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| import gradio as gr | |
| import pandas as pd | |
| from transformers import pipeline | |
| pipe = pipeline("fill-mask", model="aminghias/Clinical-BERT-finetuned") | |
| pipe2 = pipeline("fill-mask", model="emilyalsentzer/Bio_ClinicalBERT") | |
| pipe3= pipeline("fill-mask", model="medicalai/ClinicalBERT") | |
| def predict(text): | |
| pred1 = pipe(text) | |
| pred2 = pipe2(text) | |
| pred3= pipe3(text) | |
| df_sum=pd.DataFrame(pred1) | |
| df_sum | |
| df_sum['score_finetuned_CBERT']=df_sum['score'] | |
| df_sum2=pd.DataFrame(pred2) | |
| df_sum2['score_Bio_CBERT']=df_sum2['score'] | |
| df_sum2 | |
| df_sum3= pd.DataFrame(pred3) | |
| df_sum3['score_CBERT']=df_sum3['score'] | |
| # # join the two dataframes on token do outer join | |
| df_join=pd.merge(df_sum,df_sum2,on='token_str',how='outer') | |
| df_join=pd.merge(df_sum3,df_join,on='token_str',how='outer') | |
| df_join | |
| df_join['sum_sequence']=df_join['sequence_x'].fillna(df_join['sequence_y']) | |
| df_join['sum_sequence']=df_join['sum_sequence'].fillna(df_join['sequence']) | |
| df_join=df_join.fillna(0) | |
| df_join['score_average']=(df_join['score_finetuned_CBERT']+df_join['score_Bio_CBERT']+df_join['score_CBERT'])/3 | |
| df_join=df_join.sort_values(by='score_average',ascending=False) | |
| df_join=df_join.reset_index(drop=True) | |
| df=df_join.copy() | |
| df_join=df_join[['token_str','score_average','score_finetuned_CBERT','score_Bio_CBERT','score_CBERT']].head() | |
| return (df['sum_sequence'][0],df_join) | |
| demo = gr.Interface( | |
| fn=predict, | |
| inputs='text', | |
| # outputs='text', | |
| outputs=['text', gr.Dataframe()], | |
| title="Filling Missing Clinical/Medical Data ", | |
| examples=[ ['The high blood pressure was due to [MASK] which is critical.'], | |
| ['The patient is suffering from throat infection causing [MASK] and cough.'] | |
| ], | |
| description="This application fills any missing words in the medical domain", | |
| ) | |
| demo.launch() |