Update app.py

app.py

@@ -7,7 +7,7 @@ import numpy as np
 def greet(name):
     return "Hello " + name + "!!"
 
-def predict(SpO2, Age, Weight, Height, Temperature, Gender, Race):
+def predicter(SpO2, Age, Weight, Height, Temperature, Gender, Race):
     xgb_reg = xgboost.XGBClassifier(tree_method = 'approx',
                                 enable_categorical = True,
                                 learning_rate=.1,
@@ -23,14 +23,18 @@ def predict(SpO2, Age, Weight, Height, Temperature, Gender, Race):
     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])
+    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(user_input)
 
-    return user_input['gen']
+    return pred
     
 
 demo = gr.Interface(
-    fn=predict,
+    fn=predicter,
     inputs=[gr.Slider(0, 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=["text"],
 )