| 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) | |
| ''' | |
| loaded_models = [] | |
| 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) | |
| ''' | |
| 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(loaded_models),2)) | |
| for i in range(len(loaded_models)): | |
| predictions[i] = loaded_models[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() |