Update app.py

app.py

@@ -2,6 +2,7 @@ import streamlit as st
 import pickle
 import pandas as pd
 from sklearn.pipeline import Pipeline
+import numpy as np
 
 # Load the model and encoders
 with open('model_penguin_706.pkl', 'rb') as file:
@@ -41,6 +42,11 @@ input_data = pd.DataFrame({
     'culmen_depth_mm': [culmen_depth_mm]
 })
 
+# Check for NaN values in the input data
+if input_data.isna().any().any():
+    st.warning("Input data contains NaN values. Filling with default values.")
+    input_data = input_data.fillna(0)  # Replace NaN values with 0 or appropriate value
+
 # Apply encoding to categorical features (check column names here!)
 input_data['species'] = species_encoder.transform(input_data['species'])
 input_data['island'] = island_encoder.transform(input_data['island'])