app.py

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)

    print(f"list detected entities:")
    while len(document.entities.predictions) > 0:
        entity = document.entities.predictions.pop(0)
        print(f"entity detected: {entity}")
        document.entities.append(entity)

    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()