diff --git a/BETA_E_Model_T&T.ipynb b/BETA_E_Model_T&T.ipynb
index a263439..37e899a 100644
--- a/BETA_E_Model_T&T.ipynb
+++ b/BETA_E_Model_T&T.ipynb
@@ -19738,7 +19738,103 @@
       "Epoch 176/180\n",
       "256/256 [==============================] - 46s 180ms/step - loss: 0.1612 - accuracy: 0.9509 - val_loss: 0.1555 - val_accuracy: 0.9519\n",
       "Epoch 177/180\n",
-      "  6/256 [..............................] - ETA: 39s - loss: 0.0660 - accuracy: 0.9792"
+      "256/256 [==============================] - 46s 178ms/step - loss: 0.1049 - accuracy: 0.9700 - val_loss: 0.1565 - val_accuracy: 0.9423\n",
+      "Epoch 178/180\n",
+      "256/256 [==============================] - 47s 185ms/step - loss: 0.0882 - accuracy: 0.9766 - val_loss: 0.1467 - val_accuracy: 0.9551\n",
+      "Epoch 179/180\n",
+      "256/256 [==============================] - 45s 177ms/step - loss: 0.0484 - accuracy: 0.9880 - val_loss: 0.2215 - val_accuracy: 0.9295\n",
+      "Epoch 180/180\n",
+      "256/256 [==============================] - 45s 174ms/step - loss: 0.0314 - accuracy: 0.9946 - val_loss: 0.1765 - val_accuracy: 0.9487\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9551}, \u001b[0m\u001b[0;33mloss{0.1467}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1345}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9487\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1765\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m357.65 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m280.88 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m76.77 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [30] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m31\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 180)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01064\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 181/186\n",
+      "256/256 [==============================] - 51s 186ms/step - loss: 0.1757 - accuracy: 0.9451 - val_loss: 0.2124 - val_accuracy: 0.9295\n",
+      "Epoch 182/186\n",
+      "256/256 [==============================] - 47s 182ms/step - loss: 0.1258 - accuracy: 0.9612 - val_loss: 0.1451 - val_accuracy: 0.9471\n",
+      "Epoch 183/186\n",
+      "256/256 [==============================] - 46s 180ms/step - loss: 0.0908 - accuracy: 0.9714 - val_loss: 0.1544 - val_accuracy: 0.9407\n",
+      "Epoch 184/186\n",
+      "256/256 [==============================] - 46s 179ms/step - loss: 0.0597 - accuracy: 0.9814 - val_loss: 0.1675 - val_accuracy: 0.9407\n",
+      "Epoch 185/186\n",
+      "256/256 [==============================] - 47s 183ms/step - loss: 0.0437 - accuracy: 0.9885 - val_loss: 0.1770 - val_accuracy: 0.9423\n",
+      "Epoch 186/186\n",
+      "256/256 [==============================] - 46s 181ms/step - loss: 0.0309 - accuracy: 0.9934 - val_loss: 0.1718 - val_accuracy: 0.9455\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mNot loading weights\u001b[0m\u001b[0;32m[\u001b[0m\u001b[0;94mBSR:\u001b[0m\u001b[0;33macc{0.9471}, \u001b[0m\u001b[0;33mloss{0.1451}\u001b[0m\u001b[0;95m|\u001b[0m\u001b[0;94mBTR:\u001b[0m\u001b[0;32macc{0.9615}, loss{0.1345}]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9455\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1718\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel loss did not improve from 0.1344851404. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m356.89 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m284.16 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m72.73 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [31] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m32\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 186)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33m└───Shuffling data...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01058\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 187/192\n",
+      "256/256 [==============================] - 52s 190ms/step - loss: 0.1776 - accuracy: 0.9463 - val_loss: 0.1711 - val_accuracy: 0.9391\n",
+      "Epoch 188/192\n",
+      "256/256 [==============================] - 46s 180ms/step - loss: 0.1528 - accuracy: 0.9534 - val_loss: 0.1292 - val_accuracy: 0.9471\n",
+      "Epoch 189/192\n",
+      "256/256 [==============================] - 45s 175ms/step - loss: 0.0933 - accuracy: 0.9744 - val_loss: 0.2000 - val_accuracy: 0.9327\n",
+      "Epoch 190/192\n",
+      "256/256 [==============================] - 45s 176ms/step - loss: 0.0623 - accuracy: 0.9827 - val_loss: 0.2264 - val_accuracy: 0.9375\n",
+      "Epoch 191/192\n",
+      "256/256 [==============================] - 45s 175ms/step - loss: 0.0344 - accuracy: 0.9927 - val_loss: 0.1794 - val_accuracy: 0.9423\n",
+      "Epoch 192/192\n",
+      "256/256 [==============================] - 45s 175ms/step - loss: 0.0360 - accuracy: 0.9907 - val_loss: 0.1729 - val_accuracy: 0.9423\n",
+      "\u001b[0;32mSubset training done.\u001b[0m\n",
+      "\u001b[0;33mLoading the best weights...\u001b[0m\n",
+      "\u001b[0;33mLoading weights from file cache\\model_SUB_checkpoint-188-0.9471.h5...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test acc: \u001b[0m\u001b[0;32m0.9471\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mModel Test loss: \u001b[0m\u001b[0;32m0.1292\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;91mModel accuracy did not improve from 0.9615384340. Not saving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mImproved model loss from \u001b[0m\u001b[0;32m0.1344851404 \u001b[0m\u001b[0;33mto \u001b[0m\u001b[0;32m0.1292192638\u001b[0m\u001b[0;33m. \u001b[0m\u001b[0;96mSaving model.\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;36mSaving full model H5 format...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(FULL): \u001b[0m\u001b[0;32m371.26 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(SUBo): \u001b[0m\u001b[0;32m279.52 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTime taken for epoch(OTHERo): \u001b[0m\u001b[0;32m91.75 \u001b[0m\u001b[0;36msec\u001b[0m\n",
+      "\u001b[0;36m<---------------------------------------|Epoch [32] END|--------------------------------------->\u001b[0m\n",
+      "\u001b[0m\n",
+      "\u001b[0m\u001b[0mEpoch: \u001b[0m\u001b[0;36m33\u001b[0m\u001b[0m/\u001b[0m\u001b[0;32m489 (TSEC: 192)\u001b[0m\u001b[0;34m | \u001b[0m\u001b[0;32m[Fine tuning]\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mTaking a subset of \u001b[0m\u001b[0;32m[|4096|AdvSubset:True]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;33mPreparing train data...\u001b[0m\n",
+      "\u001b[0;33m- Augmenting Image Data...\u001b[0m\n",
+      "\u001b[0;33m- Normalizing Image Data...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training OneCycleLr::maxlr to \u001b[0m\u001b[0;32m[0.01052\u001b[0m\u001b[0;31m\u001b[0m\u001b[0;32m]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0m\u001b[0m\u001b[0;33mSetting training subset epoch.c to \u001b[0m\u001b[0;32m[6]\u001b[0m\u001b[0;33m...\u001b[0m\n",
+      "\u001b[0;32mTraining on subset...\u001b[0m\n",
+      "Epoch 193/198\n",
+      "256/256 [==============================] - 49s 179ms/step - loss: 0.1605 - accuracy: 0.9482 - val_loss: 0.1399 - val_accuracy: 0.9503\n",
+      "Epoch 194/198\n",
+      "256/256 [==============================] - 45s 175ms/step - loss: 0.1440 - accuracy: 0.9563 - val_loss: 0.1420 - val_accuracy: 0.9503\n",
+      "Epoch 195/198\n",
+      "207/256 [=======================>......] - ETA: 7s - loss: 0.0962 - accuracy: 0.9716"
      ]
     }
    ],
diff --git a/Interface/GUI/Data/GUI_main.py b/Interface/GUI/Data/GUI_main.py
index aaa16ef..067b38f 100644
--- a/Interface/GUI/Data/GUI_main.py
+++ b/Interface/GUI/Data/GUI_main.py
@@ -370,9 +370,10 @@ def main():
     # Main loop for the Graphical User Interface (GUI)
     while True:
         # Read events and values from the GUI window
-        event, values = GUI_window.read(timeout=2)
-        logger.debug(f'GUI_window:event {event}')
-        logger.debug(f'GUI_window:values {values}')
+        event, values = GUI_window.read(timeout=250, timeout_key='-TIMEOUT-')
+        if not event == '-TIMEOUT-':
+            logger.debug(f'GUI_window:event: {event}')
+            logger.debug(f'GUI_window:values: {values}')
         
         # Check if the window has been closed or the 'Close' button has been clicked
         if event == sg.WINDOW_CLOSED or event == 'Close':
@@ -424,12 +425,13 @@ def main():
             result_expanded = ''
             result = Queue_ins.get()
             print(f'Queue Data: {result}')
-            logger.debug(f'Queue:get {result}')
+            logger.debug(f'Queue:get: {result}')
             # Update the GUI with the result message
             for block in result:
                 result_expanded += f'> {block}\n'
             GUI_window['-OUTPUT_ST-'].update(result_expanded, text_color='yellow')
             GUI_window.finalize()
+
 # start>>>
 # clear the 'start L1' prompt
 print('                  ', end='\r')