ames-house-price-predictor

import torch
from huggingface_hub import hf_hub_download

# Assuming you have the RegularizedModel class defined as above
# input_features and output_features would be derived from your preprocessed data
input_features = 289 # Example value, replace with actual if different
output_features = 1

model = RegularizedModel(input_features, output_features)

# Download the model weights from Hugging Face Hub
model_path = hf_hub_download(repo_id="ShiroOnigami23/ames-house-price-predictor", filename="regularized_model.pth")
model.load_state_dict(torch.load(model_path))
model.eval()

# Example prediction (replace with your actual preprocessed input)
# dummy_input = torch.randn(1, input_features) # Ensure input is scaled like training data
# with torch.no_grad():
#    prediction = model(dummy_input)
# print(f"Predicted price (scaled): {prediction.item()}")

# To inverse transform the prediction to original price scale, you'd use your target_scaler
# original_price = target_scaler.inverse_transform(prediction.numpy())