demo.py

#!/usr/bin/env python3
"""
Machine Learning Price Predictor Demo
=====================================

This script demonstrates how to use the trained price prediction model
to predict house prices based on various features.
"""

import sys
import os
sys.path.append('src')

from simple_model import predict_price_from_saved_model

def main():
    print("🏠 Machine Learning Price Predictor Demo")
    print("=" * 50)
    
    model_path = 'models/simple_price_model.pkl'
    
    # Check if model exists
    if not os.path.exists(model_path):
        print("❌ Model not found! Please run 'python src/simple_model.py' first to train the model.")
        return
    
    print("✅ Model loaded successfully!")
    print("\nThis model predicts house prices based on:")
    print("• Size (square feet)")
    print("• Number of bedrooms")
    print("• Number of bathrooms") 
    print("• Location score (1-10)")
    print("• Age (years)")
    print("• Garage (0=No, 1=Yes)")
    
    # Demo predictions
    print("\n🔮 Sample Predictions:")
    print("-" * 30)
    
    sample_houses = [
        {
            'name': 'Starter Home',
            'size': 1200,
            'bedrooms': 2,
            'bathrooms': 1,
            'location_score': 6.5,
            'age': 15,
            'garage': 0
        },
        {
            'name': 'Family Home',
            'size': 2000,
            'bedrooms': 3,
            'bathrooms': 2,
            'location_score': 8.0,
            'age': 7,
            'garage': 1
        },
        {
            'name': 'Luxury Home',
            'size': 3000,
            'bedrooms': 5,
            'bathrooms': 4,
            'location_score': 9.5,
            'age': 2,
            'garage': 1
        }
    ]
    
    for house in sample_houses:
        try:
            predicted_price = predict_price_from_saved_model(
                model_path,
                house['size'],
                house['bedrooms'],
                house['bathrooms'],
                house['location_score'],
                house['age'],
                house['garage']
            )
            
            print(f"\n🏡 {house['name']}:")
            print(f"   Size: {house['size']:,} sqft")
            print(f"   Bedrooms: {house['bedrooms']}")
            print(f"   Bathrooms: {house['bathrooms']}")
            print(f"   Location Score: {house['location_score']}")
            print(f"   Age: {house['age']} years")
            print(f"   Garage: {'Yes' if house['garage'] else 'No'}")
            print(f"   💰 Predicted Price: ${predicted_price:,.2f}")
            
        except Exception as e:
            print(f"❌ Error predicting price for {house['name']}: {e}")
    
    # Interactive prediction
    print("\n" + "=" * 50)
    print("🎯 Try Your Own Prediction!")
    print("=" * 50)
    
    try:
        print("\nEnter house details:")
        size = float(input("Size (square feet): "))
        bedrooms = int(input("Bedrooms: "))
        bathrooms = int(input("Bathrooms: "))
        location_score = float(input("Location score (1-10): "))
        age = int(input("Age (years): "))
        garage = int(input("Garage (0=No, 1=Yes): "))
        
        predicted_price = predict_price_from_saved_model(
            model_path, size, bedrooms, bathrooms, location_score, age, garage
        )
        
        print(f"\n🎉 Predicted Price: ${predicted_price:,.2f}")
        
    except KeyboardInterrupt:
        print("\n\n👋 Thanks for using the Price Predictor!")
    except ValueError:
        print("❌ Please enter valid numbers for all fields.")
    except Exception as e:
        print(f"❌ Error: {e}")

if __name__ == "__main__":
    main()