feat: Update inference code

inference.py

@@ -0,0 +1,39 @@
+
+import pandas as pd
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.utils.data import Dataset, DataLoader
+from model import HousePriceModel
+from data import  HousePriceTestDataset
+
+# Load Data
+data_path = 'data/public_dataset.csv'  # Update with the path of your data file
+data = pd.read_csv(data_path)
+normalize_columns = {'橫坐標': 'min-max', '縱坐標': 'min-max'}
+selected_features = ['橫坐標', '縱坐標']
+target_column = '單價'
+dataset = HousePriceTestDataset(data, selected_features, normalize_columns)
+data_loader = DataLoader(dataset, batch_size=len(dataset), shuffle=False)
+
+# Load Model
+model_path = 'model.pth'  # Update with the path of your trained model file
+input_dim = len(selected_features)
+model = HousePriceModel(input_dim)
+model.load_state_dict(torch.load(model_path))
+model.eval()
+
+# Inference
+with torch.no_grad():
+    for batch in data_loader:
+        features = batch['features']
+        predictions = model(features).numpy().flatten()
+
+# Create a DataFrame to hold the IDs and predicted prices
+ids = [f"PU-{i}" for i in range(1, len(predictions) + 1)]  # Adjust ID format as needed
+predicted_prices_df = pd.DataFrame({"ID": ids, "predicted_price": predictions})
+
+# Save to CSV
+output_csv_path = 'predicted_prices.csv'  # Update with the desired output path
+predicted_prices_df.to_csv(output_csv_path, index=False)
+print(f"Predictions saved to {output_csv_path}")