Llama3_2_3B_Instruct_with_Gradio5.ipynb

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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/yvrjsharma/HugginFace_Gradio/blob/main/Llama3_2_3B_Instruct_with_Gradio5.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Llama 3.2 Release -- Experince with Gradio 5.0 and Huggingface\n",
        "\n"
      ],
      "metadata": {
        "id": "NNJW4XghZZDk"
      }
    },
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      "source": [
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      ],
      "metadata": {
        "id": "Cj9LutYt4mtl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install Gradio 5 beta"
      ],
      "metadata": {
        "id": "8eGILAsy6cdx"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kMfoqW_96V18",
        "outputId": "725f7706-cfbf-4db9-e517-840ffcca8426"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 MB\u001b[0m \u001b[31m36.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m319.2/319.2 kB\u001b[0m \u001b[31m20.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m94.6/94.6 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.4/76.4 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m141.9/141.9 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.9/10.9 MB\u001b[0m \u001b[31m60.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m4.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 kB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m130.2/130.2 kB\u001b[0m \u001b[31m9.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q gradio --pre"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install required packages"
      ],
      "metadata": {
        "id": "-WCOYGEv6gq5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install accelerate==0.33.0 bitsandbytes==0.43.2 transformers==4.43.3 -q"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhUw4yow6mTk",
        "outputId": "c2b47dd7-0530-4c25-9d3f-6ee539957c7b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m870.3 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m315.1/315.1 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.5/137.5 MB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.4/9.4 MB\u001b[0m \u001b[31m82.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Set up your Huggingfacetoken as environ variable"
      ],
      "metadata": {
        "id": "skHiJfnR6ns8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"HF_TOKEN\"] = \"hf_xx\""
      ],
      "metadata": {
        "id": "rTgF_9qJ6sWR"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Download model from Huggingface Hub"
      ],
      "metadata": {
        "id": "mQxuokpy6xbi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#llama 3.2 3b IT\n",
        "import os\n",
        "from threading import Thread\n",
        "from typing import Iterator\n",
        "\n",
        "import gradio as gr\n",
        "#import spaces\n",
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer\n",
        "\n",
        "DESCRIPTION = \"\"\"\\\n",
        "# Llama 3.2 3B Instruct\n",
        "Llama 3.2 3B is Meta's latest iteration of open LLMs.\n",
        "This is a demo of [`meta-llama/Llama-3.2-3B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct), fine-tuned for instruction following.\n",
        "For more details, please check [our post](https://huggingface.co/blog/llama32).\n",
        "\"\"\"\n",
        "\n",
        "MAX_MAX_NEW_TOKENS = 2048\n",
        "DEFAULT_MAX_NEW_TOKENS = 1024\n",
        "MAX_INPUT_TOKEN_LENGTH = int(os.getenv(\"MAX_INPUT_TOKEN_LENGTH\", \"4096\"))\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Change this to meta-llama or the correct org name from Huggingface Hub\n",
        "model_id = \"meta-internal/Llama-3.2-3B-Instruct\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    device_map=\"auto\",\n",
        "    torch_dtype=torch.bfloat16,\n",
        ")\n",
        "model.eval()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 910,
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          ]
        },
        "id": "NHWU7Oxq60SO",
        "outputId": "6a06b593-24ca-4511-cd6d-662cc68b810d"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
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            ],
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            }
          },
          "metadata": {}
        },
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            }
          },
          "metadata": {}
        },
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
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          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
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          "metadata": {}
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            ],
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            }
          },
          "metadata": {}
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          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(128256, 3072)\n",
              "    (layers): ModuleList(\n",
              "      (0-27): 28 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaSdpaAttention(\n",
              "          (q_proj): Linear(in_features=3072, out_features=3072, bias=False)\n",
              "          (k_proj): Linear(in_features=3072, out_features=1024, bias=False)\n",
              "          (v_proj): Linear(in_features=3072, out_features=1024, bias=False)\n",
              "          (o_proj): Linear(in_features=3072, out_features=3072, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear(in_features=3072, out_features=8192, bias=False)\n",
              "          (up_proj): Linear(in_features=3072, out_features=8192, bias=False)\n",
              "          (down_proj): Linear(in_features=8192, out_features=3072, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): LlamaRMSNorm()\n",
              "        (post_attention_layernorm): LlamaRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): LlamaRMSNorm()\n",
              "    (rotary_emb): LlamaRotaryEmbedding()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=3072, out_features=128256, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Main Gradio inference function"
      ],
      "metadata": {
        "id": "bJNZmfS_7EsV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generate(\n",
        "    message: str,\n",
        "    chat_history: list[tuple[str, str]],\n",
        "    max_new_tokens: int = 1024,\n",
        "    temperature: float = 0.6,\n",
        "    top_p: float = 0.9,\n",
        "    top_k: int = 50,\n",
        "    repetition_penalty: float = 1.2,\n",
        ") -> Iterator[str]:\n",
        "\n",
        "    conversation = [{k: v for k, v in d.items() if k != 'metadata'} for d in chat_history]\n",
        "    conversation.append({\"role\": \"user\", \"content\": message})\n",
        "\n",
        "    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors=\"pt\")\n",
        "    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:\n",
        "        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]\n",
        "        gr.Warning(f\"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.\")\n",
        "    input_ids = input_ids.to(model.device)\n",
        "\n",
        "    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)\n",
        "    generate_kwargs = dict(\n",
        "        {\"input_ids\": input_ids},\n",
        "        streamer=streamer,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        do_sample=True,\n",
        "        top_p=top_p,\n",
        "        top_k=top_k,\n",
        "        temperature=temperature,\n",
        "        num_beams=1,\n",
        "        repetition_penalty=repetition_penalty,\n",
        "    )\n",
        "    t = Thread(target=model.generate, kwargs=generate_kwargs)\n",
        "    t.start()\n",
        "\n",
        "    conversation.append({\"role\": \"assistant\", \"content\": \"\"})\n",
        "    outputs = []\n",
        "    for text in streamer:\n",
        "        outputs.append(text)\n",
        "        bot_response = \"\".join(outputs)\n",
        "        conversation[-1]['content'] = bot_response\n",
        "        yield \"\", conversation\n"
      ],
      "metadata": {
        "id": "OJDo0sY17HJ6"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Implementing Gradio 5 features and building a ChatInterface UI yourself"
      ],
      "metadata": {
        "id": "L8-AUrI67SFh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "PLACEHOLDER = \"\"\"<div style=\"padding: 20px; text-align: center; display: flex; flex-direction: column; align-items: center;\">\n",
        "   <img src=\"https://ysharma-dummy-chat-app.hf.space/file=/tmp/gradio/c21ff9c8e7ecb2f7d957a72f2ef03c610ac7bbc4/Meta_lockup_positive%20primary_RGB_small.jpg\" style=\"width: 80%; max-width: 550px; height: auto; opacity: 0.55; margin-bottom: 10px;\">\n",
        "   <h1 style=\"font-size: 28px; margin: 0;\">Meta llama3.2</h1>\n",
        "   <p style=\"font-size: 18px; margin: 5px 0 0; opacity: 0.65;\">\n",
        "       <a href=\"https://huggingface.co/blog/llama32\" target=\"_blank\" style=\"color: inherit; text-decoration: none;\">Learn more about Llama 3.2</a>\n",
        "   </p>\n",
        "</div>\"\"\"\n"
      ],
      "metadata": {
        "id": "J6l1IPHXgOCu"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "import random\n",
        "import time\n",
        "\n",
        "def handle_retry(history, retry_data: gr.RetryData):\n",
        "    new_history = history[:retry_data.index]\n",
        "    previous_prompt = history[retry_data.index]['content']\n",
        "    yield from generate(previous_prompt, chat_history = new_history, max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)\n",
        "\n",
        "def handle_like(data: gr.LikeData):\n",
        "    if data.liked:\n",
        "        print(\"You upvoted this response: \", data.value)\n",
        "    else:\n",
        "        print(\"You downvoted this response: \", data.value)\n",
        "\n",
        "def handle_undo(history, undo_data: gr.UndoData):\n",
        "    chatbot = history[:undo_data.index]\n",
        "    prompt = history[undo_data.index]['content']\n",
        "    return chatbot, prompt\n",
        "\n",
        "def chat_examples_fill(data: gr.SelectData):\n",
        "    yield from generate(data.value['text'], chat_history = [], max_new_tokens = 1024, temperature = 0.6, top_p = 0.9, top_k = 50, repetition_penalty = 1.2)\n",
        "\n",
        "\n",
        "with gr.Blocks(theme=gr.themes.Soft(), fill_height=True) as demo:\n",
        "    with gr.Column(elem_id=\"container\", scale=1):\n",
        "        chatbot = gr.Chatbot(\n",
        "            label=\"Llama3.2 3B Instruct Chatbotw using Gradio 5\",\n",
        "            show_label=False,\n",
        "            type=\"messages\",\n",
        "            scale=1,\n",
        "            suggestions = [\n",
        "                {\"text\": \"How many R are there in a Strawberry?\"},\n",
        "                {\"text\": \"What is the meaning of life for an AI?\"},\n",
        "                {\"text\": \"Are tomatoes vegetables?\"},\n",
        "                {\"text\": \"There's a llama in my garden 😱 What should I do?\"},\n",
        "                {\"text\": \"What is the best way to open a can of worms?\"},\n",
        "                {\"text\": \"The odd numbers in this group add up to an even number: 15, 32, 5, 13, 82, 7, 1. \"},\n",
        "                {\"text\": 'How to setup a human base on Mars? Give short answer.'},\n",
        "                {\"text\": 'Explain theory of relativity to me like I’m 8 years old.'},\n",
        "                {\"text\": 'What is 9,000 * 9,000?'},\n",
        "                {\"text\": 'Write a pun-filled happy birthday message to my friend Alex.'},\n",
        "                {\"text\": 'Justify why a penguin might make a good king of the jungle.'}\n",
        "                ],\n",
        "            placeholder = PLACEHOLDER,\n",
        "            )\n",
        "\n",
        "    msg = gr.Textbox(submit_btn=True, show_label=False)\n",
        "    with gr.Accordion('Additional inputs', open=False):\n",
        "        max_new_tokens = gr.Slider(label=\"Max new tokens\", minimum=1, maximum=10, step=1, value=23, )\n",
        "        temperature = gr.Slider(label=\"Temperature\",minimum=0.1, maximum=4.0, step=0.1, value=0.6,)\n",
        "        top_p = gr.Slider(label=\"Top-p (nucleus sampling)\", minimum=0.05, maximum=1.0, step=0.05, value=0.9, )\n",
        "        top_k = gr.Slider(label=\"Top-k\", minimum=1, maximum=1000, step=1, value=50, )\n",
        "        repetition_penalty = gr.Slider(label=\"Repetition penalty\", minimum=1.0, maximum=2.0, step=0.05, value=1.2, )\n",
        "\n",
        "    msg.submit(generate, [msg, chatbot, max_new_tokens, temperature, top_p, top_k, repetition_penalty], [msg, chatbot])\n",
        "    chatbot.retry(handle_retry, chatbot, [msg, chatbot])\n",
        "    chatbot.like(handle_like, None, None)\n",
        "    chatbot.undo(handle_undo, chatbot, [chatbot, msg])\n",
        "    chatbot.suggestion_select(chat_examples_fill, None, [msg, chatbot] )\n",
        "\n",
        "\n",
        "demo.launch(debug=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "id": "gaYC_DI87V4d",
        "outputId": "c4e450b2-2085-48dc-ce54-462ba0afa114"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "* Running on public URL: https://6e0ad5d6a70fdcdcf3.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<div><iframe src=\"https://6e0ad5d6a70fdcdcf3.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    }
  ]
}