Update app.py

app.py

@@ -122,24 +122,22 @@ def predict(data: OneHotPatientData):
         feature_order = [
             'age', 'bmi', 'cholesterol_level', 'hypertension', 'asthma',
             'cirrhosis', 'other_cancer', 'gender_Male',
-            'country_Belgium','country_Bulgaria','country_Croatia','country_Cyprus',
-            'country_Czech_Republic','country_Denmark','country_Estonia','country_Finland',
-            'country_France','country_Germany','country_Greece','country_Hungary',
-            'country_Ireland','country_Italy','country_Latvia','country_Lithuania',
-            'country_Luxembourg','country_Malta','country_Netherlands','country_Poland',
-            'country_Portugal','country_Romania','country_Slovakia','country_Slovenia',
-            'country_Spain','country_Sweden',
-            'cancer_stage_Stage II','cancer_stage_Stage III','cancer_stage_Stage IV', # Corrected names
+            'country_Belgium', 'country_Bulgaria', 'country_Croatia', 'country_Cyprus',
+            'country_Czech Republic', 'country_Denmark', 'country_Estonia', 'country_Finland',
+            'country_France', 'country_Germany', 'country_Greece', 'country_Hungary',
+            'country_Ireland', 'country_Italy', 'country_Latvia', 'country_Lithuania',
+            'country_Luxembourg', 'country_Malta', 'country_Netherlands', 'country_Poland',
+            'country_Portugal', 'country_Romania', 'country_Slovakia', 'country_Slovenia',
+            'country_Spain', 'country_Sweden',
+            'cancer_stage_Stage Ii', 'cancer_stage_Stage Iii', 'cancer_stage_Stage Iv',
             'family_history_Yes',
-            'smoking_status_Former Smoker','smoking_status_Never Smoked','smoking_status_Passive Smoker', # Corrected names
-            'treatment_type_Combined','treatment_type_Radiation','treatment_type_Surgery'
+            'smoking_status_Former Smoker', 'smoking_status_Never Smoked', 'smoking_status_Passive Smoker',
+            'treatment_type_Combined', 'treatment_type_Radiation', 'treatment_type_Surgery'
         ]
 
-        # Fill missing fields with 0
         input_dict_complete = {col: input_dict.get(col, 0) for col in feature_order}
         input_df = pd.DataFrame([input_dict_complete], columns=feature_order)
 
-        # Predict probabilities
         probabilities = model.predict_proba(input_df)[0]
         confidence_high_risk = probabilities[1]
         risk_level = "High Risk of Non-Survival" if confidence_high_risk > 0.5 else "Low Risk of Non-Survival"