DocLayNet_image_viewer_APP.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Document AI | DocLayNet image viewer APP"
      ],
      "metadata": {
        "id": "YzZu0H0to1Sk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Author: [Pierre GUILLOU](https://www.linkedin.com/in/pierreguillou/)\n",
        "- Date: 14/02/2023 (changes from updated date 29/01/2023: list of blog posts and notebooks)\n",
        "- Blog posts: \n",
        "  - (02/14/2023) [Document AI | Inference APP for Document Understanding at line level](https://medium.com/@pierre_guillou/document-ai-inference-app-for-document-understanding-at-line-level-a35bbfa98893)\n",
        "  - (02/10/2023) [Document AI | Document Understanding model at line level with LiLT, Tesseract and DocLayNet dataset](https://medium.com/@pierre_guillou/document-ai-document-understanding-model-at-line-level-with-lilt-tesseract-and-doclaynet-dataset-347107a643b8)\n",
        "  - (01/31/2023) [Document AI | DocLayNet image viewer APP](https://medium.com/@pierre_guillou/document-ai-doclaynet-image-viewer-app-3ac54c19956)\n",
        "  - (01/27/2023) [Document AI | Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)](https://medium.com/@pierre_guillou/document-ai-processing-of-doclaynet-dataset-to-be-used-by-layout-models-of-the-hugging-face-hub-308d8bd81cdb)\n",
        "- Notebooks: \n",
        "  - [Document AI | Inference at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)\n",
        "  - [Document AI | Inference APP at line level with a Document Understanding model (LiLT fine-tuned on DocLayNet dataset)](https://github.com/piegu/language-models/blob/master/Gradio_inference_on_LiLT_model_finetuned_on_DocLayNet_base_in_any_language_at_levellines_ml384.ipynb)\n",
        "  - [Document AI | Fine-tune LiLT on DocLayNet base in any language at line level (chunk of 384 tokens with overlap)](https://github.com/piegu/language-models/blob/master/Fine_tune_LiLT_on_DocLayNet_base_in_any_language_at_linelevel_ml_384.ipynb)\n",
        "  - [DocLayNet image viewer APP](https://github.com/piegu/language-models/blob/master/DocLayNet_image_viewer_APP.ipynb)\n",
        "  - [Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)](processing_DocLayNet_dataset_to_be_used_by_layout_models_of_HF_hub.ipynb)"
      ],
      "metadata": {
        "id": "ZbaGasAEoaS8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "KFZM9dE_o88i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive \n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8r8FlyUtL5XS",
        "outputId": "b488c5e2-ef10-45ea-a9cc-b952689323b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q datasets gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcZEeEpBo4VZ",
        "outputId": "c0359a7b-178a-44c1-b89e-f92a4fc5587d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.2/14.2 MB\u001b[0m \u001b[31m50.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.0/132.0 KB\u001b[0m \u001b[31m14.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m15.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m213.0/213.0 KB\u001b[0m \u001b[31m22.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m71.5/71.5 KB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.6/140.6 KB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.6/80.6 KB\u001b[0m \u001b[31m9.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 KB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 KB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for python-multipart (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import gradio as gr\n",
        "from PIL import Image, ImageDraw, ImageFont\n",
        "import random\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from datasets import concatenate_datasets\n",
        "from operator import itemgetter\n",
        "import collections"
      ],
      "metadata": {
        "id": "L63zGASp9m2O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Download DocLayNet small and base"
      ],
      "metadata": {
        "id": "NAHsXHeFpX1S"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- The size of the DocLayNet small is about 1% of the DocLayNet dataset (random selection respectively in the train, val and test files).\n",
        "- The size of the DocLayNet base is about 10% of the DocLayNet dataset (random selection respectively in the train, val and test files)."
      ],
      "metadata": {
        "id": "0Frf_gmh2Dlp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**WARNING**: in Google Colab, we advise you to use the code as is (ie, only the dataset DocLayNet small) due to its limitations."
      ],
      "metadata": {
        "id": "N9z7Hu5lLcBY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# download datasets\n",
        "from datasets import load_dataset\n",
        "\n",
        "dataset_small = load_dataset(\"pierreguillou/DocLayNet-small\")\n",
        "dataset_base = load_dataset(\"pierreguillou/DocLayNet-base\")\n",
        "\n",
        "id2label = {idx:label for idx,label in enumerate(dataset_small[\"train\"].features[\"categories\"].feature.names)}\n",
        "label2id = {label:idx for idx,label in id2label.items()}\n",
        "labels = [label for idx, label in id2label.items()]"
      ],
      "metadata": {
        "colab": {
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          "height": 478,
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          ]
        },
        "outputId": "f486a5b9-e3e5-4975-a8ee-733a81681ea3",
        "id": "-viqFmDA2YPD"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/13.4k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "883da4d56a524dba9ffdc8ac826813f5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/13.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "85636027ef374867953789eb9af30be3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:No config specified, defaulting to: doc_lay_net-small/DocLayNet_2022.08_processed_on_2023.01\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset doc_lay_net-small/DocLayNet_2022.08_processed_on_2023.01 to /root/.cache/huggingface/datasets/pierreguillou___doc_lay_net-small/DocLayNet_2022.08_processed_on_2023.01/1.1.0/9a545168f32e0dc98a6deb7670c5c48a0fb7261b6248da74b38a596072625332...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/381M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5658efb33ae3462781d99772edd89414"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e72ebe41e6747afa19cb87e7456ebcf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4c3efd78f58b47d19eb27ff78ceffee1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "309f6fe06dad43e2930f23eb5afbc639"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset doc_lay_net-small downloaded and prepared to /root/.cache/huggingface/datasets/pierreguillou___doc_lay_net-small/DocLayNet_2022.08_processed_on_2023.01/1.1.0/9a545168f32e0dc98a6deb7670c5c48a0fb7261b6248da74b38a596072625332. Subsequent calls will reuse this data.\n"
          ]
        },
        {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading builder script:   0%|          | 0.00/13.3k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "83bccdff434949268311d69db9cee2bc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading readme:   0%|          | 0.00/13.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "49c3073822204c5a8ed2b71ea137a85d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:No config specified, defaulting to: doc_lay_net-base/DocLayNet_2022.08_processed_on_2023.01\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading and preparing dataset doc_lay_net-base/DocLayNet_2022.08_processed_on_2023.01 to /root/.cache/huggingface/datasets/pierreguillou___doc_lay_net-base/DocLayNet_2022.08_processed_on_2023.01/1.1.0/9688182ec245305add7ef9f98480e6d437d65b7cb5786787ff85e1420bfccaf6...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "e7240d7a67db4acd81bc5d71e6a0b870"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.download.download_manager:Computing checksums of downloaded files. They can be used for integrity verification. You can disable this by passing ignore_verifications=True to load_dataset\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Computing checksums: 100%|##########| 1/1 [00:12<00:00, 12.66s/it]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "f5c4fee9ae664f58afa2c66e91b6f930"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "557787ce4e9f479da7ae9f527cf027fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating validation split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "40f9bf2e81d04032b5fabc3476d5cfa5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating test split: 0 examples [00:00, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43c60878f1bb4bd5a4501b3123727c1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset doc_lay_net-base downloaded and prepared to /root/.cache/huggingface/datasets/pierreguillou___doc_lay_net-base/DocLayNet_2022.08_processed_on_2023.01/1.1.0/9688182ec245305add7ef9f98480e6d437d65b7cb5786787ff85e1420bfccaf6. Subsequent calls will reuse this data.\n"
          ]
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              "model_id": "d04e7540e2ca4532b09f7c5f521985ca"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_small"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uJjmw9Czw1L7",
        "outputId": "5236f514-c411-45f3-d702-11ce50ef0b79"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],\n",
              "        num_rows: 691\n",
              "    })\n",
              "    validation: Dataset({\n",
              "        features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],\n",
              "        num_rows: 64\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['id', 'texts', 'bboxes_block', 'bboxes_line', 'categories', 'image', 'page_hash', 'original_filename', 'page_no', 'num_pages', 'original_width', 'original_height', 'coco_width', 'coco_height', 'collection', 'doc_category'],\n",
              "        num_rows: 49\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#save\n",
        "dataset_small.save_to_disk(\"/content/drive/MyDrive/DocLayNet/datasets/small\")\n",
        "dataset_base.save_to_disk(\"/content/drive/MyDrive/DocLayNet/datasets/base\")"
      ],
      "metadata": {
        "colab": {
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          "height": 81,
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        "outputId": "f46eadf1-f604-455c-9075-b2a8e99c25c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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        {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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        },
        {
          "output_type": "display_data",
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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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        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WV9og2NOd3q8"
      },
      "source": [
        "## Visualizing the labeled bounding boxes (by paragraphes and lines)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# need to change the coordinates format\n",
        "def convert_box(box):\n",
        "    x, y, w, h = tuple(box) # the row comes in (left, top, width, height) format\n",
        "    actual_box = [x, y, x+w, y+h] # we turn it into (left, top, left+widght, top+height) to get the actual box \n",
        "    return actual_box\n",
        "\n",
        "# get back original size\n",
        "def original_box(box, original_width, original_height, coco_width, coco_height):\n",
        "    return [\n",
        "        int(original_width * (box[0] / coco_width)),\n",
        "        int(original_height * (box[1] / coco_height)),\n",
        "        int(original_width * (box[2] / coco_width)),\n",
        "        int(original_height* (box[3] / coco_height)),\n",
        "    ]\n",
        "\n",
        "# function to sort bounding boxes\n",
        "def get_sorted_boxes(bboxes):\n",
        "\n",
        "  # sort by y from page top to bottom \n",
        "  bboxes = sorted(bboxes, key=itemgetter(1), reverse=False)\n",
        "  y_list = [bbox[1] for bbox in bboxes]\n",
        "\n",
        "  # sort by x from page left to right when boxes with same y\n",
        "  if len(list(set(y_list))) != len(y_list):\n",
        "    y_list_duplicates_indexes = dict()\n",
        "    y_list_duplicates = [item for item, count in collections.Counter(y_list).items() if count > 1]\n",
        "    for item in y_list_duplicates:\n",
        "      y_list_duplicates_indexes[item] = [i for i, e in enumerate(y_list) if e == item]\n",
        "      bbox_list_y_duplicates = sorted(np.array(bboxes)[y_list_duplicates_indexes[item]].tolist(), key=itemgetter(0), reverse=False)\n",
        "      np_array_bboxes = np.array(bboxes)\n",
        "      np_array_bboxes[y_list_duplicates_indexes[item]] = np.array(bbox_list_y_duplicates)\n",
        "      bboxes = np_array_bboxes.tolist()\n",
        "\n",
        "  return bboxes\n",
        "\n",
        "# categories colors\n",
        "label2color = {\n",
        "    'Caption': 'brown',\n",
        "    'Footnote': 'orange',\n",
        "    'Formula': 'gray',\n",
        "    'List-item': 'yellow',\n",
        "    'Page-footer': 'red',\n",
        "    'Page-header': 'red',\n",
        "    'Picture': 'violet',\n",
        "    'Section-header': 'orange',\n",
        "    'Table': 'green',\n",
        "    'Text': 'blue',\n",
        "    'Title': 'pink'\n",
        "    }\n",
        "\n",
        "# image witout content\n",
        "examples_dir = 'samples/'\n",
        "images_wo_content = examples_dir + \"wo_content.png\"\n",
        "\n",
        "df_paragraphs_wo_content, df_lines_wo_content = pd.DataFrame(), pd.DataFrame()\n",
        "\n",
        "df_paragraphs_wo_content[\"paragraphs\"] = [0]\n",
        "df_paragraphs_wo_content[\"categories\"] = [\"no content\"]\n",
        "df_paragraphs_wo_content[\"texts\"] = [\"no content\"]\n",
        "df_paragraphs_wo_content[\"bounding boxes\"] = [\"no content\"]\n",
        "\n",
        "df_lines_wo_content[\"lines\"] = [0]\n",
        "df_lines_wo_content[\"categories\"] = [\"no content\"]\n",
        "df_lines_wo_content[\"texts\"] = [\"no content\"]\n",
        "df_lines_wo_content[\"bounding boxes\"] = [\"no content\"]\n",
        "\n",
        "# lists\n",
        "font = ImageFont.load_default()\n",
        "\n",
        "dataset_names = [\"small\", \"base\"]\n",
        "splits = [\"all\", \"train\", \"validation\", \"test\"]\n",
        "domains = [\"all\", \"Financial Reports\", \"Manuals\", \"Scientific Articles\", \"Laws & Regulations\", \"Patents\", \"Government Tenders\"]\n",
        "domains_names = [domain_name.lower().replace(\" \", \"_\").replace(\"&\", \"and\") for domain_name in domains]\n",
        "categories = labels + [\"all\"]"
      ],
      "metadata": {
        "id": "8dcmoXe2zo80"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# function to get a random image and all data from DocLayNet\n",
        "def generate_annotated_image(dataset_name, split, domain, category):\n",
        "\n",
        "  # error message\n",
        "  msg_error = \"\"\n",
        "\n",
        "  # get dataset\n",
        "  if dataset_name == \"small\": example = dataset_small\n",
        "  else: example = dataset_base\n",
        "\n",
        "  # get split\n",
        "  if split == \"all\":\n",
        "    example = concatenate_datasets([example[\"train\"], example[\"validation\"], example[\"test\"]])\n",
        "  else:\n",
        "    example = example[split]\n",
        "\n",
        "  # get domain\n",
        "  domain_name = domains_names[domains.index(domain)]\n",
        "  if domain_name != \"all\":\n",
        "    example = example.filter(lambda example: example[\"doc_category\"] == domain_name)\n",
        "    if len(example) == 0:\n",
        "      msg_error = f'There is no image with at least one labeled bounding box that matches your settings (dataset: \"DocLayNet {dataset_name}\" / domain: \"{domain}\" /  split: \"{split}\").'\n",
        "      example = dict()\n",
        "\n",
        "  # get category\n",
        "  idx_list = list()\n",
        "  if category != \"all\":\n",
        "    for idx, categories_list in enumerate(example[\"categories\"]):\n",
        "      if int(label2id[category]) in categories_list:\n",
        "        idx_list.append(idx)\n",
        "    if len(idx_list) > 0:\n",
        "      example = example.select(idx_list)\n",
        "    else:\n",
        "      msg_error = f'There is no image with at least one labeled bounding box that matches your settings (dataset: \"DocLayNet {dataset_name}\" / split: \"{split}\" / domain: \"{domain}\" / category: \"{category}\").'\n",
        "      example = dict()\n",
        "\n",
        "  if len(msg_error) > 0:\n",
        "    # save image files\n",
        "    Image.open(images_wo_content).save(\"wo_content.png\")\n",
        "    # save csv files\n",
        "    df_paragraphs_wo_content.to_csv(\"paragraphs_wo_content.csv\", encoding=\"utf-8\", index=False)\n",
        "    df_lines_wo_content.to_csv(\"lines_wo_content.csv\", encoding=\"utf-8\", index=False)\n",
        "\n",
        "    return msg_error, \"wo_content.png\", images_wo_content, images_wo_content, \"wo_content.png\", \"wo_content.png\", df_paragraphs_wo_content, df_lines_wo_content, gr.File.update(value=\"paragraphs_wo_content.csv\", visible=False), gr.File.update(value=\"lines_wo_content.csv\", visible=False)\n",
        "  else:\n",
        "    # get random image & PDF data\n",
        "    index = random.randint(0, len(example)-1)\n",
        "    image = example[index][\"image\"] # original image\n",
        "    coco_width, coco_height = example[index][\"coco_width\"], example[index][\"coco_height\"]\n",
        "    original_width, original_height = example[index][\"original_width\"], example[index][\"original_height\"]\n",
        "    original_filename = example[index][\"original_filename\"]\n",
        "    page_no = example[index][\"page_no\"]\n",
        "    num_pages = example[index][\"num_pages\"]\n",
        "\n",
        "    # resize image to original\n",
        "    image = image.resize((original_width, original_height))\n",
        "\n",
        "    # get image of PDF without bounding boxes\n",
        "    img_file = original_filename.replace(\".pdf\", \".png\")\n",
        "    image.save(img_file)\n",
        "\n",
        "    # get corresponding annotations\n",
        "    texts = example[index][\"texts\"]\n",
        "    bboxes_block = example[index][\"bboxes_block\"]\n",
        "    bboxes_line = example[index][\"bboxes_line\"]\n",
        "    categories = example[index][\"categories\"]\n",
        "    domain = example[index][\"doc_category\"]\n",
        "\n",
        "    # convert boxes to original\n",
        "    original_bboxes_block = [original_box(convert_box(box), original_width, original_height, coco_width, coco_height) for box in bboxes_block]\n",
        "    original_bboxes_line = [original_box(convert_box(box), original_width, original_height, coco_width, coco_height) for box in bboxes_line]\n",
        "    original_bboxes = [original_bboxes_block, original_bboxes_line]\n",
        "\n",
        "    ##### block boxes #####\n",
        "\n",
        "    # get list of unique block boxes\n",
        "    original_blocks = dict()\n",
        "    original_bboxes_block_list = list()\n",
        "    original_bbox_block_prec = list()\n",
        "    for count_block, original_bbox_block in enumerate(original_bboxes_block):\n",
        "      if original_bbox_block != original_bbox_block_prec:\n",
        "        original_bbox_block_indexes = [i for i, original_bbox in enumerate(original_bboxes_block) if original_bbox == original_bbox_block]\n",
        "        original_blocks[count_block] = original_bbox_block_indexes\n",
        "        original_bboxes_block_list.append(original_bbox_block)\n",
        "      original_bbox_block_prec = original_bbox_block\n",
        "\n",
        "    # get list of categories and texts by unique block boxes\n",
        "    category_block_list, text_block_list = list(), list()\n",
        "    for original_bbox_block in original_bboxes_block_list:\n",
        "      count_block = original_bboxes_block.index(original_bbox_block)\n",
        "      original_bbox_block_indexes = original_blocks[count_block]\n",
        "      category_block = categories[original_bbox_block_indexes[0]]\n",
        "      category_block_list.append(category_block)\n",
        "      if id2label[category_block] == \"Text\" or id2label[category_block] == \"Caption\" or id2label[category_block] == \"Footnote\":\n",
        "        text_block = ' '.join(np.array(texts)[original_bbox_block_indexes].tolist())\n",
        "      elif id2label[category_block] == \"Section-header\" or id2label[category_block] == \"Title\" or id2label[category_block] == \"Picture\" or id2label[category_block] == \"Formula\" or id2label[category_block] == \"List-item\" or id2label[category_block] == \"Table\" or id2label[category_block] == \"Page-header\" or id2label[category_block] == \"Page-footer\":\n",
        "        text_block = '\\n'.join(np.array(texts)[original_bbox_block_indexes].tolist())\n",
        "      text_block_list.append(text_block)\n",
        "\n",
        "    # sort data from y = 0 to end of page (and after, x=0 to end of page when necessary)\n",
        "    sorted_original_bboxes_block_list = get_sorted_boxes(original_bboxes_block_list)\n",
        "    sorted_original_bboxes_block_list_indexes = [original_bboxes_block_list.index(item) for item in sorted_original_bboxes_block_list]\n",
        "    sorted_category_block_list = np.array(category_block_list)[sorted_original_bboxes_block_list_indexes].tolist()\n",
        "    sorted_text_block_list = np.array(text_block_list)[sorted_original_bboxes_block_list_indexes].tolist()\n",
        "\n",
        "    ##### line boxes ####\n",
        "\n",
        "    # sort data from y = 0 to end of page (and after, x=0 to end of page when necessary)\n",
        "    original_bboxes_line_list = original_bboxes_line\n",
        "    category_line_list = categories\n",
        "    text_line_list = texts\n",
        "    sorted_original_bboxes_line_list = get_sorted_boxes(original_bboxes_line_list)\n",
        "    sorted_original_bboxes_line_list_indexes = [original_bboxes_line_list.index(item) for item in sorted_original_bboxes_line_list]\n",
        "    sorted_category_line_list = np.array(category_line_list)[sorted_original_bboxes_line_list_indexes].tolist()\n",
        "    sorted_text_line_list = np.array(text_line_list)[sorted_original_bboxes_line_list_indexes].tolist()\n",
        "    \n",
        "    # setup images & PDF data\n",
        "    columns = 2\n",
        "    images = [image.copy(), image.copy()]\n",
        "    num_imgs = len(images)\n",
        "\n",
        "    imgs, df_paragraphs, df_lines = dict(), pd.DataFrame(), pd.DataFrame()\n",
        "    for i, img in enumerate(images):\n",
        "        \n",
        "        draw = ImageDraw.Draw(img)\n",
        "        \n",
        "        for box, label_idx, text in zip(original_bboxes[i], categories, texts):\n",
        "            label = id2label[label_idx]\n",
        "            color = label2color[label]\n",
        "            draw.rectangle(box, outline=color)\n",
        "            text = text.encode('latin-1', 'replace').decode('latin-1') # https://stackoverflow.com/questions/56761449/unicodeencodeerror-latin-1-codec-cant-encode-character-u2013-writing-to\n",
        "            draw.text((box[0] + 10, box[1] - 10), text=label, fill=color, font=font)\n",
        "\n",
        "        if i == 0: \n",
        "          imgs[\"paragraphs\"] = img\n",
        "\n",
        "          # save\n",
        "          img_paragraphs = \"img_paragraphs_\" + original_filename.replace(\".pdf\", \".png\")\n",
        "          img.save(img_paragraphs)\n",
        "        \n",
        "          df_paragraphs[\"paragraphs\"] = list(range(len(sorted_original_bboxes_block_list)))\n",
        "          df_paragraphs[\"categories\"] = [id2label[label_idx] for label_idx in sorted_category_block_list]\n",
        "          df_paragraphs[\"texts\"] = sorted_text_block_list\n",
        "          df_paragraphs[\"bounding boxes\"] = [str(bbox) for bbox in sorted_original_bboxes_block_list]\n",
        "\n",
        "          # save \n",
        "          csv_paragraphs = \"csv_paragraphs_\" + original_filename.replace(\".pdf\", \".csv\")\n",
        "          df_paragraphs.to_csv(csv_paragraphs, encoding=\"utf-8\", index=False)\n",
        "\n",
        "        else: \n",
        "          imgs[\"lines\"] = img\n",
        "\n",
        "          # save \n",
        "          img_lines = \"img_lines_\" + original_filename.replace(\".pdf\", \".png\")\n",
        "          img.save(img_lines)\n",
        "\n",
        "          df_lines[\"lines\"] = list(range(len(sorted_original_bboxes_line_list)))\n",
        "          df_lines[\"categories\"] = [id2label[label_idx] for label_idx in sorted_category_line_list]\n",
        "          df_lines[\"texts\"] = sorted_text_line_list\n",
        "          df_lines[\"bounding boxes\"] = [str(bbox) for bbox in sorted_original_bboxes_line_list]\n",
        "          \n",
        "          # save \n",
        "          csv_lines = \"csv_lines_\" + original_filename.replace(\".pdf\", \".csv\")\n",
        "          df_lines.to_csv(csv_lines, encoding=\"utf-8\", index=False)\n",
        "\n",
        "    msg = f'The page {page_no} of the PDF \"{original_filename}\" (domain: \"{domain}\") matches your settings.'\n",
        "\n",
        "    return msg, img_file, imgs[\"paragraphs\"], imgs[\"lines\"], img_paragraphs, img_lines, df_paragraphs, df_lines, gr.File.update(value=csv_paragraphs, visible=True), gr.File.update(value=csv_lines, visible=True)"
      ],
      "metadata": {
        "id": "RIOyWMksqBZq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# gradio APP\n",
        "with gr.Blocks(title=\"DocLayNet image viewer\", css=\".gradio-container\") as demo:\n",
        "    gr.HTML(\"\"\"\n",
        "    <div style=\"font-family:'Times New Roman', 'Serif'; font-size:26pt; font-weight:bold; text-align:center;\"><h1>DocLayNet image viewer</h1></div>\n",
        "    <div style=\"margin-top: 40px\"><p>(01/29/2023) This APP is an image viewer of the DocLayNet dataset and a data extraction tool.</p></div>\n",
        "    <div><p>It uses the datasets <a style=\"text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6\" href=\"https://huggingface.co/datasets/pierreguillou/DocLayNet-small\" target=\"_blank\">DocLayNet small</a> and <a style=\"text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6\" style=\"text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6\" href=\"https://huggingface.co/datasets/pierreguillou/DocLayNet-base\" target=\"_blank\">DocLayNet base</a> (you can also run this APP in Google Colab by running this <a style=\"text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6\" href=\"https://github.com/piegu/language-models/blob/master/DocLayNet_image_viewer_APP.ipynb\" target=\"_blank\">notebook</a>).</p></div>\n",
        "    <div><p>Make your settings and the output will show 2 images of a randomly selected PDF with labeled bounding boxes, one of paragraphs and the other of lines, and their corresponding tables of texts with their labels.</p></div>\n",
        "    <div><p>For example, if you select the domain \"laws_and_regulations\" and the category \"Caption\", you will get a random PDF that corresponds to these settings (ie, it will have at least one bounding box labeled with \"Caption\" in the PDF).</p></div>\n",
        "    <div><p><b>WARNING</b>: if the app crashes or runs without providing a result, refresh the page (<a style=\"text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6\" href=\"https://huggingface.co/spaces/pierreguillou/DocLayNet-image-viewer\">DocLayNet image viewer</a>) and run a search again. If the same problem occurs again, prefer the DocLayNet small. Thanks.</p></div>\n",
        "    <div style=\"margin-top: 20px\"><p>More information about the DocLayNet datasets and this APP in the following blog post: <a style=\"text-decoration: none; border-bottom: #64b5f6 0.125em solid; color: #64b5f6\" href=\"https://medium.com/@pierre_guillou/document-ai-processing-of-doclaynet-dataset-to-be-used-by-layout-models-of-the-hugging-face-hub-308d8bd81cdb\" target=\"_blank\">(01/27/2023) Document AI | Processing of DocLayNet dataset to be used by layout models of the Hugging Face hub (finetuning, inference)</a></div> \n",
        "    \"\"\")\n",
        "    with gr.Row():\n",
        "        with gr.Column():\n",
        "            dataset_name_gr = gr.Radio(dataset_names, value=\"small\", label=\"DocLayNet dataset\")\n",
        "        with gr.Column():\n",
        "            split_gr = gr.Dropdown(splits, value=\"all\", label=\"Split\")\n",
        "        with gr.Column():\n",
        "            domain_gr = gr.Dropdown(domains, value=\"all\", label=\"Domain\")\n",
        "        with gr.Column():\n",
        "            category_gr = gr.Dropdown(categories, value=\"all\", label=\"Category\")\n",
        "    btn = gr.Button(\"Display labeled PDF image & data\")\n",
        "    with gr.Row():\n",
        "      with gr.Column():\n",
        "        output_msg = gr.Textbox(label=\"Output message\")\n",
        "      with gr.Column():\n",
        "        img_file = gr.File(visible=True, label=\"Image file of the PDF\")\n",
        "    with gr.Row():\n",
        "        with gr.Column():\n",
        "          img_paragraphs_file = gr.File(visible=True, label=\"Image file (labeled paragraphs)\")\n",
        "          img_paragraphs = gr.Image(type=\"pil\", label=\"Bounding boxes of labeled paragraphs\", visible=True)\n",
        "        with gr.Column():\n",
        "          img_lines_file = gr.File(visible=True, label=\"Image file (labeled lines)\")\n",
        "          img_lines = gr.Image(type=\"pil\", label=\"Bounding boxes of labeled lines\", visible=True)\n",
        "    with gr.Row():\n",
        "      with gr.Column():\n",
        "        with gr.Row():\n",
        "          csv_paragraphs = gr.File(visible=False, label=\"CSV file (paragraphs)\")\n",
        "        with gr.Row():\n",
        "          df_paragraphs = gr.Dataframe(\n",
        "              headers=[\"paragraphs\", \"categories\", \"texts\", \"bounding boxes\"],\n",
        "              datatype=[\"number\", \"str\", \"str\", \"str\"],\n",
        "              col_count=(4, \"fixed\"), \n",
        "              visible=True,\n",
        "              label=\"Paragraphs data\",\n",
        "              type=\"pandas\",\n",
        "              wrap=True\n",
        "            )\n",
        "      with gr.Column():\n",
        "        with gr.Row():\n",
        "          csv_lines = gr.File(visible=False, label=\"CSV file (lines)\")\n",
        "        with gr.Row():\n",
        "          df_lines = gr.Dataframe(\n",
        "              headers=[\"lines\", \"categories\", \"texts\", \"bounding boxes\"],\n",
        "              datatype=[\"number\", \"str\", \"str\", \"str\"],\n",
        "              col_count=(4, \"fixed\"), \n",
        "              visible=True,\n",
        "              label=\"Lines data\",\n",
        "              type=\"pandas\",\n",
        "              wrap=True\n",
        "            )\n",
        "    btn.click(generate_annotated_image, inputs=[dataset_name_gr, split_gr, domain_gr, category_gr], outputs=[output_msg, img_file, img_paragraphs, img_lines, img_paragraphs_file, img_lines_file, df_paragraphs, df_lines, csv_paragraphs, csv_lines])\n",
        "\n",
        "    gr.Markdown(\"## Example\")\n",
        "    gr.Examples(\n",
        "        [[\"small\", \"all\", \"all\", \"all\"]],\n",
        "        [dataset_name_gr, split_gr, domain_gr, category_gr],\n",
        "        [output_msg, img_file, img_paragraphs, img_lines, img_paragraphs_file, img_lines_file, df_paragraphs, df_lines, csv_paragraphs, csv_lines],\n",
        "        fn=generate_annotated_image,\n",
        "        cache_examples=True,\n",
        "    )\n",
        "\n",
        "demo.launch(share=True)"
      ],
      "metadata": {
        "id": "AjR0cnLt342p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# END"
      ],
      "metadata": {
        "id": "BvfeA1x1_Di_"
      }
    }
  ]
}