import gradio as gr
import xgboost
import pandas as pd
import numpy as np

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')

def greet(name):
    return "Hello " + name + "!!"

def predict(SpO2, Age, Weight, Height, Temperature, Gender, Race)

demo = gr.Interface(
    fn=greet,
    inputs=[gr.Slider(0, 100),"number","number","number",gr.Radio(["Male", "Female"]),gr.Radio(["White", "Black", "Asian", "Hispanic", "Other"])],
    outputs=["text", "number"],
)
demo.launch()