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import re |
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import gradio as gr |
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from dataclasses import dataclass |
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from prettytable import PrettyTable |
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from pytorch_ie.annotations import LabeledSpan, BinaryRelation |
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from pytorch_ie.auto import AutoPipeline |
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from pytorch_ie.core import AnnotationList, annotation_field |
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from pytorch_ie.documents import TextBasedDocument |
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from pytorch_ie.taskmodules import * |
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from pytorch_ie.models import * |
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from typing import List |
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@dataclass |
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class ExampleDocument(TextBasedDocument): |
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entities: AnnotationList[LabeledSpan] = annotation_field(target="text") |
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relations: AnnotationList[BinaryRelation] = annotation_field(target="entities") |
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ner_model_name_or_path = "pie/example-ner-spanclf-conll03" |
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re_model_name_or_path = "pie/example-re-textclf-tacred" |
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ner_pipeline = AutoPipeline.from_pretrained(ner_model_name_or_path, device=-1, num_workers=0) |
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re_pipeline = AutoPipeline.from_pretrained(re_model_name_or_path, device=-1, num_workers=0, taskmodule_kwargs=dict(create_relation_candidates=True)) |
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def predict(text): |
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document = ExampleDocument(text) |
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ner_pipeline(document) |
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print(f"detected entities:") |
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for entity in document.entities.predictions: |
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print(f"'{entity}', label={entity.label}, score={entity.score}") |
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document.entities.append(entity.copy()) |
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re_pipeline(document) |
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t = PrettyTable() |
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t.field_names = ["head", "tail", "relation"] |
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t.align = "l" |
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for relation in document.relations.predictions: |
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t.add_row([str(relation.head), str(relation.tail), relation.label]) |
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html = t.get_html_string(format=True) |
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html = ( |
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"<div style='max-width:100%; max-height:360px; overflow:auto'>" |
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+ html |
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+ "</div>" |
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) |
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return html |
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iface = gr.Interface( |
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fn=predict, |
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inputs=gr.inputs.Textbox( |
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lines=5, |
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default="“Making a super tasty alt-chicken wing is only half of it,” said Po Bronson, general partner at SOSV and managing director of IndieBio.", |
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), |
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outputs="html", |
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) |
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iface.launch() |
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