Update app.py

app.py

@@ -1,10 +1,9 @@
 import streamlit as st
 import numpy as np
 import pickle
-import pandas as pd
-from sklearn.preprocessing import OneHotEncoder
 from sklearn.compose import ColumnTransformer
 from sklearn.pipeline import Pipeline
+from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, RobustScaler
 
 # Load Model
 try:
@@ -15,6 +14,18 @@ except FileNotFoundError:
     st.error("❌ Model file not found! Please upload `final_model.pkl`.")
     model = None
 
+# Define your preprocessing pipeline (assuming it's already defined in your model)
+# Example preprocessing pipeline
+nom_pl = Pipeline(steps=[('Encoding', OneHotEncoder(sparse_output=False, handle_unknown='ignore'))])
+ord_pl = Pipeline(steps=[('Encoding', OrdinalEncoder())])
+scale_pl = Pipeline(steps=[('Scaling', RobustScaler())])
+
+ct = ColumnTransformer(
+    transformers=[
+        ('nom_pl', nom_pl, [0, 4]),  # Example for categorical columns
+        ('ord_pl', ord_pl, [1, 2, 3, 6, 7]),  # Example for ordinal columns
+        ('scale_pl', scale_pl, [5, 8, 9])  # Example for numerical columns
+    ])
 
 # Title of the application
 st.markdown("<h1 class='title'>🏡 House Price Predictor</h1>", unsafe_allow_html=True)
@@ -37,9 +48,12 @@ if st.button("🔍 Predict Price"):
     input_data = [[POSTED_BY, UNDER_CONSTRUCTION, RERA, BHK_NO_, BHK_OR_RK, SQUARE_FT,
                    READY_TO_MOVE, RESALE, LONGITUDE, LATITUDE]]
 
-    
+    # Transform input data using the ColumnTransformer (fit before prediction)
+    # You need to transform the input data before prediction
+    input_data_transformed = ct.transform(input_data)
+
     # Make prediction using the model
-    predicted_price = model.predict(input_data)[0]
+    predicted_price = model.predict(input_data_transformed)[0]
 
     # Display predicted price
     st.markdown(f"<div class='result-box'>🏠 Predicted Price: ₹ {predicted_price:.2f} Lakhs</div>", unsafe_allow_html=True)