add app file

app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+import joblib
+from sklearn.preprocessing import LabelEncoder, StandardScaler
+
+try:
+    model = tf.keras.models.load_model("banking_model.keras")  
+    scaler = joblib.load("scaler.pkl")
+    label_encoders = joblib.load("label_encoders.pkl")
+except Exception as e:
+    st.error(f"Error loading model or preprocessors: {e}")
+    st.stop()
+
+
+st.title("📊 Banking 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())
+except AttributeError:
+    st.error("Scaler or encoders are not properly loaded.")
+    st.stop()
+
+for feature in numerical_features:
+    numerical_inputs[feature] = st.number_input(f"Enter {feature}", value=0.0)
+
+for feature in categorical_features:
+    if label_encoders[feature].classes_.size > 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()
+
+
+if st.button("Predict"):
+    try:
+        for feature in categorical_inputs:
+            categorical_inputs[feature] = label_encoders[feature].transform([categorical_inputs[feature]])[0]
+
+        input_data = pd.DataFrame([{**numerical_inputs, **categorical_inputs}])
+
+        input_data[numerical_features] = scaler.transform(input_data[numerical_features])
+
+        prediction = model.predict(input_data)
+        predicted_class = np.argmax(prediction)
+
+        st.success(f"✅ Predicted Classification Stage: {predicted_class}")
+
+    except Exception as e:
+        st.error(f"Prediction error: {e}")