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import gradio as gr
import pandas as pd
from transformers import pipeline
# model_name="aminghias/distilbert-base-uncased-finetuned-imdb"
# mask_filler = pipeline(
# "fill-mask", model=model_name
# )
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_join=df_join.dropna()
# df_join=df_join.fillna(0)
df=df_join.copy()
df_join=df_join[['token_str','score_average','score_finetuned_CBERT','score_Bio_CBERT','score_CBERT']].head()
# gr.Interface(fn=lambda: df_join, inputs=None, outputs=gr.Dataframe(headers=df_join.columns)).launch()
# print(df_join)
# df_join['sum_sequence'][0]
return (df['sum_sequence'][0],df_join)
# return (pipe(text)[0]['sequence'],pipe2(text)[0]['sequence'])
demo = gr.Interface(
fn=predict,
inputs='text',
# outputs='text',
outputs=['text', gr.Dataframe()],
# outputs='text','text',
# outputs=gr.Dataframe(headers=['title', 'author', 'text']), allow_flagging='never')
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",
# fn=lambda: df, inputs=None, outputs=gr.Dataframe(headers=df_join.columns)
# fn = infer, inputs = inputs, outputs = outputs, examples = [[df_join.head()]]
)
demo.launch() |