Update app.py

app.py

@@ -3,7 +3,12 @@ import xgboost
 import pandas as pd
 import numpy as np
 
-xgb_reg = xgboost.XGBClassifier(tree_method = 'approx',
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+def predict(SpO2, Age, Weight, Height, Temperature, Gender, Race):
+    xgb_reg = xgboost.XGBClassifier(tree_method = 'approx',
                                 enable_categorical = True,
                                 learning_rate=.1,
                                    max_depth=2,
@@ -11,16 +16,22 @@ xgb_reg = xgboost.XGBClassifier(tree_method = 'approx',
                               early_stopping_rounds = 0,
                                scale_pos_weight=1)
 
-xgb_reg.load_model('classifier_fewer_features_HH.json')
+    xgb_reg.load_model('classifier_fewer_features_HH.json')
 
-def greet(name):
-    return "Hello " + name + "!!"
+    if Gender == "Male":
+        gen = "M"
+    elif Gender == "Female":
+        gen = "F"
+
+    
+    user_input = pd.DataFrame([SpO2,Age,Weight,Height,Temperature,gen,Race])
 
-def predict(SpO2, Age, Weight, Height, Temperature, Gender, Race)
+    return user_input['gen']
+    
 
 demo = gr.Interface(
-    fn=greet,
-    inputs=[gr.Slider(0, 100),"number","number","number",gr.Radio(["Male", "Female"]),gr.Radio(["White", "Black", "Asian", "Hispanic", "Other"])],
-    outputs=["text", "number"],
+    fn=predict,
+    inputs=[gr.Slider(0, 100),"number","number","number","number",gr.Radio(["Male", "Female"]),gr.Radio(["White", "Black", "Asian", "Hispanic", "Other"])],
+    outputs=["text"],
 )
 demo.launch()