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| import matplotlib.cm as cm | |
| import html | |
| import torch | |
| import numpy as np | |
| from transformers import pipeline | |
| import gradio as gr | |
| def value2rgba(x, cmap=cm.RdYlGn, alpha_mult=1.0): | |
| "Convert a value `x` from 0 to 1 (inclusive) to an RGBA tuple according to `cmap` times transparency `alpha_mult`." | |
| c = cmap(x) | |
| rgb = (np.array(c[:-1]) * 255).astype(int) | |
| a = c[-1] * alpha_mult | |
| return tuple(rgb.tolist() + [a]) | |
| def piece_prob_html(pieces, prob, colors, label, sep=' ', **kwargs): | |
| html_code,spans = ['<span style="font-family: monospace;">'], [] | |
| for p, a, cols, l in zip(pieces, prob, colors, label): | |
| p = html.escape(p) | |
| c = str(value2rgba(a, cmap=cols, alpha_mult=0.5, **kwargs)) | |
| spans.append(f'<span title="{l}: {a:.3f}" style="background-color: rgba{c};">{p}</span>') | |
| html_code.append(sep.join(spans)) | |
| html_code.append('</span>') | |
| return ''.join(html_code) | |
| def nothing_ent(i, word): | |
| return { | |
| 'entity': 'O', | |
| 'score': 0, | |
| 'index': i, | |
| 'word': word, | |
| 'start': 0, | |
| 'end': 0 | |
| } | |
| def _gradio_highlighting(text): | |
| result = ner_model(text) | |
| tokens = ner_model.tokenizer.tokenize(text) | |
| label_indeces = [i['index'] - 1 for i in result] | |
| entities = list() | |
| for i, word in enumerate(tokens): | |
| if i in label_indeces: | |
| entities.append(result[label_indeces.index(i)]) | |
| else: | |
| entities.append(nothing_ent(i, word)) | |
| entities = ner_model.group_entities(entities) | |
| spans = [e['word'] for e in entities] | |
| probs = [e['score'] for e in entities] | |
| labels = [e['entity_group'] for e in entities] | |
| colors = [cm.RdPu if label == 'ADR' else cm.YlGn for i, label in enumerate(labels)] | |
| return piece_prob_html(spans, probs, colors, labels, sep=' ') | |
| default_text = """# Pancreatitis | |
| - Lipase: 535 -> 154 -> 145 | |
| - Managed with NBM, IV fluids | |
| - CT AP and abdo USS: normal | |
| - Likely secondary to Azathioprine - ceased, never to be used again. | |
| - Resolved with conservative measures | |
| """ | |
| title = "Adverse Drug Reaction Highlighting" | |
| description = "Named Entity Recognition model to detect ADRs in discharge summaries" | |
| article = """This app was made to accompany our recent [paper](https://www.medrxiv.org/content/10.1101/2021.12.11.21267504v2). | |
| ADRs will be highlighted in <span style="color:purple">purple</span>, offending medications in <span style="color:green">green</span>. | |
| Hover over a word to see the strength of each prediction on a 0-1 scale. | |
| Our training code can be found at [github](https://github.com/AustinMOS/adr-nlp). | |
| """ | |
| ner_model = pipeline(task = 'token-classification', model = "austin/adr-ner") | |
| iface = gr.Interface(_gradio_highlighting, | |
| [ | |
| gr.inputs.Textbox( | |
| lines=7, | |
| label="Text", | |
| default=default_text), | |
| ], | |
| gr.outputs.HTML(label="ADR Prediction"), | |
| title = title, | |
| description = description, | |
| article = article, | |
| theme = "huggingface" | |
| ) | |
| iface.launch() |