Update app.py

app.py

@@ -13,9 +13,9 @@ from sklearn.decomposition import PCA
 def predict(age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country):
     features = [age, workclass, education, marital_status, occupation, relationship, race, gender, capital_gain, capital_loss, hours_per_week, native_country]
     columns = [
-    "age", "workclass", "education", "marital_status", "occupation", 
-    "relationship", "race", "gender", "capital_gain", "capital_loss", 
-    "hours_per_week", "native_country"]
+    "age", "workclass", "education-num", "marital_status", "occupation", 
+    "relationship", "race", "gender", "capital-gain", "capital-loss", 
+    "hours-per-week", "native-country"]
     df = pd.DataFrame(index=features, columns=columns)
     fixed_features = cleaning_features(df)
     # prediction = model.predict(features)
@@ -36,7 +36,7 @@ def cleaning_features(data):
 
     # 2. Label encode gender and income
     data['gender'] = le.fit_transform(data['gender'])
-    data['education-num'] = le.fit_transform(data['education'])
+    data['education-num'] = le.fit_transform(data['education-num'])
     
     # 3. One-hot encode race
     for N in columns_to_encode: