Update app.py

app.py

@@ -4,9 +4,6 @@ import pandas as pd
 import numpy as np
 
 
-def greet(name):
-    return "Hello " + name + "!!"
-
 def predicter(SpO2, Age, Weight, Height, Temperature, Gender, Race):
     xgb_reg = xgboost.XGBClassifier(tree_method = 'approx',
                                 enable_categorical = True,
@@ -28,14 +25,16 @@ def predicter(SpO2, Age, Weight, Height, Temperature, Gender, 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)
+    pred = xgb_reg.predict_proba(user_input)
 
-    return pred
+    return return {"Probability of Not Being Hidden Hypoxemic": pred[0][0], 
+                  "Probability of Having Hidden Hypoxemia": pred[0][1], }
     
 
 demo = gr.Interface(
     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"],
+    outputs=gr.outputs.Label(num_top_classes=2),
+    title = "Probability of Hidden Hypoxemia"
 )
 demo.launch()