diff --git a/src/App.js b/src/App.js
index 6df32d9..9dc7700 100755
--- a/src/App.js
+++ b/src/App.js
@@ -311,9 +311,9 @@ function App() {
 								</Tabs.List>
 							</Box>
 							<Box className="px-4 flex items-center gap-4 w-fit">
-								<Button disabled={isLoading} onClick={() => setOpenConfirm(true)} leftSection={<IconReportAnalytics size={14} />} color="#caa32c">
+								{/* <Button disabled={isLoading} onClick={() => setOpenConfirm(true)} leftSection={<IconReportAnalytics size={14} />} color="#caa32c">
 									Training
-								</Button>
+								</Button> */}
 								<Button disabled={!clickData || isLoading} onClick={handleClearSelect} leftSection={<IconRefreshDot size={14} />} color="orange">
 									Reset select
 								</Button>
diff --git a/src/server/train.py b/src/server/train.py
index d74c397..87c4146 100755
--- a/src/server/train.py
+++ b/src/server/train.py
@@ -15,6 +15,8 @@ IMAGE_EXTENSION = ".png"
 LOG_FILE = "training_logs.log"
 LATEST_DETECT_IMAGES = "latest_detect/images"
 LATEST_DETECT_LABELS = "latest_detect/labels"
+image_folder=""
+label_folder=""
 
 def get_label_names():
     conn = sqlite3.connect("my_database.db")
@@ -75,6 +77,7 @@ for folder in [train_img_folder, train_lbl_folder, val_img_folder, val_lbl_folde
 
 # 📂 Get image list
 image_files = [f for f in os.listdir(image_folder) if f.endswith(IMAGE_EXTENSION)]
+label_files = [f for f in os.listdir(label_folder) if os.path.exists(label_folder)]
 
 # 🌀 Shuffle images
 random.shuffle(image_files)
@@ -86,18 +89,18 @@ split_idx = int(len(image_files) * train_ratio)
 train_files = image_files[:split_idx]
 val_files = image_files[split_idx:]
 
-latest_img_folder = [f for f in os.listdir(LATEST_DETECT_IMAGES) if f.endswith(IMAGE_EXTENSION)]
-train_files += latest_img_folder
-# Copy latest detect images to train folder
-for img_file in latest_img_folder:
-    img_path = os.path.join(LATEST_DETECT_IMAGES, img_file)
-    label_file = os.path.splitext(img_file)[0] + ".txt"
-    label_path = os.path.join(LATEST_DETECT_LABELS, label_file)
-    
-    if os.path.exists(img_path) and os.path.exists(label_path) and not os.path.exists(os.path.join(image_folder, img_file)):
-        shutil.copy(img_path, os.path.join(image_folder, img_file))
-        shutil.copy(label_path, os.path.join(label_folder, label_file))
-
+def copy_file_latest_training():
+    latest_img_folder = [f for f in os.listdir(LATEST_DETECT_IMAGES) if f.endswith(IMAGE_EXTENSION)]
+    train_files += latest_img_folder
+    # Copy latest detect images to train folder
+    for img_file in latest_img_folder:
+        img_path = os.path.join(LATEST_DETECT_IMAGES, img_file)
+        label_file = os.path.splitext(img_file)[0] + ".txt"
+        label_path = os.path.join(LATEST_DETECT_LABELS, label_file)
+        
+        if os.path.exists(img_path) and os.path.exists(label_path) and not os.path.exists(os.path.join(image_folder, img_file)):
+            shutil.copy(img_path, os.path.join(image_folder, img_file))
+            shutil.copy(label_path, os.path.join(label_folder, label_file))
 
 def log_message(message: str):
     """Ghi log vào file"""
@@ -266,35 +269,39 @@ def train_yolo_model(
     call_reload_model_api()
     clear_images_source()   
 
-# 🚀 Copy files
-train_copied_imgs, train_copied_lbls = move_files(train_files, train_img_folder, train_lbl_folder)
-val_copied_imgs, val_copied_lbls = move_files(val_files, val_img_folder, val_lbl_folder)
+if(len(label_folder) >= 20):
+    copy_file_latest_training()
 
-# Create yml file
-create_dataset_yaml(dataset_folder, class_names)
+    # 🚀 Copy files
+    train_copied_imgs, train_copied_lbls = move_files(train_files, train_img_folder, train_lbl_folder)
+    val_copied_imgs, val_copied_lbls = move_files(val_files, val_img_folder, val_lbl_folder)
+
+    # Create yml file
+    create_dataset_yaml(dataset_folder, class_names)
 
 
-# 🏁 Log summary
-with open(LOG_FILE, "a") as log:
-    log.write(f"\n[{datetime.datetime.now()}]  Dataset split completed\n")
-    log.write(f"Source folder: {image_folder}\n")
-    log.write(f"Dataset saved in: {dataset_folder}\n")
-    log.write(f"Planned Train: {len(train_files)} images, Val: {len(val_files)} images\n")
-    log.write(f"Actual Train: {train_copied_imgs} images, {train_copied_lbls} labels\n")
-    log.write(f"Actual Val: {val_copied_imgs} images, {val_copied_lbls} labels\n")
-
-
-if(train_copied_imgs <=0 or train_copied_lbls <= 0 or val_copied_imgs <=0 or val_copied_lbls <=0):
+    # 🏁 Log summary
     with open(LOG_FILE, "a") as log:
-        log.write(f"\n[{datetime.datetime.now()}]  Data not qualified\n")
-        log.write("=" * 50 + "\n")
-else:
-    train_yolo_model(pretrained_model = get_latest_model(
-        trained_model_folder=TRAINED_MODEL_FOLDER, 
-        default_model=PRETRAINED_MODEL), 
-        dataset_folder = dataset_folder,
-        epochs = 50,
-        name=today_str,
-        project_name=model_folder_name,
-        )
+        log.write(f"\n[{datetime.datetime.now()}]  Dataset split completed\n")
+        log.write(f"Source folder: {image_folder}\n")
+        log.write(f"Dataset saved in: {dataset_folder}\n")
+        log.write(f"Planned Train: {len(train_files)} images, Val: {len(val_files)} images\n")
+        log.write(f"Actual Train: {train_copied_imgs} images, {train_copied_lbls} labels\n")
+        log.write(f"Actual Val: {val_copied_imgs} images, {val_copied_lbls} labels\n")
 
+
+    if(train_copied_imgs <=0 or train_copied_lbls <= 0 or val_copied_imgs <=0 or val_copied_lbls <=0):
+        with open(LOG_FILE, "a") as log:
+            log.write(f"\n[{datetime.datetime.now()}]  Data not qualified\n")
+            log.write("=" * 50 + "\n")
+    else:
+        train_yolo_model(pretrained_model = get_latest_model(
+            trained_model_folder=TRAINED_MODEL_FOLDER, 
+            default_model=PRETRAINED_MODEL), 
+            dataset_folder = dataset_folder,
+            epochs = 50,
+            name=today_str,
+            project_name=model_folder_name,
+            )
+else:
+    log_message(f"Date: {today_str}. Need more image for training!!")