Update app.py

app.py

@@ -13,11 +13,11 @@ import pickle
 # # Define the prediction function
 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", "educational-num", "marital-status", "occupation", 
-    "relationship", "race", "gender", "capital-gain", "capital-loss", 
-    "hours-per-week", "native-country"]
-    df = pd.DataFrame(data=features, columns=columns)
+    columns = {
+    "age": age, "workclass":workclass, "educational-num":education, "marital-status":marital_status, "occupation":occupation, 
+    "relationship":relationship, "race":race, "gender":gender, "capital-gain":capital_gain, "capital-loss":capital_loss, 
+    "hours-per-week":hours_per_week, "native-country":native_country}
+    df = pd.DataFrame(data=columns)
     fixed_features = cleaning_features(df)
     # prediction = model.predict(features)
     # prediction = 1