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| import gradio as gr | |
| import nltk | |
| from qanom.qanom_end_to_end_pipeline import QANomEndToEndPipeline | |
| from typing import List | |
| models = ["kleinay/qanom-seq2seq-model-baseline", | |
| "kleinay/qanom-seq2seq-model-joint"] | |
| pipelines = {model: QANomEndToEndPipeline(model) for model in models} | |
| description = f"""This is a demo of the full QANom Pipeline - identifying deverbal nominalizations and parsing them with question-answer driven semantic role labeling (QASRL) """ | |
| title="QANom End-to-End Pipeline Demo" | |
| examples = [[models[1], "the construction of the officer 's building finished right after the beginning of the destruction of the previous construction .", 0.7], | |
| [models[1], "The doctor asked about the progress in Luke 's treatment .", 0.75], | |
| [models[0], "The Veterinary student was interested in Luke 's treatment of sea animals .", 0.75], | |
| [models[1], "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://www.qasrl.org' target='_blank'>QASRL Website</a> | <a href='https://huggingface.co/kleinay/qanom-seq2seq-model-baseline' target='_blank'>Model Repo at Huggingface Hub</a> | |
| </p>""" | |
| def call(model_name, sentence, detection_threshold): | |
| pipeline = pipelines[model_name] | |
| pred_infos = pipeline([sentence], detection_threshold=detection_threshold)[0] | |
| def pretty_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] | |
| all_qas = [qa for pred_info in pred_infos for qa in pretty_qas(pred_info)] | |
| if not pred_infos: | |
| pretty_qa_output = "NO NOMINALIZATION FOUND" | |
| elif not all_qas: | |
| pretty_qa_output = "NO QA GENERATED" | |
| else: | |
| pretty_qa_output = "\n".join(all_qas) | |
| # also present highlighted predicates | |
| positives = [pred_info['predicate_idx'] for pred_info in pred_infos] | |
| def color(idx): | |
| if idx in positives: return "lightgreen" | |
| idx2verb = {d["predicate_idx"] : d["verb_form"] for d in pred_infos} | |
| idx2prob = {d["predicate_idx"] : d["predicate_detector_probability"] for d in pred_infos} | |
| def word_span(word, idx): | |
| tooltip = f'title=" probability={idx2prob[idx]:.2}
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, pretty_qa_output , pred_infos | |
| iface = gr.Interface(fn=call, | |
| inputs=[gr.inputs.Radio(choices=models, default=models[0], label="Model"), | |
| 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 Nominalizations"), | |
| gr.outputs.Textbox(label="Generated QAs"), | |
| gr.outputs.JSON(label="Raw Model Output")], | |
| title=title, | |
| description=description, | |
| article=links, | |
| examples=examples) | |
| iface.launch() |