Spaces:
Runtime error
Runtime error
File size: 2,150 Bytes
864a486 03f1ac5 9d06087 3572a2d 864a486 9d06087 864a486 03f1ac5 864a486 03f1ac5 9d06087 864a486 9d06087 7c8f86e 864a486 9d06087 2895baa 9d06087 2895baa 9d06087 864a486 9d06087 7c8f86e 864a486 03f1ac5 8b928dc 03f1ac5 864a486 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 |
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 TextBasedDocument
from pytorch_ie.taskmodules import *
from pytorch_ie.models import *
from typing import List
@dataclass
class ExampleDocument(TextBasedDocument):
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, taskmodule_kwargs=dict(create_relation_candidates=True))
def predict(text):
document = ExampleDocument(text)
# execute NER pipeline
ner_pipeline(document)
# show predicted entities and promote them from predictions to ground-truth annotations
print(f"detected entities:")
for entity in document.entities.predictions:
print(f"'{entity}', label={entity.label}, score={entity.score}")
document.entities.append(entity.copy())
# execute RE pipeline
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="“Making a super tasty alt-chicken wing is only half of it,” said Po Bronson, general partner at SOSV and managing director of IndieBio.",
),
outputs="html",
)
iface.launch()
|