app.py

import json
import logging
from functools import partial
from typing import Any, Optional, Tuple

import gradio as gr
from pie_modules.document.processing import tokenize_document
from pie_modules.documents import TokenDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions
from pie_modules.models import *  # noqa: F403
from pie_modules.taskmodules import *  # noqa: F403
from pytorch_ie import Pipeline
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
from rendering_utils import render_displacy, render_pretty_table
from transformers import AutoModel, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer

logger = logging.getLogger(__name__)

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

DEFAULT_MODEL_NAME = "ArneBinder/sam-pointer-bart-base-v0.3"
DEFAULT_MODEL_REVISION = "76300f8e534e2fcf695f00cb49bba166739b8d8a"
# local path
# DEFAULT_MODEL_NAME = "models/dataset-sciarg/task-ner_re/v0.3/2024-05-28_23-33-46"
# DEFAULT_MODEL_REVISION = None
DEFAULT_EMBEDDING_MODEL_NAME = "allenai/scibert_scivocab_uncased"


def embed_text_annotations(
    document: TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
    model: PreTrainedModel,
    tokenizer: PreTrainedTokenizer,
    text_layer_name: str,
) -> dict:
    # to not modify the original document
    document = document.copy()
    # tokenize_document does not yet consider predictions, so we need to add them manually
    document[text_layer_name].extend(document[text_layer_name].predictions.clear())
    added_annotations = []
    # TODO: set return_overflowing_tokens=True and max_length=...?
    tokenizer_kwargs = {}
    tokenized_documents = tokenize_document(
        document,
        tokenizer=tokenizer,
        result_document_type=TokenDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions,
        partition_layer="labeled_partitions",
        added_annotations=added_annotations,
        **tokenizer_kwargs,
    )
    # just tokenize again to get tensors in the correct format for the model
    model_inputs = tokenizer(document.text, return_tensors="pt", **tokenizer_kwargs)
    assert len(model_inputs.encodings) == len(tokenized_documents)
    model_output = model(**model_inputs)

    # get embeddings for all text annotations
    embeddings = {}
    for batch_idx in range(len(model_output.last_hidden_state)):
        text2tok_ann = added_annotations[batch_idx][text_layer_name]
        tok2text_ann = {v: k for k, v in text2tok_ann.items()}
        for tok_ann in tokenized_documents[batch_idx].labeled_spans:
            # skip "empty" annotations
            if tok_ann.start == tok_ann.end:
                continue
            # use the max pooling strategy to get a single embedding for the annotation text
            embedding = model_output.last_hidden_state[batch_idx, tok_ann.start : tok_ann.end].max(
                dim=0
            )[0]
            text_ann = tok2text_ann[tok_ann]

            if text_ann in embeddings:
                logger.warning(
                    f"Overwriting embedding for annotation '{text_ann}' (do you use striding?)"
                )
            embeddings[text_ann] = embedding

    return embeddings


def predict(
    text: str,
    pipeline: Pipeline,
    embedding_model: Optional[PreTrainedModel] = None,
    embedding_tokenizer: Optional[PreTrainedTokenizer] = None,
) -> 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()

    if embedding_model is not None and embedding_tokenizer is not None:
        adu_embeddings = embed_text_annotations(
            document=document,
            model=embedding_model,
            tokenizer=embedding_tokenizer,
            text_layer_name="labeled_spans",
        )
        # convert keys to str because JSON keys must be strings
        adu_embeddings_dict = {str(k._id): v.detach().tolist() for k, v in adu_embeddings.items()}
        document_dict["embeddings"] = adu_embeddings_dict
    else:
        gr.Warning(
            "No embedding model provided. Skipping embedding extraction. You can load an embedding model in the 'Model Configuration' section."
        )

    # Return as dict and JSON string. The latter is required because the JSON component converts floats
    # to ints which destroys de-serialization of the document (the scores of the annotations need to be floats)
    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)
    # remove embeddings from document_dict to make it de-serializable
    document_dict.pop("embeddings", None)
    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_displacy(document, **render_kwargs)
    else:
        raise ValueError(f"Unknown render_with value: {render_with}")

    return html


def add_to_index(
    output_txt: str, doc_id: str, processed_documents: dict, vector_store: Any
) -> None:
    try:
        if doc_id in processed_documents:
            gr.Warning(f"Document {doc_id} already in index. Overwriting.")
        output = json.loads(output_txt)
        # get the embeddings from the output and remove them from the output
        embeddings = output.pop("embeddings")
        # save the processed document to the index
        processed_documents[doc_id] = output
        # save the embeddings to the vector store
        for adu_id, embedding in embeddings.items():
            emb_id = f"{doc_id}:{adu_id}"
            # TODO: save embedding to vector store at emb_id (embedding is a list of 768 floats)

        gr.Info(
            f"Added document {doc_id} to index (index contains {len(processed_documents)} entries). (NOT YET IMPLEMENTED)"
        )
    except Exception as e:
        raise gr.Error(f"Failed to add document {doc_id} to index: {e}")


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


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


def load_argumentation_model(model_name: str, revision: Optional[str] = None) -> Pipeline:
    try:
        model = AutoPipeline.from_pretrained(
            model_name,
            device=-1,
            num_workers=0,
            taskmodule_kwargs=dict(revision=revision),
            model_kwargs=dict(revision=revision),
        )
    except Exception as e:
        raise gr.Error(f"Failed to load argumentation model: {e}")
    gr.Info(f"Loaded argumentation model: model_name={model_name}, revision={revision})")
    return model


def load_embedding_model(model_name: str) -> Tuple[PreTrainedModel, PreTrainedTokenizer]:
    try:
        embedding_model = AutoModel.from_pretrained(model_name)
        embedding_tokenizer = AutoTokenizer.from_pretrained(model_name)
    except Exception as e:
        raise gr.Error(f"Failed to load embedding model: {e}")
    gr.Info(f"Loaded embedding model: model_name={model_name})")
    return embedding_model, embedding_tokenizer


def load_models(
    model_name: str, revision: Optional[str] = None, embedding_model_name: Optional[str] = None
) -> Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]]:
    argumentation_model = load_argumentation_model(model_name, revision)
    embedding_model = None
    embedding_tokenizer = None
    if embedding_model_name is not None and embedding_model_name.strip():
        embedding_model, embedding_tokenizer = load_embedding_model(embedding_model_name)

    return argumentation_model, embedding_model, embedding_tokenizer


def main():

    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."

    print("Loading argumentation model ...")
    argumentation_model = load_argumentation_model(
        model_name=DEFAULT_MODEL_NAME, revision=DEFAULT_MODEL_REVISION
    )

    default_render_kwargs = {
        "entity_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,
        },
    }

    # TODO: setup the vector store
    vector_store = None

    with gr.Blocks() as demo:
        processed_documents_state = gr.State(dict())
        vector_store_state = gr.State(vector_store)
        # wrap the pipeline and the embedding model/tokenizer in a tuple to avoid that it gets called
        models_state = gr.State((argumentation_model, None, None))
        with gr.Row():
            with gr.Column(scale=1):
                doc_id = gr.Textbox(
                    label="Document ID",
                    value="user_input",
                )
                text = gr.Textbox(
                    label="Text",
                    lines=20,
                    value=example_text,
                )
                with gr.Accordion("Model Configuration", open=False):
                    model_name = gr.Textbox(
                        label="Model Name",
                        value=DEFAULT_MODEL_NAME,
                    )
                    model_revision = gr.Textbox(
                        label="Model Revision",
                        value=DEFAULT_MODEL_REVISION,
                    )
                    embedding_model_name = gr.Textbox(
                        label=f"Embedding Model Name (e.g. {DEFAULT_EMBEDDING_MODEL_NAME})",
                        value="",
                    )
                    load_models_btn = gr.Button("Load Models")
                    load_models_btn.click(
                        fn=load_models,
                        inputs=[model_name, model_revision, embedding_model_name],
                        outputs=models_state,
                    )

                predict_btn = gr.Button("Analyse")

                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")

                add_to_index_btn = gr.Button("Add current result to Index")

        render_button_kwargs = dict(
            fn=render, inputs=[output_txt, render_as, render_kwargs], outputs=rendered_output
        )

        def _predict(
            text: str,
            models: Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]],
        ) -> Tuple[dict, str]:
            return predict(text, *models)

        predict_btn.click(open_accordion, inputs=[], outputs=[output_accordion]).then(
            fn=_predict,
            inputs=[text, models_state],
            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")

        add_to_index_btn.click(
            fn=add_to_index,
            inputs=[output_txt, doc_id, processed_documents_state, vector_store_state],
            outputs=[],
        )

        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()


if __name__ == "__main__":
    # configure logging
    logging.basicConfig()

    main()