app.py

import json
from collections import defaultdict
from typing import Dict, List, Optional, Tuple, Union

import gradio as gr
from pie_modules.models import *  # noqa: F403
from pie_modules.taskmodules import *  # noqa: F403
from pytorch_ie.annotations import BinaryRelation, LabeledSpan
from pytorch_ie.auto import AutoPipeline
from pytorch_ie.documents import TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions
from pytorch_ie.models import *  # noqa: F403
from pytorch_ie.taskmodules import *  # noqa: F403

RENDER_WITH_DISPLACY = "displaCy + highlighted arguments"
RENDER_WITH_PRETTY_TABLE = "Pretty Table"


def render_pretty_table(
    document: TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions, **render_kwargs
):
    from prettytable import PrettyTable

    t = PrettyTable()
    t.field_names = ["head", "tail", "relation"]
    t.align = "l"
    for relation in list(document.binary_relations) + list(document.binary_relations.predictions):
        t.add_row([str(relation.head), str(relation.tail), relation.label])

    html = t.get_html_string(format=True)
    html = "<div style='max-width:100%; max-height:360px; overflow:auto'>" + html + "</div>"

    return html


def render_spacy(
    document: TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
    style="ent",
    inject_relations=True,
    colors_hover=None,
    **render_kwargs,
):
    from spacy import displacy

    spans = list(document.labeled_spans) + list(document.labeled_spans.predictions)
    spacy_doc = {
        "text": document.text,
        "ents": [
            {"start": entity.start, "end": entity.end, "label": entity.label} for entity in spans
        ],
        "title": None,
    }

    html = displacy.render(
        spacy_doc, page=True, manual=True, minify=True, style=style, **render_kwargs
    )
    html = "<div style='max-width:100%; max-height:360px; overflow:auto'>" + html + "</div>"
    if inject_relations:
        binary_relations = list(document.binary_relations) + list(
            document.binary_relations.predictions
        )
        sorted_entities = sorted(spans, key=lambda x: (x.start, x.end))
        html = inject_relation_data(
            html,
            sorted_entities=sorted_entities,
            binary_relations=binary_relations,
            additional_colors=colors_hover,
        )
    return html


def inject_relation_data(
    html: str,
    sorted_entities,
    binary_relations: List[BinaryRelation],
    additional_colors: Optional[Dict[str, Union[str, dict]]] = None,
) -> str:
    from bs4 import BeautifulSoup

    # Parse the HTML using BeautifulSoup
    soup = BeautifulSoup(html, "html.parser")

    entity2tails = defaultdict(list)
    entity2heads = defaultdict(list)
    for relation in binary_relations:
        entity2heads[relation.tail].append((relation.head, relation.label))
        entity2tails[relation.head].append((relation.tail, relation.label))

    entity2id = {entity: f"entity-{idx}" for idx, entity in enumerate(sorted_entities)}

    # Add unique IDs to each entity
    entities = soup.find_all(class_="entity")
    for idx, entity in enumerate(entities):
        entity["id"] = f"entity-{idx}"
        original_color = entity["style"].split("background:")[1].split(";")[0].strip()
        entity["data-color-original"] = original_color
        if additional_colors is not None:
            for key, color in additional_colors.items():
                entity[f"data-color-{key}"] = (
                    json.dumps(color) if isinstance(color, dict) else color
                )
        entity_annotation = sorted_entities[idx]
        # sanity check
        if str(entity_annotation) != entity.next:
            raise ValueError(f"Entity text mismatch: {entity_annotation} != {entity.text}")
        entity["data-label"] = entity_annotation.label
        entity["data-relation-tails"] = json.dumps(
            [
                {"entity-id": entity2id[tail], "label": label}
                for tail, label in entity2tails.get(entity_annotation, [])
            ]
        )
        entity["data-relation-heads"] = json.dumps(
            [
                {"entity-id": entity2id[head], "label": label}
                for head, label in entity2heads.get(entity_annotation, [])
            ]
        )

    # Return the modified HTML as a string
    return str(soup)


def predict(text: str) -> Tuple[dict, str]:
    document = TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions(text=text)

    # add single partition from the whole text (the model only considers text in partitions)
    document.labeled_partitions.append(LabeledSpan(start=0, end=len(text), label="text"))

    # execute prediction pipeline
    pipeline(document)

    document_dict = document.asdict()
    return document_dict, json.dumps(document_dict)


def render(document_txt: str, render_with: str, render_kwargs_json: str) -> str:
    document_dict = json.loads(document_txt)
    document = TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions.fromdict(
        document_dict
    )
    render_kwargs = json.loads(render_kwargs_json)
    if render_with == RENDER_WITH_PRETTY_TABLE:
        html = render_pretty_table(document, **render_kwargs)
    elif render_with == RENDER_WITH_DISPLACY:
        html = render_spacy(document, **render_kwargs)
    else:
        raise ValueError(f"Unknown render_with value: {render_with}")

    return html


def open_accordion():
    return gr.Accordion(open=True)


def close_accordion():
    return gr.Accordion(open=False)


if __name__ == "__main__":

    model_name_or_path = "ArneBinder/sam-pointer-bart-base-v0.3"
    # local path
    # model_name_or_path = "models/dataset-sciarg/task-ner_re/v0.3/2024-03-01_18-25-32"

    example_text = "Scholarly Argumentation Mining (SAM) has recently gained attention due to its potential to help scholars with the rapid growth of published scientific literature. It comprises two subtasks: argumentative discourse unit recognition (ADUR) and argumentative relation extraction (ARE), both of which are challenging since they require e.g. the integration of domain knowledge, the detection of implicit statements, and the disambiguation of argument structure. While previous work focused on dataset construction and baseline methods for specific document sections, such as abstract or results, full-text scholarly argumentation mining has seen little progress. In this work, we introduce a sequential pipeline model combining ADUR and ARE for full-text SAM, and provide a first analysis of the performance of pretrained language models (PLMs) on both subtasks. We establish a new SotA for ADUR on the Sci-Arg corpus, outperforming the previous best reported result by a large margin (+7% F1). We also present the first results for ARE, and thus for the full AM pipeline, on this benchmark dataset. Our detailed error analysis reveals that non-contiguous ADUs as well as the interpretation of discourse connectors pose major challenges and that data annotation needs to be more consistent."

    pipeline = AutoPipeline.from_pretrained(model_name_or_path, device=-1, num_workers=0)
    re_pipeline = AutoPipeline.from_pretrained(
        model_name_or_path,
        device=-1,
        num_workers=0,
        # taskmodule_kwargs=dict(create_relation_candidates=True),
    )

    default_render_kwargs = {
        "style": "ent",
        "options": {
            # we need to convert the keys to uppercase because the spacy rendering function expects them in uppercase
            "colors": {
                "own_claim".upper(): "#009933",
                "background_claim".upper(): "#99ccff",
                "data".upper(): "#993399",
            }
        },
        "colors_hover": {
            "selected": "#ffa",
            # "tail": "#aff",
            "tail": {
                # green
                "supports": "#9f9",
                # red
                "contradicts": "#f99",
                # do not highlight
                "parts_of_same": None,
            },
            "head": None,  # "#faf",
            "other": None,
        },
    }

    with gr.Blocks() as demo:
        with gr.Row():
            with gr.Column(scale=1):
                text = gr.Textbox(
                    label="Input Text",
                    lines=20,
                    value=example_text,
                )

                predict_btn = gr.Button("Predict")

                output_txt = gr.Textbox(visible=False)

            with gr.Column(scale=1):

                with gr.Accordion("See plain result ...", open=False) as output_accordion:
                    output_json = gr.JSON(label="Model Output")

                with gr.Accordion("Render Options", open=False):
                    render_as = gr.Dropdown(
                        label="Render with",
                        choices=[RENDER_WITH_PRETTY_TABLE, RENDER_WITH_DISPLACY],
                        value=RENDER_WITH_DISPLACY,
                    )
                    render_kwargs = gr.Textbox(
                        label="Render Arguments",
                        lines=5,
                        value=json.dumps(default_render_kwargs, indent=2),
                    )
                render_btn = gr.Button("Re-render")

                rendered_output = gr.HTML(label="Rendered Output")

        render_button_kwargs = dict(
            fn=render, inputs=[output_txt, render_as, render_kwargs], outputs=rendered_output
        )
        predict_btn.click(open_accordion, inputs=[], outputs=[output_accordion]).then(
            fn=predict, inputs=text, outputs=[output_json, output_txt], api_name="predict"
        ).success(**render_button_kwargs).success(
            close_accordion, inputs=[], outputs=[output_accordion]
        )
        render_btn.click(**render_button_kwargs, api_name="render")

        js = """
        () => {
            function maybeSetColor(entity, colorAttributeKey, colorDictKey) {
                var color = entity.getAttribute('data-color-' + colorAttributeKey);
                // if color is a json string, parse it and use the value at colorDictKey
                try {
                    const colors = JSON.parse(color);
                    color = colors[colorDictKey];
                } catch (e) {}
                if (color) {
                    console.log('setting color', color);
                    console.log('entity', entity);
                    entity.style.backgroundColor = color;
                    entity.style.color = '#000';
                }
            }

            function highlightRelationArguments(entityId) {
                const entities = document.querySelectorAll('.entity');
                // reset all entities
                entities.forEach(entity => {
                    const color = entity.getAttribute('data-color-original');
                    entity.style.backgroundColor = color;
                    entity.style.color = '';
                });

                if (entityId !== null) {
                    var visitedEntities = new Set();
                    // highlight selected entity
                    const selectedEntity = document.getElementById(entityId);
                    if (selectedEntity) {
                        const label = selectedEntity.getAttribute('data-label');
                        maybeSetColor(selectedEntity, 'selected', label);
                        visitedEntities.add(selectedEntity);
                    }
                    // highlight tails
                    const relationTailsAndLabels = JSON.parse(selectedEntity.getAttribute('data-relation-tails'));
                    relationTailsAndLabels.forEach(relationTail => {
                        const tailEntity = document.getElementById(relationTail['entity-id']);
                        if (tailEntity) {
                            const label = relationTail['label'];
                            maybeSetColor(tailEntity, 'tail', label);
                            visitedEntities.add(tailEntity);
                        }
                    });
                    // highlight heads
                    const relationHeadsAndLabels = JSON.parse(selectedEntity.getAttribute('data-relation-heads'));
                    relationHeadsAndLabels.forEach(relationHead => {
                        const headEntity = document.getElementById(relationHead['entity-id']);
                        if (headEntity) {
                            const label = relationHead['label'];
                            maybeSetColor(headEntity, 'head', label);
                            visitedEntities.add(headEntity);
                        }
                    });
                    // highlight other entities
                    entities.forEach(entity => {
                        if (!visitedEntities.has(entity)) {
                            const label = entity.getAttribute('data-label');
                            maybeSetColor(entity, 'other', label);
                        }
                    });
                }
            }

            const entities = document.querySelectorAll('.entity');
            entities.forEach(entity => {
                const alreadyHasListener = entity.getAttribute('data-has-listener');
                if (alreadyHasListener) {
                    return;
                }
                entity.addEventListener('mouseover', () => {
                    highlightRelationArguments(entity.id);
                });
                entity.addEventListener('mouseout', () => {
                    highlightRelationArguments(null);
                });
                entity.setAttribute('data-has-listener', 'true');
            });
        }
        """

        rendered_output.change(fn=None, js=js, inputs=[], outputs=[])

    demo.launch()