...\"\n",
" if input_ids_dict[str(bboxes_list[-1])][0] == (tokenizer.convert_tokens_to_ids('')):\n",
" del input_ids_dict[str(bboxes_list[-1])]\n",
" bboxes_list = bboxes_list[:-1]\n",
"\n",
" # get texts by line\n",
" input_ids_list = input_ids_dict.values()\n",
" texts_list = [tokenizer.decode(input_ids) for input_ids in input_ids_list]\n",
" \n",
" # display DataFrame\n",
" df = pd.DataFrame({\"texts\": texts_list, \"input_ids\": input_ids_list, \"bboxes\": bboxes_list})\n",
"\n",
" return image, df, num_tokens, page_no, num_pages"
],
"metadata": {
"id": "5AyQ5NfQomMV"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"# display chunk of PDF image and its data\n",
"def display_chunk_paragraphs_inference(index_chunk=None):\n",
"\n",
" # get image and image data\n",
" image, df, num_tokens, page_no, num_pages = get_encoded_chunk_inference(index_chunk=index_chunk)\n",
"\n",
" # get data from dataframe\n",
" input_ids = df[\"input_ids\"]\n",
" texts = df[\"texts\"]\n",
" bboxes = df[\"bboxes\"]\n",
"\n",
" print(f'Chunk ({num_tokens} tokens) of the PDF (page: {page_no+1} / {num_pages})\\n')\n",
"\n",
" # display image with bounding boxes\n",
" print(\">> PDF image with bounding boxes of paragraphs\\n\")\n",
" draw = ImageDraw.Draw(image)\n",
" \n",
" labels = list()\n",
" for box, text in zip(bboxes, texts):\n",
" color = \"red\"\n",
" draw.rectangle(box, outline=color)\n",
"\n",
" # resize image to original\n",
" width, height = image.size\n",
" image = image.resize((int(0.5*width), int(0.5*height)))\n",
"\n",
" # convert to cv and display\n",
" img = np.array(image, dtype='uint8') # PIL to cv2\n",
" cv2_imshow(img)\n",
" cv2.waitKey(0)\n",
"\n",
" # display image dataframe\n",
" print(\"\\n>> Dataframe of annotated paragraphs\\n\")\n",
" cols = [\"texts\",\t\"bboxes\"]\n",
" df = df[cols]\n",
" display(df)"
],
"metadata": {
"id": "yYeb88pWoUFS"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"#### APP function"
],
"metadata": {
"id": "QO-nRIOxCeko"
}
},
{
"cell_type": "code",
"source": [
"# APP outputs \n",
"def app_outputs(uploaded_pdf):\n",
" filename, msg, images = pdf_to_images(uploaded_pdf)\n",
" num_images = len(images)\n",
"\n",
" if not msg.startswith(\"Error with the PDF\"):\n",
"\n",
" # Extraction of image data (text and bounding boxes)\n",
" dataset, texts_lines, texts_pars, texts_lines_par, row_indexes, par_boxes, line_boxes, lines_par_boxes = extraction_data_from_image(images)\n",
" # prepare our data in the format of the model\n",
" encoded_dataset = dataset.map(prepare_inference_features_paragraph, batched=True, batch_size=64, remove_columns=dataset.column_names)\n",
" custom_encoded_dataset = CustomDataset(encoded_dataset, tokenizer)\n",
" # Get predictions (token level)\n",
" outputs, images_ids_list, chunk_ids, input_ids, bboxes = predictions_token_level(images, custom_encoded_dataset)\n",
" # Get predictions (paragraph level)\n",
" probs_bbox, bboxes_list_dict, input_ids_dict_dict, probs_dict_dict, df = predictions_paragraph_level(dataset, outputs, images_ids_list, chunk_ids, input_ids, bboxes)\n",
" # Get labeled images with lines bounding boxes\n",
" images = get_labeled_images(dataset, images_ids_list, bboxes_list_dict, probs_dict_dict)\n",
"\n",
" img_files = list()\n",
" # get image of PDF without bounding boxes\n",
" for i in range(num_images):\n",
" if filename != \"files/blank.png\": img_file = f\"img_{i}_\" + filename.replace(\".pdf\", \".png\")\n",
" else: img_file = filename.replace(\".pdf\", \".png\")\n",
" img_file = img_file.replace(\"/\", \"_\")\n",
" images[i].save(img_file)\n",
" img_files.append(img_file)\n",
"\n",
" if num_images < max_imgboxes:\n",
" img_files += [image_blank]*(max_imgboxes - num_images)\n",
" images += [Image.open(image_blank)]*(max_imgboxes - num_images)\n",
" for count in range(max_imgboxes - num_images):\n",
" df[num_images + count] = pd.DataFrame()\n",
" else:\n",
" img_files = img_files[:max_imgboxes]\n",
" images = images[:max_imgboxes]\n",
" df = dict(itertools.islice(df.items(), max_imgboxes))\n",
"\n",
" # save \n",
" csv_files = list()\n",
" for i in range(max_imgboxes):\n",
" csv_file = f\"csv_{i}_\" + filename.replace(\".pdf\", \".csv\")\n",
" csv_file = csv_file.replace(\"/\", \"_\")\n",
" csv_files.append(gr.File.update(value=csv_file, visible=True))\n",
" df[i].to_csv(csv_file, encoding=\"utf-8\", index=False)\n",
"\n",
" else: \n",
" img_files, images, csv_files = [\"\"]*max_imgboxes, [\"\"]*max_imgboxes, [\"\"]*max_imgboxes\n",
" img_files[0], img_files[1] = image_blank, image_blank\n",
" images[0], images[1] = Image.open(image_blank), Image.open(image_blank)\n",
" csv_file = \"csv_wo_content.csv\"\n",
" csv_files[0], csv_files[1] = gr.File.update(value=csv_file, visible=True), gr.File.update(value=csv_file, visible=True)\n",
" df, df_empty = dict(), pd.DataFrame()\n",
" df[0], df[1] = df_empty.to_csv(csv_file, encoding=\"utf-8\", index=False), df_empty.to_csv(csv_file, encoding=\"utf-8\", index=False)\n",
" \n",
" return msg, img_files[0], img_files[1], images[0], images[1], csv_files[0], csv_files[1], df[0], df[1]"
],
"metadata": {
"id": "MbBPAYF8Cg8d"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "CQdS0CydNesH"
},
"source": [
"## Model & tokenizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"referenced_widgets": [
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},
"id": "RtvKqWjUNhM_",
"outputId": "e345f95e-9a5a-426b-f92f-eefa158e6fb4"
},
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/3.32k [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "027eee36bc97498bb709774dc0d3aa58"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading pytorch_model.bin: 0%| | 0.00/1.48G [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "2395de11a9d6435b933e96fd4caf25da"
}
},
"metadata": {}
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.9/dist-packages/transformers/models/layoutlmv2/feature_extraction_layoutlmv2.py:30: FutureWarning: The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please use LayoutLMv2ImageProcessor instead.\n",
" warnings.warn(\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)lve/main/config.json: 0%| | 0.00/615 [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "b506e265aae2423db0389026fb2bdc6e"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)tencepiece.bpe.model: 0%| | 0.00/5.07M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "31ab2545e4844b08b4c2cd4979f44505"
}
},
"metadata": {}
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Downloading (…)/main/tokenizer.json: 0%| | 0.00/9.10M [00:00, ?B/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "176789e82ec44a5daca0fb9a003dc6d2"
}
},
"metadata": {}
}
],
"source": [
"from transformers import LayoutLMv2ForTokenClassification # LayoutXLMTokenizerFast, \n",
"\n",
"import torch\n",
"device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
"\n",
"# model\n",
"# tokenizer = LayoutXLMTokenizerFast.from_pretrained(model_id)\n",
"model = LayoutLMv2ForTokenClassification.from_pretrained(model_id);\n",
"model.to(device);\n",
"\n",
"# feature extractor\n",
"from transformers import LayoutLMv2FeatureExtractor\n",
"feature_extractor = LayoutLMv2FeatureExtractor(apply_ocr=False)\n",
"\n",
"# tokenizer\n",
"from transformers import AutoTokenizer\n",
"tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "KpeG4pMbO4yF"
},
"outputs": [],
"source": [
"# get labels\n",
"id2label = model.config.id2label\n",
"label2id = model.config.label2id\n",
"num_labels = len(id2label)"
]
},
{
"cell_type": "markdown",
"source": [
"## Gradio APP"
],
"metadata": {
"id": "7PHpV5RSXgyc"
}
},
{
"cell_type": "code",
"source": [
"with gr.Blocks(title=\"Inference APP for Document Understanding at paragraph level (v2 - LayoutXLM base)\", css=\".gradio-container\") as demo:\n",
" gr.HTML(\"\"\"\n",
" Inference APP for Document Understanding at paragraph level (v2 - LayoutXLM base)
\n",
" \n",
" LayoutXLM: Multimodal Pre-training for Multilingual Visually-rich Document Understanding is a Document Understanding model that uses both layout and text in order to detect labels of bounding boxes. Combined with the model XML-RoBERTa base, this finetuned model has the capacity to understand any language. Finetuned on the dataset DocLayNet base, it can classifly any bounding box (and its OCR text) to 11 labels (Caption, Footnote, Formula, List-item, Page-footer, Page-header, Picture, Section-header, Table, Text, Title).
\n",
" It relies on an external OCR engine to get words and bounding boxes from the document image. Thus, let's run in this APP an OCR engine (PyTesseract) to get the bounding boxes, then run Layout XLM base (already fine-tuned on the dataset DocLayNet base at paragraph level) on the individual tokens and then, visualize the result at paragraph level!
\n",
" It allows to get all pages of any PDF (of any language) with bounding boxes labeled at paragraph level and the associated dataframes with labeled data (bounding boxes, texts, labels) :-)
\n",
" \n",
" More information about the DocLayNet datasets, the finetuning of the model and this APP in the following blog posts:
\n",
"
\n",
" \"\"\")\n",
" with gr.Row():\n",
" pdf_file = gr.File(label=\"PDF\")\n",
" with gr.Row():\n",
" submit_btn = gr.Button(f\"Display first {max_imgboxes} labeled PDF pages\")\n",
" reset_btn = gr.Button(value=\"Clear\")\n",
" with gr.Row():\n",
" output_msg = gr.Textbox(label=\"Output message\")\n",
" with gr.Row():\n",
" fileboxes = []\n",
" for num_page in range(max_imgboxes):\n",
" file_path = gr.File(visible=True, label=f\"Image file of the PDF page n°{num_page}\")\n",
" fileboxes.append(file_path)\n",
" with gr.Row():\n",
" imgboxes = []\n",
" for num_page in range(max_imgboxes):\n",
" img = gr.Image(type=\"pil\", label=f\"Image of the PDF page n°{num_page}\")\n",
" imgboxes.append(img)\n",
" with gr.Row():\n",
" csvboxes = []\n",
" for num_page in range(max_imgboxes):\n",
" csv = gr.File(visible=True, label=f\"CSV file at paragraph level (page {num_page})\")\n",
" csvboxes.append(csv)\n",
" with gr.Row():\n",
" dfboxes = []\n",
" for num_page in range(max_imgboxes):\n",
" df = gr.Dataframe(\n",
" headers=[\"bounding boxes\", \"texts\", \"labels\"],\n",
" datatype=[\"str\", \"str\", \"str\"],\n",
" col_count=(3, \"fixed\"), \n",
" visible=True,\n",
" label=f\"Data of page {num_page}\",\n",
" type=\"pandas\",\n",
" wrap=True\n",
" )\n",
" dfboxes.append(df)\n",
"\n",
" outputboxes = [output_msg] + fileboxes + imgboxes + csvboxes + dfboxes\n",
" submit_btn.click(app_outputs, inputs=[pdf_file], outputs=outputboxes)\n",
" # https://github.com/gradio-app/gradio/pull/2044/files#diff-a91dd2749f68bb7d0099a0f4079a4fd2d10281e299e7b451cb1bb876a7c21975R91\n",
" reset_btn.click(\n",
" lambda: [pdf_file.update(value=None), output_msg.update(value=None)] + [filebox.update(value=None) for filebox in fileboxes] + [imgbox.update(value=None) for imgbox in imgboxes] + [csvbox.update(value=None) for csvbox in csvboxes] + [dfbox.update(value=None) for dfbox in dfboxes],\n",
" inputs=[],\n",
" outputs=[pdf_file, output_msg] + fileboxes + imgboxes + csvboxes + dfboxes\n",
" )\n",
" \n",
" gr.Examples(\n",
" [[\"files/example.pdf\"]],\n",
" [pdf_file],\n",
" outputboxes,\n",
" fn=app_outputs,\n",
" cache_examples=True,\n",
" )\n",
" \n",
"demo.launch()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 636,
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"id": "RvYyMF7jXgJP",
"outputId": "4c475214-bc7c-4d3d-e19c-78fbf4a4b375"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Caching examples at: '/content/gradio_cached_examples/92'\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Map: 0%| | 0/2 [00:00, ? examples/s]"
],
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{
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"data": {
"text/plain": [
"Filter: 0%| | 0/2 [00:00, ? examples/s]"
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"Filter: 0%| | 0/2 [00:00, ? examples/s]"
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