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| import gradio as gr | |
| from joblib import dump, load | |
| #loading models | |
| log = load('logistic_model.joblib') | |
| knn = load('knn_model.joblib') | |
| decision = load('decision_tree_model.joblib') | |
| deep = load('deep_model.joblib') | |
| #input/output modules | |
| input_module1 = gr.Dropdown(choices=["Logistic Regression", "KNN", "Decision Tree","Neural Network"], label = "method") | |
| input_module2 = gr.Dropdown(choices=["male", 'female'], label = "gender") | |
| input_module3 = gr.Dropdown(choices=['african-american','asian','black','black african','black or african american',\ | |
| 'caucasian', 'han chinese', 'hispanic', 'intermediate', 'japanese', 'korean', 'other',\ | |
| 'Other (Black British)','Other Mixed Race','White'], label = "race") | |
| input_module4 = gr.Number(label='age') | |
| input_module5 = gr.Number(label='height (cm)') | |
| input_module6 = gr.Number(label='weight (kg)') | |
| input_module7 = gr.Checkbox(label='Diabetes') | |
| input_module8 = gr.Checkbox(label='Simvastatin (Zocor)') | |
| input_module9 = gr.Checkbox(label='Amiodarone (Cordarone)') | |
| input_module10 = gr.Number(label='target INR') | |
| input_module11 = gr.Number(label='INR on Reported Therapeutic Dose of Warfarin') | |
| input_module12 = gr.Number(label='Cyp2C9 genotypes (1-13)') | |
| input_module13 = gr.Dropdown(choices=['A/A','A/G','G/G'], label = "VKORC1 genotype") | |
| output_module = gr.Number(label='Therapeutic Dose of Warfarin (>=30 mg/wk) (1=true, 0=false)') | |
| race_options = ['african-american','asian','black','black african','black or african american',\ | |
| 'caucasian', 'han chinese', 'hispanic', 'intermediate', 'japanese', 'korean', 'other',\ | |
| 'Other (Black British)','Other Mixed Race','White'] | |
| #gradio function | |
| def predict(method, gender, race, age, height, weight, diabetes, simv, amio, targetINR, INR, cyp2c9, vkorc1): | |
| #converting inputs into numeric data | |
| if gender == 'male': | |
| gender = 0 | |
| else: | |
| gender = 1 | |
| for i in range(len(race_options)): | |
| if race_options[i] == race: | |
| race_options[i] = 1 | |
| else: | |
| race_options[i] = 0 | |
| if diabetes == True: | |
| diabetes = 1 | |
| else: | |
| diabetes = 0 | |
| if simv == True: | |
| simv = 1 | |
| else: | |
| simv = 0 | |
| if amio == True: | |
| amio = 1 | |
| else: | |
| amio = 0 | |
| if vkorc1 == 'A/A': | |
| vkorc1 = 0 | |
| elif vkorc1 == 'A/G': | |
| vkorc1 = 1 | |
| else: | |
| vkorc1 = 2 | |
| #compiling data | |
| data = [gender, age, height, weight, diabetes, simv, amio, targetINR, INR, cyp2c9, vkorc1] | |
| data.extend(race_options) | |
| data.extend([0]) #accounting for extra unused race category (there are 2 'other' options for race) | |
| #predicting using given method | |
| if method == "Logistic Regression": | |
| value = log.predict([data])[0] | |
| elif method == "KNN": | |
| value = knn.predict([data])[0] | |
| elif method == "Decision Tree": | |
| value = decision.predict([data])[0] | |
| else: | |
| value = deep.predict([data])[0] | |
| if value >= 0.5: | |
| value = 1 | |
| else: | |
| value = 0 | |
| return value | |
| gr.Interface(fn=predict, inputs=[input_module1,input_module2,input_module3,input_module4,input_module5,input_module6,input_module7,\ | |
| input_module8,input_module9,input_module10,input_module11,input_module12,input_module13], outputs=output_module).launch(debug=True) |