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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}")
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