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import pickle | |
import pandas as pd | |
import sklearn | |
import gradio as gr | |
import joblib | |
# In[38]: | |
def encode_df(df): | |
# Gender | |
sex_map = {"Male": 1, "Female": 0} | |
df = df.replace({"Gender": sex_map}) | |
# Tipe Angina | |
chestpain_map = {"Typical": 0, "Asymptomatic": 1, | |
"Nonanginal": 2, "Nontypical": 3} | |
df = df.replace({"Tipe_angina": chestpain_map}) | |
# Gula_darah_puasa | |
fastingBLP_map = {"<120": 0, ">120": 1, "120": 2} | |
df = df.replace({"Gula_darah_puasa": fastingBLP_map}) | |
# Angina_aktivitas | |
exang_map = {"No": 0, "Yes": 1} | |
df = df.replace({"Angina_aktivitas": exang_map}) | |
# Hasil_Elektrokardiografi | |
recg_map = {"Normal": 0, "Abnormal Wave": 1, | |
"Left Ventricular Hypertrophy": 2} | |
df = df.replace({"Hasil_Elektrokardiografi": recg_map}) | |
# st_slope_ECG | |
slope_map = {"Upsloping": 1, "Flat": 2, "Downsloping": 3} | |
df = df.replace({"st_slope_ECG": slope_map}) | |
df = df[ | |
[ | |
"Umur", | |
"Gender", | |
"Tipe_angina", | |
"Tekanan_darah_istirahat", | |
"Kolesterol", | |
"Gula_darah_puasa", | |
"Hasil_Elektrokardiografi", | |
"Denyut_jantung_max", | |
"Angina_aktivitas", | |
"st_depression_ECG", | |
"st_slope_ECG", | |
] | |
] | |
return df | |
filename = 'ACS_model_random_forest.sav' | |
# load the model from disk | |
loaded_model = joblib.load(filename) | |
# In[51]: | |
def predict(Umur, Gender, Tipe_angina, Tekanan_darah_istirahat, Kolesterol, | |
Gula_darah_puasa, Hasil_Elektrokardiografi, | |
Denyut_jantung_max, Angina_aktivitas, st_depression_ECG, st_slope_ECG): | |
df = pd.DataFrame.from_dict( | |
{ | |
"Umur": [Umur], | |
"Gender": [Gender], | |
"Tipe_angina": [Tipe_angina], | |
"Tekanan_darah_istirahat": [Tekanan_darah_istirahat], | |
"Kolesterol": [Kolesterol], | |
"Gula_darah_puasa": [Gula_darah_puasa], | |
"Hasil_Elektrokardiografi": [Hasil_Elektrokardiografi], | |
"Denyut_jantung_max": [Denyut_jantung_max], | |
"Angina_aktivitas": [Angina_aktivitas], | |
"st_depression_ECG": [st_depression_ECG], | |
"st_slope_ECG": [st_slope_ECG], | |
} | |
) | |
df = encode_df(df) | |
pred = loaded_model.predict_proba(df)[0] | |
output = {"Possible Heart Disease": float(pred[1]), | |
"Less chance of Heart Disease": float(pred[0])} | |
return output | |
title = "Interactive Demonstration for Acute Coronary Syndrome Prediction System" | |
des = '''This model predicts the possibility of a heart disease using a hybrid sampling SMOTE-TOMEK model that achieved an high accuracy of 85% with Random Forest Algorithm''' | |
article = "<p style='text-align: center'><a href='https://www.linkedin.com/in/m-afif-rizky-a-a96048182/'>Created by @Vrooh933 Production</a> | <a href='https://github.com/afifrizkyandika11551100310'>GitHub Profile</a>" | |
demo = gr.Interface(predict, | |
[gr.Slider(0, 88, value=25, label='Umur'), | |
gr.Radio(["Male", "Female"], label='Gender'), | |
gr.Dropdown(["Typical", "Asymptomatic", "Nonanginal", | |
"Nontypical"], label="Tipe_angina"), | |
gr.Slider(0, 200, value=125, | |
label='Tekanan_darah_istirahat'), | |
gr.Slider(0, 603, value=50, label='Kolesterol'), | |
gr.Radio(["<120", ">120", '120'], | |
label='Gula_darah_puasa'), | |
gr.Dropdown(["Normal", "Abnormal Wave", "Left Ventricular Hypertrophy"], | |
label='Hasil_Elektrokardiografi'), | |
gr.Number(value=100, label='Denyut_jantung_max'), | |
gr.Dropdown(["No", "Yes"], label='Angina_aktivitas'), | |
gr.Slider(-2.6, 6.2, value=3.1, | |
label='st_depression_ECG'), | |
gr.Radio(["Upsloping", "Flat", "Downsloping"], | |
label='st_slope_ECG'), | |
], "label", | |
examples=[[37, 'Male', 'Typical', 89, 276, '>120', "Left Ventricular Hypertrophy", 150, 'No', 2.3, 'Downsloping', 0, 'Fixed'], | |
[63, 'Male', 'Typical', 50, 100, '<120', "Abnormal Wave", | |
90, 'No', 1.0, 'Downsloping', 1, 'Reversable'], | |
[58, 'Female', 'Asymptomatic', 70, 500, '>120', | |
"Normal", 111, 'Yes', 0.8, 'Flat', 0, 'Normal'] | |
], | |
title=title, | |
description=des, | |
article=article, | |
live=True, | |
) | |
# In[53]: | |
if __name__ == "__main__": | |
demo.launch() |