| |
| import gradio as gr |
| import joblib |
| import numpy as np |
| 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, |
| |
| Abs_Diff_t_0_5_MaxValue,Abs_Diff_t_1_0_MaxValue,Abs_Diff_t_2_0_MaxValue,Optional_Custom_Message='No_Message'): |
| print('------------------') |
| print(Optional_Custom_Message) |
| |
| X = [t_0_5_MaxValue,t_1_0_MaxValue,t_2_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(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(Optional_Custom_Message) |
| 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') |
|
|
| probs_2way = loaded_rf_2way.predict_proba([X]) |
| probs_2way = str(np.round(probs_2way[0], decimals=2)) |
| print('2 way class Probabilities (Normal/KC) are ', probs_2way) |
|
|
| result_3way = loaded_rf_3way.predict([X]) |
|
|
| probs_3way = loaded_rf_3way.predict_proba([X]) |
| probs_3way = str(np.round(probs_3way[0], decimals=2)) |
| print('3 way class Probabilities (Normal/Suspect/KC) are ', probs_3way) |
|
|
| if result_2way == 0: |
| print('The patient is ', outcome_decoded[int(result_3way)], 'through the 3way method') |
| |
| |
| return 'The 3-way classification resulted in a ' + outcome_decoded[int(result_3way)] + ' patient. Futher analysis using the 2-way classification resulted in a ' + outcome_decoded[int(result_2way)] + ' label. ' + '2 way class Probabilities (Normal/KC) are ' + probs_2way + ' and 3 way class Probabilities (Normal/Suspect/KC) are ' + probs_3way |
|
|
| |
| |
|
|
| return 'The 2-way classification resulted in a ' + outcome_decoded[int(result_2way)] + ' patient. Futher analysis using the 3-way classification resulted in a ' + outcome_decoded[int(result_3way)] + ' label. ' + '2 way class Probabilities (Normal/KC) are ' + probs_2way + ' and 3 way class Probabilities (Normal/Suspect/KC) are ' + probs_3way |
|
|
| 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","text"], |
| outputs="text") |
| iface.launch( |
| |
| ) |
| |
|
|
