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   "cell_type": "code",
   "execution_count": 4,
   "id": "9138adfe-71b0-4db2-a08f-dd9e472fdd63",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15d71dd6-cc03-485e-8a34-7a33ed5dee0e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1358921d-173b-4d5d-828c-b6c3726a5eb3",
   "metadata": {},
   "source": [
    "#### Connect to bedrock models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b3827087-182f-48be-8b59-b2741f8ded44",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "94c11534-6847-4e4a-b8e4-8066e0cc6aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the Conversation API to send a text message to Amazon Nova.\n",
    "\n",
    "import boto3\n",
    "from botocore.exceptions import ClientError\n",
    "\n",
    "# Create a Bedrock Runtime client in the AWS Region you want to use.\n",
    "client = boto3.client(\"bedrock-runtime\", region_name=\"us-east-1\")\n",
    "\n",
    "# Set the model ID, e.g., Amazon Nova Lite.\n",
    "model_id = \"amazon.nova-lite-v1:0\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a8ad65f-abaa-475c-892c-2e2b4e668f5d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ac20bb00-e93f-4a95-a1de-dd2688bce591",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Start a conversation with the user message.\n",
    "user_message = \"\"\"\n",
    "List the best parks to see in London with number of google ratings and value ie. 4.5 out of 5 etc. \n",
    "Give number of ratings and give output in table form\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a29f0055-48c4-4f25-b33f-cde1eaf755c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "conversation = [\n",
    "    {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [{\"text\": user_message}],\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e68b2d5-4d43-4b80-8574-d3c847b33661",
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    # Send the message to the model, using a basic inference configuration.\n",
    "    response = client.converse(\n",
    "        modelId=model_id,\n",
    "        messages=conversation,\n",
    "        inferenceConfig={\"maxTokens\": 512, \"temperature\": 0.5, \"topP\": 0.9},\n",
    "    )\n",
    "\n",
    "    # Extract and print the response text.\n",
    "    response_text = response[\"output\"][\"message\"][\"content\"][0][\"text\"]\n",
    "    print(response_text)\n",
    "\n",
    "except (ClientError, Exception) as e:\n",
    "    print(f\"ERROR: Can't invoke '{model_id}'. Reason: {e}\")\n",
    "    exit(1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ed16ee7-3f09-4780-8dfc-d1c5f3cffdbe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f8c7a18-0907-430d-bfe7-86ecb8933bfd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2183994b-cde5-45b0-b18b-37be3277d73b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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