Update app.py

app.py

@@ -16,12 +16,9 @@ categorical_features = ["person_home_ownership", "loan_intent", "loan_grade", "c
 X = pd.get_dummies(X, categorical_features)
 X.columns
 
-
-
 from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2)
 
-X_train.head()
 
 from sklearn.preprocessing import StandardScaler
 scaler_normal = StandardScaler()
@@ -39,6 +36,10 @@ X_test = scaler(X_test, True)
 rf_model = RandomForestClassifier(max_depth = 5)
 rf_model.fit(X_train, y_train)
 
+y_predict = rf_model.predict(X_test)
+y_predict
+
+
 features = {
     "person_home_ownership": ['MORTGAGE', 'OTHER','OWN', 'RENT',],
     "loan_intent": ['DEBTCONSOLIDATION', 'EDUCATION', 'HOMEIMPROVEMENT', 'MEDICAL', 'PERSONAL', 'VENTURE'],
@@ -53,15 +54,13 @@ def preprocess(model_input):
                 model_input[f'{feature}_{option}'] = 1
             else:
                 model_input[f'{feature}_{option}'] = 0
-                
+
     model_input.drop([_ for _ in features], inplace = True, axis = 1)
     return model_input
 
-    
 
-def credit_run(person_age, person_income, person_home_ownership,
-       person_emp_length, loan_intent, loan_grade, loan_amnt,
-       loan_int_rate, cb_person_default_on_file, cb_person_cred_hist_length):
+def credit_run(person_age, person_emp_length,person_home_ownership,cb_person_default_on_file,loan_intent,loan_grade,person_income, loan_amnt,
+       loan_int_rate, cb_person_cred_hist_length):
     model_input = preprocess(
     pd.DataFrame( { 'person_age': person_age,
                    'person_income': person_income,
@@ -76,7 +75,7 @@ def credit_run(person_age, person_income, person_home_ownership,
                   }, index = [0]
     ))
     out = rf_model.predict(model_input)
-    return "High risk of defaulting" if out[0] == 1 else "Low risk of defaulting" 
+    return "High risk of defaulting" if out[0] == 1 else "Low risk of defaulting"
            
 import gradio as gr
 with gr.Blocks(theme=gr.themes.Soft()) as demo: