app.py

import joblib
import gradio as gr
import numpy as np

# Load the pre-trained model
model = joblib.load("house_price_model.pkl")

# Define a prediction function
def predict_price(area):
    # Convert input to the correct format
    area_array = np.array([[float(area)]])  # Reshape to 2D array
    predicted_price = model.predict(area_array)
    return f"The predicted price for {area} sq ft house is ${predicted_price[0]:,.2f}"

# Create a Gradio interface
interface = gr.Interface(
    fn=predict_price,  # Function to call for predictions
    inputs=gr.Number(label="Enter Area (sq ft)"),  # Input field
    outputs=gr.Textbox(label="Predicted Price"),  # Output display
    title="House Price Prediction",
    description="Enter the area of a house (in square feet), and this application will predict its price."
)

# Launch the app
interface.launch()