app.py

import gradio as gr
import nltk
nltk.download('omw-1.4')
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>  |  
</p>"""
examples = [["The doctor was interested in Luke 's treatment .", True, True, 0.6], 
            ["the construction of the officer 's building finished right after the beginning of the destruction of the previous construction .", True, True, 0.7]]

def call(sentence: str, return_all_candidates: bool, threshold: float):
    ret = detector([sentence], return_all_candidates, True, threshold)[0]
    return ret, ret

iface = gr.Interface(fn=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.JSON(label="Model Output"), gr.outputs.JSON(label="Model Output - QASRL")],
                     title=title,
                     description=description,
                     article=links,
                     examples=examples )
iface.launch()