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| !pip install https://huggingface.co/spacy/en_core_web_md/resolve/main/en_core_web_md-any-py3-none-any.whl | |
| import gradio as gr | |
| import spacy | |
| from spacy.pipeline import EntityRuler | |
| from spacy.language import Language | |
| from spacy.matcher import PhraseMatcher | |
| from spacy.tokens import Span | |
| nlp = spacy.load("en_core_web_md") | |
| def load(txt1, txt2, txt3, txt4): | |
| user_input = str(txt1.strip()) | |
| doc1 = nlp(user_input) | |
| entities = [(ent.text, ent.label_) for ent in doc1.ents] | |
| pattern_list = [] | |
| for i in txt2.strip().split(): | |
| pattern_list.append(i) | |
| patterns = list(nlp.pipe(pattern_list)) | |
| matcher = PhraseMatcher(nlp.vocab) | |
| user_named = str(txt3.strip()) | |
| matcher.add(user_named, patterns) | |
| def component_function(doc): | |
| matches = matcher(doc) | |
| spans = [Span(doc, start, end, label=user_named) for match_id, start, end in matches] | |
| doc.ents = spans | |
| return doc | |
| if "added_component" not in nlp.pipe_names: | |
| nlp.add_pipe(("added_component"), after="ner") | |
| user_input4 = str(txt4.strip()) | |
| apply_doc = nlp(user_input4) | |
| entities2 = [(ent.text, ent.label_) for ent in apply_doc.ents] | |
| from collections import Counter | |
| labels = [ent.label_ for ent in apply_doc.ents] | |
| lab_counts = Counter(labels) | |
| return(entities, entities2, lab_counts) | |
| description = "Use this space to produce and test your own customized NER" | |
| iface = gr.Interface( | |
| title = "Customized Named Entity Recognition", | |
| description = description, | |
| fn = load, | |
| interpretation = "shap", | |
| inputs = [gr.inputs.Textbox(lines=10, default="The coronavirus disease 2019 (COVID-19) pandemic is the result of widespread infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).", label="Text to Run through Entity Recognition"), gr.inputs.Textbox(lines=3, default= "Coronavirus, coronavirus, COVID-19, SARS-CoV-2, SARS‐CoV‐2", label="Enter entity references"), gr.inputs.Textbox(lines=1, default="COVID", label="Enter entity label"), gr.inputs.Textbox(lines=10, default="The tissue distribution of the virus-targeted receptor protein, angiotensin converting enzyme II (ACE2), determines which organs will be attacked by SARS‐CoV‐2.", label="Enter new sentence containing named entity")], | |
| outputs = [gr.outputs.Textbox(type="str", label="Entities recognized before"), | |
| gr.outputs.Textbox(type="str", label="Entites recognized after"), | |
| gr.outputs.Textbox(type="str", label="Count of entities captured for new label")], | |
| theme = "dark" | |
| ) | |
| iface.launch() |