app.py

import gradio as gr
import numpy as np
import pandas as pd
import joblib

# Load saved files
model = joblib.load("house_price_model.pkl")
scaler = joblib.load("scaler.pkl")
columns = joblib.load("columns.pkl")

def predict_price(bedrooms, bathrooms, sqft_living, sqft_lot, floors,
                  waterfront, view, condition, sqft_above,
                  sqft_basement, yr_built, yr_renovated, year, month):

    # Create dataframe from user input
    input_data = pd.DataFrame([[bedrooms, bathrooms, sqft_living, sqft_lot, floors,
                                waterfront, view, condition, sqft_above,
                                sqft_basement, yr_built, yr_renovated, year, month]],
                              columns=['bedrooms','bathrooms','sqft_living','sqft_lot','floors',
                                       'waterfront','view','condition','sqft_above',
                                       'sqft_basement','yr_built','yr_renovated','year','month'])

    # Match training columns
    input_data = input_data.reindex(columns=columns, fill_value=0)

    # Scale input
    input_scaled = scaler.transform(input_data)

    # Predict log price
    log_price = model.predict(input_scaled)

    # Convert back to original price
    price = np.exp(log_price)

    return f"Predicted House Price: ₹ {int(price[0])}"


# Gradio Interface
interface = gr.Interface(
    fn=predict_price,
    inputs=[
        gr.Number(label="Bedrooms", value=3),
        gr.Number(label="Bathrooms", value=2),
        gr.Number(label="Sqft Living", value=1800),
        gr.Number(label="Sqft Lot", value=4000),
        gr.Number(label="Floors", value=1),
        gr.Number(label="Waterfront (0/1)", value=0),
        gr.Number(label="View (0-4)", value=0),
        gr.Number(label="Condition (1-5)", value=3),
        gr.Number(label="Sqft Above", value=1500),
        gr.Number(label="Sqft Basement", value=300),
        gr.Number(label="Year Built", value=2005),
        gr.Number(label="Year Renovated", value=0),
        gr.Number(label="Year Sold", value=2014),
        gr.Number(label="Month Sold", value=5)
    ],
    outputs="text",
    title="House Price Prediction App"
)

interface.launch(debug = True,share = False)