Update app.py

app.py

@@ -81,8 +81,16 @@ def init_model():
 
             print("[INFO] Loading RF-DETR model (CPU mode)...")
             MODEL = RFDETRSegPreview(pretrain_weights=CHECKPOINT_PATH)
-            MODEL.optimize_for_inference()
             
+            # Try to optimize for inference
+            try:
+                print("[INFO] Optimizing model for inference...")
+                MODEL.optimize_for_inference()
+                print("[INFO] Model optimization complete")
+            except Exception as e:
+                print(f"[WARN] optimize_for_inference() skipped/failed: {e}")
+            
+            print("[INFO] Model ready for inference")
             return MODEL
         except Exception as e:
             print(f"[ERROR] Model initialization failed: {e}")
@@ -120,7 +128,7 @@ def annotate_segmentation(image: Image.Image, detections: sv.Detections,
     Args:
         image: Input PIL Image
         detections: Supervision Detections object
-        show_labels: Whether to show "ROI" label text
+        show_labels: Whether to show "Tulsi" label text
         show_confidence: Whether to show confidence scores
     """
     try:
@@ -160,7 +168,7 @@ def annotate_segmentation(image: Image.Image, detections: sv.Detections,
             for conf in detections.confidence:
                 label_parts = []
                 if show_labels:
-                    label_parts.append("Disease")
+                    label_parts.append("Disease")   #class name: Disease
                 if show_confidence:
                     label_parts.append(f"{float(conf):.2f}")
                 labels.append(" ".join(label_parts))
@@ -327,7 +335,9 @@ def predict():
 
 
 if __name__ == "__main__":
-    print("Starting disease Segmentation Server")
+    print("\n" + "="*60)
+    print("Starting Tulsi Leaf Segmentation Server")
+    print("="*60 + "\n")
     
     # Warm model in background thread
     def warm():