJlYHAOPrXN3E0kdrIxOF6D8a2oLhZFkC428DAGKx9VtH+xgqC2G3HHQAVdNWlZms7uF0YUXyyg+hrrLbULmcYthBajqXChcY9+tcnEMOp9DXVQQXF+yAK0jkAKqr0H0reozjgrky6pHahmhD3M5PLzZweP7vQ/jXQaPbavdS2sl7IYoDghZJtu5QRkYHSix0VtDH2udN1+wxBEvzbSeMsP6U8vsUXd9I0t0//LAHkem4/wBKwdRdCvZtnrF9bw63phgQqyngFTkDFReHNNfT7K4smk2k5Ct9fSuO8L6vfWmotbXMjEeWDsLYWMfT1ruElhlSG+OcLySpzkUqc0qql1MJwnGDp9GfOXjjwp9h1a7kuWZXQkMxbgnjBrzuZVSQqhyB3r6p8faFb+MrADTzHJKnyzDO1sHBFfP994VNteS28lvMjRsQcntX1CpfWIqULXPMacXqclWlYJbz3EUL/KW43EZGahOnXB1A2kcTu4bGAMmvVPCPgCG5gXUbwR21lBMqSyS8Nz6VnRpO7ctkJ3Nn4b+BvtV+krx7bZcSO6jgkfw17dO0Ua7XACqO9crF4t0LS9mkaUhkjCcSQcgN7+prZLp9l8y6OWkXBU9/wrz8xxCl7trI6KVGUdZHh3jvxes+v3umeaXhThJouqdz7EV5pfReTNvSRZI2OVkQ9T/Q10vxA8P/APCO+MJAiYtLhvNiHop6j8K4hn2uynoCa6aMoumnHYwmmpu5tWl806+Tcn7RH/zzb7wHqp7Gtjw3H9l1JhG++GVCQe4IPQ+9cfAXeVVTI561ox6xJYXokhYE4+YdiaK0eek0aYaoqdaMn0PTQBz65rifHaNPeWwjBYoh3Y7Vf/4TKzWOHqzMB5mONvr9a5bVtZuL2/mniZhCTheOMV5uEw8o1LzWh6+YYqnOjywd2zHZSjFWGCO1CKXYKo5PFOO+aQnlmNXQq2EQZhmVhxkdPavRSPDbPQPCUBt/hL48QsCd1geP+u1cRXZ+DXaT4R+PWY5Jex/9HVxlergP4b9f8iWFFFJXcAlFFFBzhXdzTtIQGJPoOgFcJXcvtH1rx82V+T5/odeFe5A+98jOF9KiaPYQc9DmnvN6DFQM5Oa8ix1XO68LRNNrJ2plSg+b05rrJ9UiMjQQokuzKlcnJI69RXmem6ootktklkEjttbaD0r0uw06NNPhJVVcrknJJ/GuODUU11N566mbZqlzqq3aWvkpFk46Asau3FppMglnvLe2yR8xKKc/WnAvJM+1vlzjAqy1hbvbFZI1kPfIzRGs+hMqaPNdYtvDN1fLaWcsdtdP93YSFP4dPyrCbT59PuXiumIdf4V5BHqDXea34LsbpDPFE8ci8/JkDjpXL2tvPMBZSuJfLbguCW/Cur2qSRkoMW1mix5YUR/TvVl7qKIcDJqrrOlxWOnyXSTyZjGWXGa5u3vDOCUkY4OCCelXGPMrkt2djauLl5X7hfSsjWnZrRMtn5xx+BqZJXzyc/WodZXFkh/6aD+RrtwaSrw9TGq/cZh16n4U06ObwXZ3UiSTxQzGSWPeBhC7K2OeMABuoxya8sr0jwRoWo69pccNnuEaqxkbd0G854zz9K9XNf4K9f0Zy0leR6HZ+PtOispnvYNiRyG2Cxr8zoQedvYDA5ye9cprl3FJq8Oo2xeW4mjkZX8sOGV1BGRjH3iRyO1dbL8OtH0+yF3M15feWgd4QdhwB2XH6GsPV/Guh2cNqdBsrWWSRCjR3FsBJbEc7w449eB3rwFzSWpvyWZzT6ZrE9o8S2N1HKrKI2ClACcYLN04/lmrviW1OiwfaWulZAN8jLz25575b8elc7ZeMtW/ti0kvbqa5gjLRqrkkKp64H5flXR22hv4v1cy3kbro9o5xGc4mYd/cH+X1rOp7m+x10UuXR69jJ8Mx6n4ncn7E0FqD/x8s+A3PTGOv0Nek2XhfQ7SFY7qJrl9uGEh+9+Aq5Bbx2NspijWKNV2xxoAAB2xVWT7REpmuUCxA53E84ry6tdyfu6I76dKys3c3LfTPD0LCSHSbJJApAdYFBA+uKrTaLoN2Qp0+BDksDEoHPrjpWYl9bywrJE7MmcDcMEf4ilttVRJyrEcH1/Wo9tNPRiWGiUvEOiXNlbrfafcqIrYZCHjYOP/AK/51z2peMZrRLa3hTlIwxaUZJJB+7+GOT3Fd/JrFnDlZghjZfmDenfiua8XL4c0nTri7urKOaS4iEdrsxuJ6gjngA9f/r104OonOzVzPERaha5y+q+KY9c09EmimS7jlBJD/uymOm31ziuYu599lc5Ocxtj8qhF4Rb4/wBWW7IcD06VNFZSXtnevGSIoLd5Gc88hSQPqcV7dJJSXqeVK7OUpaKK+sOIKKKKYgooooAKKKWmAUUUUAFFFFMDuvAn/Hjd/wDXQfyrp7m5hs7d553CRoMljXJeDbmGz0m+nncJGkgJY/SsLXtem1m4wMpbIf3cf9T71qpWieNPDSr4mXYXXtem1m4wMpbIf3cf9T71j0UVG560IRhHlitAooopFhRRRQB6B4JvRPpT2rH54H4H+yef55rpq8v8OakNM1iKR2xDJ+7k9ge/4HBr1CtoO6Pn8fS5KrfR6hRRRVHEFFFFAGKunX9t4iuL63a2a2ugglEhYOu3j5cDB/GmWei3VvoN9YmdEmuJJWSSMnC7icdhW7RWKoQvf1/Hc29vO1vT8NjkNP8ADN1DfabcNZ6bai0J8xoCxeX5cZJwO/Y/nVgeHLsaalt5sG9dS+1k7jjZuzjp1rp6KiOEpxVv66f5GksXUbv/AF1/zMq40yaXxDBqCtGIo7Z4ipJ3ZJGO3Ss6x8O3dsmiq8kBNjJK0uCeQ2cY49++K6aireHg3zP+tv8AJELETS5V/W/+bMC80M3N7q09xNGlrd2qxbs8ptzkntj8ayJpb2W60OzmutPuPLukZfsrFmZVByx7AY+tdqQGBBAIPBBqC3sLO1dnt7SCF26tHGFJ/IVnUw137unf77mlPE2Xva9vusWKKKK6jkCiiigArB8XXotNCkjDYkuCI1+nf9OPxrerzfxdqQv9XMUbZitxsHoW/iP9PwqZuyOzBUvaVl2WpgUUUVzH0YUUUUAFFFFABRRRSGFFFFAG54M/5Hrw9/2E7b/0atfVl8P9Kf8AD+VfKfgz/kevD3/YTtv/AEatfV17xcN+FfNcQfZO3B7s4n4hceFZz/tCvnHUPJYwrMxVTnkfWvoz4inHhOf6ivm/Ugj+SHbauG5/E15+A/hs0xPxIieLTQMCZuO45qPyrHJxO/8A3zSeRZBMfaDu9cUnkWf/AD3b8q7jmLFv9jhZHWeRXHXArtrDxjpkFrHHMSzIoAbacmuCENplf37Hnniur8JeGbbVdS+2S4/s6E87jyzemPSpnUVOLkwVBVpKJb8N+AZ/Fd9JqsziLTd5OG4ZwOoHpXZ32v6RptsLbRrRY/3ZiOUwAPX3rE13xFcQX722lvLawRjZ+6bAf3rnvOlfq0hH0rx6kpVnzS2PZpUo0lZGs2qynA2xD6LVSWRpSZCw5PYVPa2bPHuZ1JZc7WHIqeWxQgsCFIHIA4rOyWxtzFBWIYYYg1XcszksSTnvU8oQOAhPXvUB5PrQUOhuDbyBuq55FT315GbGQJlmcYHtVeW1kkwwXauM8HpUDRlWELDvnIoSTdyuaSVinp1s8tygwvXJ3dK9W0Oyg0Wwe8uFEkkigRkdCMdPzrkdK023g1zyJHE0BHLRnOMiu/eGCK1hstrSWxBcsTzxzWGJqXaQU4WRjC4ktRJcTBXuJl3RtnJjHr9az4bhbffqEy72VsRqw4ZvX8K02eS9uTGoXJ4TOAFA/pioLiOO4ktoVIWCEgAeozkn6k1jFrqatdEaegB4x+8BM053Sk9Tmuz02dd0unnC5jygzn61zNkmJl4YvuwtaU039m69DKW3CNQGUe/b9aiEvfuZ1YqS5VueXeJPtlj4sbyriaMk8FXIq7ceI7yXT7ezntYXEOQZyCXf6mtrx/pks+tW17DHmP8Ai29qit9Lt5IgTu5HIzX1uHfLTVyHRU/eaMdNWiihiIs0Eisd74xuB6Cqt7rGpXVrLaLLItju8wwj7v1NdH/YdmpJ8vcD2J6VS1SzSKwlSFTnbgKOa2U09g9loW/htprXmqNcsuYoupI716fOFmmMpkUKvygHoK5vwJZXGleGmeZApmO9QDzj3rSu0hSJwAVYDeSW4PtXy2ZVL1nEhrmkcp8XND/tPwmt/Cha4t3DfIuSVPB/xr54WBpZio4GeSa+rtPvPMtLi3BImKnYScjp0r5i1kT6ZqE1o6eVcIxEnQ4P4V6GVzTpuMuh52MpuEynNLHbp5UP3jwxNUiDjdg49amggMrbnOIx95uuKLicOPLjG2FTlVr0Zaq7ORaFfNSRtIw8pTwx6UxQWYADJNX41SxQSuAZj0U1CGxcJYRhjlpTxjtVCSR5G3OxJ9zRLK0rbmOf6U63RXlw/QDNG7sgWh6N4KBHwh8eZ/v2P/o6uOrtfCLmT4TePmIx89iAPT98K4qvWwCtTfr/AJCYlLRSGu0QlFFFBzAK7VlOea4uu1lJxivIzX7Hz/Q68N1K0mFJ6VVmckd+KufZnyC5Cj36013ihHyJlv7xrx2zqLPhuEreGVosgEYDCvbtIt45rBF6kDOO+K8c0dg8bMk4S53AhWTcCOf8/jXdaFrV7BP5ZSORI8lnjbqB6A81zSg3K7RnLF04vkbs0dZLpDdYF+XuBT1tIoYd0nmbgeVC5pLbXIWuXVX2yAjKuMA56VsSSpMFcIVcjp2NHsEtTRV+bY57UL620+1kkmVnVeoCk5/KuQl8U6L529ka2Yn+OHbn8xXb6iokQhlGOp4rj9S0eCdmkwgPcsccVPMlpY1SurnOa7q2n3of7M3moOpVSRj09K4+4sIdPKCMjMq+aVxjbnoPywfxr0GfT9NigYrIrSAZEaNyT7+grFGmA3BnaMPK5yWc8A+1dsakVHQ53FuRi6bpd1fSKFTYh/ifipPFenQ2WkRMjs8hnALHgfdboK6i3iMa/wB5u5FYPjX/AJA0P/XwP/QWrbBVHLEw9RVYpU2cHXQWGsXlhYWht7mWFotzL5ZK4+YnPA9ePrj0rn6uxRvcpbwxKXkCPwenc/ngV7uZfwl6/ozio/EereHvirrDlbGe2ivLjJIuZG2fLjJzgdRg8+lTa1qGg63LDdw6eml3KZ8y6MatEIsYPy4+ZjnA46jrXnumW8ml3dy95G+Gj8tHXkDPUdR7+3WujtdesdBmsZbmDzm3Rs0WBvZQe/b8/pXhuKSujquzqfDtlodyxbSrG4aRvlM32bapweTuAAHbiu02w2MSK7KcfwgZ+bvms2PX7Kfw+lzpSNb2rAkRFQuw55AA461zOr+ILi001GRm+c/MwPJ9s9cfSvHxFZzlypHp0KPu8zOue6jZ2uZpAI05AJrzvxj48hMbRW7EDplMZP0rRaGXVtMMKyld/wAzBeOK5e78FWJcHDFh1BOazpKF/fNJqVvcK/h/xFc3sIUMdpPC+9d1YWE11apIDljz05rltI0SCwvVJUKo6CvRNMu12LEuFXp0xU1nFS9zY0hzKGu5zniPQdVfTWls97SlCMdw1cRq8eoNa6LY6mCbgghpS2CAW6EdOOa96MsSEZIbI5B715v8VNLuxBa3lmMWbsFeVcnyzycHAJwc8Vtgqi51FnNiruFzhZ449R1y30zT4jHFvChzycADLn27/SvQdZaw03wze2kDWwQ2D7ImdRJ80Zw3qeoPGelc3odha2V2bKW9El1JCVebY3+jwr87cEfKfr/drib6T/SZ0DtIAWw7jDMPUg969qEeaa9Tzb8sdDOooor6888KKKKBBRRRTABS0UUAFFFFMAooooAf5snk+TvPl7t23PGfWmUUU0AUUUUAFFFFABRRRQAV6J4T1gX9gLWVv9Itxjn+Jex/p/8ArrzurNhezadeR3UBw6HoehHcGnGVmc2KoKtT5evQ9doqnpmpQarZJcwHg8Mp6qe4NXK3PnZRcXZ7hRRRQSFFFFABRRRQAUUUUAFFFFABRRRQAUUVXvbyHT7SS5uG2xoM+59h70DSbdkZ3iTWBpOmtsb/AEmYFYh6erfhXmPU5NXdV1ObVr97mXjPCJ2VewqlXPOV2fR4TD+xp2e73CiiioOsKKKKACiiikNBRRRQAUUUUgNzwZ/yPXh7/sJ23/o1a+rr0f6Q1fKHgv8A5Hvw9/2E7b/0atfWN7/r2r5vP/snZhN2cJ8SP+RRn+or5xuovtE1tHgkNngfWvo74k/8ihP/ALwr5ylu0tLm2lcHAU5x9TXBgP4bNMTe6sX00K0MY3I+703UNodoP4XHH9/pTF8S2gzkSdeOBTP+EjtT/BIT3yBzXo3gcFqg5vD6yFIrVZXmdgFUc5rq9Tkt9A0uHR7OPa7oGml7k/5Brll1mdWWa1leFsfeHWoZby4nYPPOXc/xE5rzsU+eVlsj08GnCN5blnfyTu/MVfsZxHgSbCmc85zWMtwUf5myO9W47iMnAPOeM1yuJ1xkdRDdx8MkQ54yD2qxcyBYWYjjpj1qjZxKUUtngD2FOvb+IYi2bx13A8ZrKxpcolg0hO0AHp7VFnmkEjF9x656UuMk9KC0yVJP3bLk89KliQSMqBc5wPxqqD7c10Gj2Udts1HUJEhtVG5NxyXPoAKzloi0+51Hh3QU026SaSUlihyAQBk/zqe9vZgWgcgAEgjHNcrc+LJ9Unjs7GwErnhAoJJPqBXe6L4E/tDT7a51u4uhOVyYd23Zz0J61zypSbvMHXhA50TW0URR8Rb2CmUckA9qgRVN6I4pBIobhh3rvH+GuhvEwH2kkkEEzE4q6PBOhKpUWKqMdnP+NDgktyFjI32OfjukMaiNTujOVYipBY/bY5HdmWdTkknrWb4i0m88K3a31kss+lNzMD8xi/8ArVuaGqzrcSbyVYAgeorCdNw1NY1YuPNEwvEAknhRsk7O1Z2nzDbsY811Oo2RkRtv3enArl5dLuLdnmhBMSjIY8fpX1mDrQxFBKO6O2DjKnylx2GKyXkae7Cqe+KRprmUhQrA4x8o61paXo1wGWeWMqmeST0rpjH2UXOehUYKnrJna2qgWMULfdCgHBqnq0TbIwqtsAqvqOu2+jNEjMrrIO9cp4j8ZQXhSOAMjxDa2OQT7V8bVUq1VyR5yupXN5L6HSSJruQRRudoZl4zXFr4X0Lx98RNQdJGjs4YEdmtgAJH6HqK47Wtev55jBJPJ5fXZuyK9i+GPhq107QbbVwsn2u8hzIS3BGSRxXZTpvDQc76s5q8lUdjfHh/w/BbrHHotkY1ABJgU9PwrntX+G3hHxDZy21nbwWF5I4fz4FJI9QBnGK6bXC4gQIxUEkEZ61R0o/6Qh75xmudYyqp7lLDQlT5j5k1Gxj0DWdQsJdzy28jIjldu7BxnHb1rKaUzzBpTmu4+KtvP/wsDUWuN+Sy7GYfw7RjFcI6MnUcHofWvoYtuCkeTJWbFmUK5wCFPTNOtpFikLN0xjpSIyNxKSQOmKRonGeOAM/hVdeZC8j0vwfz8IvHr9mksSP+/wBXFV2Xgsk/B/x4Owex/wDR1cbXq4F3pv1/RCYUlFFdgCUtJS1RyhXfSMqsQige/euBrvrhVJ+UkD+deRmq+D5/odWHe5Tllw/OKgWPzpQgGcnirQtzPIsUYJZjgCvSfDngeK2tRcXKCSQjPK9K8d2R03MjR7G2s4ESIfv2wGJ659K73TdLto4EE0CSyhsqx6rn3rn7zScX8c0RKyoflwvB+tRa7rN3aMlrbSsku394wHT2Fc0XySbkaSjzpJHavp1ggkaVF+fls9M1hS6jd6fMRp7C5ts5ZGzx9DXHRw3ZUztcTPIecsxNdLomvxRARXqEEcbgK2hWUn2MZYfkWhm+IPiFPaqIYNOPnMOWm4Vfw6muAuL6XULv7ReSvMzdVzhR9BXf+MNIbWoBd2nlsUyeCAce9eeGzMcYLTQtliNqPuI/KtJRW6CD0szcshCsYMagA81pxRofmPesCwlMRWNsbG9e1dDCCOcHb2YjisGrM1LMUIBz8p9q5Px4oGkRc8/aRwBwPlauq82GMIwBc+3TP0rlfHs5m0uL5Aqi4Xoc5O1vyrrwK/2iHqZ1vgZ59XYaBolpeaCZbolXdyYnVsFcHGPzHv1rj69k8AeG01HwjZXNxJsRmkKhMZI3kHOR7foK9nOqip4dN91+TMMDHmqtW6DtB+HGrWujrqC3MTpdKzNbzDBjXs/uSP51b/4V3ply/mzNiVhncuSPUHmu9gQW+npaxyEQRLtCg54+tUbq7ihiIGdxHHrXyVbGylbk0PUo4WOvNqc5eNHBClhbKqxwptUAeneufuY1uNOmQ5IjyAT19z+v6VoXc/nzsVJHPbvUJgFtbzufuKjb8nrXInd3Z3WSViXwtepNanDLuwBjNbjWKCMzSOuByFH8Vec6QLvTCjK4aAn7ynOK9Ka9tZLC2MsrHzMADPGaqcWpaGSkranNCNZ7rcOPm/StSTU/7LjwsPy45fvT2sBZ3UjKOOq54HNc9rsKXkLrJvjYggHOKEuZpFOVoksnjLzbtI4tpA4Lsa6nTPE1l5DWuoTxyW8wKttP3frXzvf3V3YSmKJiAOjZp2ipqOuatb2Szyb5nCcHsa9SOFopXseXPEVJOzOz8QR3XhvX9Ws7OGKZpVZPOJ5SFhnCg+3euTKSsXaUMWKk88k+9eqfEbStLtr6xSdmCrZrErsASNi9SSDk4HSuIOnNHp9zMLiK5iEZC7UKuBtJBIxwOK7MNLmcWc1RWuczRRRX2J54UUUUwClpKWgAooooAKKKKYBRRRQAUUUUwCiiigAooooAKKKKGAUUUUgNDSNXuNHuxNCdyHiSMnhh/j716Xp+o22p2qz2z7lP3l7qfQivJKt6fqNzplyJ7aTa3cHow9CKuM7HFisGqy5lpI9borE0bxNaaqBG5EFz/wA82PDf7p7/AErbrZNPY8KpTlTfLJWYUUUUEBRRRQAUUUUAFFFFABRRWTrHiGz0hCrt5txjiFDz+PoKG7blwhKb5Yq7L95eW9hbNcXMgjjXue/sPU15rruuzazc5OUt0P7uPP6n3qDVNXutXuPNuH+UfcjX7qj2qhWE530R7uEwSo+9LWX5BRRRWZ3hRRRQAUUUUAgooopDCiiigApDS0lIDc8F/wDI9+Hv+wnbf+jVr6xvP9c1fJ3gv/ke/D3/AGE7b/0atfWN7/rmr5zP/snZhd2cH8SiB4Rn/wB4V81agglaFSwGVPJ+pr6T+Jpx4RmHqwr5o1MZWIf7P9a8/A/w2a4j4kUza4JxIh59antrUBiWKsewFQrafJudwh9DT7MYmbnt1reatHYzh8RoAcYxingZpmcjrTsfLkkVxnYhelTRfu23Y5qIrxu7Uodh3NJjRtpqcxidWCjcKv2XkyD5FUsR82TmsCN9yjnmrlvKiDBLZJ5C9xUNGqZs3FqpGUwuKz95Wr9vc/aAylduBVaYZLKq8jj8KzsaJkaZdwM8k1qa5PJBo1lbbuMkNWXaxl3yvUHpVzVL1RPpUQeOR45hvUjI6jr61KjeSCUrRPVfBPgS10H7Nql1OZb148rg4Vdw/Xiptf1i4lvWhhkKxpkfKetbeqyeXAmzICqMAdBXF3rtK/3ctnJwOtedUquctTfC0k/fkdzpOqsNHt2nlUyHgnPP41k6/q9zb6nH9nlO0KCMHjNc9HeSxRBdowOlRfNMS5BIHU+lRdvc2hhYxnzM7qC+OraHLFOFeWaN1244IxjmvOfCWt3NhbzpqEgRrdvJKEfMcdK7Hw/KUuNuBhhjPpXlni6Z9P8AHeom4T7zbgFPUEcGuigvaxcWck4qlUaWzPTYtdtrstElxGV4yO/Nadtphu7YSKyn5sYPQV47HiZVljcCMjPPaux8NeJrrTrV7PyleIK7B8nOaqNN0neLLlJ29w73T9FgdH+0wpvRsDbXOa/rFhp0LxxZjwed3QVz+n+PdR0sypOFkVm4LnByfU15t4z8VtqV28UDEDfuYhs8+ldL9riLQk2YupKm3ObNLxF4pfVphCFURwk4ZT96uZm1JYX2gZ/vDNY8fmTuTkj3qX7Nk8sc11QoU6ehhKvOeqL2lWkuua3b2cXD3EgTPXaCetfUujWlvpWk2ulwXHmm2jEe5iMnFeQfB3RVS5vNXkUN5a+VCSe55bj6YrttQ1KLRtatWdnAnk2/L2J7GuLGVXKSpx6F0aXMm2zqNWt2msw65LA/dArO0i2ka7UFSO4zXTQlXgVmUBsU6IxbjsAyveuH2d2mVGu4wcbHIfEPwHF4u0Xbb+Wmow/NDIw6+qk+hr5f1ewuNPuZbO7jZLm2co6noMV9pSTxxrudgo9a8g+N3hizn0aPxDbRKs0Lhbl1GPMVuAT6kH+detg8RZ+zezOGpB25j54qdJ8rsYDBGM039z/tUqiAsAWYD1r043WzMGek+EAo+EXjsKMfNYZ/7/VxJrtfCLq/wm8elem+wA/7/CuJr1cF8D9f8hBRRRXWMKKKKs5Ar0UQGQ4AJrzqvaNO0t3GAMZ6142cVFBQ+f6HXhYczZiadAkWpRGUYQHPcV7Dpl7aSWqeVKeBXnmp6Wbe1Z92WxkVS8P+ImSfyXbODgj0rwXUu7nZyHrRa2G6TGT65rzjWJvO1iaRectgYrpVvfPg2x5xis2009ZJ3lKElTnJrnqycrI1pxUdSoXktrQSSIcehpWtVuIVkUbGPTNW/FNwbXQWeKDfkgH/AGfesfw3qst7F9nuAMfwt71py2SFzXNK2SSINGwIyMH3rgNXhWw1KWNzt3Hcue9epRwsEKSAhl6EGuS8W6bDdWYu1X97EcH3FdFN6GMtzj1unbHkIc5+81b9i9xPGDPMWY8bRWFEgGCTx6A/1ra0mUrvUKBGacn2GjbQAxCM4QD15JrmPHcSR6FBtUBvtK5JPP3WrolORgY6jjpXOeOn3aHAvGftC9P91q2wGuJh6k1v4bPPq95+HzA+ANLTP/PXgf8AXV68Hr3b4dlh4G04gcDzcn/tq1enxGr4WP8AiX5Myy12qv0/VHYROI4yqADI6AVz9y8jXTZO04xn2zW7HMm3k5HXrz+JrH1Nlvr2Kytot0knBIP3Rjk/lXxnLc9yDsznppl+0vGhByRjHp3NM1KbytOdWHzNkc+4z+lSxwwaSZTeMWlV9uccD61SurpdWuo4IVJROWc9D7CqW/oaPYp2DeSgQgNERytW2CTwtECwhJyADyjdsGrL2YijZOg6EVnTkQIApyR8wx2PH/1q0jK5jOHY6yDUhJbqlwR5irg4GOneuf1nBLOu1mPc5qW3v7e6jVbhfmB6jv6VHeLp8VsRLLlwCVDMATge9awjdmEnZHA6nZ/aZnZ4UC/3mGaveFI7PRdRW4kcwr1O4YO3NZOo+KpUuDHaQooHAZhubOaxp717u8edpJA6nG4NzknjHHavQjCbVnojilKKeh3XiHxGNfv5pIWE1pKvELKxI24C9Bxzxwe5q+vhbUNO0XUNT1O1nVp7SUxwgb5APLb5328IowvX8qk+FtloVlBNrOsXdqbiH93bxSyAyrtJJITqCcjHU9fWuv1DxFrfiLw1rRTT7fTrKKxuROs8ha4wI22gLxtznv7+1dFJ8s4xXkYyV02eA0UUV9meaFFFKKYBRRRQAUUUUwCiiigAooopgFFFFABRRRQAUUUUAFFFFIAooooAKWikoAK39M8W39gBHKftMI7SH5h9G/xzWBRQm1sRUpQqK01c9LsfFWl3oAab7PIf4ZuP16VtI6yKGRgynoQcg141U0F3c2pzb3EsR/2HI/lWiqdzz6mWResHY9gorzKHxXrMIx9r3j/bQH9cZq2vjfVFGDHbN7lD/Q1XtEcry2strHoVFefN431RhgRWq+4Rv8aqy+LNZlBAuQgP9yNR/Sj2iBZbWe9j0okKCSQAOpNZF94n0qxBBuBM4/gh+Y/n0/WvObi+u7v/AI+LmaX2dyRVc1Lq9jpp5ZFfHI6PU/GN9eAx2o+yxHupy5/Ht+Fc4SWYsxJJ5JPekorNtvc9KnShSVoKwUUUVJoFFFFABRRRQAUUUUhhRRRQAUUUUgEooooGbngv/ke/D3/YTtv/AEatfWN6MzPXyd4L/wCR78Pf9hO2/wDRq19ZXgzK1fN599k68LucB8TgT4Qk9m5/KvmnUUaQRhRkhf6mvpT4ntt8JSe7/wBK+dJLaW7dY4QxbYegyetcOBV6bReI0kjGMEwJ3IfU1pwIBAvAzgVt6V4PvNRaWOW4aDYB99Ouc/4VqJ4Du1XH2uHAxzg812ezfKzn9vCMldnKClz+VdYPAl3g5u4cjHY0h8CXu3IuoffrXJ9Xqdjo+uUf5jluQKOvOcV1q+Ar1gubqEE8EYPFMPgTUOMTQEfU8Uvq9TsNYyj/ADHLAkCpknkUgqSOO1dGPAmp4yJYTjnqaVvA2qoPvwEdfvGl9XqdivrdH+Yyra5KHerEHvk9a0J/3gRlGcnJOahufDmoWFqlxMsRQnj5un1qu32+4kDxRMBjgIOKh4ao+hqsXStuWzMlnEznJLccVP4HsYtc8Y2sOoKZYfmYjdjoMj9arajol/DZrdXMsawjBPXjPtVvwRNFZ+Jbe589D95QMHnI+lKeHnCDdgWIhUkkme66kPOh2oGO3jjtWfaaPLFK0hZdxQhR7mtnTYZJ7BLiQ8Nzn2qYYLnAJ5xXzzi1qz0FVcU4RPPZbWaOVoyjZBwcCul0nTgdNyU2ysCDkdfSrzWsAugQSSSSfrV0olsqrk4NS3c2qV3JJIxrC1lhkzINvPA715V8Sntz42uNhYv5SeYSeA2P8MV7vHaiWMSxkcA9utfPXxJvUv8AxfP5cToYkETZGCSO9ehgqcufbc461ZS959DDSU7flJ2+xq9a6jLAQr7mQAgAHGM1m2Ok6jeL/oqFlBxjcBVmTwtrkgG2L5jkj94OK9T6rN9DleMpx6mVrOtzXgW3Rz5SHnAwSazIbZpDl+FPPua6KPwPrSv5jQLnrjeKsL4R1gHH2dP+/grb2U4K0Uc6r05vmlIxVUIAo4Apcc1s/wDCL6v1+zgj/fFIfDOrAEm24/3hWfsanY2WIo/zI1/h3rD6X4jWLcfLuVMe3tuPQ1r+Or67XxXZRwtlotrxqOcsT6VzOk2F1peuWdzcxbY4pQ7YIJwK9DtfDtv4w8SR6xbXgjjtnTfE8Zycc1x1aTp1VOS0sdNOtCUGos9IZ5Y9Ji3jErRjfjjDY5rD8PXtxD50Nxv3+acFjnI7V1k8ccsLKFyGGARXOW0DR3UhkTBUYGfevLq3izai4yg0y7qrO1ltUsGLcUXWkR6z4LudMu494nhZSCcfN2OfrirFzbSy6f8AKCTuHyj0qTULSa58MXVlERHPLbvGhY4AJGAc1pQT50zCrJez5fM+N76wmtr6aBo9jRuUKk5wRwar/Z5Mjjr710svgbxAJGzGjndgnzQcmmjwP4h4IgXI6HzRX06h5HlOrDudJ4NQp8IvHoPXfY/+jq4uvRdC0TUNG+EnjgX8YRpXsivzA5xMM/zrzqvSwKtB+v8AkUmmroKKKK7BhRRQK0OQWvfLVyj4Xn2rwOvo21tVVtwXPvXz+e/8u/n+h34H7XyI7qMPbkOOorhrq1FhqbTxr8p+9Xo15Huhxt/GuY1XTvMtXKDLGvEa0OuL1NTw7KZtPSWU8tWxbbEaSNgSGGAK57QZlht/JZhuQAV1VvGkqrMyhj0pRjdjk7Iy7uynu4TZbVOTlgOT7ZrKuNAfR4xcxJuH8aDtXcwRLFICiqqnsBUl1Gu8M6ho24ZTXbyxlHlOW8ou5zmmXkGqwGE/LOq/L2JHofes3WbDdYvb5BZ+Olal5ogt9QE8IK5+6R6U97TKfPljWbi479CrqWx5JcWU9k+yZSOeD61Np9wkMjA8b+Ov9K9D1HRYbyFo3TORwe4Nec6lpk+l3LJIpKZ+Vh3paNFJm4JC4XZnjrXPeNQ39kQ7uP36/wDoLVf0+7Lfu3POOD61neMm3aPFzn/SF/8AQWrowH+8w9RVv4bOHr3b4fDzPAWmx84/e5x/11evCa9q8BTyR+DdOG4bB5nH/bRq9TiJf7LH/EvyZjlv8V+n6o6S5jbDbAVC857Zq14Utk/0i92vj/Vx7z34LGs65uWmTyl4ye3U111nFDa6ZFBEu1Yxhj6nufzzXyEIqKcj2Kkm1Y5nXPD8GoXBlcMGzn5TVS20aGzQeXHnHQV0ty0fIIJrMvbnbGccZH3R2rByZrF3Rkm368jk5zWZd2Mbk8jPsK1Dt2neSMj0p0VrAeNoLseP/r1UBzZm6ZonmTh35iTnGOprm/iVptv9v0RJZli8xZXxnGfu8Z+lelwW+5hAnyxjmRh6elcn47l8MXUcp1aWE+VGYo8H94rdflxz6V6OH0kjkr/AeFWlvNcXqWwXMjuEAB/izivb9C+FeixWu3UQ13NIwctuMarx0wD168+9eX+CtFbWfEqLDPJaiFfN85cEoQeOvvivo/RbC5htIRcXAuJQoDOF2BvyOK6q02tEclKCtdljTvD+j2kkMlvYWkLRLtiZYwCAetV/FcFnZ+EfEEihEeWxnDsoxvbyyBk+vQVLr/iCx8OadLd3cpSFCPlABZ2P8Kg9TXgXirxPP4o1KW9KmG2IZYYnk3bVC9x2PU5x1J9KMNFyqJ+ZNWXLFnKUUUV92eQFLRRQAUUUUAFFFFMAooooQBRRRTAKKKKACiiigAooopMAooooAKKKWgApKKKBhRRRQAUUUUAFFFFIAooooAKSg0UAFFFFAwooopAFFFFABRRRSGgooooAKKKKACkoopAFFFFAzc8F/wDI9+Hv+wnbf+jVr6yvP9c1fJvgv/ke/D3/AGE7b/0atfWV7/rWr5vPvsnXhdzzr4pt/wAUk+em/wDoa8u8N6GIpLXUhNuJQjy9vrnvXp3xXOPB789X/oa8w8IzXkrr5jn7MkeFyO9ceXfwyMwbWx14AJ6Y96eMdulR56gnH1qvfXLW1m88YDFccfWvSPHtcuDH/wCugH5evy5rmD4jmJb90h5/Knf8JDOR/q4+PrwPSmHs5HUK+APm/P1qlea3Y2M/kTSMH27jxnNc/PrE1zFsf5VJO7YcE+1Y0sZdgSSxP940Fxprqej6fqNpc2yMsqqZPuoWGSfSnzahZW2Fup44mxlVc9q84sZ3sLpJlAPlktsY/hU+r6i2qzxyOiARgqAmfXvSK9krjRc3mo3SWjXTtC0pC56deK6VLjT/AA4PI1OUK5AdAwLcdD0HqKbY6eNL05b0FXIBk298Y6Vz3iDUE1+6ikSLy1iTb97knrSuXbmduhkXN9dX0subqWS23EhWbjHbip9NxbX9vIuQA4P51FFFh1hQn5uAeoya2tRtYdG8OzSymNrssNh3e47VE1eLRvCVpKx9G6fCsek2yBfl8tc/lVO0hL3EyE5RXrnPAHj2z8QeFHmuWSK5skxPED0AHDDNbXgzVY9c0QajGMLLK5A9txA/SvnauHfMrrY9iFTRvuVpUk/tEoATg8j2rR1C2MdgJEJLLjrVia1H9pCUcdz71ZnCPavu4AGa4o0N0zeVa/K0LZqUs4gcE7Rk14z8T7Kxg8cW1xKEQXFsd5PAZgcDNej+HvFVnqcRWOUPtOCv8S/WvJ/i/qFrqniWKCA5NrEUdvc8162Bi/aJdjixStF36nIalObS+P8AZ1xiIqDiJ+M1veF57tbkG4WRo5QWEjv/AErn9MstrecxxGvJYfStXUdQiu7WOK2ZgUIJbbgDivdR5E9VyncrJE5ZI3BK8kA5Ipxwevb0rgtF1T+yZJHlR5S4wcHkc1s/8JbFni0kxk8Ej8Ko5ZUpJ6HSbSc5/wD1UhQEYx17Vzo8VxEcWzkHJyWHWpB4ojY4Fs/PON3WgnkkTapbAxFlGcdqf4S1h9KvntmYRwzcs7HG0gVTfXI5hxCxXA5z+v8AOqzpFMSy8DsTWNehGtBwkdeFryoTUj1fw54ttNWke1WQ+chIAYY3D1FdJcqGjHAJ+lfPsf2iyukureR1MbDBQ4Ne3vr1mNDi1Rp/LtmjDl27fX8a8HG4L2KXLsz2cPifbu70ZtxLstxnrjpWVr2qwabotzPO/AQgDOMn0qnrHiO3s/Dk92Lhd5iPksDnexHGPWvEdQ13VtWiRb++klQHhWxgH8KeDwkqrUuiFiKqp3T3N6x1a0vW+RyGXhlbjFaW8YB9a8+ACZ2kg98cE+1XYtXvEURrOQFyc46+1fRWPClTu9DudbP/ABabxV7G1/8ARy14bXrMd3NdfCPxiZZCxVrPAPb98K8mrpwvwy9f0R6FBWppBRRRXSbBS0UVocgV9BWfifRFXDavp4/7eUH9a+faK48Zgo4rl5naxtSrOne3U+gL3xXow+VdWsGU8fJcIT/Osm61rTFk2prNi0OM4Fwh/rXitFcbyWm/tM1WKkuh7DbazpULl01KzBY5OZ1/xrprHxbo6wgPq+nrjsblB/WvnmilHJKa+2xvGN9D6gh8VeHWQFtf0tT73kf+NTDxb4blUrJr2len/H5H/jXyzQK2WUQ/mZm8S30Pp9fE/huYeTLr+mDYflb7ZHg/rSp4h8LjO/xBpZ/7fI/8a+YKK0eVwas5GarNbH0pd+IvDjH93rmlkf8AX5H/AI1z+sX/AIevYmRtX05iR2ukOP1rwuisnk1N/aZosS10O8kewtpsf2lauAeGjlUj+f8AhVXxPf291oEKRXMLuLgExpKp42tzgVx1Fa0cqhSqKopPQUsS5RasFer+D9Z0208JWcM2oWkUy+ZlJJlUrl2PIJzXlFLXRjsFHGU1Tk7Wd/z/AMycPXdGXMlc9y0rXNGk1RGn1exSOIb8vcoAzdup/H8K6uXxZ4fWNUTXdMJ74vI/8a+YqK8t8O0mrc7Op5jJu/KfQ9x4p0YElda04nPOLlP8ax7nxLpUhJTU7LjoftCf414hRUf6s0v+fj/AtZpNfZR7ENf0ySYZ1O0AHU+co/rW7aaxoEY3NrenbuvN2n+NeAUVceHKUftsUszm/snvt/4w0Wzspmj1O0kCKWIimV2b2UA5Jr571u7n1jWrm/aF086UuE5O0dhmrFFdNLJKdP7TMauOlUSTRJpMmqJqHladeCzZ1G6RpxCpA55YkD8K9ljkbVPC8dpffEaws7h1XzEhaEBT1wSGBP1BFeLUVUsmg/tfgZrFNK1ja8RxXMGsPb3GtrqyR8R3KXHmqy/mcfSoIrdG0+WYTW4fJUI0oD4C5JwT36fWsyitYZVCLT5tiHXb6BRRS16pgFFFFABRRRTAKKKKACiiimAUUUUAFFFFABRRRQwCiiikAUUUUAAopaSgAooooGFFFFABRRRQAUUUUgCg0UlABRRRQMKKKKQBRRRQAUUUUAFFFFIYUUUUAFFFJSAKKKKBhRRRSA3PBf8AyPfh7/sJ23/o1a+sr3/WuK+TfBf/ACPfh7/sJ23/AKNWvrS7H71jXzuffZOvC7nmfxaP/FJhcdZP6V8/2+s31gClvOUXj3r6X8e6Dea/oyWtlCJH3EnLAY4968WX4TeJJbgxJbAuP9tf8a87CVIxp2bsaVoc8tjlj4n1jAAvHwDnoOaY/iLVZE2tdsVIxtwMV2Evwb8VQoXa1QADP+sX/GsSTwLqsTFHRQQefmFdPto/zGKoX2iYX9q3mT+96+woGrXvaY9uwrY/4QzUR2X/AL6FJ/whuof7H/fQo9tH+Yr2L/lMkavergiX9KX+2b3H+sH5Vqnwdf46p+Ypv/CHah/s/mKPbL+YPYf3TL/ti8znePyru/B+taMNHlGpzwLc7jxIOT6Vyx8H6h/sfnQPB9/nnbj601XXcmeG5lawy78X6uzSwpcjyDlQNo+76Vnprd4HzuQAnJOwVpnwhfeq/nTR4RveuV/Ol7Zdx+wdtjqNc8SaTBo1udJNu92ThhsOQMc/riuAvb24v7lp7glnbrjpWyPCV6O607/hFbzGTim60H1FHDuOyMiwvrjT5WkhYDcpRlJ4YEYwRXcfD34izeFr+C1uPn0x3Pmjume4/niuePhW8IyMfnTf+EVvc8BamU6clytlqE072Poy/wD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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-09 04:18:23,048] p24755 {3951696921.py:24} INFO - Image=s3://sagemaker-us-west-2-597703351594/multimodal/img/CMP301_TrainDeploy_E1_20230607_SPEdited_image_9.jpg\n",
      "Question: What are quarks in particle physics?\n",
      "Answer: I did not find the answer to this question in the slide deck.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create prompt and convert to embeddings\n",
    "question: str = \"What are quarks in particle physics?\"\n",
    "prompt = prompt_template.format(question=question)\n",
    "text_embeddings = get_text_embeddings(bedrock, question)\n",
    "\n",
    "# vector db search\n",
    "vector_db_response: Dict = find_similar_data(text_embeddings)\n",
    "\n",
    "# download image for local notebook display\n",
    "s3_img_path = vector_db_response.get('hits', {}).get('hits')[0].get('_source').get('image_path')\n",
    "logger.info(f\"going to answer the question=\\\"{question}\\\" using the image \\\"{s3_img_path}\\\"\")\n",
    "\n",
    "!aws s3 cp {s3_img_path} .\n",
    "local_img_path = os.path.basename(s3_img_path)\n",
    "display(Image(filename=local_img_path))\n",
    "\n",
    "# Ask LLaVA\n",
    "data = {\n",
    "    \"image\" : s3_img_path,\n",
    "    \"question\" : prompt,\n",
    "    \"temperature\" : 0.1,\n",
    "}\n",
    "output = predictor.predict(data)\n",
    "logger.info(f\"Image={s3_img_path}\\nQuestion: {question}\\nAnswer: {output}\\n\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clean Up\n",
    "\n",
    "We need to delete the endpoint created. Uncomment the following cell to delete the endpoint after you are done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[2024-01-10 04:13:38,790] p24755 {session.py:4324} INFO - Deleting endpoint configuration with name: huggingface-pytorch-inference-2024-01-08-23-24-45-284\n",
      "[2024-01-10 04:13:38,919] p24755 {session.py:4314} INFO - Deleting endpoint with name: huggingface-pytorch-inference-2024-01-08-23-24-45-284\n"
     ]
    }
   ],
   "source": [
    "#predictor.delete_endpoint()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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