Spaces:
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19d65ea
1
Parent(s):
05c0b1b
Create app.py
Browse files
app.py
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import gradio as gr
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import spacy
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import medspacy
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from medspacy.visualization import visualize_dep, visualize_ent
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from spacy import displacy
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med_ner = medspacy.load(r"./ner_models//ner_model_v2//training//model-best")
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def merge_tokens(tokens):
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merged_tokens = []
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for token in tokens:
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if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
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# If current token continues the entity of the last one, merge them
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last_token = merged_tokens[-1]
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last_token['word'] += token['word'].replace('##', '')
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last_token['end'] = token['end']
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# last_token['score'] = (last_token['score'] + token['score']) / 2
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else:
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# Otherwise, add the token to the list
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merged_tokens.append(token)
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return merged_tokens
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def ner(inp):
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output = med_ner(inp)
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formatted_ents = []
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for i in output.ents:
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ent = {}
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ent['entity']= i.label_
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ent['word']= i.text
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ent['start']= int(i.start_char)
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ent['end']= int(i.end_char)
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print(i.label_,"->",i.text,"->",i.start_char,"->",i.end_char,"->",type(i.start_char))
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formatted_ents.append(ent)
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print(formatted_ents)
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merged_tokens = merge_tokens(formatted_ents)
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# return {"text": str(inp), "entities": formatted_ents}
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return {"text": str(inp), "entities": merged_tokens}
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demo = gr.Interface(fn=ner,
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inputs=[gr.Textbox(label="Text to find entities", lines=2)],
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outputs=[gr.HighlightedText(label="Text with entites")],
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title="Custom-NER with Spacy3 and MedSpacy",
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description="Find medical entities using the NER model under the hood!",
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allow_flagging = True,
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examples=["Patient has hx of stroke. Mother diagnosed with diabetes. No evidence of pna.", "I have fever and cough since 2 days."]
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)
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demo.launch()
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