Update app.py

app.py

@@ -9,41 +9,12 @@ from sklearn.pipeline import Pipeline
 # Load Model
 try:
     with open("final_model_1.pkl", "rb") as f:
-        model, ct = pickle.load(f)  # Load both the model and the fitted transformer
+        model = pickle.load(f)
     st.success("✅ Model loaded successfully!")
 except FileNotFoundError:
     st.error("❌ Model file not found! Please upload `final_model.pkl`.")
     model = None
-    ct = None
 
-# Define your preprocessing pipeline (to match the one used during training)
-if model and ct is None:
-    nom_pl = Pipeline(steps=[('Encoding', OneHotEncoder(drop='first', sparse_output=False, handle_unknown='ignore'))])
-
-    # Create a ColumnTransformer for the preprocessing steps
-    ct = ColumnTransformer(
-        transformers=[
-            ('nom_pl', nom_pl, [0, 4]),  # Apply OneHotEncoder on 'POSTED_BY' and 'BHK_OR_RK'
-        ], 
-        remainder='passthrough'  # Leave other columns as they are
-    )
-
-    # Here we simulate fitting the transformer on a sample of data
-    train_data = pd.DataFrame({
-        'POSTED_BY': ['Owner', 'Dealer', 'Builder'],
-        'UNDER_CONSTRUCTION': [1, 0, 1],
-        'RERA': [1, 0, 1],
-        'BHK_NO_': [2, 3, 2],
-        'BHK_OR_RK': ['BHK', 'RK', 'BHK'],
-        'SQUARE_FT': [1200, 1500, 1300],
-        'READY_TO_MOVE': [1, 0, 1],
-        'RESALE': [1, 0, 1],
-        'LONGITUDE': [20.75, 21.00, 19.80],
-        'LATITUDE': [77.32, 78.00, 76.50]
-    })
-
-    # Fit the transformer on sample data
-    ct.fit(train_data)
 
 # Title of the application
 st.markdown("<h1 class='title'>🏡 House Price Predictor</h1>", unsafe_allow_html=True)
@@ -65,12 +36,10 @@ if st.button("🔍 Predict Price"):
     # Create input data for prediction
     input_data = [[POSTED_BY, UNDER_CONSTRUCTION, RERA, BHK_NO_, BHK_OR_RK, SQUARE_FT,
                    READY_TO_MOVE, RESALE, LONGITUDE, LATITUDE]]
-    
-    # Preprocess the input data using the same transformer pipeline used during training
-    input_data_transformed = ct.transform(input_data)
+
     
     # Make prediction using the model
-    predicted_price = model.predict(input_data_transformed)[0]
+    predicted_price = model.predict(input_data)[0]
 
     # Display predicted price
     st.markdown(f"<div class='result-box'>🏠 Predicted Price: ₹ {predicted_price:.2f} Lakhs</div>", unsafe_allow_html=True)