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import re | |
import gradio as gr | |
from dataclasses import dataclass | |
from prettytable import PrettyTable | |
from pytorch_ie.annotations import LabeledSpan, BinaryRelation | |
from pytorch_ie.auto import AutoPipeline | |
from pytorch_ie.core import AnnotationList, annotation_field | |
from pytorch_ie.documents import TextDocument | |
from typing import List | |
class ExampleDocument(TextDocument): | |
entities: AnnotationList[LabeledSpan] = annotation_field(target="text") | |
relations: AnnotationList[BinaryRelation] = annotation_field(target="entities") | |
ner_model_name_or_path = "pie/example-ner-spanclf-conll03" | |
re_model_name_or_path = "pie/example-re-textclf-tacred" | |
ner_pipeline = AutoPipeline.from_pretrained(ner_model_name_or_path, device=-1, num_workers=0) | |
re_pipeline = AutoPipeline.from_pretrained(re_model_name_or_path, device=-1, num_workers=0) | |
def predict(text): | |
document = ExampleDocument(text) | |
ner_pipeline(document) | |
while len(document.entities.predictions) > 0: | |
document.entities.append(document.entities.predictions.pop(0)) | |
re_pipeline(document) | |
t = PrettyTable() | |
t.field_names = ["head", "tail", "relation"] | |
t.align = "l" | |
for relation in document.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 | |
iface = gr.Interface( | |
fn=predict, | |
inputs=gr.inputs.Textbox( | |
lines=5, | |
default="There is still some uncertainty that Musk - also chief executive of electric car maker Tesla and rocket company SpaceX - will pull off his planned buyout.", | |
), | |
outputs="html", | |
) | |
iface.launch() | |