diff --git "a/app.ipynb" "b/app.ipynb"
--- "a/app.ipynb"
+++ "b/app.ipynb"
@@ -2,41 +2,44 @@
   "cells": [
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "lm2GQwhxCNQw",
-        "outputId": "9f599db7-0b70-4a02-b363-8e09496a4b84"
+        "outputId": "19ebc8a3-09b8-48bd-c783-266682437745"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.7/19.7 MB\u001b[0m \u001b[31m46.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.8/64.8 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m56.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.0/57.0 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.9/19.9 MB\u001b[0m \u001b[31m39.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m64.8/64.8 kB\u001b[0m \u001b[31m5.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m65.8/65.8 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m288.4/288.4 kB\u001b[0m \u001b[31m23.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 kB\u001b[0m \u001b[31m4.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m137.0/137.0 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.7/58.7 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.1/82.1 kB\u001b[0m \u001b[31m8.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━���━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m12.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m22.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m14.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m6.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m72.5/72.5 kB\u001b[0m \u001b[31m7.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
-            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m65.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m294.2/294.2 kB\u001b[0m \u001b[31m16.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.4/75.4 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m10.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m138.7/138.7 kB\u001b[0m \u001b[31m1.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.5/59.5 kB\u001b[0m \u001b[31m2.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.9/129.9 kB\u001b[0m \u001b[31m5.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.7/58.7 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.1/82.1 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.4/50.4 kB\u001b[0m \u001b[31m434.1 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m46.5/46.5 kB\u001b[0m \u001b[31m3.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.7/43.7 kB\u001b[0m \u001b[31m4.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/41.0 kB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/41.0 kB\u001b[0m \u001b[31m2.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m7.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.5/84.5 kB\u001b[0m \u001b[31m5.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m67.0/67.0 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m74.5/74.5 kB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m57.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
             "\u001b[?25h  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
           ]
         }
@@ -47,7 +50,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
         "id": "KjVdtCKTWf4g"
       },
@@ -58,7 +61,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
         "id": "SBhTQPpDWlLE"
       },
@@ -71,48 +74,48 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
         "id": "sSpSwwC1Wny7"
       },
       "outputs": [],
       "source": [
         "#!export\n",
-        "model = load_learner('tradiotional_clothing_recognition-v1.pkl')"
+        "model = load_learner('tradiotional_clothing_recognition-v0.pkl')"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
         "id": "j2TCH9wVXe7c"
       },
       "outputs": [],
       "source": [
         "categories = [\n",
-        "    \"kimono (Japan)\",\n",
-        "    \"hanbok (South Korea)\",\n",
-        "    \"cheongsam/qipao (China)\",\n",
-        "    \"sari (India)\",\n",
-        "    \"thawb/dishdasha (Saudi Arabia)\",\n",
-        "    \"dirndl (Germany)\",\n",
-        "    \"kilt (Scotland)\",\n",
-        "    \"ao dai (Vietnam)\",\n",
-        "    \"boubou (West Africa)\",\n",
-        "    \"huipil (Mexico)\",\n",
-        "    \"sarong (Indonesia)\",\n",
-        "    \"chador (Iran)\",\n",
-        "    \"traje de flamenca (Spain)\",\n",
-        "    \"batik (Malaysia)\",\n",
-        "    \"thobe (Palestine)\",\n",
-        "    \"national dress (Norway)\",\n",
-        "    \"national costume (Philippines)\",\n",
-        "    \"barong tagalog (Philippines)\",\n",
-        "    \"abaya (United Arab Emirates)\",\n",
-        "    \"folkdräkt (Sweden)\",\n",
-        "    \"ao po'i (Paraguay)\",\n",
-        "    \"pounamu piupiu (New Zealand)\",\n",
-        "    \"kaftan (Morocco)\"\n",
+        "   \"KIMONO (JAPAN)\",\n",
+        "    \"HANBOK (SOUTH KOREA)\",\n",
+        "    \"CHEONGSAM/QIPAO (CHINA)\",\n",
+        "    \"SARI (INDIA)\",\n",
+        "    \"THAWB/DISHDASHA (SAUDI ARABIA)\",\n",
+        "    \"DIRNDL (GERMANY)\",\n",
+        "    \"KILT (SCOTLAND)\",\n",
+        "    \"AO DAI (VIETNAM)\",\n",
+        "    \"BOUBOU (WEST AFRICA)\",\n",
+        "    \"HUIPIL (MEXICO)\",\n",
+        "    \"SARONG (INDONESIA)\",\n",
+        "    \"CHADOR (IRAN)\",\n",
+        "    \"TRAJE DE FLAMENCA (SPAIN)\",\n",
+        "    \"BATIK (MALAYSIA)\",\n",
+        "    \"THOBE (PALESTINE)\",\n",
+        "    \"NATIONAL DRESS (NORWAY)\",\n",
+        "    \"NATIONAL COSTUME (PHILIPPINES)\",\n",
+        "    \"BARONG TAGALOG (PHILIPPINES)\",\n",
+        "    \"ABAYA (UNITED ARAB EMIRATES)\",\n",
+        "    \"FOLKDRÄKT (SWEDEN)\",\n",
+        "    \"AO PO'I (PARAGUAY)\",\n",
+        "    \"POUNAMU PIUPIU (NEW ZEALAND)\",\n",
+        "    \"KAFTAN (MOROCCO)\"\n",
         "]\n",
         "def recognize_image(image):\n",
         "  pred, idx, probs = model.predict(image)\n",
@@ -121,44 +124,44 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 209
         },
         "id": "7CpDz53hX-n6",
-        "outputId": "f028d264-15f1-4723-a425-e8ca22abf6a4"
+        "outputId": "2ef208f7-ab6b-4fa6-bb68-fbdd088c9adc"
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "PILImage mode=RGB size=192x192"
             ]
           },
-          "execution_count": 10,
+          "execution_count": 8,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
-        "img = PILImage.create(f'unknown-1.jpg')\n",
+        "img = PILImage.create(f'test_images/unknown-1.jpg')\n",
         "img.thumbnail((192,192))\n",
         "img"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 436
+          "height": 363
         },
         "id": "V5HdXkkPYC0y",
-        "outputId": "225b6ae5-7b9a-4cc0-b722-52195286931d"
+        "outputId": "913819b1-497e-439f-e08e-2f29ca688dad"
       },
       "outputs": [
         {
@@ -201,32 +204,28 @@
         {
           "data": {
             "text/plain": [
-              "{'kimono (Japan)': 3.039119837922044e-05,\n",
-              " 'hanbok (South Korea)': 0.00026097308727912605,\n",
-              " 'cheongsam/qipao (China)': 9.200982162838045e-07,\n",
-              " 'sari (India)': 1.4729216673003975e-06,\n",
-              " 'thawb/dishdasha (Saudi Arabia)': 1.62081287271576e-05,\n",
-              " 'dirndl (Germany)': 4.745931437355466e-05,\n",
-              " 'kilt (Scotland)': 2.069252877845429e-05,\n",
-              " 'ao dai (Vietnam)': 0.00028415146516636014,\n",
-              " 'boubou (West Africa)': 7.29018381662172e-07,\n",
-              " 'huipil (Mexico)': 0.00010681543062673882,\n",
-              " 'sarong (Indonesia)': 0.00015180723858065903,\n",
-              " 'chador (Iran)': 1.9500197595334612e-05,\n",
-              " 'traje de flamenca (Spain)': 1.9817616703221574e-05,\n",
-              " 'batik (Malaysia)': 4.4932971832167823e-07,\n",
-              " 'thobe (Palestine)': 0.6253378391265869,\n",
-              " 'national dress (Norway)': 0.37217476963996887,\n",
-              " 'national costume (Philippines)': 0.00039766140980646014,\n",
-              " 'barong tagalog (Philippines)': 3.984975137427682e-06,\n",
-              " 'abaya (United Arab Emirates)': 1.5120702983040246e-06,\n",
-              " 'folkdräkt (Sweden)': 2.1541298337979242e-05,\n",
-              " \"ao po'i (Paraguay)\": 0.00011518658720888197,\n",
-              " 'pounamu piupiu (New Zealand)': 9.098471491597593e-05,\n",
-              " 'kaftan (Morocco)': 0.0008950466872192919}"
+              "{'KIMONO (JAPAN)': 2.181729541916866e-05,\n",
+              " 'HANBOK (SOUTH KOREA)': 0.00015175332373473793,\n",
+              " 'CHEONGSAM/QIPAO (CHINA)': 4.405498202686431e-06,\n",
+              " 'SARI (INDIA)': 7.586645267565473e-08,\n",
+              " 'THAWB/DISHDASHA (SAUDI ARABIA)': 1.4588684962291154e-06,\n",
+              " 'DIRNDL (GERMANY)': 1.627047083729849e-07,\n",
+              " 'KILT (SCOTLAND)': 5.46255876088253e-08,\n",
+              " 'AO DAI (VIETNAM)': 5.861677720986336e-08,\n",
+              " 'BOUBOU (WEST AFRICA)': 2.7282158043817617e-05,\n",
+              " 'HUIPIL (MEXICO)': 1.2585975355250412e-06,\n",
+              " 'SARONG (INDONESIA)': 1.237753849636647e-06,\n",
+              " 'CHADOR (IRAN)': 4.4873812043988437e-07,\n",
+              " 'TRAJE DE FLAMENCA (SPAIN)': 0.9997422099113464,\n",
+              " 'BATIK (MALAYSIA)': 1.4894385458319448e-07,\n",
+              " 'THOBE (PALESTINE)': 5.477321792568546e-06,\n",
+              " 'NATIONAL DRESS (NORWAY)': 2.511950469852309e-07,\n",
+              " 'NATIONAL COSTUME (PHILIPPINES)': 8.023681630220381e-07,\n",
+              " 'BARONG TAGALOG (PHILIPPINES)': 4.507920493779238e-06,\n",
+              " 'ABAYA (UNITED ARAB EMIRATES)': 3.662340168375522e-05}"
             ]
           },
-          "execution_count": 11,
+          "execution_count": 9,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -237,27 +236,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "SB4lBAhvYFVy",
-        "outputId": "d79c2d4b-bc5d-4fa8-9f02-88692f5c0d36"
+        "outputId": "6ce7ae5e-6bb6-4d69-be1f-ed697d06a5ed"
       },
       "outputs": [
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:259: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
-            "  warnings.warn(\n",
-            "/usr/local/lib/python3.10/dist-packages/gradio/inputs.py:262: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
-            "  super().__init__(\n",
-            "/usr/local/lib/python3.10/dist-packages/gradio/outputs.py:197: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
-            "  warnings.warn(\n",
-            "/usr/local/lib/python3.10/dist-packages/gradio/outputs.py:200: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
-            "  super().__init__(num_top_classes=num_top_classes, type=type, label=label)\n"
+            "<ipython-input-10-de29e51b94b7>:2: GradioDeprecationWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
+            "  image = gr.inputs.Image(shape=(192,192))\n",
+            "<ipython-input-10-de29e51b94b7>:2: GradioDeprecationWarning: `optional` parameter is deprecated, and it has no effect\n",
+            "  image = gr.inputs.Image(shape=(192,192))\n",
+            "<ipython-input-10-de29e51b94b7>:3: GradioDeprecationWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
+            "  label = gr.outputs.Label()\n",
+            "<ipython-input-10-de29e51b94b7>:3: GradioUnusedKwargWarning: You have unused kwarg parameters in Label, please remove them: {'type': 'auto'}\n",
+            "  label = gr.outputs.Label()\n"
           ]
         },
         {
@@ -265,7 +264,7 @@
           "output_type": "stream",
           "text": [
             "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
-            "Running on public URL: https://897f24d5bf9ba00855.gradio.live\n",
+            "Running on public URL: https://9a514f26346dd8a8af.gradio.live\n",
             "\n",
             "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n"
           ]
@@ -274,7 +273,7 @@
           "data": {
             "text/plain": []
           },
-          "execution_count": 13,
+          "execution_count": 10,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -286,7 +285,6 @@
         "examples = [\n",
         "    'unknown-1.jpg',\n",
         "    'unknown-2.jpg',\n",
-        "    'unknown-3.jpg'\n",
         "    ]\n",
         "\n",
         "iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples)\n",
@@ -305,20 +303,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "6tfDvQm2YgdM",
-        "outputId": "3f8f9f02-443a-4d69-9b89-c88e75fc6bed"
+        "outputId": "1c5faa50-7c04-40fa-ee8f-33242c280fa7"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
             "Requirement already satisfied: nbdev in /usr/local/lib/python3.10/dist-packages (2.3.12)\n",
             "Requirement already satisfied: fastcore>=1.5.27 in /usr/local/lib/python3.10/dist-packages (from nbdev) (1.5.29)\n",
             "Requirement already satisfied: execnb>=0.1.4 in /usr/local/lib/python3.10/dist-packages (from nbdev) (0.1.5)\n",
@@ -326,18 +323,18 @@
             "Requirement already satisfied: ghapi>=1.0.3 in /usr/local/lib/python3.10/dist-packages (from nbdev) (1.0.4)\n",
             "Requirement already satisfied: watchdog in /usr/local/lib/python3.10/dist-packages (from nbdev) (3.0.0)\n",
             "Requirement already satisfied: asttokens in /usr/local/lib/python3.10/dist-packages (from nbdev) (2.2.1)\n",
-            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from nbdev) (6.0)\n",
+            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.10/dist-packages (from nbdev) (6.0.1)\n",
             "Requirement already satisfied: ipython in /usr/local/lib/python3.10/dist-packages (from execnb>=0.1.4->nbdev) (7.34.0)\n",
             "Requirement already satisfied: pip in /usr/local/lib/python3.10/dist-packages (from fastcore>=1.5.27->nbdev) (23.1.2)\n",
             "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from fastcore>=1.5.27->nbdev) (23.1)\n",
             "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from asttokens->nbdev) (1.16.0)\n",
-            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse->nbdev) (0.40.0)\n",
+            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse->nbdev) (0.41.0)\n",
             "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (67.7.2)\n",
             "Requirement already satisfied: jedi>=0.16 in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (0.18.2)\n",
             "Requirement already satisfied: decorator in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (4.4.2)\n",
             "Requirement already satisfied: pickleshare in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (0.7.5)\n",
             "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (5.7.1)\n",
-            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (3.0.38)\n",
+            "Requirement already satisfied: prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (3.0.39)\n",
             "Requirement already satisfied: pygments in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (2.14.0)\n",
             "Requirement already satisfied: backcall in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (0.2.0)\n",
             "Requirement already satisfied: matplotlib-inline in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (0.1.6)\n",
@@ -376,7 +373,9 @@
     }
   ],
   "metadata": {
+    "accelerator": "GPU",
     "colab": {
+      "gpuType": "T4",
       "provenance": []
     },
     "kernelspec": {