app.py

# app.py - The code that runs on the Hugging Face server

import gradio as gr
import joblib
import pandas as pd
import os

# --- Load the Model and Columns ONCE when the app starts ---
# This is efficient because the model stays loaded in memory on the server.
try:
    model = joblib.load("housing_model.joblib")
    model_columns = joblib.load("model_columns.joblib")
except FileNotFoundError:
    # Handle the case where files are in a subdirectory (sometimes happens on HF)
    model = joblib.load(os.path.join(os.path.dirname(__file__), "housing_model.joblib"))
    model_columns = joblib.load(os.path.join(os.path.dirname(__file__), "model_columns.joblib"))


# --- This is the core prediction function ---
def predict_price(sqft, bedrooms, house_age, condition, year_sold, interest_rate, region, sub_type, style, has_garage, has_pool):
    
    # 1. Create a DataFrame from the input data
    input_data = {
        'SquareFootage': sqft, 'Bedrooms': bedrooms, 'HouseAge': house_age,
        'PropertyCondition': condition, 'HasGarage': has_garage, 'HasPool': has_pool,
        'YearSold': year_sold, 'InterestRate': interest_rate,
        'Region': region, 'SubType': sub_type, 'ArchitecturalStyle': style
    }
    input_df = pd.DataFrame([input_data])
    
    # 2. Preprocess the data exactly like in training
    input_processed = pd.get_dummies(input_df)
    final_input = input_processed.reindex(columns=model_columns, fill_value=0)
    
    # 3. Make the prediction
    predicted_price = model.predict(final_input)[0]
    
    # 4. Return a nicely formatted string
    return f"${predicted_price:,.0f}"

# --- Create the Gradio Interface ---
# This automatically creates a simple web UI and a usable API endpoint.
demo = gr.Interface(
    fn=predict_price,
    inputs=[
        gr.Number(label="Square Footage", value=2500),
        gr.Number(label="Bedrooms", value=4),
        gr.Number(label="House Age (years)", value=15),
        gr.Slider(label="Property Condition", minimum=1, maximum=10, step=1, value=8),
        gr.Number(label="Year Sold", value=2024),
        gr.Number(label="Interest Rate (%)", value=5.5),
        gr.Radio(['Sunbelt', 'Pacific Northwest', 'Rust Belt', 'New England', 'Mountain West'], label="Region", value="Sunbelt"),
        gr.Radio(['Urban', 'Suburban', 'Rural', 'Historic District'], label="Sub-Type", value="Suburban"),
        gr.Radio(['Modern', 'Ranch', 'Colonial', 'Craftsman', 'Victorian'], label="Architectural Style", value="Colonial"),
        gr.Checkbox(label="Has Garage?", value=True),
        gr.Checkbox(label="Has Pool?", value=False)
    ],
    outputs=gr.Textbox(label="Predicted Price"),
    title="AI House Price Predictor",
    description="Describe a property, and our AI will estimate its market value. Powered by a model trained on 9.2GB of simulated data."
)

# Launch the app
demo.launch()