Update inference.py

inference.py

@@ -35,7 +35,6 @@ txt_results_file = os.path.join(safe_data_dir, "inference_results.txt")
 csv_results_file = os.path.join(safe_data_dir, "inference_results.csv")
 
 # --- إعداد بيانات inference ---
-
 inference_dataset = VRP_Dataset(
     dataset_size=1,
     num_nodes=params['num_nodes'],
@@ -69,6 +68,7 @@ nn_actor.nearest_neighbors()
 # --- استدلال وتخزين النتائج ---
 dataloader = DataLoader(inference_dataset, batch_size=1, collate_fn=inference_dataset.collate)
 output_lines = []
+summary_text = ""
 
 for batch in dataloader:
     with torch.no_grad():
@@ -80,7 +80,6 @@ for batch in dataloader:
         nn_time = nn_output['total_time'].item()
 
         improvement = (nn_time - total_time) / nn_time * 100
-
         timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
         result_text = (
@@ -93,6 +92,12 @@ for batch in dataloader:
         print(result_text)
         output_lines.append(result_text)
 
+        summary_text = (
+            f"Inference Time: {timestamp}\n"
+            f"Actor Cost = {total_time:.4f} | NN Cost = {nn_time:.4f} "
+            f"| Improvement = {improvement:.2f}%"
+        )
+
         # حفظ النتائج إلى CSV
         write_header = not os.path.exists(csv_results_file)
         with open(csv_results_file, 'a', newline='') as csvfile:
@@ -106,4 +111,5 @@ with open(txt_results_file, 'a') as f:
     f.write("\n".join(output_lines))
     f.write("\n=============================\n")
 
-print(f"\n✅ Results saved to:\n- {txt_results_file}\n- {csv_results_file}")
+# هذا السطر هو الجديد لعرض النتائج في واجهة Gradio
+print(f"\n🔍 Summary for UI:\n{summary_text}")