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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 greet(name): |
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return "Hello " + name + "!!" |
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def predict(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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user_input = pd.DataFrame([SpO2,Age,Weight,Height,Temperature,gen,Race]) |
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return user_input['gen'] |
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demo = gr.Interface( |
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fn=predict, |
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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"])], |
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outputs=["text"], |
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) |
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demo.launch() |