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Duplicate from pie/Joint-NER-and-Relation-Extraction
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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
@dataclass
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()