File size: 3,422 Bytes
13c4e9f cf82ef8 13c4e9f cf82ef8 13c4e9f 581b1cc 13c4e9f 581b1cc cf82ef8 581b1cc cf82ef8 581b1cc cf82ef8 581b1cc 13c4e9f cf82ef8 13c4e9f cf82ef8 13c4e9f cf82ef8 13c4e9f | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 | """
Gradio app for House Price Prediction Model
Deploy this to Hugging Face Spaces for interactive inference
"""
import gradio as gr # type: ignore
import joblib # type: ignore
import pandas as pd
from huggingface_hub import hf_hub_download # type: ignore
from typing import Any
print("🔄 Downloading model files...")
# Download model files
try:
model_path: str = hf_hub_download( # type: ignore
repo_id="niru-nny/house-price-prediction",
filename="house_price_model.joblib"
)
print(f"✅ Model downloaded: {model_path}")
pipeline_path: str = hf_hub_download( # type: ignore
repo_id="niru-nny/house-price-prediction",
filename="preprocessing_pipeline.joblib"
)
print(f"✅ Pipeline downloaded: {pipeline_path}")
# Load model and pipeline
print("🔄 Loading model and pipeline...")
model: Any = joblib.load(model_path) # type: ignore
pipeline: Any = joblib.load(pipeline_path) # type: ignore
print("✅ Model and pipeline loaded successfully!")
except Exception as e:
print(f"❌ Error loading model: {e}")
raise
def predict_price(
longitude: float,
latitude: float,
housing_median_age: int,
total_rooms: int,
total_bedrooms: int,
population: int,
households: int,
median_income: float,
ocean_proximity: str
) -> str:
"""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: Any = pipeline.transform(input_data) # type: ignore
prediction: float = model.predict(processed_data)[0] # type: ignore
return f"${prediction:,.2f}"
# Create Gradio interface
demo: Any = gr.Interface( # type: ignore
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()
|