import gradio as gr import pickle import numpy as np # Load model and scaler model = pickle.load(open('model.pkl', 'rb')) scaler = pickle.load(open('scaler.pkl', 'rb')) def predict_price(area, bedrooms, bathrooms, stories, parking, mainroad, guestroom, basement, hotwaterheating, airconditioning, prefarea, furnishingstatus): # Encode inputs mainroad = 1.0 if mainroad == "Yes" else 0.0 guestroom = 1.0 if guestroom == "Yes" else 0.0 basement = 1.0 if basement == "Yes" else 0.0 hotwaterheating = 1.0 if hotwaterheating == "Yes" else 0.0 airconditioning = 1.0 if airconditioning == "Yes" else 0.0 prefarea = 1.0 if prefarea == "Yes" else 0.0 furnishing_map = {"furnished": 0.0, "semi-furnished": 1.0, "unfurnished": 2.0} furnishingstatus = furnishing_map[furnishingstatus] # Create input array input_data = np.array([[area, bedrooms, bathrooms, stories, mainroad, guestroom, basement, hotwaterheating, airconditioning, parking, prefarea, furnishingstatus]]) # Scale and predict input_scaled = scaler.transform(input_data) prediction = model.predict(input_scaled) return f" {prediction[0]:,.2f}" # Create Gradio interface demo = gr.Interface( fn=predict_price, inputs=[ gr.Number(label="Area (sq ft)", value=5000), gr.Dropdown(choices=[1, 2, 3, 4, 5, 6], label="Bedrooms", value=3), gr.Dropdown(choices=[1, 2, 3, 4], label="Bathrooms", value=2), gr.Dropdown(choices=[1, 2, 3, 4], label="Stories", value=2), gr.Dropdown(choices=[0, 1, 2, 3], label="Parking", value=1), gr.Dropdown(choices=["Yes", "No"], label="Main Road", value="Yes"), gr.Dropdown(choices=["Yes", "No"], label="Guest Room", value="No"), gr.Dropdown(choices=["Yes", "No"], label="Basement", value="No"), gr.Dropdown(choices=["Yes", "No"], label="Hot Water Heating", value="No"), gr.Dropdown(choices=["Yes", "No"], label="Air Conditioning", value="Yes"), gr.Dropdown(choices=["Yes", "No"], label="Preferred Area", value="Yes"), gr.Dropdown(choices=["furnished", "semi-furnished", "unfurnished"], label="Furnishing", value="furnished") ], outputs=gr.Textbox(label="Predicted Price"), title="🏠 House Price Prediction", description="Enter house features to predict the price" ) demo.launch(share=True)