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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/pgurazada/captioning-with-multimodal-models/blob/main/newyorker_cartoon_description.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1K3KG4dgExk9",
        "outputId": "434f598d-0866-4f32-9066-782b859c4c4a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m521.2/521.2 kB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.1/61.1 kB\u001b[0m \u001b[31m6.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 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[32m134.8/134.8 kB\u001b[0m \u001b[31m14.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip install -q \"google-cloud-aiplatform>=1.38\" datasets evaluate bert_score"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "import json\n",
        "import vertexai\n",
        "import evaluate\n",
        "\n",
        "from vertexai.preview.generative_models import (\n",
        "    GenerationConfig,\n",
        "    GenerativeModel,\n",
        "    Image\n",
        ")\n",
        "\n",
        "from datasets import load_dataset\n",
        "\n",
        "from google.colab import auth"
      ],
      "metadata": {
        "id": "AvUkUsTJFC7x"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Setup"
      ],
      "metadata": {
        "id": "OLg3weXdFIrq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "auth.authenticate_user()"
      ],
      "metadata": {
        "id": "o46p5k4uFHEg"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open(\"config-vertexai.json\") as f:\n",
        "    data = f.read()"
      ],
      "metadata": {
        "id": "_wQn68SYFaL4"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "creds = json.loads(data)"
      ],
      "metadata": {
        "id": "cMeWplkPFlQW"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vertexai.init(\n",
        "    project=creds[\"project\"],\n",
        "    location=creds[\"location\"]\n",
        ")"
      ],
      "metadata": {
        "id": "oBliYhcuFoPH"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "multimodal_model = GenerativeModel(\"gemini-pro-vision\")"
      ],
      "metadata": {
        "id": "8r5AwGIPFqHE"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data"
      ],
      "metadata": {
        "id": "gKtH0p5lFt6s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "captions_examples_ds = load_dataset(\n",
        "    \"jmhessel/newyorker_caption_contest\", \"explanation\",\n",
        "    split=\"validation\"\n",
        ")\n",
        "\n",
        "captions_gold_examples_ds = load_dataset(\n",
        "    \"jmhessel/newyorker_caption_contest\", \"explanation\",\n",
        "    split=\"test\"\n",
        ")"
      ],
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        "colab": {
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      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/40.3k [00:00<?, ?B/s]"
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          "data": {
            "text/plain": [
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          "metadata": {}
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        {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/2340 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c9a2ad545864345be848c233c666c6c"
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          },
          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split:   0%|          | 0/130 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2b2311158dfd4395a63d86f218245f33"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split:   0%|          | 0/131 [00:00<?, ? examples/s]"
            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "7b48be5e09264dd3ac8ce48627c023b3"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "description_prompt = \"\"\"\n",
        "Your task is to generate a description for the cartoon presented in the input.\n",
        "Write a 2-3 sentence description focusing on:\n",
        "- Where is the scene taking place?\n",
        "- Who/Whats in the scene? What are they doing?\n",
        "- What objects and actions are being depicted?\n",
        "- Is anyone particularly happy/unhappy/mad/etc?\n",
        "There is no need to be formal, but please do your best to write full, grammatical sentences.\n",
        "Here are a few examples to guide your generation process.\n",
        "\"\"\""
      ],
      "metadata": {
        "id": "t7fe8zGwGZSZ"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "task_prompt = \"\"\"Now generate a description for the following cartoon:\"\"\""
      ],
      "metadata": {
        "id": "ttdd3jYaGc3R"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "examples_for_prompt = []\n",
        "\n",
        "n_examples = 5 # 5-shot as in the paper\n",
        "few_shot_examples = captions_examples_ds.shuffle()[0: n_examples]"
      ],
      "metadata": {
        "id": "L6tT9kzUGd-m"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for example_image, example_description in zip(\n",
        "    few_shot_examples['image'],\n",
        "    few_shot_examples['image_description']):\n",
        "\n",
        "    with io.BytesIO() as buffer:\n",
        "        example_image.save(buffer, format='JPEG')\n",
        "        example_image_bytes = buffer.getvalue()\n",
        "        example_image_input = Image.from_bytes(example_image_bytes)\n",
        "\n",
        "        examples_for_prompt.append(example_image_input)\n",
        "        examples_for_prompt.append(example_description)\n",
        "\n",
        "few_shot_prompt = [description_prompt] + examples_for_prompt + [task_prompt]\n"
      ],
      "metadata": {
        "id": "GUNsWgNrGggx"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Generation"
      ],
      "metadata": {
        "id": "fX0ivRt2Gqj0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "description_generation_config = GenerationConfig(\n",
        "    temperature=0.8,\n",
        "    top_p=.95,\n",
        "    max_output_tokens=64\n",
        ")"
      ],
      "metadata": {
        "id": "1cyXLZ2iGnv5"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "Lih69HLdGzq7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "n_test_examples = 30\n",
        "\n",
        "gold_examples = captions_gold_examples_ds.shuffle()[0: n_test_examples]"
      ],
      "metadata": {
        "id": "uebtouXRGrdV"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_predictions, ground_truths = [], []\n",
        "\n",
        "for gold_example_image, gold_example_description in zip(\n",
        "    gold_examples['image'],\n",
        "    gold_examples['image_description']):\n",
        "\n",
        "    with io.BytesIO() as buffer:\n",
        "        gold_example_image.save(buffer, format='JPEG')\n",
        "        gold_example_image_bytes = buffer.getvalue()\n",
        "        gold_example_image_input = Image.from_bytes(gold_example_image_bytes)\n",
        "\n",
        "        gold_example_prompt = few_shot_prompt + [gold_example_image_input]\n",
        "\n",
        "        try:\n",
        "            generated_description = multimodal_model.generate_content(\n",
        "                gold_example_prompt,\n",
        "                generation_config=description_generation_config\n",
        "            )\n",
        "        except Exception as e:\n",
        "            print(e)\n",
        "            continue\n",
        "\n",
        "        model_predictions.append(generated_description.text.strip())\n",
        "        ground_truths.append(gold_example_description)"
      ],
      "metadata": {
        "id": "YuOo8RH_G1jM"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with open('generated-descriptions.txt', 'w') as f:\n",
        "    for prediction in model_predictions:\n",
        "        f.write(f'{prediction}\\n')\n",
        "\n",
        "with open('gold-descriptions.txt', 'w') as f:\n",
        "    for description in ground_truths:\n",
        "        f.write(f'{description}\\n')"
      ],
      "metadata": {
        "id": "SqG80nB3G3wV"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "bert_scorer = evaluate.load(\"bertscore\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
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        "outputId": "38561781-58c2-4ed4-f1ca-8503ea3b4e42"
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      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/7.95k [00:00<?, ?B/s]"
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    {
      "cell_type": "code",
      "source": [
        "score = bert_scorer.compute(\n",
        "    predictions=model_predictions,\n",
        "    references=ground_truths,\n",
        "    lang=\"en\",\n",
        "    rescale_with_baseline=True\n",
        ")"
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      "execution_count": 25,
      "outputs": [
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              "config.json:   0%|          | 0.00/482 [00:00<?, ?B/s]"
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaModel were not initialized from the model checkpoint at roberta-large and are newly initialized: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sum(score['f1'])/len(score['f1'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wP6XmCITJ5lr",
        "outputId": "cfc7f1c8-ad57-4241-baad-11a1f1a827d6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.39895525376001995"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    }
  ]
}