Update app.py

app.py

@@ -7,29 +7,31 @@ model = joblib.load("best_random_forest_model.pkl")
 
 # Define the prediction function
 def predict_alzheimers(
-    age, gender, bmi, physical_activity, smoking, alcohol,
-    diabetes, hypertension, cholesterol, family_history,
-    cognitive_score, depression, sleep_quality, dietary,
+    age, gender, bmi, diabetes, hypertension, cholesterol,
+    physical_activity, smoking, alcohol, family_history,
+    cognitive_score, depression, sleep, dietary,
     genetic_risk, social_engagement, income, stress
 ):
-    # Mapping categorical inputs to numerical values
-    gender = 1 if gender == "Female" else 0
-    physical_activity = {"Low": 0, "Medium": 1, "High": 2}[physical_activity]
-    smoking = {"Never": 0, "Former": 1, "Current": 2}[smoking]
-    alcohol = {"Never": 0, "Occasionally": 1, "Regularly": 2}[alcohol]
+    # Map categorical values to numerical ones
+    gender = 1 if gender == "Male" else 0
     diabetes = 1 if diabetes == "Yes" else 0
     hypertension = 1 if hypertension == "Yes" else 0
-   cholesterol = {"Low": 0, "Normal": 1, "High": 2}.get(cholesterol, 1)  # default: Normal
-
     family_history = 1 if family_history == "Yes" else 0
-    depression = {"Low": 0, "Medium": 1, "High": 2}[depression]
-    sleep_quality = {"Poor": 0, "Average": 1, "Good": 2}[sleep_quality]
-    dietary = {"Healthy": 0, "Average": 1, "Unhealthy": 2}.get(dietary, 1)  # default: Average
-
     genetic_risk = 1 if genetic_risk == "Yes" else 0
-    social_engagement = {"Low": 0, "Medium": 1, "High": 2}[social_engagement]
-    income = {"Low": 0, "Medium": 1, "High": 2}[income]
-    stress = {"Low": 0, "Medium": 1, "High": 2}[stress]
+
+    cholesterol = {"Low": 0, "Normal": 1, "High": 2}.get(cholesterol, 1)
+    physical_activity = {"Low": 0, "Medium": 1, "High": 2}.get(physical_activity, 1)
+    smoking = {"Never": 0, "Former": 1, "Current": 2}.get(smoking, 1)
+    alcohol = {"Never": 0, "Occasionally": 1, "Regularly": 2}.get(alcohol, 1)
+    depression = {"Low": 0, "Medium": 1, "High": 2}.get(depression, 1)
+    sleep = {"Poor": 0, "Average": 1, "Good": 2}.get(sleep, 1)
+    dietary = {"Healthy": 0, "Average": 1, "Unhealthy": 2}.get(dietary, 1)
+    social_engagement = {"Low": 0, "Medium": 1, "High": 2}.get(social_engagement, 1)
+    income = {"Low": 0, "Medium": 1, "High": 2}.get(income, 1)
+    stress = {"Low": 0, "Medium": 1, "High": 2}.get(stress, 1)
+
+    # ... your model input code continues ...
+
 
     # Create input array
     features = np.array([[