{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/donbcolab/composable_vlms/blob/main/notebooks/roboflow_bccd_hf_load.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Roboflow 100 - Blood Cell Dataset (BCCD)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JFMi8nxJsqct",
        "outputId": "ba0118ff-f3b4-42a6-a182-3edd52f369dd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/76.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━\u001b[0m \u001b[32m71.7/76.7 kB\u001b[0m \u001b[31m3.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.7/76.7 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m547.8/547.8 kB\u001b[0m \u001b[31m13.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m178.7/178.7 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.5/54.5 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m39.9/39.9 MB\u001b[0m \u001b[31m40.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.9/64.9 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m8.8 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[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n",
            "google-colab 1.0.0 requires requests==2.31.0, but you have requests 2.32.3 which is incompatible.\n",
            "ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q roboflow datasets datasets[vision]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Setup Roboflow connection parameters and configuration BCCD dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cWyUczWctate",
        "outputId": "44c0f011-b3d5-401f-ca39-375c84858499"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "loading Roboflow workspace...\n",
            "loading Roboflow project...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading Dataset Version Zip in /content/coco/ to coco:: 100%|██████████| 9042/9042 [00:01<00:00, 5811.25it/s] "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to /content/coco/ in coco:: 100%|██████████| 372/372 [00:00<00:00, 6072.69it/s]\n",
            "Downloading Dataset Version Zip in /content/paligemma/ to paligemma:: 100%|██████████| 7541/7541 [00:01<00:00, 5042.83it/s] "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to /content/paligemma/ in paligemma:: 100%|██████████| 370/370 [00:00<00:00, 5702.03it/s]\n",
            "Downloading Dataset Version Zip in /content/florence2_od/ to florence2-od:: 100%|██████████| 7538/7538 [00:01<00:00, 5536.18it/s] "
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\n",
            "Extracting Dataset Version Zip to /content/florence2_od/ in florence2-od:: 100%|██████████| 372/372 [00:00<00:00, 4983.98it/s]\n"
          ]
        }
      ],
      "source": [
        "from google.colab import userdata\n",
        "from roboflow import Roboflow\n",
        "\n",
        "ROBOFLOW_API_KEY = userdata.get('ROBOFLOW_API_KEY')\n",
        "rf = Roboflow(api_key=ROBOFLOW_API_KEY)\n",
        "\n",
        "project = rf.workspace(\"roboflow-100\").project(\"bccd-ouzjz\")\n",
        "version = project.version(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### select dataset model formats - for models you want to finetune"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "dataset = version.download(model_format=\"paligemma\", location=\"/content/paligemma/\", overwrite=True)\n",
        "dataset = version.download(model_format=\"florence2-od\", location=\"/content/florence2_od/\", overwrite=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### validation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rtzP2xV9sV52",
        "outputId": "6dc6f65b-ac6b-4b85-f452-7d8cc648f562"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "-rw-r--r-- 1 root root 1122675 Jul 23 03:30 coco/train/_annotations.coco.json\n",
            "-rw-r--r-- 1 root root 153472 Jul 23 03:30 coco/test/_annotations.coco.json\n",
            "-rw-r--r-- 1 root root 314313 Jul 23 03:30 coco/valid/_annotations.coco.json\n",
            "-rw-r--r-- 1 root root 21993 Jul 23 03:30 florence2_od/test/annotations.jsonl\n",
            "-rw-r--r-- 1 root root 160106 Jul 23 03:30 florence2_od/train/annotations.jsonl\n",
            "-rw-r--r-- 1 root root 45081 Jul 23 03:30 florence2_od/valid/annotations.jsonl\n",
            "-rw-r--r-- 1 root root  24860 Jul 23 03:30 paligemma/dataset/_annotations.test.jsonl\n",
            "-rw-r--r-- 1 root root 180953 Jul 23 03:30 paligemma/dataset/_annotations.train.jsonl\n",
            "-rw-r--r-- 1 root root  50942 Jul 23 03:30 paligemma/dataset/_annotations.valid.jsonl\n"
          ]
        }
      ],
      "source": [
        "# for each of the dataset model formats (florence2_od / paligemma)\n",
        "# do a \"ls -l\" of the json files\n",
        "!ls -l florence2_od/test/*.json*\n",
        "!ls -l florence2_od/train/*.json*\n",
        "!ls -l florence2_od/valid/*.json*\n",
        "\n",
        "!ls -l paligemma/dataset/*.json*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oSgcUrNPu6Xq",
        "outputId": "6571f3cf-dd52-4c88-e3a0-de95432caaca"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/coco/train: 255 files\n",
            "/content/coco/test: 36 files\n",
            "/content/coco/valid: 73 files\n",
            "/content/florence2_od/test: 36 files\n",
            "/content/florence2_od/train: 255 files\n",
            "/content/florence2_od/valid: 73 files\n",
            "/content/paligemma/dataset: 364 files\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "\n",
        "def count_jpg_files(directory):\n",
        "    return len([f for f in os.listdir(directory) if f.endswith('.jpg')])\n",
        "\n",
        "directories = [\n",
        "    \"/content/florence2_od/test\",\n",
        "    \"/content/florence2_od/train\",\n",
        "    \"/content/florence2_od/valid\",\n",
        "    \"/content/paligemma/dataset\"\n",
        "]\n",
        "\n",
        "for directory in directories:\n",
        "    try:\n",
        "        count = count_jpg_files(directory)\n",
        "        print(f\"{directory}: {count} files\")\n",
        "    except FileNotFoundError:\n",
        "        print(f\"{directory}: Directory not found\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Florence 2 - dataset preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "0r7c4TQq9MyS"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "from datasets import Dataset, Features, Image, Value, DatasetDict\n",
        "\n",
        "def load_florence2_annotations(annotation_file, image_dir):\n",
        "    dataset_entries = []\n",
        "    with open(annotation_file, 'r') as f:\n",
        "        for line in f:\n",
        "            ann = json.loads(line)\n",
        "            entry = {\n",
        "                'image_id': ann['image'],\n",
        "                'image': os.path.join(image_dir, ann['image']),\n",
        "                'annotations': ann  # Keep all original fields\n",
        "            }\n",
        "            dataset_entries.append(entry)\n",
        "    return dataset_entries\n",
        "\n",
        "# Load annotations\n",
        "train_data = load_florence2_annotations('/content/florence2_od/train/annotations.jsonl', '/content/florence2_od/train')\n",
        "test_data = load_florence2_annotations('/content/florence2_od/test/annotations.jsonl', '/content/florence2_od/test')\n",
        "valid_data = load_florence2_annotations('/content/florence2_od/valid/annotations.jsonl', '/content/florence2_od/valid')\n",
        "\n",
        "# Define features\n",
        "features = Features({\n",
        "    'image_id': Value('string'),\n",
        "    'image': Image(),\n",
        "    'annotations': {\n",
        "        'image': Value('string'),\n",
        "        'prefix': Value('string'),\n",
        "        'suffix': Value('string')\n",
        "    }\n",
        "})\n",
        "\n",
        "# Create datasets\n",
        "florence2_train_dataset = Dataset.from_list(train_data, features=features)\n",
        "florence2_test_dataset = Dataset.from_list(test_data, features=features)\n",
        "florence2_valid_dataset = Dataset.from_list(valid_data, features=features)\n",
        "\n",
        "# Combine into a DatasetDict\n",
        "florence2_dataset = DatasetDict({\n",
        "    'train': florence2_train_dataset,\n",
        "    'test': florence2_test_dataset,\n",
        "    'validation': florence2_valid_dataset\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### push dataset to hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
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        "id": "TTp5OEYU-K2c",
        "outputId": "32cbb7dd-47dd-4629-c490-5d48a77980ed"
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              "version_major": 2,
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          },
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          },
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          },
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          },
          "metadata": {},
          "output_type": "display_data"
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        {
          "data": {
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          },
          "metadata": {},
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset splits: dict_keys(['train', 'test', 'validation'])\n",
            "Number of training examples: 255\n",
            "Number of testing examples: 36\n",
            "Number of validation examples: 73\n",
            "Features: {'image_id': Value(dtype='string', id=None), 'image': Image(mode=None, decode=True, id=None), 'annotations': {'image': Value(dtype='string', id=None), 'prefix': Value(dtype='string', id=None), 'suffix': Value(dtype='string', id=None)}}\n",
            "\n",
            "Sample entry:\n",
            "{'image_id': 'BloodImage_00343_jpg.rf.d8c56063ce5e40c50efb00a7e0c83c3b.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7B72E44569E0>, 'annotations': {'image': 'BloodImage_00343_jpg.rf.d8c56063ce5e40c50efb00a7e0c83c3b.jpg', 'prefix': '<OD>', 'suffix': 'RBC<loc_756><loc_406><loc_958><loc_631>RBC<loc_820><loc_623><loc_995><loc_825>RBC<loc_150><loc_327><loc_355><loc_508>RBC<loc_283><loc_685><loc_283><loc_685>RBC<loc_400><loc_417><loc_588><loc_640>RBC<loc_817><loc_2><loc_999><loc_248>RBC<loc_50><loc_10><loc_208><loc_204>RBC<loc_206><loc_46><loc_314><loc_302>RBC<loc_528><loc_677><loc_670><loc_925>Platelets<loc_2><loc_752><loc_75><loc_854>Platelets<loc_109><loc_429><loc_184><loc_531>WBC<loc_286><loc_2><loc_664><loc_319>'}}\n"
          ]
        }
      ],
      "source": [
        "# Push to Hub - uncomment next line when ready to push (update with your id)\n",
        "# florence2_dataset.push_to_hub(\"dwb2023/roboflow100-bccd-florence2\")\n",
        "\n",
        "# Print some information about the dataset\n",
        "print(f\"Dataset splits: {florence2_dataset.keys()}\")\n",
        "print(f\"Number of training examples: {len(florence2_dataset['train'])}\")\n",
        "print(f\"Number of testing examples: {len(florence2_dataset['test'])}\")\n",
        "print(f\"Number of validation examples: {len(florence2_dataset['validation'])}\")\n",
        "print(f\"Features: {florence2_dataset['train'].features}\")\n",
        "\n",
        "# Print a sample entry\n",
        "print(\"\\nSample entry:\")\n",
        "print(florence2_dataset['train'][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Paligemma - dataset preparation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "QAppgYrYDm4a"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import json\n",
        "from datasets import Dataset, Features, Image, Value, DatasetDict\n",
        "\n",
        "def load_paligemma_annotations(annotation_file, image_dir):\n",
        "    dataset_entries = []\n",
        "    with open(annotation_file, 'r') as f:\n",
        "        for line in f:\n",
        "            ann = json.loads(line)\n",
        "            entry = {\n",
        "                'image_id': ann['image'],\n",
        "                'image': os.path.join(image_dir, ann['image']),\n",
        "                'annotations': ann  # Keep all original fields\n",
        "            }\n",
        "            dataset_entries.append(entry)\n",
        "    return dataset_entries\n",
        "\n",
        "# Load annotations\n",
        "train_data = load_paligemma_annotations('/content/paligemma/dataset/_annotations.train.jsonl', '/content/paligemma/dataset')\n",
        "test_data = load_paligemma_annotations('/content/paligemma/dataset/_annotations.test.jsonl', '/content/paligemma/dataset')\n",
        "valid_data = load_paligemma_annotations('/content/paligemma/dataset/_annotations.valid.jsonl', '/content/paligemma/dataset')\n",
        "\n",
        "# Define features\n",
        "features = Features({\n",
        "    'image_id': Value('string'),\n",
        "    'image': Image(),\n",
        "    'annotations': {\n",
        "        'image': Value('string'),\n",
        "        'prefix': Value('string'),\n",
        "        'suffix': Value('string')\n",
        "    }\n",
        "})\n",
        "\n",
        "# Create datasets\n",
        "paligemma_train_dataset = Dataset.from_list(train_data, features=features)\n",
        "paligemma_test_dataset = Dataset.from_list(test_data, features=features)\n",
        "paligemma_valid_dataset = Dataset.from_list(valid_data, features=features)\n",
        "\n",
        "# Combine into a DatasetDict\n",
        "paligemma_dataset = DatasetDict({\n",
        "    'train': paligemma_train_dataset,\n",
        "    'test': paligemma_test_dataset,\n",
        "    'validation': paligemma_valid_dataset\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### push dataset to hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
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            "text/plain": [
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Dataset splits: dict_keys(['train', 'test', 'validation'])\n",
            "Number of training examples: 255\n",
            "Number of testing examples: 36\n",
            "Number of validation examples: 73\n",
            "Features: {'image_id': Value(dtype='string', id=None), 'image': Image(mode=None, decode=True, id=None), 'annotations': {'image': Value(dtype='string', id=None), 'prefix': Value(dtype='string', id=None), 'suffix': Value(dtype='string', id=None)}}\n",
            "\n",
            "Sample entry:\n",
            "{'image_id': 'BloodImage_00327_jpg.rf.dc708dec2caf7249ddca49d41055ca4c.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7B72E44FFB20>, 'annotations': {'image': 'BloodImage_00327_jpg.rf.dc708dec2caf7249ddca49d41055ca4c.jpg', 'prefix': 'detect Platelets ; RBC ; WBC', 'suffix': '<loc0232><loc0169><loc0296><loc0233> Platelets ; <loc0217><loc0529><loc0302><loc0606> Platelets'}}\n"
          ]
        }
      ],
      "source": [
        "# Push to Hub - uncomment next line when ready to push (update with your id)\n",
        "# paligemma_dataset.push_to_hub(\"dwb2023/roboflow-bccd-paligemma\")\n",
        "\n",
        "# Print some information about the dataset\n",
        "print(f\"Dataset splits: {paligemma_dataset.keys()}\")\n",
        "print(f\"Number of training examples: {len(paligemma_dataset['train'])}\")\n",
        "print(f\"Number of testing examples: {len(paligemma_dataset['test'])}\")\n",
        "print(f\"Number of validation examples: {len(paligemma_dataset['validation'])}\")\n",
        "print(f\"Features: {paligemma_dataset['train'].features}\")\n",
        "\n",
        "# Print a sample entry\n",
        "print(\"\\nSample entry:\")\n",
        "print(paligemma_dataset['train'][0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Compare records across datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "nSiuyM3kKVro"
      },
      "outputs": [],
      "source": [
        "# Search for same image across datasets\n",
        "image_id_to_find = 'BloodImage_00343_jpg.rf.d8c56063ce5e40c50efb00a7e0c83c3b.jpg'\n",
        "\n",
        "# Function to find a record by image_id\n",
        "def find_record_by_image_id(dataset, image_id):\n",
        "    for record in dataset['train']:\n",
        "        if record['image_id'] == image_id:\n",
        "            return record\n",
        "    return None"
      ]
    },
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      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
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        },
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        "outputId": "33c30ff1-a5cc-47e2-f772-2a5e8e5ba585"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Record found: {'image_id': 'BloodImage_00343_jpg.rf.d8c56063ce5e40c50efb00a7e0c83c3b.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7B72E4454BB0>, 'annotations': {'image': 'BloodImage_00343_jpg.rf.d8c56063ce5e40c50efb00a7e0c83c3b.jpg', 'prefix': '<OD>', 'suffix': 'RBC<loc_756><loc_406><loc_958><loc_631>RBC<loc_820><loc_623><loc_995><loc_825>RBC<loc_150><loc_327><loc_355><loc_508>RBC<loc_283><loc_685><loc_283><loc_685>RBC<loc_400><loc_417><loc_588><loc_640>RBC<loc_817><loc_2><loc_999><loc_248>RBC<loc_50><loc_10><loc_208><loc_204>RBC<loc_206><loc_46><loc_314><loc_302>RBC<loc_528><loc_677><loc_670><loc_925>Platelets<loc_2><loc_752><loc_75><loc_854>Platelets<loc_109><loc_429><loc_184><loc_531>WBC<loc_286><loc_2><loc_664><loc_319>'}}\n"
          ]
        }
      ],
      "source": [
        "# Find the Roboflow Florence2 record\n",
        "record = find_record_by_image_id(florence2_dataset, image_id_to_find)\n",
        "\n",
        "# Display the record\n",
        "if record:\n",
        "    print(f\"Record found: {record}\")\n",
        "else:\n",
        "    print(\"Record not found\")"
      ]
    },
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      "cell_type": "code",
      "execution_count": 16,
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        },
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        "outputId": "117fbead-cfa9-48af-abd8-95c7cc979892"
      },
      "outputs": [
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          "output_type": "stream",
          "text": [
            "Record found: {'image_id': 'BloodImage_00343_jpg.rf.d8c56063ce5e40c50efb00a7e0c83c3b.jpg', 'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480 at 0x7B72E4456E60>, 'annotations': {'image': 'BloodImage_00343_jpg.rf.d8c56063ce5e40c50efb00a7e0c83c3b.jpg', 'prefix': 'detect Platelets ; RBC ; WBC', 'suffix': '<loc0416><loc0774><loc0646><loc0980> RBC ; <loc0637><loc0840><loc0844><loc1019> RBC ; <loc0334><loc0153><loc0520><loc0363> RBC ; <loc0701><loc0289><loc0701><loc0289> RBC ; <loc0426><loc0409><loc0654><loc0601> RBC ; <loc0002><loc0836><loc0253><loc1023> RBC ; <loc0010><loc0051><loc0209><loc0212> RBC ; <loc0046><loc0211><loc0309><loc0321> RBC ; <loc0693><loc0540><loc0947><loc0686> RBC ; <loc0770><loc0001><loc0874><loc0076> Platelets ; <loc0439><loc0112><loc0544><loc0188> Platelets ; <loc0002><loc0292><loc0326><loc0680> WBC'}}\n"
          ]
        }
      ],
      "source": [
        "# Find the Roboflow Paligemma record\n",
        "record = find_record_by_image_id(paligemma_dataset, image_id_to_find)\n",
        "\n",
        "# Display the record\n",
        "if record:\n",
        "    print(f\"Record found: {record}\")\n",
        "else:\n",
        "    print(\"Record not found\")"
      ]
    }
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