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Update app.py
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app.py
CHANGED
@@ -27,11 +27,13 @@ def predict(text):
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df_sum=pd.DataFrame(pred1)
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df_sum
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df_sum2=pd.DataFrame(pred2)
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-
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df_sum2
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df_sum3= pd.DataFrame(pred3)
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# # join the two dataframes on token do outer join
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@@ -42,14 +44,14 @@ def predict(text):
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df_join['sum_sequence']=df_join['sequence_x'].fillna(df_join['sequence_y'])
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df_join['sum_sequence']=df_join['sum_sequence'].fillna(df_join['sequence'])
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df_join=df_join.fillna(0)
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df_join['
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df_join=df_join.sort_values(by='
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df_join=df_join.reset_index(drop=True)
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# df_join=df_join.dropna()
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# df_join=df_join.fillna(0)
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df=df_join.copy()
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df_join=df_join[['
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# gr.Interface(fn=lambda: df_join, inputs=None, outputs=gr.Dataframe(headers=df_join.columns)).launch()
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@@ -69,9 +71,9 @@ demo = gr.Interface(
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# outputs=gr.Dataframe(headers=['title', 'author', 'text']), allow_flagging='never')
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title="Filling Missing
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examples=[ ['The high blood pressure was due to [MASK] which is critical.'],
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['The [MASK]
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],
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description="This application fills any missing words in the medical domain",
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# fn=lambda: df, inputs=None, outputs=gr.Dataframe(headers=df_join.columns)
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df_sum=pd.DataFrame(pred1)
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df_sum
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df_sum['score_finetuned_CBERT']=df_sum['score']
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df_sum2=pd.DataFrame(pred2)
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df_sum2['score_Bio_CBERT']=df_sum2['score']
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df_sum2
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df_sum3= pd.DataFrame(pred3)
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df_sum3['score_CBERT']=df_sum3['score']
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# # join the two dataframes on token do outer join
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df_join['sum_sequence']=df_join['sequence_x'].fillna(df_join['sequence_y'])
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df_join['sum_sequence']=df_join['sum_sequence'].fillna(df_join['sequence'])
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df_join=df_join.fillna(0)
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df_join['score_average']=(df_join['score_finetuned_CBERT']+df_join['score_Bio_CBERT']+df_join['score_CBERT'])/3
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df_join=df_join.sort_values(by='score_average',ascending=False)
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df_join=df_join.reset_index(drop=True)
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# df_join=df_join.dropna()
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# df_join=df_join.fillna(0)
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df=df_join.copy()
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df_join=df_join[['score_finetuned_CBERT','score_Bio_CBERT','score_CBERT','score_average','token_str']]
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# gr.Interface(fn=lambda: df_join, inputs=None, outputs=gr.Dataframe(headers=df_join.columns)).launch()
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# outputs=gr.Dataframe(headers=['title', 'author', 'text']), allow_flagging='never')
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title="Filling Missing Clinical/Medical Data ",
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examples=[ ['The high blood pressure was due to [MASK] which is critical.'],
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['The patient is suffering from throat infection causing [MASK] and cough.']
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],
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description="This application fills any missing words in the medical domain",
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# fn=lambda: df, inputs=None, outputs=gr.Dataframe(headers=df_join.columns)
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