|
|
|
import gradio as gr |
|
import joblib |
|
loaded_rf_2way = joblib.load("STPI_2WAY_RandomForest.joblib") |
|
loaded_rf_3way = joblib.load("STPI_3WAY_RandomForest.joblib") |
|
|
|
|
|
def STPI(t_0_5_MaxValue,t_1_0_MaxValue,t_2_0_MaxValue,Acc_0_5__1_0_MaxValue,Abs_Diff_t_0_5_MaxValue,Abs_Diff_t_1_0_MaxValue,Abs_Diff_t_2_0_MaxValue): |
|
X = [t_0_5_MaxValue,t_1_0_MaxValue,t_2_0_MaxValue,Acc_0_5__1_0_MaxValue,Abs_Diff_t_0_5_MaxValue,Abs_Diff_t_1_0_MaxValue,Abs_Diff_t_2_0_MaxValue] |
|
outcome_decoded = ['Normal','Keratoconic','Suspect'] |
|
file_object = open('stpi_data.txt', 'a') |
|
file_object.write(str(t_0_5_MaxValue)) |
|
file_object.write(';') |
|
file_object.write(str(t_1_0_MaxValue)) |
|
file_object.write(';') |
|
file_object.write(str(t_2_0_MaxValue)) |
|
file_object.write(';') |
|
file_object.write(str(Acc_0_5__1_0_MaxValue)) |
|
file_object.write(';') |
|
file_object.write(str(Abs_Diff_t_0_5_MaxValue)) |
|
file_object.write(';') |
|
file_object.write(str(Abs_Diff_t_1_0_MaxValue)) |
|
file_object.write(';') |
|
file_object.write(str(Abs_Diff_t_2_0_MaxValue)) |
|
file_object.write('\n') |
|
file_object.close() |
|
|
|
result_2way = loaded_rf_2way.predict([X]) |
|
print('The patient is ', outcome_decoded[int(result_2way)], 'through the 2way method') |
|
|
|
if result_2way == 0: |
|
result_3way = loaded_rf_3way.predict([X]) |
|
print('The patient is ', outcome_decoded[int(result_3way)], 'through the 3way method') |
|
return "The patient is ", outcome_decoded[int(result_3way)], "through the 3way method" |
|
|
|
|
|
return "The patient is ", outcome_decoded[int(result_2way)], "through the 2way method" |
|
|
|
iface = gr.Interface(fn=STPI, |
|
title='STPI Calculator', |
|
description='Calculates the STPI through summarized tomographic parameters. Beta version by Prof. Shady Awwad, Jad Assaf MD and Jawad Kaisania.', |
|
inputs=["number", "number","number", "number","number", "number","number"], |
|
outputs="text") |
|
iface.launch(share=True) |
|
|
|
|
