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


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

def predict(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"

    
    user_input = pd.DataFrame([SpO2,Age,Weight,Height,Temperature,gen,Race])

    return user_input['gen']
    

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