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| # %% | |
| 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): | |
| print('------------------') | |
| 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] | |
| print(X) | |
| 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(';') | |
| 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') | |
| result_3way = loaded_rf_3way.predict([X]) | |
| if result_2way == 0: | |
| print('The patient is ', outcome_decoded[int(result_3way)], 'through the 3way method') | |
| # result = 'The 3-way classification resulted in a ', outcome_decoded[int(result_3way)] + ' patient.' | |
| # further_analysis = 'Futher analysis using the 2-way classification resulted in a ' + outcome_decoded[int(result_2way)] + ' label.' | |
| return 'The patient is ' + outcome_decoded[int(result_3way)] + '.' | |
| # result = 'The 2-way classification resulted in a ', outcome_decoded[int(result_2way)] + ' patient.' | |
| # further_analysis = 'Futher analysis using the 3-way classification resulted in a ' + outcome_decoded[int(result_3way)] + ' label.' | |
| return 'The patient is ' + outcome_decoded[int(result_2way)] + '.' | |
| iface = gr.Interface( | |
| fn=STPI, | |
| title='TSPI Calculator', | |
| description='Calculates the Thickness Speed Progression Index (TSPI) through summarized tomographic parameters. Beta version made for Zeimer by Prof. Shady Awwad and Jad Assaf MD.', | |
| inputs=["number", "number","number", | |
| # "number", | |
| "number", "number","number"], | |
| outputs="text") | |
| iface.launch( | |
| # share=True | |
| ) | |
| # %% | |