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import gradio as gr
import xgboost
import pandas as pd
import numpy as np
import json
import pickle
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)
'''
with open('HH_ensemble_classifier_online.json', 'r') as file:
model_data = json.load(file)
for item in model_data:
index = item['index']
model = pickle.loads(item['model'].encode('latin1'))
loaded_models.append(model)
classifier_list = loaded_models
'''
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')
predictions = np.zeros((len(classifier_list),2))
for i in range(len(classifier_list)):
predictions[i] = classifier_list[i].predict_proba(user_input[cont_features + cat_features])
averaged_prediction = predictions.mean(axis=0)
'''
pred = xgb_reg.predict_proba(user_input)
'''
return {"No Hidden Hypoxemia": float(averaged_prediction[0]), "Hidden Hypoxemia": float(averaged_prediction[1])}
demo = gr.Interface(
fn=predicter,
inputs=[gr.Slider(88.1, 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 Predictions"
)
demo.launch() |