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import gradio as gr
import pickle
import numpy as np
save_file_name="xgboost-model.pkl"
loaded_model = pickle.load(open(save_file_name, 'rb'))
def predict_death_event(age, anaemia, creatinine_phosphokinase ,diabetes ,ejection_fraction, high_blood_pressure ,platelets ,serum_creatinine, serum_sodium, sex ,smoking ,time):
input=[[age, anaemia, creatinine_phosphokinase ,diabetes ,ejection_fraction, high_blood_pressure ,platelets ,serum_creatinine, serum_sodium, sex ,smoking ,time]]
result=loaded_model.predict(input)
if result[0]==1:
return 'Positive'
else:
return 'Negative'
return result
title = "Patient Survival Prediction"
description = "Predict survival of patient with heart failure, given their clinical record"
out_response = gr.components.Textbox(type="text", label='Death_event')
iface = gr.Interface(fn=predict_death_event,
inputs=[
gr.Slider(18, 100, value=20, label="Age"),
gr.Slider(0, 1, value=1, label="anaemia"),
gr.Slider(100, 2000, value=20, label="creatinine_phosphokinase"),
gr.Slider(0, 1, value=1, label="diabetes"),
gr.Slider(18, 100, value=20, label="ejection_fraction"),
gr.Slider(0, 1, value=1, label="high_blood_pressure"),
gr.Slider(18, 400000, value=20, label="platelets"),
gr.Slider(1, 10, value=20, label="serum_creatinine"),
gr.Slider(100, 200, value=20, label="serum_sodium"),
gr.Slider(0, 1, value=1, label="sex"),
gr.Slider(0, 1, value=1, label="smoking"),
gr.Slider(1, 10, value=20, label="time"),
],
outputs = [out_response])
iface.launch()
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