Update inference.py

inference.py

@@ -4,6 +4,7 @@ import json
 import os
 import csv
 from datetime import datetime
+import numpy as np
 
 from nets.model import Model
 from Actor.actor import Actor
@@ -52,7 +53,7 @@ model = Model(
     decoder_input_size=params["decoder_input_size"]
 )
 model.load_state_dict(torch.load(model_path, map_location=device))
-model.eval()  # ← هذا هو التعديل المهم
+model.eval()
 
 # --- تهيئة الممثل والـ NN Actor ---
 actor = Actor(model=model,
@@ -62,7 +63,6 @@ actor = Actor(model=model,
               device=device,
               normalize=False)
 
-
 nn_actor = Actor(model=None, num_movers=1, num_neighbors_action=1, device=device)
 nn_actor.nearest_neighbors()
 
@@ -70,6 +70,7 @@ nn_actor.nearest_neighbors()
 dataloader = DataLoader(inference_dataset, batch_size=1, collate_fn=inference_dataset.collate)
 output_lines = []
 summary_text = ""
+input_summary = ""
 
 for batch in dataloader:
     with torch.no_grad():
@@ -83,23 +84,39 @@ for batch in dataloader:
         improvement = (nn_time - total_time) / nn_time * 100
         timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
 
+        # --- ملخص البيانات ---
+        coords = batch['coords'][0].cpu().numpy()  # shape: (N, 2)
+        coords_preview = "\n".join(
+            [f"Node {i}: x={x:.3f}, y={y:.3f}" for i, (x, y) in enumerate(coords[:5])]
+        )
+        if coords.shape[0] > 5:
+            coords_preview += f"\n... (showing 5 of {coords.shape[0]} nodes)"
+
+        input_summary = f"📌 **Input Coordinates Preview:**\n{coords_preview}"
+
+        # --- نص كامل للعرض والطباعة ---
         result_text = (
+            "\n===== INPUT SUMMARY =====\n"
+            f"{coords_preview}\n"
             "\n===== INFERENCE RESULT =====\n"
             f"Time: {timestamp}\n"
-            f"Actor Model Total Cost: {total_time:.4f}\n"
-            f"Nearest Neighbor Cost : {nn_time:.4f}\n"
+            f"Actor Model Total Cost: {total_time:.4f} units\n"
+            f"Nearest Neighbor Cost : {nn_time:.4f} units\n"
             f"Improvement over NN    : {improvement:.2f}%\n"
         )
         print(result_text)
         output_lines.append(result_text)
 
+        # --- للواجهة Gradio ---
         summary_text = (
-            f"Inference Time: {timestamp}\n"
-            f"Actor Cost = {total_time:.4f} | NN Cost = {nn_time:.4f} "
-            f"| Improvement = {improvement:.2f}%"
+            f"🕒 Time: {timestamp}\n"
+            f"🚚 Actor Cost: {total_time:.4f} units\n"
+            f"📍 NN Cost: {nn_time:.4f} units\n"
+            f"📈 Improvement: {improvement:.2f}%\n\n"
+            f"{input_summary}"
         )
 
-        # حفظ النتائج إلى CSV
+        # --- CSV حفظ ---
         write_header = not os.path.exists(csv_results_file)
         with open(csv_results_file, 'a', newline='') as csvfile:
             writer = csv.writer(csvfile)
@@ -112,5 +129,5 @@ with open(txt_results_file, 'a') as f:
     f.write("\n".join(output_lines))
     f.write("\n=============================\n")
 
-# هذا السطر هو الجديد لعرض النتائج في واجهة Gradio
+# --- طباعة للـ UI
 print(f"\n🔍 Summary for UI:\n{summary_text}")