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| import gradio as gr | |
| from typing import List | |
| from qasem.end_to_end_pipeline import QASemEndToEndPipeline | |
| pipeline = QASemEndToEndPipeline(spacy_model="en_core_web_lg") | |
| 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" | |
| all_layers = ["qasrl", "qanom", "qadiscourse"] | |
| examples = [["Both were shot in the confrontation with police and have been recovering in hospital since the attack .", all_layers, False, 0.75], | |
| ["the construction of the officer 's building was delayed by the lockdown and is expected to continue for at least 10 more months.", all_layers, False, 0.75], | |
| ["While President Obama expressed condolences regarding the death of Margaret Thatcher upon her death earlier this year , he did not issue an executive order that flags be lowered in her honor .", all_layers, False, 0.75], | |
| ["We made a very clear commitment : if there is any proposal in the next parliament for a transfer of powers to Brussels ( the EU ) we will have an in/out referendum .", all_layers, False, 0.75], | |
| ["The doctor asked about the progress in Luke 's treatment .", all_layers, False, 0.75], | |
| ["The Veterinary student was interested in Luke 's treatment of sea animals .", all_layers, False, 0.7], | |
| ["Some reviewers agreed that the criticism raised by the AC is mostly justified .", all_layers, False, 0.6]] | |
| 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, layers, show_openie: bool, detection_threshold: float): | |
| outputs = pipeline([sentence], nominalization_detection_threshold=detection_threshold, output_openie=show_openie) | |
| if show_openie: | |
| openie_outputs = outputs["openie"][0] # list of OpenIE tuples | |
| outputs = outputs["qasem"] | |
| outputs = outputs[0] # only one sentence in input batch | |
| def pretty_qadisc_qas(qa_infos) -> List[str]: | |
| if not qa_infos: return [] | |
| return ["- " + f"{qa['question']} --- {qa['answer']}".lstrip() | |
| 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'])}".lstrip() | |
| for qa in pred_info['QAs'] if qa is not None] | |
| # filter outputs by requested `layers` | |
| outputs = {layer: qas if layer in layers else [] | |
| for layer, qas in outputs.items()} | |
| # Prettify outputs | |
| 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 = [] | |
| if "qasrl" in layers: all_qas += ['\nQASRL:'] + qasrl_qas | |
| if "qanom" in layers: all_qas += ['\nQANom:'] + qanom_qas | |
| if "qadiscourse" in layers: all_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>' | |
| # show openie_outputs | |
| if show_openie: | |
| repr_oie = lambda tup: f"({','.join(e for e in tup)})" | |
| openie_html = '<span><b>Open Information Extraction:</b><br>' + '<br>'.join([repr_oie(tup) for tup in openie_outputs]) + '</span>' | |
| else: | |
| openie_html = '' | |
| return html, pretty_qa_output, openie_html, outputs | |
| iface = gr.Interface(fn=call, | |
| inputs=[gr.components.Textbox(placeholder=input_sent_box_label, label="Sentence", lines=4), | |
| gr.components.CheckboxGroup(all_layers, value=all_layers, label="Annotation Layers"), | |
| gr.components.Checkbox(value=False, label="Show OpenIE format (converted from verbal QASRL only)"), | |
| gr.components.Slider(minimum=0., maximum=1., step=0.01, value=0.75, label="Nominalization Detection Threshold")], | |
| outputs=[gr.components.HTML(label="Detected Predicates"), | |
| gr.components.Textbox(label="Generated QAs"), | |
| gr.components.HTML(label="OpenIE Output"), | |
| gr.components.JSON(label="Raw QASemEndToEndPipeline Output")], | |
| title=title, | |
| description=description, | |
| article=links, | |
| examples=examples) | |
| iface.launch() |