app.py

import gradio as gr
import joblib
import numpy as np

# Load trained model
model = joblib.load("model/house_price_model.pkl")

# Define prediction function
def predict_price(rm, lstat, ptratio):
    input_data = np.array([[rm, lstat, ptratio]])
    prediction = model.predict(input_data)[0]
    return round(prediction, 2)

# Define Gradio interface
iface = gr.Interface(
    fn=predict_price,
    inputs=[
        gr.Slider(3.0, 9.0, step=0.1, label="Average Rooms (RM)"),
        gr.Slider(1.0, 40.0, step=0.1, label="Lower Status Population (%) (LSTAT)"),
        gr.Slider(12.0, 22.0, step=0.1, label="Pupil-Teacher Ratio (PTRATIO)")
    ],
    outputs=gr.Number(label="Predicted House Price (in $1000s)"),
    title="🏠 Boston Housing Price Predictor",
    description="Enter the house features to predict the price using a linear regression model.",
)

# Launch app
iface.launch()