Update app.py

app.py

@@ -65,11 +65,56 @@ def map_DiffWalk(value):
     mapping = {'No': 0, 'Yes': 1}
     return mapping[value]
 
+def map_gender(value):
+    mapping = {'Female': 0, 'Male': 1}
+    return mapping[value]
+
+def map_education(value):
+    mapping = {
+        "Never attended school": 0,
+        "Grades 1-8": 1,
+        "Grades 9-11": 2,
+        "Grade 12 or GED": 3,
+        "College 1-3 years": 4,
+        "College 4+ years": 5
+    }
+    return mapping[value]
 
+def map_income(value):
+    mapping = {
+        "< $10,000": 0,
+        "$10,000 - $24,999": 1,
+        "$25,000 - $49,999": 2,
+        "$50,000 - $74,999": 3,
+        "$75,000 or more": 4 
+    }
+    return mapping[value]
 
 # Create the main function for server
 def main_func(HighBP, HighChol, CholCheck, BMI, Smoker, Stroke, HeartDiseaseorAttack, PhysActivity, Fruits, Veggies, HvyAlcoholConsump, AnyHealthcare, NoDocbcCost, GenHlth, MentHlth, PhysHlth, DiffWalk, Sex, Age, Education, Income):
-    new_row = pd.DataFrame.from_dict({'HighBP': map_HighBP(HighBP), 'HighChol': map_HighChol(HighChol), 'CholCheck': map_CholCheck(CholCheck), 'BMI': BMI, 'Smoker': map_Smoker(Smoker), 'Stroke': map_Stroke(Stroke), 'HeartDiseaseorAttack': map_HeartDiseaseorAttack(HeartDiseaseorAttack), 'PhysActivity': map_PhysActivity(PhysActivity), 'Fruits': map_Fruits(Fruits), 'Veggies': map_Veggies(Veggies), 'HvyAlcoholConsump': map_HvyAlcoholConsump(HvyAlcoholConsump), 'AnyHealthcare': map_AnyHealthcare(AnyHealthcare), 'NoDocbcCost': map_NoDocbcCost(NoDocbcCost), 'GenHlth': GenHlth, 'MentHlth': MentHlth, 'PhysHlth': PhysHlth, 'DiffWalk': map_DiffWalk(DiffWalk), 'Sex': Sex, 'Age': Age, 'Education': Education, 'Income': Income}, orient='index').transpose()
+    new_row = pd.DataFrame.from_dict({        
+        'HighBP': map_yes_no(HighBP),
+        'HighChol': map_yes_no(HighChol),
+        'CholCheck': map_yes_no(CholCheck),
+        'BMI': BMI,
+        'Smoker': map_yes_no(Smoker),
+        'Stroke': map_yes_no(Stroke),
+        'HeartDiseaseorAttack': map_yes_no(HeartDiseaseorAttack),
+        'PhysActivity': map_yes_no(PhysActivity),
+        'Fruits': map_yes_no(Fruits),
+        'Veggies': map_yes_no(Veggies),
+        'HvyAlcoholConsump': map_yes_no(HvyAlcoholConsump),
+        'AnyHealthcare': map_yes_no(AnyHealthcare),
+        'NoDocbcCost': map_yes_no(NoDocbcCost),
+        'GenHlth': GenHlth,
+        'MentHlth': MentHlth,
+        'PhysHlth': PhysHlth,
+        'DiffWalk': map_yes_no(DiffWalk),
+        'Sex': map_gender(Sex),
+        'Age': Age,
+        'Education': map_education(Education),
+        'Income': map_income(Income)
+    }, orient='index').transpose()
    
 
     prob = loaded_model.predict_proba(new_row)