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"""
Gradio app for House Price Prediction Model
Deploy this to Hugging Face Spaces for interactive inference
"""
import gradio as gr
import joblib
import pandas as pd
from huggingface_hub import hf_hub_download
# Download model files
model_path = hf_hub_download(
repo_id="niru-nny/house-price-prediction",
filename="house_price_model.joblib"
)
pipeline_path = hf_hub_download(
repo_id="niru-nny/house-price-prediction",
filename="preprocessing_pipeline.joblib"
)
# Load model and pipeline
model = joblib.load(model_path)
pipeline = joblib.load(pipeline_path)
def predict_price(longitude, latitude, housing_median_age, total_rooms,
total_bedrooms, population, households, median_income,
ocean_proximity):
"""Predict house price based on input features"""
# Create input dataframe
input_data = pd.DataFrame({
'longitude': [longitude],
'latitude': [latitude],
'housing_median_age': [housing_median_age],
'total_rooms': [total_rooms],
'total_bedrooms': [total_bedrooms],
'population': [population],
'households': [households],
'median_income': [median_income],
'ocean_proximity': [ocean_proximity]
})
# Preprocess and predict
processed_data = pipeline.transform(input_data)
prediction = model.predict(processed_data)[0]
return f"${prediction:,.2f}"
# Create Gradio interface
demo = gr.Interface(
fn=predict_price,
inputs=[
gr.Slider(-124.5, -114.0, value=-122.23, label="Longitude"),
gr.Slider(32.5, 42.0, value=37.88, label="Latitude"),
gr.Slider(0, 52, value=41, step=1, label="Housing Median Age"),
gr.Slider(0, 40000, value=880, step=10, label="Total Rooms"),
gr.Slider(0, 6500, value=129, step=1, label="Total Bedrooms"),
gr.Slider(0, 35000, value=322, step=1, label="Population"),
gr.Slider(0, 6000, value=126, step=1, label="Households"),
gr.Slider(0, 15, value=8.3252, step=0.1, label="Median Income (in $10,000s)"),
gr.Dropdown(
choices=["NEAR BAY", "INLAND", "<1H OCEAN", "NEAR OCEAN", "ISLAND"],
value="NEAR BAY",
label="Ocean Proximity"
)
],
outputs=gr.Textbox(label="Predicted House Price"),
title="🏠 California House Price Prediction",
description="Predict California house prices based on location and features",
examples=[
[-122.23, 37.88, 41, 880, 129, 322, 126, 8.3252, "NEAR BAY"],
[-121.22, 39.43, 7, 1430, 244, 515, 226, 3.8462, "INLAND"],
]
)
if __name__ == "__main__":
demo.launch()
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