diff --git "a/app.ipynb" "b/app.ipynb"
--- "a/app.ipynb"
+++ "b/app.ipynb"
@@ -8,38 +8,38 @@
           "base_uri": "https://localhost:8080/"
         },
         "id": "lm2GQwhxCNQw",
-        "outputId": "19ebc8a3-09b8-48bd-c783-266682437745"
+        "outputId": "66896a6f-87e8-437a-8ef1-c798ad19e067"
       },
       "outputs": [
         {
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "\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[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m19.9/19.9 MB\u001b[0m \u001b[31m27.7 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[31m1.9 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[31m4.7 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[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[32m294.2/294.2 kB\u001b[0m \u001b[31m19.5 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[31m4.6 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[31m16.8 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[31m3.3 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[31m2.5 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[31m2.2 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[31m2.5 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[31m5.5 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[31m2.4 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[31m2.7 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[31m2.3 MB/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[31m966.9 kB/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[31m1.8 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[31m2.5 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[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m87.5/87.5 kB\u001b[0m \u001b[31m3.4 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[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[31m2.6 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[31m2.6 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[31m4.4 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[31m42.3 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"
           ]
         }
@@ -74,19 +74,19 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 21,
       "metadata": {
         "id": "sSpSwwC1Wny7"
       },
       "outputs": [],
       "source": [
         "#!export\n",
-        "model = load_learner('tradiotional_clothing_recognition-v0.pkl')"
+        "model = load_learner('tradiotional_clothing_recognition-v1.pkl')"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 22,
       "metadata": {
         "id": "j2TCH9wVXe7c"
       },
@@ -117,21 +117,22 @@
         "    \"POUNAMU PIUPIU (NEW ZEALAND)\",\n",
         "    \"KAFTAN (MOROCCO)\"\n",
         "]\n",
+        "\n",
         "def recognize_image(image):\n",
-        "  pred, idx, probs = model.predict(image)\n",
-        "  return dict(zip(categories, map(float, probs)))"
+        "    pred, idx, probs = model.predict(image)\n",
+        "    return dict(zip(sorted(categories), map(float, probs)))\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 23,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 209
         },
         "id": "7CpDz53hX-n6",
-        "outputId": "2ef208f7-ab6b-4fa6-bb68-fbdd088c9adc"
+        "outputId": "d9812550-1d05-42d8-e683-a9a86b323b87"
       },
       "outputs": [
         {
@@ -141,27 +142,139 @@
               "PILImage mode=RGB size=192x192"
             ]
           },
-          "execution_count": 8,
+          "execution_count": 23,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
-        "img = PILImage.create(f'test_images/unknown-1.jpg')\n",
+        "img = PILImage.create('unknown-1.jpg')\n",
         "img.thumbnail((192,192))\n",
         "img"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 24,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 363
         },
         "id": "V5HdXkkPYC0y",
-        "outputId": "913819b1-497e-439f-e08e-2f29ca688dad"
+        "outputId": "c3cf1b48-5aa9-4336-96c8-589246897442"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/html": [
+              "\n",
+              "<style>\n",
+              "    /* Turns off some styling */\n",
+              "    progress {\n",
+              "        /* gets rid of default border in Firefox and Opera. */\n",
+              "        border: none;\n",
+              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
+              "        background-size: auto;\n",
+              "    }\n",
+              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
+              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
+              "    }\n",
+              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
+              "        background: #F44336;\n",
+              "    }\n",
+              "</style>\n"
+            ],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/html": [],
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "text/plain": [
+              "{'ABAYA (UNITED ARAB EMIRATES)': 5.2136801542701505e-09,\n",
+              " 'AO DAI (VIETNAM)': 3.8817680092506635e-07,\n",
+              " \"AO PO'I (PARAGUAY)\": 4.2737188787889124e-10,\n",
+              " 'BARONG TAGALOG (PHILIPPINES)': 7.477908570141523e-11,\n",
+              " 'BATIK (MALAYSIA)': 2.3851260966267773e-09,\n",
+              " 'BOUBOU (WEST AFRICA)': 6.892130199886992e-10,\n",
+              " 'CHADOR (IRAN)': 2.8864597378941426e-09,\n",
+              " 'CHEONGSAM/QIPAO (CHINA)': 1.2007833616323182e-10,\n",
+              " 'DIRNDL (GERMANY)': 6.495823434704562e-09,\n",
+              " 'FOLKDRÄKT (SWEDEN)': 8.678026297559427e-09,\n",
+              " 'HANBOK (SOUTH KOREA)': 8.89525952896264e-10,\n",
+              " 'HUIPIL (MEXICO)': 9.05117847338488e-09,\n",
+              " 'KAFTAN (MOROCCO)': 0.9999996423721313,\n",
+              " 'KILT (SCOTLAND)': 3.810919135815993e-09,\n",
+              " 'KIMONO (JAPAN)': 1.6788022083602527e-09,\n",
+              " 'NATIONAL COSTUME (PHILIPPINES)': 2.3898119039245103e-09,\n",
+              " 'NATIONAL DRESS (NORWAY)': 3.5316976010335566e-09,\n",
+              " 'POUNAMU PIUPIU (NEW ZEALAND)': 1.3970392620521466e-09,\n",
+              " 'SARI (INDIA)': 1.5023390309565343e-09}"
+            ]
+          },
+          "execution_count": 24,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "recognize_image(img)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 25,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 209
+        },
+        "id": "dBuKUCY21C9c",
+        "outputId": "40b9423a-cb57-42a1-b73f-273bb8647358"
+      },
+      "outputs": [
+        {
+          "data": {
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",
+            "text/plain": [
+              "PILImage mode=RGB size=144x192"
+            ]
+          },
+          "execution_count": 25,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "img = PILImage.create('unknown-6.jpg')\n",
+        "img.thumbnail((192,192))\n",
+        "img"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 26,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 363
+        },
+        "id": "oClnCZ2W1Mvj",
+        "outputId": "15d6ace6-3e72-44b8-f9a6-4f43393aa6c8"
       },
       "outputs": [
         {
@@ -204,28 +317,28 @@
         {
           "data": {
             "text/plain": [
-              "{'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}"
+              "{'ABAYA (UNITED ARAB EMIRATES)': 2.0577258510456886e-06,\n",
+              " 'AO DAI (VIETNAM)': 2.978152906507603e-07,\n",
+              " \"AO PO'I (PARAGUAY)\": 0.00011985862511210144,\n",
+              " 'BARONG TAGALOG (PHILIPPINES)': 0.00019182419055141509,\n",
+              " 'BATIK (MALAYSIA)': 2.0184272216283716e-05,\n",
+              " 'BOUBOU (WEST AFRICA)': 0.9944612979888916,\n",
+              " 'CHADOR (IRAN)': 5.0027159886667505e-05,\n",
+              " 'CHEONGSAM/QIPAO (CHINA)': 7.236670171550941e-06,\n",
+              " 'DIRNDL (GERMANY)': 3.200794890290126e-05,\n",
+              " 'FOLKDRÄKT (SWEDEN)': 0.0021258401684463024,\n",
+              " 'HANBOK (SOUTH KOREA)': 7.068156264722347e-06,\n",
+              " 'HUIPIL (MEXICO)': 1.9892893305950565e-06,\n",
+              " 'KAFTAN (MOROCCO)': 5.319330739439465e-06,\n",
+              " 'KILT (SCOTLAND)': 0.002798256231471896,\n",
+              " 'KIMONO (JAPAN)': 1.070639154931996e-05,\n",
+              " 'NATIONAL COSTUME (PHILIPPINES)': 4.875407284998801e-06,\n",
+              " 'NATIONAL DRESS (NORWAY)': 3.6706040873468737e-07,\n",
+              " 'POUNAMU PIUPIU (NEW ZEALAND)': 7.359711162280291e-05,\n",
+              " 'SARI (INDIA)': 8.728736429475248e-05}"
             ]
           },
-          "execution_count": 9,
+          "execution_count": 26,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -236,26 +349,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 33,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "SB4lBAhvYFVy",
-        "outputId": "6ce7ae5e-6bb6-4d69-be1f-ed697d06a5ed"
+        "outputId": "bab1e514-68c3-4182-c026-de81b0a9ec29"
       },
       "outputs": [
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "<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",
+            "<ipython-input-33-002e914b88ef>: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",
+            "<ipython-input-33-002e914b88ef>: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",
+            "<ipython-input-33-002e914b88ef>: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",
+            "<ipython-input-33-002e914b88ef>:3: GradioUnusedKwargWarning: You have unused kwarg parameters in Label, please remove them: {'type': 'auto'}\n",
             "  label = gr.outputs.Label()\n"
           ]
         },
@@ -264,7 +377,7 @@
           "output_type": "stream",
           "text": [
             "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
-            "Running on public URL: https://9a514f26346dd8a8af.gradio.live\n",
+            "Running on public URL: https://2dc61d1241a78b8fcb.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"
           ]
@@ -273,7 +386,7 @@
           "data": {
             "text/plain": []
           },
-          "execution_count": 10,
+          "execution_count": 33,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -283,12 +396,9 @@
         "image = gr.inputs.Image(shape=(192,192))\n",
         "label = gr.outputs.Label()\n",
         "examples = [\n",
-        "    'unknown-1.jpg',\n",
-        "    'unknown-2.jpg',\n",
-        "    'unknown-3.jpg',\n",
-        "    'unknown-4.jpg',\n",
-        "    'unknown-5.jpg',\n",
-        "    'unknown-6.jpg'\n",
+        "    'unknown-6.jpg',\n",
+        "    'unknown-16.jpg',\n",
+        "    'unknown-18.jpg',\n",
         "    ]\n",
         "\n",
         "iface = gr.Interface(fn=recognize_image, inputs=image, outputs=label, examples=examples)\n",
@@ -307,48 +417,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "6tfDvQm2YgdM",
-        "outputId": "1c5faa50-7c04-40fa-ee8f-33242c280fa7"
+        "id": "6tfDvQm2YgdM"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "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",
-            "Requirement already satisfied: astunparse in /usr/local/lib/python3.10/dist-packages (from nbdev) (1.6.3)\n",
-            "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) (6.0.1)\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) (0.41.0)\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) (3.0.39)\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",
-            "Requirement already satisfied: pexpect>4.3 in /usr/local/lib/python3.10/dist-packages (from ipython->execnb>=0.1.4->nbdev) (4.8.0)\n",
-            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.10/dist-packages (from jedi>=0.16->ipython->execnb>=0.1.4->nbdev) (0.8.3)\n",
-            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.10/dist-packages (from pexpect>4.3->ipython->execnb>=0.1.4->nbdev) (0.7.0)\n",
-            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.10/dist-packages (from prompt-toolkit!=3.0.0,!=3.0.1,<3.1.0,>=2.0.0->ipython->execnb>=0.1.4->nbdev) (0.2.6)\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "!pip install nbdev\n"
       ]
@@ -387,8 +460,7 @@
       "name": "python3"
     },
     "language_info": {
-      "name": "python",
-      "version": "3.9.7"
+      "name": "python"
     }
   },
   "nbformat": 4,