Update inference.py

inference.py

@@ -129,50 +129,29 @@ def evaluate_performance(args, model, scaler, device):
 
 # --- 5. 메인 로직 ---
 if __name__ == '__main__':
-    final_output = {}
-    try:
-        model, scaler, device = load_model_and_scaler(args)
-        output_dir = 'pred_results'
-        os.makedirs(output_dir, exist_ok=True)
-
-        if args.predict_input_file:
-            print("--- Running in Single Prediction Mode ---", file=sys.stderr)
-            prediction = predict_future(args, model, scaler, device)
-            
-            # ⭐️ 결과 파일을 저장하고, 그 경로를 final_output에 추가
-            output_path = os.path.join(output_dir, 'prediction_future.npy')
-            np.save(output_path, prediction)
-            print(f"Future prediction saved to {output_path}", file=sys.stderr)
-            
-            final_output = {
-                "status": "success",
-                "mode": "single_prediction",
-                "files": [output_path] # 👈 파일 경로 추가
-            }
-
-        elif args.evaluate_file:
-            print("--- Running in Rolling Evaluation Mode ---", file=sys.stderr)
-            eval_preds, eval_trues = evaluate_performance(args, model, scaler, device)
-            
-            # ⭐️ 결과 파일들을 저장하고, 그 경로들을 final_output에 추가
-            pred_path = os.path.join(output_dir, 'evaluation_preds.npy')
-            true_path = os.path.join(output_dir, 'evaluation_trues.npy')
-            np.save(pred_path, eval_preds)
-            np.save(true_path, eval_trues)
-            print(f"Evaluation results saved to {output_dir}", file=sys.stderr)
-
-            mae, mse, _, _, _ = metric(eval_preds, eval_trues)
-            final_output = {
-                "status": "success",
-                "mode": "rolling_evaluation",
-                "mse": mse,
-                "mae": mae,
-                "files": [pred_path, true_path] # 👈 파일 경로들 추가
-            }
-        else:
-            final_output = {"status": "error", "message": "No mode selected."}
-
-    except Exception as e:
-        final_output = {"status": "error", "message": traceback.format_exc()}
-
-    print(json.dumps(final_output, indent=2))
+    # 결과 저장 폴더 생성
+    output_dir = 'pred_results'
+    os.makedirs(output_dir, exist_ok=True)
+    
+    model, scaler, device = load_model_and_scaler(args)
+
+    if args.predict_input_file:
+        print("\n--- Running in Single Prediction Mode ---")
+        prediction = predict_future(args, model, scaler, device)
+        output_path = os.path.join(output_dir, 'prediction_future.npy')
+        np.save(output_path, prediction)
+        print(f"\n✅ Future prediction saved to {output_path}")
+
+    elif args.evaluate_file:
+        print("\n--- Running in Rolling Evaluation Mode ---")
+        eval_preds, eval_trues = evaluate_performance(args, model, scaler, device)
+        pred_path = os.path.join(output_dir, 'evaluation_preds.npy')
+        true_path = os.path.join(output_dir, 'evaluation_trues.npy')
+        np.save(pred_path, eval_preds)
+        np.save(true_path, eval_trues)
+        print(f"\n✅ Evaluation results saved to {output_dir}")
+        print(f"   - Predictions shape: {eval_preds.shape}")
+        print(f"   - Truths shape: {eval_trues.shape}")
+        
+    else:
+        print("오류: --predict_input_file 또는 --evaluate_file 중 하나의 모드를 선택해야 합니다.")