v.0.2

app.py

@@ -3,44 +3,57 @@ import numpy as np
 import pandas as pd
 import tensorflow as tf
 import joblib
-from sklearn.preprocessing import LabelEncoder, StandardScaler
+import os
+
+
+if not os.path.exists("banking_model.keras"):
+    st.error("🚨 Model file not found! Train and save the model first.")
+    st.stop()
+
+if not os.path.exists("scaler.pkl"):
+    st.error("🚨 Scaler file 'scaler.pkl' not found! Make sure preprocessing was done correctly.")
+    st.stop()
+
+if not os.path.exists("label_encoders.pkl"):
+    st.error("🚨 Label encoder file 'label_encoders.pkl' not found! Ensure encoding was saved properly.")
+    st.stop()
+
 
 try:
-    model = tf.keras.models.load_model("banking_model.keras")  
+    model = tf.keras.models.load_model("banking_model.keras")
     scaler = joblib.load("scaler.pkl")
     label_encoders = joblib.load("label_encoders.pkl")
+
+    if not label_encoders:  
+        raise ValueError("Label encoders are empty!")
+
 except Exception as e:
     st.error(f"Error loading model or preprocessors: {e}")
     st.stop()
 
 
-st.title("📊 Banking App")
+st.title("📊 Classification Prediction App")
 st.write("Enter the feature values below to predict the classification stage.")
 
-if not label_encoders:
-    st.error("Label encoders are empty. Make sure the model was trained correctly.")
-    st.stop()
-
 numerical_inputs = {}
 categorical_inputs = {}
 
 try:
-    numerical_features = list(scaler.feature_names_in_)
-    categorical_features = list(label_encoders.keys())
+    numerical_features = list(scaler.feature_names_in_) 
+    categorical_features = list(label_encoders.keys()) 
 except AttributeError:
     st.error("Scaler or encoders are not properly loaded.")
     st.stop()
 
-# ✅ FIXED: Ensure numerical inputs always have valid default values
 for feature in numerical_features:
-    numerical_inputs[feature] = st.number_input(f"Enter {feature}", value=0.0, step=0.1, format="%.2f")
+    numerical_inputs[feature] = float(st.number_input(f"Enter {feature}", value=0.0, step=0.1, format="%.2f"))
 
 for feature in categorical_features:
-    if label_encoders[feature].classes_.size > 0:
+    if len(label_encoders[feature].classes_) > 0:
         categorical_inputs[feature] = st.selectbox(f"Select {feature}", label_encoders[feature].classes_)
     else:
-        st.error(f"Label encoder for {feature} is empty.")
-        st.stop()
+        st.warning(f"⚠️ No classes found for '{feature}'. Skipping this feature.")
+
 
 if st.button("Predict"):
     try:
@@ -58,4 +71,3 @@ if st.button("Predict"):
 
     except Exception as e:
         st.error(f"Prediction error: {e}")
-