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