|
import gradio as gr |
|
import xgboost |
|
import pandas as pd |
|
import numpy as np |
|
|
|
|
|
def predicter(SpO2, Age, Weight, Height, Temperature, Gender, Race): |
|
xgb_reg = xgboost.XGBClassifier(tree_method = 'approx', |
|
enable_categorical = True, |
|
learning_rate=.1, |
|
max_depth=2, |
|
n_estimators=70, |
|
early_stopping_rounds = 0, |
|
scale_pos_weight=1) |
|
|
|
xgb_reg.load_model('classifier_fewer_features_HH.json') |
|
|
|
if Gender == "Male": |
|
gen = "M" |
|
elif Gender == "Female": |
|
gen = "F" |
|
|
|
cont_features = ['SpO2','anchor_age','weight','height','temperature'] |
|
cat_features = ['gender','race_group'] |
|
|
|
user_input = pd.DataFrame([[SpO2/100,Age/91,Weight/309,Height/213,Temperature/42.06,gen,Race]],columns = cont_features+cat_features) |
|
user_input[cat_features] = user_input[cat_features].copy().astype('category') |
|
pred = xgb_reg.predict_proba(user_input) |
|
|
|
|
|
return {"No Hidden Hypoxemia": float(pred[0][0]), "Hidden Hypoxemia": float(pred[0][1])} |
|
|
|
|
|
demo = gr.Interface( |
|
fn=predicter, |
|
inputs=[gr.Slider(0, 100),"number",gr.inputs.Number(label = "Weight in kg"),gr.inputs.Number(label = "Height in cm"),gr.inputs.Number(label = "Temperature in Celcius"),gr.Radio(["Male", "Female"]),gr.Radio(["White", "Black", "Asian", "Hispanic", "Other"])], |
|
outputs=[gr.Label(label = "Probabilities")], |
|
title = "Model Predictioned Probabilities" |
|
) |
|
demo.launch() |
