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| import gradio as gr | |
| from typing import List | |
| from qasem.end_to_end_pipeline import QASemEndToEndPipeline | |
| pipeline = QASemEndToEndPipeline() | |
| description = f"""This is a demo of the QASem Parsing pipeline. It wraps models of three QA-based semantic tasks, composing a comprehensive semi-structured representation of sentence meaning - covering verbal and nominal semantic role labeling together with discourse relations.""" | |
| title="QASem Parsing Demo" | |
| examples = [["the construction of the officer 's building finished right after the beginning of the destruction of the previous construction .", 0.7], | |
| ["The doctor asked about the progress in Luke 's treatment .", 0.7], | |
| ["The Veterinary student was interested in Luke 's treatment of sea animals .", 0.7], | |
| ["Both were shot in the confrontation with police and have been recovering in hospital since the attack .", 0.7], | |
| ["Some reviewers agreed that the criticism raised by the AC is mostly justified .", 0.5]] | |
| input_sent_box_label = "Insert sentence here, or select from the examples below" | |
| links = """<p style='text-align: center'> | |
| <a href='https://github.com/kleinay/QASem' target='_blank'>Github Repo</a> | <a href='https://arxiv.org/abs/2205.11413' target='_blank'>Paper</a> | |
| </p>""" | |
| def call(sentence, detection_threshold): | |
| outputs = pipeline([sentence], nominalization_detection_threshold=detection_threshold)[0] | |
| def pretty_qadisc_qas(qa_infos) -> List[str]: | |
| if not qa_infos: return [] | |
| return [f"{qa['question']} --- {qa['answer']}" | |
| for qa in qa_infos if qa is not None] | |
| def pretty_qasrl_qas(pred_info) -> List[str]: | |
| if not pred_info or not pred_info['QAs']: return [] | |
| return [f"{qa['question']} --- {';'.join(qa['answers'])}" | |
| for qa in pred_info['QAs'] if qa is not None] | |
| qasrl_qas = [qa for pred_info in outputs['qasrl'] for qa in pretty_qasrl_qas(pred_info)] | |
| qanom_qas = [qa for pred_info in outputs['qanom'] for qa in pretty_qasrl_qas(pred_info)] | |
| qadisc_qas= pretty_qadisc_qas(outputs['qadiscourse']) | |
| all_qas = ['QASRL:'] + qasrl_qas + ['\nQANom:'] + qanom_qas + ['\nQADiscourse:'] + qadisc_qas | |
| if not qasrl_qas + qanom_qas + qadisc_qas: | |
| pretty_qa_output = "NO QA GENERATED" | |
| else: | |
| pretty_qa_output = "\n".join(all_qas) | |
| # also present highlighted predicates | |
| qasrl_predicates = [pred_info['predicate_idx'] for pred_info in outputs['qasrl']] | |
| qanom_predicates = [pred_info['predicate_idx'] for pred_info in outputs['qanom']] | |
| def color(idx): | |
| if idx in qasrl_predicates : return "aquamarine" | |
| if idx in qanom_predicates : return "aqua" | |
| def word_span(word, idx): | |
| return f'<span style="background-color: {color(idx)}">{word}</span>' | |
| html = '<span>' + ' '.join(word_span(word, idx) for idx, word in enumerate(sentence.split(" "))) + '</span>' | |
| return html, pretty_qa_output , outputs | |
| iface = gr.Interface(fn=call, | |
| inputs=[gr.inputs.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), | |
| gr.inputs.Slider(minimum=0., maximum=1., step=0.01, default=0.5, label="Nominalization Detection Threshold")], | |
| outputs=[gr.outputs.HTML(label="Detected Predicates"), | |
| gr.outputs.Textbox(label="Generated QAs"), | |
| gr.outputs.JSON(label="Raw QASemEndToEndPipeline Output")], | |
| title=title, | |
| description=description, | |
| article=links, | |
| examples=examples) | |
| iface.launch() |