update interface

app.py

@@ -1,73 +1,58 @@
 import streamlit as st
-import numpy as np
 import pandas as pd
+import numpy as np
 import tensorflow as tf
-import joblib
-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")
-    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("📊 Classification Prediction App")
-st.write("Enter the feature values below to predict the classification stage.")
-
-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] = float(st.number_input(f"Enter {feature}", value=0.0, step=0.1, format="%.2f"))
-
-for feature in categorical_features:
-    if len(label_encoders[feature].classes_) > 0:
-        categorical_inputs[feature] = st.selectbox(f"Select {feature}", label_encoders[feature].classes_)
-    else:
-        st.warning(f"⚠️ No classes found for '{feature}'. Skipping this feature.")
-
-
-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}")
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+
+# Load the model
+model = tf.keras.models.load_model("banking_model.keras")
+
+# Function to preprocess input data
+def preprocess_input(input_data, label_encoders, scaler):
+    # Convert input data to DataFrame
+    input_df = pd.DataFrame([input_data])
+    
+    # Encode categorical variables
+    for col in label_encoders:
+        input_df[col] = label_encoders[col].transform(input_df[col])
+    
+    # Scale numerical variables
+    numerical_columns = input_df.select_dtypes(include=["int64", "float64"]).columns
+    input_df[numerical_columns] = scaler.transform(input_df[numerical_columns])
+    
+    return input_df
+
+# Streamlit app
+def main():
+    st.title("Banking Stage Classification")
+    
+    # Input fields
+    st.sidebar.header("User Input Features")
+    
+    # Example feature inputs, adjust according to your actual features
+    credit_expiration = st.sidebar.number_input("Credit Expiration", min_value=0, value=0)
+    dpd = st.sidebar.number_input("DPD", min_value=0, value=0)
+    feature1 = st.sidebar.selectbox("Feature 1", options=["Yes", "No"])
+    feature2 = st.sidebar.selectbox("Feature 2", options=["Yes", "No"])
+    stage_last_month = st.sidebar.selectbox("Stage As Last Month", options=[1, 2, 3])
+    
+    # Prepare input data
+    input_data = {
+        'Credit Expiration': credit_expiration,
+        'DPD': dpd,
+        'Feature 1': feature1,
+        'Feature 2': feature2,
+        'Stage As Last Month': stage_last_month
+    }
+    
+    # Preprocess the input
+    processed_input = preprocess_input(input_data, label_encoders, scaler)
+    
+    # Make prediction
+    if st.sidebar.button("Predict"):
+        prediction = model.predict(processed_input)
+        predicted_stage = np.argmax(prediction, axis=1) + 1  # Adjust if necessary
+        st.success(f"Predicted Current Stage: {predicted_stage[0]}")
+
+if __name__ == "__main__":
+    main()