Update app.py

app.py

@@ -2,9 +2,12 @@ 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,
@@ -12,9 +15,19 @@ def predicter(SpO2, Age, Weight, Height, Temperature, Gender, Race):
                                    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":
@@ -22,13 +35,19 @@ def predicter(SpO2, Age, Weight, Height, Temperature, Gender, Race):
 
     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')
-    pred = xgb_reg.predict_proba(user_input)
 
-    #return str(pred)
-    return {"No Hidden Hypoxemia": float(pred[0][0]), "Hidden Hypoxemia": float(pred[0][1])}
+
+    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(