app.py

import gradio as gr
import pickle

# Load the trained model
with open('model.pkl', 'rb') as file:
    model = pickle.load(file)

# Define the prediction function
def predict_price(area, bedrooms, bathrooms):
    features = [[area, bedrooms, bathrooms]]
    prediction = model.predict(features)
    return float(prediction[0])


# Create a Gradio interface
interface = gr.Interface(
    fn=predict_price,
    inputs=["number", "number", "number"],
    outputs="number",
    title="House Price Predictor",
    description="Enter the area (in sqft), number of bedrooms, and number of bathrooms to predict the house price."
)

# Launch the interface
interface.launch()