{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "923343b0-d4bd-4361-b8d4-dd29f86a0fbd",
   "metadata": {},
   "source": [
    "## Getting Started with LlamaStack Vision API\n",
    "\n",
    "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html).\n",
    "\n",
    "Let's import the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eae04594-49f9-43af-bb42-9df114d9ddd6",
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "import base64\n",
    "import mimetypes\n",
    "from llama_stack_client import LlamaStackClient\n",
    "from llama_stack_client.lib.inference.event_logger import EventLogger\n",
    "from llama_stack_client.types import UserMessage\n",
    "from termcolor import cprint"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "143837c6-1072-4015-8297-514712704087",
   "metadata": {},
   "source": [
    "## Configuration\n",
    "Set up your connection parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1d293479-9dde-4b68-94ab-d0c4c61ab08c",
   "metadata": {},
   "outputs": [],
   "source": [
    "HOST = \"localhost\"  # Replace with your host\n",
    "PORT = 5000        # Replace with your port"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "51984856-dfc7-4226-817a-1d44853e6661",
   "metadata": {},
   "source": [
    "## Helper Functions\n",
    "Let's create some utility functions to handle image processing and API interaction:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8e65aae0-3ef0-4084-8c59-273a89ac9510",
   "metadata": {},
   "outputs": [],
   "source": [
    "import base64\n",
    "import mimetypes\n",
    "from termcolor import cprint\n",
    "from llama_stack_client.lib.inference.event_logger import EventLogger\n",
    "\n",
    "def encode_image_to_data_url(file_path: str) -> str:\n",
    "    \"\"\"\n",
    "    Encode an image file to a data URL.\n",
    "\n",
    "    Args:\n",
    "        file_path (str): Path to the image file\n",
    "\n",
    "    Returns:\n",
    "        str: Data URL string\n",
    "    \"\"\"\n",
    "    mime_type, _ = mimetypes.guess_type(file_path)\n",
    "    if mime_type is None:\n",
    "        raise ValueError(\"Could not determine MIME type of the file\")\n",
    "\n",
    "    with open(file_path, \"rb\") as image_file:\n",
    "        encoded_string = base64.b64encode(image_file.read()).decode(\"utf-8\")\n",
    "\n",
    "    return f\"data:{mime_type};base64,{encoded_string}\"\n",
    "\n",
    "async def process_image(client, image_path: str, stream: bool = True):\n",
    "    \"\"\"\n",
    "    Process an image through the LlamaStack Vision API.\n",
    "\n",
    "    Args:\n",
    "        client (LlamaStackClient): Initialized client\n",
    "        image_path (str): Path to image file\n",
    "        stream (bool): Whether to stream the response\n",
    "    \"\"\"\n",
    "    data_url = encode_image_to_data_url(image_path)\n",
    "\n",
    "    message = {\n",
    "        \"role\": \"user\",\n",
    "        \"content\": [\n",
    "            {\"image\": {\"uri\": data_url}},\n",
    "            \"Describe what is in this image.\"\n",
    "        ]\n",
    "    }\n",
    "\n",
    "    cprint(\"User> Sending image for analysis...\", \"green\")\n",
    "    response = client.inference.chat_completion(\n",
    "        messages=[message],\n",
    "        model=\"Llama3.2-11B-Vision-Instruct\",\n",
    "        stream=stream,\n",
    "    )\n",
    "\n",
    "    if not stream:\n",
    "        cprint(f\"> Response: {response}\", \"cyan\")\n",
    "    else:\n",
    "        async for log in EventLogger().log(response):\n",
    "            log.print()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8073b673-e730-4557-8980-fd8b7ea11975",
   "metadata": {},
   "source": [
    "## Chat with Image\n",
    "\n",
    "Now let's put it all together:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "64d36476-95d7-49f9-a548-312cf8d8c49e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32mUser> Sending image for analysis...\u001b[0m\n",
      "\u001b[36mAssistant> \u001b[0m\u001b[33mThe\u001b[0m\u001b[33m image\u001b[0m\u001b[33m features\u001b[0m\u001b[33m a\u001b[0m\u001b[33m simple\u001b[0m\u001b[33m,\u001b[0m\u001b[33m mon\u001b[0m\u001b[33moch\u001b[0m\u001b[33mromatic\u001b[0m\u001b[33m line\u001b[0m\u001b[33m drawing\u001b[0m\u001b[33m of\u001b[0m\u001b[33m a\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m the\u001b[0m\u001b[33m words\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mLL\u001b[0m\u001b[33mAMA\u001b[0m\u001b[33m STACK\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m written\u001b[0m\u001b[33m above\u001b[0m\u001b[33m it\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m is\u001b[0m\u001b[33m depicted\u001b[0m\u001b[33m in\u001b[0m\u001b[33m a\u001b[0m\u001b[33m cartoon\u001b[0m\u001b[33mish\u001b[0m\u001b[33m style\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m a\u001b[0m\u001b[33m large\u001b[0m\u001b[33m body\u001b[0m\u001b[33m and\u001b[0m\u001b[33m a\u001b[0m\u001b[33m long\u001b[0m\u001b[33m neck\u001b[0m\u001b[33m.\u001b[0m\u001b[33m It\u001b[0m\u001b[33m has\u001b[0m\u001b[33m a\u001b[0m\u001b[33m distinctive\u001b[0m\u001b[33m head\u001b[0m\u001b[33m shape\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m a\u001b[0m\u001b[33m small\u001b[0m\u001b[33m circle\u001b[0m\u001b[33m for\u001b[0m\u001b[33m the\u001b[0m\u001b[33m eye\u001b[0m\u001b[33m and\u001b[0m\u001b[33m a\u001b[0m\u001b[33m curved\u001b[0m\u001b[33m line\u001b[0m\u001b[33m for\u001b[0m\u001b[33m the\u001b[0m\u001b[33m mouth\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m's\u001b[0m\u001b[33m body\u001b[0m\u001b[33m is\u001b[0m\u001b[33m composed\u001b[0m\u001b[33m of\u001b[0m\u001b[33m several\u001b[0m\u001b[33m rounded\u001b[0m\u001b[33m shapes\u001b[0m\u001b[33m,\u001b[0m\u001b[33m giving\u001b[0m\u001b[33m it\u001b[0m\u001b[33m a\u001b[0m\u001b[33m soft\u001b[0m\u001b[33m and\u001b[0m\u001b[33m cudd\u001b[0m\u001b[33mly\u001b[0m\u001b[33m appearance\u001b[0m\u001b[33m.\n",
      "\n",
      "\u001b[0m\u001b[33mThe\u001b[0m\u001b[33m words\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mLL\u001b[0m\u001b[33mAMA\u001b[0m\u001b[33m STACK\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m are\u001b[0m\u001b[33m written\u001b[0m\u001b[33m in\u001b[0m\u001b[33m a\u001b[0m\u001b[33m playful\u001b[0m\u001b[33m,\u001b[0m\u001b[33m handwritten\u001b[0m\u001b[33m font\u001b[0m\u001b[33m above\u001b[0m\u001b[33m the\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m's\u001b[0m\u001b[33m head\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m text\u001b[0m\u001b[33m is\u001b[0m\u001b[33m also\u001b[0m\u001b[33m in\u001b[0m\u001b[33m a\u001b[0m\u001b[33m mon\u001b[0m\u001b[33moch\u001b[0m\u001b[33mromatic\u001b[0m\u001b[33m color\u001b[0m\u001b[33m scheme\u001b[0m\u001b[33m,\u001b[0m\u001b[33m matching\u001b[0m\u001b[33m the\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m's\u001b[0m\u001b[33m outline\u001b[0m\u001b[33m.\u001b[0m\u001b[33m The\u001b[0m\u001b[33m background\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m image\u001b[0m\u001b[33m is\u001b[0m\u001b[33m a\u001b[0m\u001b[33m solid\u001b[0m\u001b[33m black\u001b[0m\u001b[33m color\u001b[0m\u001b[33m,\u001b[0m\u001b[33m which\u001b[0m\u001b[33m provides\u001b[0m\u001b[33m a\u001b[0m\u001b[33m clean\u001b[0m\u001b[33m and\u001b[0m\u001b[33m simple\u001b[0m\u001b[33m contrast\u001b[0m\u001b[33m to\u001b[0m\u001b[33m the\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m's\u001b[0m\u001b[33m design\u001b[0m\u001b[33m.\n",
      "\n",
      "\u001b[0m\u001b[33mOverall\u001b[0m\u001b[33m,\u001b[0m\u001b[33m the\u001b[0m\u001b[33m image\u001b[0m\u001b[33m appears\u001b[0m\u001b[33m to\u001b[0m\u001b[33m be\u001b[0m\u001b[33m a\u001b[0m\u001b[33m logo\u001b[0m\u001b[33m or\u001b[0m\u001b[33m icon\u001b[0m\u001b[33m for\u001b[0m\u001b[33m a\u001b[0m\u001b[33m brand\u001b[0m\u001b[33m or\u001b[0m\u001b[33m product\u001b[0m\u001b[33m called\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mL\u001b[0m\u001b[33mlama\u001b[0m\u001b[33m Stack\u001b[0m\u001b[33m.\"\u001b[0m\u001b[33m The\u001b[0m\u001b[33m use\u001b[0m\u001b[33m of\u001b[0m\u001b[33m a\u001b[0m\u001b[33m cartoon\u001b[0m\u001b[33m llama\u001b[0m\u001b[33m and\u001b[0m\u001b[33m a\u001b[0m\u001b[33m playful\u001b[0m\u001b[33m font\u001b[0m\u001b[33m suggests\u001b[0m\u001b[33m a\u001b[0m\u001b[33m l\u001b[0m\u001b[33migh\u001b[0m\u001b[33mthe\u001b[0m\u001b[33mart\u001b[0m\u001b[33med\u001b[0m\u001b[33m and\u001b[0m\u001b[33m humorous\u001b[0m\u001b[33m tone\u001b[0m\u001b[33m,\u001b[0m\u001b[33m while\u001b[0m\u001b[33m the\u001b[0m\u001b[33m mon\u001b[0m\u001b[33moch\u001b[0m\u001b[33mromatic\u001b[0m\u001b[33m color\u001b[0m\u001b[33m scheme\u001b[0m\u001b[33m gives\u001b[0m\u001b[33m the\u001b[0m\u001b[33m image\u001b[0m\u001b[33m a\u001b[0m\u001b[33m clean\u001b[0m\u001b[33m and\u001b[0m\u001b[33m modern\u001b[0m\u001b[33m feel\u001b[0m\u001b[33m.\u001b[0m\u001b[97m\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# [Cell 5] - Initialize client and process image\n",
    "async def main():\n",
    "    # Initialize client\n",
    "    client = LlamaStackClient(\n",
    "        base_url=f\"http://{HOST}:{PORT}\",\n",
    "    )\n",
    "\n",
    "    # Process image\n",
    "    await process_image(client, \"../_static/llama-stack-logo.png\")\n",
    "\n",
    "\n",
    "\n",
    "# Execute the main function\n",
    "await main()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b39efb4",
   "metadata": {},
   "source": [
    "Thanks for checking out this notebook! \n",
    "\n",
    "The next one in the series will teach you one of the favorite applications of Large Language Models: [Tool Calling](./03_Tool_Calling101.ipynb). Enjoy!"
   ]
  }
 ],
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