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import gradio as gr
from dataclasses import dataclass
from pytorch_ie.annotations import LabeledSpan
from pytorch_ie.auto import AutoPipeline
from pytorch_ie.core import AnnotationList, annotation_field
from pytorch_ie.documents import TextDocument
from spacy import displacy
@dataclass
class ExampleDocument(TextDocument):
entities: AnnotationList[LabeledSpan] = annotation_field(target="text")
model_name_or_path = "pie/example-ner-spanclf-conll03"
ner_pipeline = AutoPipeline.from_pretrained(model_name_or_path, device=-1, num_workers=0)
def predict(text):
document = ExampleDocument(text)
ner_pipeline(document)
doc = {
"text": document.text,
"ents": [{
"start": entity.start,
"end": entity.end,
"label": entity.label
} for entity in sorted(document.entities.predictions, key=lambda e: e.start)],
"title": None
}
html = displacy.render(doc, style="ent", page=True, manual=True, minify=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()
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