app.py

import json
from collections import defaultdict
from typing import Tuple

import gradio as gr
from pie_modules.models import *  # noqa: F403
from pie_modules.taskmodules import *  # noqa: F403
from pytorch_ie.annotations import 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


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,
    **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:
        print("Injecting relation data")
        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
        )
    else:
        print("Not injecting relation data")
    return html


def inject_relation_data(html: str, sorted_entities, binary_relations) -> 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}"
        entity["data-original-color"] = (
            entity["style"].split("background:")[1].split(";")[0].strip()
        )
        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-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_as: 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_as == "Pretty Table":
        html = render_pretty_table(document, **render_kwargs)
    elif render_as == "spaCy":
        html = render_spacy(document, **render_kwargs)
    else:
        raise ValueError(f"Unknown render_as value: {render_as}")

    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(): "#0033cc",
                "data".upper(): "#993399",
            }
        },
    }

    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 as",
                        choices=["Pretty Table", "spaCy"],
                        value="spaCy",
                    )
                    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 highlightRelations(entityId) {
                const entities = document.querySelectorAll('.entity');
                entities.forEach(entity => {
                    entity.style.backgroundColor = entity.getAttribute('data-original-color');
                    entity.style.color = '';
                });

                if (entityId !== null) {
                    const selectedEntity = document.getElementById(entityId);
                    if (selectedEntity) {
                        selectedEntity.style.backgroundColor = '#ffa';
                        selectedEntity.style.color = '#000';
                    }
                    // highlight tails
                    const relationTailsAndLabels = JSON.parse(selectedEntity.getAttribute('data-relation-tails'));
                    relationTailsAndLabels.forEach(relationTail => {
                        const tailEntity = document.getElementById(relationTail['entity-id']);
                        if (tailEntity) {
                            tailEntity.style.backgroundColor = '#aff';
                            tailEntity.style.color = '#000';
                        }
                    });
                    // highlight heads
                    const relationHeadsAndLabels = JSON.parse(selectedEntity.getAttribute('data-relation-heads'));
                    relationHeadsAndLabels.forEach(relationHead => {
                        const headEntity = document.getElementById(relationHead['entity-id']);
                        if (headEntity) {
                            headEntity.style.backgroundColor = '#faf';
                            headEntity.style.color = '#000';
                        }
                    });
                }
            }

            const entities = document.querySelectorAll('.entity');
            entities.forEach(entity => {
                entity.addEventListener('mouseover', () => {
                    highlightRelations(entity.id);
                });
                entity.addEventListener('mouseout', () => {
                    highlightRelations(null);
                });
            });
        }
        """

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

    demo.launch()