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import json |
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import logging |
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import os.path |
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from functools import partial |
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from typing import Dict, List, Optional, Tuple |
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import gradio as gr |
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import pandas as pd |
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from backend import get_annotation_from_document, get_relevant_adus, get_similar_adus, process_text |
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from pie_modules.taskmodules import PointerNetworkTaskModuleForEnd2EndRE |
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from pytorch_ie import Pipeline |
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from pytorch_ie.auto import AutoPipeline |
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from pytorch_ie.documents import TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions |
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from rendering_utils import render_displacy, render_pretty_table |
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from transformers import AutoModel, AutoTokenizer, PreTrainedModel, PreTrainedTokenizer |
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from vector_store import SimpleVectorStore, VectorStore |
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logger = logging.getLogger(__name__) |
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RENDER_WITH_DISPLACY = "displaCy + highlighted arguments" |
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RENDER_WITH_PRETTY_TABLE = "Pretty Table" |
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DEFAULT_MODEL_NAME = "ArneBinder/sam-pointer-bart-base-v0.3" |
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DEFAULT_MODEL_REVISION = "76300f8e534e2fcf695f00cb49bba166739b8d8a" |
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DEFAULT_EMBEDDING_MODEL_NAME = "allenai/scibert_scivocab_uncased" |
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def render_annotated_document( |
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document: TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions, |
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render_with: str, |
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render_kwargs_json: str, |
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) -> str: |
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render_kwargs = json.loads(render_kwargs_json) |
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if render_with == RENDER_WITH_PRETTY_TABLE: |
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html = render_pretty_table(document, **render_kwargs) |
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elif render_with == RENDER_WITH_DISPLACY: |
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html = render_displacy(document, **render_kwargs) |
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else: |
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raise ValueError(f"Unknown render_with value: {render_with}") |
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return html |
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def wrapped_process_text( |
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text: str, |
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doc_id: str, |
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models: Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]], |
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processed_documents: dict[ |
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str, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions |
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], |
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vector_store: VectorStore[Tuple[str, str]], |
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) -> Tuple[dict, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions]: |
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document = process_text( |
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text=text, |
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doc_id=doc_id, |
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models=models, |
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processed_documents=processed_documents, |
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vector_store=vector_store, |
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) |
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return document.asdict(), document |
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def process_uploaded_files( |
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file_names: List[str], |
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models: Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]], |
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processed_documents: dict[ |
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str, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions |
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], |
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vector_store: VectorStore[Tuple[str, str]], |
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) -> None: |
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try: |
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for file_name in file_names: |
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if file_name.lower().endswith(".txt"): |
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with open(file_name, "r", encoding="utf-8") as f: |
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text = f.read() |
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base_file_name = os.path.basename(file_name) |
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gr.Info(f"Processing file '{base_file_name}' ...") |
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process_text(text, base_file_name, models, processed_documents, vector_store) |
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else: |
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raise gr.Error(f"Unsupported file format: {file_name}") |
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except Exception as e: |
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raise gr.Error(f"Failed to process uploaded files: {e}") |
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def open_accordion(): |
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return gr.Accordion(open=True) |
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def close_accordion(): |
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return gr.Accordion(open=False) |
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def load_argumentation_model(model_name: str, revision: Optional[str] = None) -> Pipeline: |
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try: |
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model = AutoPipeline.from_pretrained( |
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model_name, |
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device=-1, |
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num_workers=0, |
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taskmodule_kwargs=dict(revision=revision), |
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model_kwargs=dict(revision=revision), |
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) |
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except Exception as e: |
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raise gr.Error(f"Failed to load argumentation model: {e}") |
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gr.Info(f"Loaded argumentation model: model_name={model_name}, revision={revision})") |
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return model |
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def load_embedding_model(model_name: str) -> Tuple[PreTrainedModel, PreTrainedTokenizer]: |
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try: |
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embedding_model = AutoModel.from_pretrained(model_name) |
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embedding_tokenizer = AutoTokenizer.from_pretrained(model_name) |
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except Exception as e: |
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raise gr.Error(f"Failed to load embedding model: {e}") |
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gr.Info(f"Loaded embedding model: model_name={model_name})") |
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return embedding_model, embedding_tokenizer |
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def load_models( |
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model_name: str, revision: Optional[str] = None, embedding_model_name: Optional[str] = None |
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) -> Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]]: |
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argumentation_model = load_argumentation_model(model_name, revision) |
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embedding_model = None |
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embedding_tokenizer = None |
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if embedding_model_name is not None and embedding_model_name.strip(): |
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embedding_model, embedding_tokenizer = load_embedding_model(embedding_model_name) |
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return argumentation_model, embedding_model, embedding_tokenizer |
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def update_processed_documents_df( |
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processed_documents: dict[str, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions] |
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) -> pd.DataFrame: |
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df = pd.DataFrame( |
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[ |
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( |
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doc_id, |
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len(document.labeled_spans.predictions), |
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len(document.binary_relations.predictions), |
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) |
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for doc_id, document in processed_documents.items() |
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], |
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columns=["doc_id", "num_adus", "num_relations"], |
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) |
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return df |
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def select_processed_document( |
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evt: gr.SelectData, |
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processed_documents_df: pd.DataFrame, |
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processed_documents: Dict[ |
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str, TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions |
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], |
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) -> TextDocumentWithLabeledSpansBinaryRelationsAndLabeledPartitions: |
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row_idx, col_idx = evt.index |
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doc_id = processed_documents_df.iloc[row_idx]["doc_id"] |
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gr.Info(f"Select document: {doc_id}") |
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doc = processed_documents[doc_id] |
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return doc |
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def set_relation_types( |
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models: Tuple[Pipeline, Optional[PreTrainedModel], Optional[PreTrainedTokenizer]], |
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default: Optional[List[str]] = None, |
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) -> gr.Dropdown: |
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arg_pipeline = models[0] |
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if isinstance(arg_pipeline.taskmodule, PointerNetworkTaskModuleForEnd2EndRE): |
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relation_types = arg_pipeline.taskmodule.labels_per_layer["binary_relations"] |
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else: |
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raise gr.Error("Unsupported taskmodule for relation types") |
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return gr.Dropdown( |
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choices=relation_types, |
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label="Relation Types", |
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value=default, |
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multiselect=True, |
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) |
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def main(): |
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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." |
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print("Loading models ...") |
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argumentation_model, embedding_model, embedding_tokenizer = load_models( |
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model_name=DEFAULT_MODEL_NAME, |
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revision=DEFAULT_MODEL_REVISION, |
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embedding_model_name=DEFAULT_EMBEDDING_MODEL_NAME, |
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) |
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default_render_kwargs = { |
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"entity_options": { |
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"colors": { |
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"own_claim".upper(): "#009933", |
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"background_claim".upper(): "#99ccff", |
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"data".upper(): "#993399", |
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} |
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}, |
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"colors_hover": { |
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"selected": "#ffa", |
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"tail": { |
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"supports": "#9f9", |
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"contradicts": "#f99", |
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"parts_of_same": None, |
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}, |
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"head": None, |
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"other": None, |
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}, |
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} |
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with gr.Blocks() as demo: |
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processed_documents_state = gr.State(dict()) |
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vector_store_state = gr.State(SimpleVectorStore()) |
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models_state = gr.State((argumentation_model, embedding_model, embedding_tokenizer)) |
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with gr.Row(): |
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with gr.Column(scale=1): |
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doc_id = gr.Textbox( |
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label="Document ID", |
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value="user_input", |
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) |
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doc_text = gr.Textbox( |
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label="Text", |
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lines=20, |
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value=example_text, |
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) |
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with gr.Accordion("Model Configuration", open=False): |
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model_name = gr.Textbox( |
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label="Model Name", |
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value=DEFAULT_MODEL_NAME, |
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) |
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model_revision = gr.Textbox( |
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label="Model Revision", |
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value=DEFAULT_MODEL_REVISION, |
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) |
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embedding_model_name = gr.Textbox( |
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label=f"Embedding Model Name (e.g. {DEFAULT_EMBEDDING_MODEL_NAME})", |
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value=DEFAULT_EMBEDDING_MODEL_NAME, |
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) |
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load_models_btn = gr.Button("Load Models") |
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load_models_btn.click( |
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fn=load_models, |
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inputs=[model_name, model_revision, embedding_model_name], |
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outputs=models_state, |
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) |
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predict_btn = gr.Button("Analyse") |
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document_state = gr.State() |
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with gr.Column(scale=1): |
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with gr.Accordion("See plain result ...", open=False) as output_accordion: |
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document_json = gr.JSON(label="Model Output") |
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with gr.Accordion("Render Options", open=False): |
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render_as = gr.Dropdown( |
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label="Render with", |
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choices=[RENDER_WITH_PRETTY_TABLE, RENDER_WITH_DISPLACY], |
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value=RENDER_WITH_DISPLACY, |
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) |
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render_kwargs = gr.Textbox( |
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label="Render Arguments", |
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lines=5, |
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value=json.dumps(default_render_kwargs, indent=2), |
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) |
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render_btn = gr.Button("Re-render") |
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rendered_output = gr.HTML(label="Rendered Output") |
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upload_btn = gr.UploadButton( |
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"Upload & Analyse Documents", file_types=["text"], file_count="multiple" |
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) |
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with gr.Column(scale=1): |
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with gr.Accordion("Indexed Documents", open=False): |
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processed_documents_df = gr.DataFrame( |
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headers=["id", "num_adus", "num_relations"], |
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interactive=False, |
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) |
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with gr.Accordion("Reference ADU", open=False): |
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reference_adu_id = gr.Textbox(label="ID", elem_id="reference_adu_id") |
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reference_adu_text = gr.Textbox(label="Text") |
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with gr.Accordion("Retrieval Configuration", open=False): |
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min_similarity = gr.Slider( |
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label="Minimum Similarity", |
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minimum=0.0, |
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maximum=1.0, |
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step=0.01, |
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value=0.8, |
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) |
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top_k = gr.Slider( |
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label="Top K", |
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minimum=2, |
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maximum=50, |
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step=1, |
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value=20, |
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) |
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retrieve_similar_adus_btn = gr.Button("Retrieve similar ADUs") |
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similar_adus = gr.DataFrame(headers=["doc_id", "adu_id", "score", "text"]) |
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relation_types = set_relation_types( |
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models_state.value, default=["supports", "contradicts"] |
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) |
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relevant_adus = gr.DataFrame( |
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label="Relevant ADUs from other documents", |
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headers=[ |
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"text", |
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"relation", |
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"doc_id", |
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"reference_adu", |
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"sim_score", |
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"rel_score", |
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], |
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) |
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render_event_kwargs = dict( |
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fn=render_annotated_document, |
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inputs=[document_state, render_as, render_kwargs], |
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outputs=rendered_output, |
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) |
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predict_btn.click(fn=open_accordion, inputs=[], outputs=[output_accordion]).then( |
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fn=wrapped_process_text, |
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inputs=[doc_text, doc_id, models_state, processed_documents_state, vector_store_state], |
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outputs=[document_json, document_state], |
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api_name="predict", |
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).success( |
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fn=update_processed_documents_df, |
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inputs=[processed_documents_state], |
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outputs=[processed_documents_df], |
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) |
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render_btn.click(**render_event_kwargs, api_name="render") |
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document_state.change( |
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fn=lambda doc: doc.asdict(), |
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inputs=[document_state], |
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outputs=[document_json], |
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).success(close_accordion, inputs=[], outputs=[output_accordion]).then( |
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**render_event_kwargs |
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) |
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upload_btn.upload( |
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fn=process_uploaded_files, |
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inputs=[upload_btn, models_state, processed_documents_state, vector_store_state], |
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outputs=[], |
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).success( |
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fn=update_processed_documents_df, |
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inputs=[processed_documents_state], |
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outputs=[processed_documents_df], |
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) |
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processed_documents_df.select( |
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select_processed_document, |
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inputs=[processed_documents_df, processed_documents_state], |
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outputs=[document_state], |
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) |
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retrieve_relevant_adus_event_kwargs = dict( |
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fn=get_relevant_adus, |
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inputs=[ |
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reference_adu_id, |
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document_state, |
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vector_store_state, |
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processed_documents_state, |
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min_similarity, |
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top_k, |
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relation_types, |
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], |
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outputs=[relevant_adus], |
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) |
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reference_adu_id.change( |
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fn=partial(get_annotation_from_document, annotation_layer="labeled_spans"), |
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inputs=[document_state, reference_adu_id], |
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outputs=[reference_adu_text], |
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).success(**retrieve_relevant_adus_event_kwargs) |
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retrieve_similar_adus_btn.click( |
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fn=get_similar_adus, |
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inputs=[ |
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reference_adu_id, |
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document_state, |
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vector_store_state, |
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processed_documents_state, |
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min_similarity, |
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top_k, |
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], |
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outputs=[similar_adus], |
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) |
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models_state.change( |
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fn=set_relation_types, |
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inputs=[models_state], |
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outputs=[relation_types], |
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) |
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js = """ |
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() => { |
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function maybeSetColor(entity, colorAttributeKey, colorDictKey) { |
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var color = entity.getAttribute('data-color-' + colorAttributeKey); |
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// if color is a json string, parse it and use the value at colorDictKey |
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try { |
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const colors = JSON.parse(color); |
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color = colors[colorDictKey]; |
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} catch (e) {} |
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if (color) { |
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entity.style.backgroundColor = color; |
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entity.style.color = '#000'; |
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} |
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} |
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function highlightRelationArguments(entityId) { |
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const entities = document.querySelectorAll('.entity'); |
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// reset all entities |
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entities.forEach(entity => { |
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const color = entity.getAttribute('data-color-original'); |
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entity.style.backgroundColor = color; |
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entity.style.color = ''; |
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}); |
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if (entityId !== null) { |
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var visitedEntities = new Set(); |
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// highlight selected entity |
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const selectedEntity = document.getElementById(entityId); |
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if (selectedEntity) { |
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const label = selectedEntity.getAttribute('data-label'); |
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maybeSetColor(selectedEntity, 'selected', label); |
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visitedEntities.add(selectedEntity); |
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} |
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// highlight tails |
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const relationTailsAndLabels = JSON.parse(selectedEntity.getAttribute('data-relation-tails')); |
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relationTailsAndLabels.forEach(relationTail => { |
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const tailEntity = document.getElementById(relationTail['entity-id']); |
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if (tailEntity) { |
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const label = relationTail['label']; |
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maybeSetColor(tailEntity, 'tail', label); |
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visitedEntities.add(tailEntity); |
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} |
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}); |
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// highlight heads |
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const relationHeadsAndLabels = JSON.parse(selectedEntity.getAttribute('data-relation-heads')); |
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relationHeadsAndLabels.forEach(relationHead => { |
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const headEntity = document.getElementById(relationHead['entity-id']); |
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if (headEntity) { |
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const label = relationHead['label']; |
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maybeSetColor(headEntity, 'head', label); |
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visitedEntities.add(headEntity); |
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} |
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}); |
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// highlight other entities |
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entities.forEach(entity => { |
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if (!visitedEntities.has(entity)) { |
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const label = entity.getAttribute('data-label'); |
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maybeSetColor(entity, 'other', label); |
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} |
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}); |
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} |
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} |
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function setReferenceAduId(entityId) { |
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// get the textarea element that holds the reference adu id |
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let referenceAduIdDiv = document.querySelector('#reference_adu_id textarea'); |
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// set the value of the input field |
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referenceAduIdDiv.value = entityId; |
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// trigger an input event to update the state |
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var event = new Event('input'); |
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referenceAduIdDiv.dispatchEvent(event); |
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} |
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const entities = document.querySelectorAll('.entity'); |
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entities.forEach(entity => { |
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const alreadyHasListener = entity.getAttribute('data-has-listener'); |
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if (alreadyHasListener) { |
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return; |
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} |
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entity.addEventListener('mouseover', () => { |
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highlightRelationArguments(entity.id); |
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setReferenceAduId(entity.id); |
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}); |
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entity.addEventListener('mouseout', () => { |
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highlightRelationArguments(null); |
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}); |
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entity.setAttribute('data-has-listener', 'true'); |
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}); |
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} |
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""" |
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rendered_output.change(fn=None, js=js, inputs=[], outputs=[]) |
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demo.launch() |
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if __name__ == "__main__": |
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logging.basicConfig() |
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main() |
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