diff --git a/Shoe Classifier_Xception.ipynb b/Shoe Classifier_Xception.ipynb
index 303f505..2eb6158 100644
--- a/Shoe Classifier_Xception.ipynb	
+++ b/Shoe Classifier_Xception.ipynb	
@@ -150,7 +150,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "train, test = train_test_split(train, test_size=0.2, random_state=42)\n",
+    "train, test = train_test_split(train, test_size=0.1, random_state=42)\n",
     "# stratify=train['PrimaryCategoryID']\n",
     "# train['PrimaryCategoryID'].value_counts()"
    ]
@@ -165,8 +165,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Found 12276 validated image filenames belonging to 7 classes.\n",
-      "Found 3068 validated image filenames belonging to 7 classes.\n"
+      "Found 17660 validated image filenames belonging to 7 classes.\n",
+      "Found 4414 validated image filenames belonging to 7 classes.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.8/dist-packages/keras_preprocessing/image/dataframe_iterator.py:279: UserWarning: Found 1 invalid image filename(s) in x_col=\"PictureURL\". These filename(s) will be ignored.\n",
+      "  warnings.warn(\n"
      ]
     }
    ],
@@ -705,8 +713,8 @@
       "                                                                                                  \n",
       "==================================================================================================\n",
       "Total params: 20,875,823\n",
-      "Trainable params: 14,343\n",
-      "Non-trainable params: 20,861,480\n",
+      "Trainable params: 20,821,295\n",
+      "Non-trainable params: 54,528\n",
       "__________________________________________________________________________________________________\n"
      ]
     }
@@ -724,19 +732,6 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "id": "ea620129",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#model.add(Dropout(.5))\n",
-    "#model.add(Dense(64, activation='softmax'))\n",
-    "# model.add(Dropout(.25))\n",
-    "#model = add_regularization(model)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
    "id": "fd5d1246",
    "metadata": {},
    "outputs": [],
@@ -748,7 +743,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "9cd2ba27",
    "metadata": {
     "scrolled": false
@@ -758,29 +753,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Epoch 1/30\n",
-      "134/192 [===================>..........] - ETA: 31s - loss: 8.9588 - accuracy: 0.1273"
+      "Epoch 1/6\n",
+      "276/276 [==============================] - 254s 903ms/step - loss: 0.9800 - accuracy: 0.6524 - val_loss: 1.3301 - val_accuracy: 0.5258\n",
+      "Epoch 2/6\n",
+      "276/276 [==============================] - 246s 891ms/step - loss: 0.4296 - accuracy: 0.8554 - val_loss: 0.8291 - val_accuracy: 0.7175\n",
+      "Epoch 3/6\n",
+      "276/276 [==============================] - 245s 885ms/step - loss: 0.1091 - accuracy: 0.9716 - val_loss: 0.9532 - val_accuracy: 0.7288\n",
+      "Epoch 4/6\n",
+      "276/276 [==============================] - 248s 895ms/step - loss: 0.0216 - accuracy: 0.9971 - val_loss: 1.0324 - val_accuracy: 0.7331\n",
+      "Epoch 5/6\n",
+      "276/276 [==============================] - 249s 900ms/step - loss: 0.0072 - accuracy: 0.9993 - val_loss: 1.1318 - val_accuracy: 0.7295\n",
+      "Epoch 6/6\n",
+      "276/276 [==============================] - 249s 899ms/step - loss: 0.0032 - accuracy: 0.9997 - val_loss: 1.1304 - val_accuracy: 0.7388\n"
      ]
     },
     {
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-4cd4443bbf2a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m model.fit(x=train_generator,\n\u001b[0m\u001b[1;32m      2\u001b[0m           \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m           \u001b[0mvalidation_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m           \u001b[0mvalidation_steps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalidation_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m           \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m30\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     62\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 64\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     65\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pylint: disable=broad-except\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     66\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1214\u001b[0m                 _r=1):\n\u001b[1;32m   1215\u001b[0m               \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1216\u001b[0;31m               \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1217\u001b[0m               \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1218\u001b[0m                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    908\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    909\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 910\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    912\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/def_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    940\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    941\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 942\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateless_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    943\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_stateful_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    944\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   3128\u001b[0m       (graph_function,\n\u001b[1;32m   3129\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m-> 3130\u001b[0;31m     return graph_function._call_flat(\n\u001b[0m\u001b[1;32m   3131\u001b[0m         filtered_flat_args, captured_inputs=graph_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m   3132\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs, cancellation_manager)\u001b[0m\n\u001b[1;32m   1957\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1958\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1959\u001b[0;31m       return self._build_call_outputs(self._inference_function.call(\n\u001b[0m\u001b[1;32m   1960\u001b[0m           ctx, args, cancellation_manager=cancellation_manager))\n\u001b[1;32m   1961\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/function.py\u001b[0m in \u001b[0;36mcall\u001b[0;34m(self, ctx, args, cancellation_manager)\u001b[0m\n\u001b[1;32m    596\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0m_InterpolateFunctionError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    597\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcancellation_manager\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 598\u001b[0;31m           outputs = execute.execute(\n\u001b[0m\u001b[1;32m    599\u001b[0m               \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    600\u001b[0m               \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_num_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     56\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 58\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     59\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     60\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x7fd5404d6580>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -788,15 +783,33 @@
     "          steps_per_epoch=len(train_generator),\n",
     "          validation_data=validation_generator,\n",
     "          validation_steps=len(validation_generator),\n",
-    "          epochs=30,\n",
+    "          epochs=6,\n",
     "          verbose=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "id": "63f791af",
    "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/usr/local/lib/python3.8/dist-packages/keras/engine/functional.py:1410: CustomMaskWarning: Custom mask layers require a config and must override get_config. When loading, the custom mask layer must be passed to the custom_objects argument.\n",
+      "  layer_config = serialize_layer_fn(layer)\n"
+     ]
+    }
+   ],
+   "source": [
+    "model.save(\"Model_1.h5\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": []
   }
diff --git a/ebay_api.py b/ebay_api.py
index 0368612..9ea17fe 100644
--- a/ebay_api.py
+++ b/ebay_api.py
@@ -527,12 +527,15 @@ class CurateData:
 
         # make custom dict, {source:target}, and name images from unique URL patt
         for k in temp_pics_source_list:
-            patt_1 = re.search(r'[^/]+(?=/\$_|.(\.jpg|\.jpeg|\.png))', k, re.IGNORECASE)
-            patt_2 = re.search(r'(\.jpg|\.jpeg|\.png)', k, re.IGNORECASE) 
-            if patt_1 and patt_2 is not None:
-                tag = patt_1.group() + patt_2.group().lower()
-                file_name = target_dir + os.sep + tag
-                dict_pics.update({k:file_name})
+            try:
+                patt_1 = re.search(r'[^/]+(?=/\$_|.(\.jpg|\.jpeg|\.png))', k, re.IGNORECASE)
+                patt_2 = re.search(r'(\.jpg|\.jpeg|\.png)', k, re.IGNORECASE) 
+                if patt_1 and patt_2 is not None:
+                    tag = patt_1.group() + patt_2.group().lower()
+                    file_name = target_dir + os.sep + tag
+                    dict_pics.update({k:file_name})
+            except TypeError:
+                pass
 
         with open('dict_pics.txt', 'w') as f:
             json.dump(dict_pics, f)
diff --git a/id_update.py b/id_update.py
new file mode 100644
index 0000000..c299a3a
--- /dev/null
+++ b/id_update.py
@@ -0,0 +1,70 @@
+import os
+import requests
+import json
+import ebaysdk
+from ebaysdk.trading import Connection as Trading
+from ebaysdk.finding import Connection as Finding
+import time
+import concurrent.futures
+# (categoryId = women's shoes = 3034)
+# Initialize loop to get number of pages needed in for loop
+start = time.time()
+fapi = Finding(config_file = "ebay.yaml")
+tapi = Trading(config_file = 'ebay.yaml')
+
+fresponse = fapi.execute(
+        'findItemsAdvanced',
+        {
+            'itemFilter':{
+                'name':'Seller',
+                'value':'chesshoebuddy'
+                },
+            'paginationInput':{
+                'entriesPerPage':'100',
+                'pageNumber':'1'
+                }
+            }
+        ).dict()
+
+page_results = int(fresponse['paginationOutput']['totalPages'])
+
+pages = []
+for i in range(0, page_results):
+    i += 1
+    pages.append(i)
+
+''' Begin definitions for getting ItemIds and SKU: '''
+
+def id_up(n):
+    ids = []
+    fresponse = fapi.execute(
+            'findItemsAdvanced',
+            {
+                'itemFilter':{
+                    'name':'Seller',
+                    'value':'chesshoebuddy'
+                    },
+                'paginationInput':{
+                    'entriesPerPage':'100',
+                    'pageNumber':str(n)
+                    }
+                }
+            ).dict()
+    for item in (fresponse['searchResult']['item']):
+        itemID = item['itemId']
+        #response = tapi.execute('GetItem',{'ItemID':itemID}).dict()
+        ids.append(itemID)
+    return ids
+
+def main():
+    ids = []
+    skus = []
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        for future in executor.map(id_up, pages):
+            ids.extend(future)
+
+    with open('ebay_ids.txt', 'w') as outfile:
+        json.dump(ids, outfile)
+
+if __name__ == '__main__':
+    main()
diff --git a/rand_revise.py b/rand_revise.py
new file mode 100644
index 0000000..83981ae
--- /dev/null
+++ b/rand_revise.py
@@ -0,0 +1,16 @@
+import ebaysdk
+import json
+import requests
+import random
+from ebaysdk.trading import Connection as Trading
+from ebaysdk.finding import Connection as Finding
+from ebaysdk.shopping import Connection as Shopping
+import concurrent.futures
+import config as cfg
+import id_update
+
+# ids = id_update.main()
+with open('ebay_ids.txt') as f:
+    ids = json.load(f)
+
+