app.py

import os
os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
credentials_kwargs={"aws_access_key_id": os.environ["ACCESS_KEY"],"aws_secret_access_key": os.environ["SECRET_KEY"]}

# work around: https://discuss.huggingface.co/t/how-to-install-a-specific-version-of-gradio-in-spaces/13552
os.system("pip uninstall -y gradio")
os.system("pip install gradio==3.4.1")
os.system(os.environ["DD_ADDONS"])

import time
from os import getcwd, path, environ
import deepdoctection as dd
from deepdoctection.dataflow.serialize import DataFromList

from dd_addons.extern import PdfTextDetector, PostProcessor, get_xsl_path
from dd_addons.pipe.conn import PostProcessorService

import gradio as gr
from botocore.config import Config


_DD_ONE = "conf_dd_one.yaml"
_XSL_PATH = get_xsl_path()

dd.ModelCatalog.register("xrf_layout/model_final_inf_only.pt",dd.ModelProfile(
            name="xrf_layout/model_final_inf_only.pt",
            description="layout_detection/morning-dragon-114",
            config="xrf_dd/layout/CASCADE_RCNN_R_50_FPN_GN.yaml",
            size=[274632215],
            tp_model=False,
            hf_repo_id=environ.get("HF_REPO_LAYOUT"),
            hf_model_name="model_final_inf_only.pt",
            hf_config_file=["Base-RCNN-FPN.yaml", "CASCADE_RCNN_R_50_FPN_GN.yaml"],
            categories={"1": dd.LayoutType.text,
                        "2": dd.LayoutType.title,
                        "3": dd.LayoutType.list,
                        "4": dd.LayoutType.table,
                        "5": dd.LayoutType.figure},
            model_wrapper="D2FrcnnDetector",
        ))

dd.ModelCatalog.register("xrf_cell/model_final_inf_only.pt", dd.ModelProfile(
            name="xrf_cell/model_final_inf_only.pt",
            description="cell_detection/restful-eon-6",
            config="xrf_dd/cell/CASCADE_RCNN_R_50_FPN_GN.yaml",
            size=[274583063],
            tp_model=False,
            hf_repo_id=environ.get("HF_REPO_CELL"),
            hf_model_name="model_final_inf_only.pt",
            hf_config_file=["Base-RCNN-FPN.yaml", "CASCADE_RCNN_R_50_FPN_GN.yaml"],
            categories={"1": dd.LayoutType.cell},
            model_wrapper="D2FrcnnDetector",
        ))

dd.ModelCatalog.register("xrf_item/model_final_inf_only.pt", dd.ModelProfile(
            name="xrf_item/model_final_inf_only.pt",
            description="item_detection/firm_plasma_14",
            config="xrf_dd/item/CASCADE_RCNN_R_50_FPN_GN.yaml",
            size=[274595351],
            tp_model=False,
            hf_repo_id=environ.get("HF_REPO_ITEM"),
            hf_model_name="model_final_inf_only.pt",
            hf_config_file=["Base-RCNN-FPN.yaml", "CASCADE_RCNN_R_50_FPN_GN.yaml"],
            categories={"1": dd.LayoutType.row, "2": dd.LayoutType.column},
            model_wrapper="D2FrcnnDetector",
        ))

# Set up of the configuration and logging. Models are globally defined, so that they are not re-loaded once the input
# updates
cfg = dd.set_config_by_yaml(path.join(getcwd(),_DD_ONE))
cfg.freeze(freezed=False)
cfg.DEVICE = "cpu"
cfg.freeze()

# layout detector
layout_config_path = dd.ModelCatalog.get_full_path_configs(cfg.CONFIG.D2LAYOUT)
layout_weights_path = dd.ModelDownloadManager.maybe_download_weights_and_configs(cfg.WEIGHTS.D2LAYOUT)
categories_layout = dd.ModelCatalog.get_profile(cfg.WEIGHTS.D2LAYOUT).categories
assert categories_layout is not None
assert layout_weights_path is not None
d_layout = dd.D2FrcnnDetector(layout_config_path, layout_weights_path, categories_layout, device=cfg.DEVICE)

# cell detector
cell_config_path = dd.ModelCatalog.get_full_path_configs(cfg.CONFIG.D2CELL)
cell_weights_path = dd.ModelDownloadManager.maybe_download_weights_and_configs(cfg.WEIGHTS.D2CELL)
categories_cell = dd.ModelCatalog.get_profile(cfg.WEIGHTS.D2CELL).categories
assert categories_cell is not None
d_cell = dd.D2FrcnnDetector(cell_config_path, cell_weights_path, categories_cell, device=cfg.DEVICE)

# row/column detector
item_config_path = dd.ModelCatalog.get_full_path_configs(cfg.CONFIG.D2ITEM)
item_weights_path = dd.ModelDownloadManager.maybe_download_weights_and_configs(cfg.WEIGHTS.D2ITEM)
categories_item = dd.ModelCatalog.get_profile(cfg.WEIGHTS.D2ITEM).categories
assert categories_item is not None
d_item = dd.D2FrcnnDetector(item_config_path, item_weights_path, categories_item, device=cfg.DEVICE)

# pdf miner
pdf_text = PdfTextDetector(_XSL_PATH)

# text detector
credentials_kwargs={"aws_access_key_id": os.environ["ACCESS_KEY"],
                    "aws_secret_access_key": os.environ["SECRET_KEY"],
                    "config": Config(region_name=os.environ["REGION"])}
tex_text = dd.TextractOcrDetector(**credentials_kwargs)


def build_gradio_analyzer():
    """Building the Detectron2/DocTr analyzer based on the given config"""

    cfg.freeze(freezed=False)
    cfg.TAB = True
    cfg.TAB_REF = True
    cfg.OCR = True
    cfg.freeze()

    pipe_component_list = []
    layout = dd.ImageLayoutService(d_layout, to_image=True, crop_image=True)
    pipe_component_list.append(layout)

    nms_service = dd.AnnotationNmsService(nms_pairs=cfg.LAYOUT_NMS_PAIRS.COMBINATIONS,
                                          thresholds=cfg.LAYOUT_NMS_PAIRS.THRESHOLDS)
    pipe_component_list.append(nms_service)

    if cfg.TAB:

        detect_result_generator = dd.DetectResultGenerator(categories_cell)
        cell = dd.SubImageLayoutService(d_cell, dd.LayoutType.table, {1: 6}, detect_result_generator)
        pipe_component_list.append(cell)

        detect_result_generator = dd.DetectResultGenerator(categories_item)
        item = dd.SubImageLayoutService(d_item, dd.LayoutType.table, {1: 7, 2: 8}, detect_result_generator)
        pipe_component_list.append(item)

        table_segmentation = dd.TableSegmentationService(
            cfg.SEGMENTATION.ASSIGNMENT_RULE,
            cfg.SEGMENTATION.THRESHOLD_ROWS,
            cfg.SEGMENTATION.THRESHOLD_COLS,
            cfg.SEGMENTATION.FULL_TABLE_TILING,
            cfg.SEGMENTATION.REMOVE_IOU_THRESHOLD_ROWS,
            cfg.SEGMENTATION.REMOVE_IOU_THRESHOLD_COLS,
            cfg.SEGMENTATION.STRETCH_RULE
        )
        pipe_component_list.append(table_segmentation)

        if cfg.TAB_REF:
            table_segmentation_refinement = dd.TableSegmentationRefinementService()
            pipe_component_list.append(table_segmentation_refinement)

    if cfg.OCR:

        d_text = dd.TextExtractionService(pdf_text)
        pipe_component_list.append(d_text)

        t_text = dd.TextExtractionService(tex_text,skip_if_text_extracted=True)
        pipe_component_list.append(t_text)

        match_words = dd.MatchingService(
            parent_categories=cfg.WORD_MATCHING.PARENTAL_CATEGORIES,
            child_categories=cfg.WORD_MATCHING.CHILD_CATEGORIES,
            matching_rule=cfg.WORD_MATCHING.RULE,
            threshold=cfg.WORD_MATCHING.THRESHOLD,
            max_parent_only=cfg.WORD_MATCHING.MAX_PARENT_ONLY
        )
        pipe_component_list.append(match_words)

        order = dd.TextOrderService(
            text_container=cfg.TEXT_ORDERING.TEXT_CONTAINER,
            floating_text_block_categories=cfg.TEXT_ORDERING.FLOATING_TEXT_BLOCK,
            text_block_categories=cfg.TEXT_ORDERING.TEXT_BLOCK,
            include_residual_text_container=cfg.TEXT_ORDERING.TEXT_CONTAINER_TO_TEXT_BLOCK)
        pipe_component_list.append(order)

    pipe = dd.DoctectionPipe(pipeline_component_list=pipe_component_list)

    post_processor = PostProcessor("deepdoctection", **credentials_kwargs)
    post_service = PostProcessorService(post_processor)
    pipe_component_list.append(post_service)

    return pipe


def analyze_image(img, pdf, max_datapoints):

    # creating an image object and passing to the analyzer by using dataflows
    analyzer = build_gradio_analyzer()

    if img is not None:
        image = dd.Image(file_name=str(time.time()).replace(".","") + ".png", location="")
        image.image = img[:, :, ::-1]

        df = DataFromList(lst=[image])
        df = analyzer.analyze(dataset_dataflow=df)
    elif pdf:
        df = analyzer.analyze(path=pdf.name, max_datapoints=max_datapoints)
    else:
        raise ValueError

    df.reset_state()

    layout_items_str = ""
    jsonl_out = []
    dpts = []
    html_list = []

    for dp in df:
        dpts.append(dp)
        out = dp.as_dict()
        jsonl_out.append(out)
        out.pop("_image")
        layout_items = dp.layouts
        layout_items.sort(key=lambda x: x.reading_order)
        layout_items_str += f"\n\n -------- PAGE NUMBER: {dp.page_number+1} ------------- \n"
        for item in layout_items:
            layout_items_str += f"\n {item.category_name}: {item.text}"
        html_list.extend([table.html for table in dp.tables])
    if html_list:
        html = ("<br /><br /><br />").join(html_list)
    else:
        html = None

    return [dp.viz(show_cells=False) for dp in dpts], layout_items_str, html, jsonl_out


demo = gr.Blocks(css="scrollbar.css")


with demo:
    with gr.Box():
        gr.Markdown("<h1><center>deepdoctection - A Document AI Package</center></h1>")
        gr.Markdown("<strong>deep</strong>doctection is a Python library that orchestrates document extraction"
                    " and document layout analysis tasks using deep learning models. It does not implement models"
                    " but enables you to build pipelines using highly acknowledged libraries for object detection,"
                    " OCR and selected NLP tasks and provides an integrated frameworks for fine-tuning, evaluating"
                    " and running models.<br />"
                    "This pipeline consists of a stack of models powered by <strong>Detectron2"
                    "</strong> for layout analysis and table recognition. OCR will be provided as well. You can process"
                    "an image or even a PDF-document. Up to nine pages can be processed. <br />")
        gr.Markdown("[https://github.com/deepdoctection/deepdoctection](https://github.com/deepdoctection/deepdoctection)")
    with gr.Box():
        gr.Markdown("<h2><center>Upload a document and choose setting</center></h2>")
        with gr.Row():
            with gr.Column():
                with gr.Tab("Image upload"):
                    with gr.Column():
                        inputs = gr.Image(type='numpy', label="Original Image")
                with gr.Tab("PDF upload (only first image will be processed) *"):
                    with gr.Column():
                        inputs_pdf = gr.File(label="PDF")
                    gr.Markdown("<sup>* If an image is cached in tab, remove it first</sup>")
            with gr.Column():
                gr.Examples(
                    examples=[path.join(getcwd(), "sample_1.jpg"), path.join(getcwd(), "sample_2.png")],
                    inputs = inputs)
                gr.Examples(examples=[path.join(getcwd(), "sample_3.pdf")], inputs = inputs_pdf)

        with gr.Row():
            max_imgs = gr.Slider(1, 8, value=2, step=1, label="Number of pages in multi page PDF",
                                 info="Will stop after 9 pages")

        with gr.Row():
            btn = gr.Button("Run model", variant="primary")

    with gr.Box():
        gr.Markdown("<h2><center>Outputs</center></h2>")
        with gr.Row():
            with gr.Column():
                with gr.Box():
                    gr.Markdown("<center><strong>Contiguous text</strong></center>")
                    image_text = gr.Textbox()
            with gr.Column():
                with gr.Box():
                    gr.Markdown("<center><strong>Layout detection</strong></center>")
                    gallery = gr.Gallery(
                        label="Output images", show_label=False, elem_id="gallery"
                    ).style(grid=2)
        with gr.Row():
            with gr.Box():
                gr.Markdown("<center><strong>Table</strong></center>")
                html = gr.HTML()

        with gr.Row():
            with gr.Box():
                gr.Markdown("<center><strong>JSON</strong></center>")
                json = gr.JSON()

    btn.click(fn=analyze_image, inputs=[inputs, inputs_pdf,  max_imgs],
              outputs=[gallery, image_text, html, json])

demo.launch()