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import gradio as gr
import nltk
from qanom.nominalization_detector import NominalizationDetector
detector = NominalizationDetector()
title = "Nominalization Detection Demo"
description = f"""This is a demo of QANom's nominalization detection algorithm,
comprised of candidate nominalization extraction followed by a contextualized binary classification model."""
links = """<p style='text-align: center'>
<a href='https://github.com/kleinay/QANom' target='_blank'>QANom repo</a> |
<a href='https://huggingface.co/kleinay/nominalization-candidate-classifier' target='_blank'>Model Repo at Huggingface Hub</a> |
<a href='https://www.aclweb.org/anthology/2020.coling-main.274/' target='_blank'>QANom Paper</a>
examples = [["The doctor was interested in Luke 's treatment .", True, 0.5],
["the construction of the officer 's building finished right after the beginning of the destruction of the previous construction .", True, 0.7]]
def call(sentence: str, return_all_candidates: bool, threshold: float):
ret = detector([sentence], return_all_candidates, True, threshold)[0]
if return_all_candidates:
positives = [d["predicate_idx"] for d in ret if d['predicate_detector_prediction']]
negatives = [d["predicate_idx"] for d in ret if not d['predicate_detector_prediction']]
positives = [d["predicate_idx"] for d in ret]
negatives = []
def color(idx):
if idx in positives: return "lightgreen"
if idx in negatives: return "pink"
idx2verb = {d["predicate_idx"] : d["verb_form"] for d in ret}
idx2prob = {d["predicate_idx"] : d["predicate_detector_probability"] for d in ret}
def word_span(word, idx):
tooltip = f'title=" probability={idx2prob[idx]:.2}&#010;verb={idx2verb[idx]}"' if idx in idx2verb else ''
return f'<span {tooltip} style="background-color: {color(idx)}">{word}</span>'
html = '<span>' + ' '.join(word_span(word, idx) for idx, word in enumerate(sentence.split(" "))) + '</span>'
return html, ret
iface = gr.Interface(call,
inputs=[gr.inputs.Textbox(label="Sentence", lines=3),
gr.inputs.Checkbox(default=True, label="Return all candidates?"),
gr.inputs.Slider(minimum=0., maximum=1., step=0.01, default=0.5, label="Threshold")],
outputs=[gr.outputs.HTML(label="Detected Nominalizations"),
gr.outputs.JSON(label="Raw Model Output")],
examples=examples )