gradio_app.py

import gradio as gr
import pandas as pd
import joblib as jb

# Load the trained pipeline
# Make sure HousePricePredictorPipeline.pkl is in the same directory as this script
MODEL_PATH = "HousePricePredictorPipeline.pkl"
pipe = jb.load(MODEL_PATH)

# Expected feature schema
NUM_FEATURES = ["area","parking","bedrooms","bathrooms","stories"]
CAT_FEATURES = ["furnishingstatus","mainroad","guestroom","basement",
                "hotwaterheating","airconditioning","prefarea"]

ALL_COLUMNS = NUM_FEATURES + CAT_FEATURES

YES_NO = ["yes","no"]
FURNISHING = ["unfurnished","semi-furnished","furnished"]

def predict_price(area, parking, bedrooms, bathrooms, stories,
                  furnishingstatus, mainroad, guestroom, basement,
                  hotwaterheating, airconditioning, prefarea):
    # Build a single-row DataFrame that matches the training-time schema
    row = {
        "area": area,
        "parking": int(parking),
        "bedrooms": int(bedrooms),
        "bathrooms": int(bathrooms),
        "stories": int(stories),
        "furnishingstatus": furnishingstatus,
        "mainroad": mainroad,
        "guestroom": guestroom,
        "basement": basement,
        "hotwaterheating": hotwaterheating,
        "airconditioning": airconditioning,
        "prefarea": prefarea
    }
    X = pd.DataFrame([row], columns=ALL_COLUMNS)
    pred = pipe.predict(X)[0]
    return float(pred)

with gr.Blocks(title="House Price Predictor") as demo:
    gr.Markdown("# 🏠 House Price Predictor")
    gr.Markdown(
        "Provide home features and get an estimated price. "
        "This app uses your trained scikit-learn pipeline."
    )

    with gr.Row():
        with gr.Column():
            area = gr.Number(label="Area (sq ft)", value=2000, precision=0)
            parking = gr.Slider(label="Parking Spots", value=1, minimum=0, maximum=5, step=1)
            bedrooms = gr.Slider(label="Bedrooms", value=3, minimum=0, maximum=10, step=1)
            bathrooms = gr.Slider(label="Bathrooms", value=2, minimum=0, maximum=10, step=1)
            stories = gr.Slider(label="Stories", value=2, minimum=0, maximum=10, step=1)

        with gr.Column():
            furnishingstatus = gr.Dropdown(FURNISHING, value="semi-furnished", label="Furnishing Status")
            mainroad = gr.Dropdown(YES_NO, value="yes", label="On Main Road?")
            guestroom = gr.Dropdown(YES_NO, value="no", label="Guest Room?")
            basement = gr.Dropdown(YES_NO, value="no", label="Basement?")
            hotwaterheating = gr.Dropdown(YES_NO, value="no", label="Hot Water Heating?")
            airconditioning = gr.Dropdown(YES_NO, value="yes", label="Air Conditioning?")
            prefarea = gr.Dropdown(YES_NO, value="no", label="Preferred Area?")

    btn = gr.Button("Predict Price")
    output = gr.Number(label="Predicted Price (same units as your training data)")

    btn.click(
        fn=predict_price,
        inputs=[area, parking, bedrooms, bathrooms, stories,
                furnishingstatus, mainroad, guestroom, basement,
                hotwaterheating, airconditioning, prefarea],
        outputs=output
    )

    gr.Markdown(
        "Tip: Ensure **HousePricePredictorPipeline.pkl** is in the same folder.\n"
        "Run with: `python gradio_app.py` and open the link in your browser."
    )

if __name__ == "__main__":
    demo.launch()