Update app.py

app.py

@@ -3,7 +3,6 @@ import pickle
 import pandas as pd
 from sklearn.pipeline import Pipeline
 
-
 # Load the model and encoders
 with open('model_penguin_706.pkl', 'rb') as file:
     model, species_encoder, island_encoder, sex_encoder = pickle.load(file)
@@ -25,6 +24,10 @@ bill_depth_mm = st.sidebar.slider('Bill Depth (mm)', 10.0, 25.0, 18.0)
 flipper_length_mm = st.sidebar.slider('Flipper Length (mm)', 170.0, 240.0, 200.0)
 body_mass_g = st.sidebar.slider('Body Mass (g)', 2500.0, 6000.0, 4000.0)
 
+# Add missing columns with default values
+culmen_length_mm = 40.0  # Default value, update with realistic values if available
+culmen_depth_mm = 15.0  # Default value, update with realistic values if available
+
 # Prepare the input data (ensure columns are in the same order as expected by the model)
 input_data = pd.DataFrame({
     'species': [species],
@@ -33,7 +36,9 @@ input_data = pd.DataFrame({
     'bill_length_mm': [bill_length_mm],
     'bill_depth_mm': [bill_depth_mm],
     'flipper_length_mm': [flipper_length_mm],
-    'body_mass_g': [body_mass_g]
+    'body_mass_g': [body_mass_g],
+    'culmen_length_mm': [culmen_length_mm],
+    'culmen_depth_mm': [culmen_depth_mm]
 })
 
 # Apply encoding to categorical features (check column names here!)
@@ -42,7 +47,6 @@ input_data['island'] = island_encoder.transform(input_data['island'])
 input_data['sex'] = sex_encoder.transform(input_data['sex'])
 
 # Ensure the columns are in the correct order
-# Check if the model includes a ColumnTransformer and use it if available
 if isinstance(model, Pipeline):
     preprocessor = model.named_steps.get('preprocessor')  # Replace with actual step name if different
     if preprocessor: