diff --git a/.ipynb_checkpoints/app-checkpoint.ipynb b/.ipynb_checkpoints/app-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..7411b23922a94f8276eecc917a047280018ef102
--- /dev/null
+++ b/.ipynb_checkpoints/app-checkpoint.ipynb
@@ -0,0 +1,134 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Searching for 'donut photos'\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'http://images.unsplash.com/photo-1551024601-bec78aea704b?ixlib=rb-1.2.1&q=80&fm=jpg&crop=entropy&cs=tinysrgb&w=1080&fit=max'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from duckduckgo_search import ddg_images\n",
+    "#from fastcore.all import *\n",
+    "def search_images(term, max_images=30):\n",
+    "    print(f\"Searching for '{term}'\")\n",
+    "    return L(ddg_images(term, max_results=max_images)).itemgot('image')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Path('donut.jpg')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastdownload import download_url\n",
+    "dest = 'donut.jpg'\n",
+    "urls = search_images('donut photos', max_images=1)\n",
+    "download_url(urls[0], dest, show_progress=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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c5zUgevFOSpqpNM/d0IIjIYUbhAvuikUo58cYc2z1PxUPcs64otMYF9xNuZd/qMo8uY4beXEBAUQoUcGDphFN1yghGiVUp6QEbIimNI9gjIhSQdd3quISXFZiUrbIUgjhfrL3bVQ2HXGXMaNuvOmGTq52qhiKICmEUFUtyFVpJD0bUlpfN99bttCoVL1RZpzVd3PpOsWywjUavxj2lV+tdPGyzeFtHARudUApcBWi9BbJOXfxixDCpbAVncZUNOgwRbw4tiJCPVijSFMOUgouQK2BlKAAJwAoSlPDSIKiyRDyVJASQgg1dEIo0XSqUY1qlBCCVWyIkZd7L9cQVI60XTMJIMqpSpXYVCVXbk4ZVG5OykXGhSIFVcqiaHVJTAgIUXpWKlIVIwEAQgoskWeZpafIpYfguWe4OsAHz1NKqYz5Yi5CIc5F8PkuuFR6KAFJJNxtJYRUeYnSnhWlogghOFM5ds44Y7ZQAZ/yf4q8trntMM44Eyo1r5YLvNy1+holtcGYuLhEYRkVQBBVl0QwJoRSgl0fiIlCNlRFGW5xk7vdMfZ2tASEPKO/aF0QQgK8RYBSjLT4U2K1sKt6rhLSa6xc2UNEiGtCJSDhCsB7nfCI1xJ94G2u0pe+1jZeJZWrbIgLopCQAhDG18Qo0sOfUkiVZ4RSxtwVnpSCqSy7FF7wzhWQ4Qp5Oowxx+ZMOAqiCskc7jFnKhxxhQcuQ40JuFUPhGAV7GmuwlHXnFJKMQGClbdUgi9F+sR7G/Ls0ruNjVDAWynbYuFfmccpUwapstkqikLqH+QpkUts0aucHELgxhUu+1mqQyReWswN2KVEQiKMQGJVseQVBlyFfK/xoODy4K5LQB6Z4xY+qTvlQkEfWeJlAATn6klQFlDl/xQHw1z7yRnnKovEFoM/5gIYyfliARoA4gLU8gmvGtOTnAogkNIxTSMYe8wmJVSjlFJN07CCL4QoOg1KNtOLIErb2pWldKv0Sj/IQ2XIq5ssVb6UbJQqRoEyA6ZE6a5wCc96Pk2CpMoGK9mBp0yKKwCv4rS0FVwKA1yLIKVUXhgEBsAIOAAC8JDq1dinVHq5aDZdC6y+lmdGAKTg0q0PdQO3UlGLdE2oFx+o/1eCc5hjOyq5q1gYwYELJT4XEiKES1k1jAkg0NyCQEQIoRRrVKM61TRCVRivCOoSAYMJoVThUDd35PkpDwnKq2/KlQOgclkKr+JYLFKHixgCSY+A9JhLD26VwIRHEZSCe+kJmYKHfaQrMeSZATeAQwipOiM3JvU+XgEEF4LJRUX2gNRvqVNFXgyvXoDdANR1DaB8oXSJFyld5FkKISTnjmdHOefCccXKmbBdclRwJoUbQrr7sITOCHFrOgEBQZgQFctjQgjVqKZRTdOI5oIXBTgJUaVlGGOiwCvBpVwhWpQRAFbViqWM7FWexTWVai08aCpcJ4MkQqACSEAq9sAlGbur6oINvChj73kPW6tdIylC5CrEoX7xXLDnmJVSy1JFNXKZPvcGwGNQPaalXIoIXBisaELFobpsiZQSSQGuDBW3wgRzc0mOwwTnzMMvnHHFuSgZK18iuJQAHvqRXJR2lVspTbxwQHMJNJfnVLQ18X4npfCiVOmiJKbMTMmuuV5FkUhedR2U5Vo9Wyo9HS0DAtL1f8Kt9FQOSgEtz6SVUyUu81Oq7PXssBcdejat9OGlOlsFScpywq4mYc9qomuLTCSAimi9YB88kttVdHc3Ko7QK3J36ye4lwJ0U/Lc488Yc5jjpnKZ+x+zGWdcMkGYQIwLKQQCgUvo2vO7CCEVfxPs5Rw0SimhmkYo0SjVNEo06lUnIQVysFeH7YWAKoGBS5ZNyQdLb/UWg0+Xb0RSVdOp2sfFmpRyQIdU04SyFhgp7OUtt7ttQN0KKOpM6QcGUASW+nThrb301l+oGxdCAAhasorqK5aBSiVD5MWHgBQfWvKCyqoLKRAgJBBCEhNQ28IzcK4p4l7+3EsfKUDJuSMElyrb4DiccS/5zhxHMFWopuJGIBwZJqeWIxgXBEDD0k9Bp4h4IlTNDZQQQjClRFsUJCUaoVRT+XYXqrqlhEqJsasKmHh45irWwrOQnnkT4KlRqQ7IXehSuAdlCBa5nM4iKenG954VUYqwCF1cIyoRRlJiKaRXAqr0XXpbAUAxywBuXk0KKgF5IseqYlh9nlTVwAhKSYqrNhpy/a/02FeEsRQCEHbNu5RSChXKqRCCcSaUyjmMq6IKxoXrC7njMIdxx2GcS4cLIcHTZ0CYCKKZnJhMMCERIogQQJgQEdCxrmFd1yjFLs9JKKWEUkyoRqiiPYnKK7ngRbVmeIHxVQbHtVrCE6IAr9LVK0nzNrlXquF5KS908BIBi4L04GSZoi6SLWUPS1H1oocSApd2CwBIoYTA3XIqt7uiLCRFkiooVU4kKGkqLfR2mhdclllmKaUQEgNwzkuvdwvhhfQyQ1y4pTBMhQ2OzRyVx2XcVVc3FygZF1wIobYXUrUqgDFI0ARQcPN/iFJsaMSv4bDOQz7sMzRd16iu7CV1K2BUsYTXb+QiF+WS8CKHVbJGi5ilVEUGLpXg+gdP2J5AkVTWyr1KSe1KgYQsPfPuGqV3C7gM4Hq0mUSCi5KwwFN3jBd7+KQnTlc6QtDysMEDMqh8+0hVwiOEQrHu1V2AJtyKJYdJrjgxL4jwnJ/jJpG443DGhOW4BBsTgrtpYLd+QILEGGMJBCNCgCIghGiUEN0niS4QlQQTSnVKdJ3oGg5o0q+Bz+cSa9jrFFMNZQDYc38YeVXBJezgruAiSi9PzyyKEy1qJ8BiIAGoXK1dRLtI34DnyEsgtOTRXPxbosYWi/yk9Jyuh3KAC+RxsyWRSyEAo6s3BCoBVkRL93n1XvHCDOmWESq/W0LhXEpVCuE4zDIt07Rts6jqX1TNC3PreIUrSC4ZFzYTQkgmgHN3HyvIoconKMEaxgQDJdTQiKFTXdc0XdMMvyA+iSlW+QdCdIopxQbmPiqVT1RRhKJlFoPrUlDvIaVyL+jaJ+TaUamsqxtiKVX0SDHPdsnF4loMAK7LgcXLAQAuqy/0ClDKn3e/mlz8GkKlC0v7DLtWAQEH/q5t5vIr3kZyw0oAhLCUgnqgCGBxF5Q+T0IpnihTXC/+lSoLaJpmNlNIZ3K5vMkc4VFt6lWg6BjOgQnJhEulKWSvIUQJUIJ1SnRN/UcNnVBCNE3z+aiua5pGsW4A9QHWJKKKSyMEESx0AI2q3lsFRTEgXNqbCLmMrnpQZs2kENK9QQCEFbTBUGom9crSpVQJV+UmESwqW3lMuaii6i9XpXIXea1F+SGEhFi08MqkK54RgSQgqBQIpOAAAnGBuRe0ILUdkVzUPrT4PyUsWvpm0t1PAhYTHerevGpAz12rIgQkVaE1tywnb9o5i2UKzLKY8Aw+CKm6BTFCArzA1Gss0gjx6UTXqGFQQ9cMjfgMTdOoW8ukAClVtCghuiGQJgFLrOoqOJZSV3GFizgkIOLpIQAIgQgOBLE/TIgGgCUiSDcQ0SRn3LaEmWWWY+ZqWC7byOaEtLJRfdjfM+FolTxTK6ygDTqZo9QUqj1GCqkCfADFay4asEVpycVw0ANQKo9f9jIof8uiPZYgpaRI6MCo5EgILiWRGCQpCiIwUV4YYbW/pAdmlHy82mvO6SIuKPsgIUroTq2NeiCQCkKv4j5AAgAmgAgQyoG7WEBKAQJUUybCOkbUBZlI5SJ0Sg2dGp4INUo0jeoUE0owcfum3bcQDBgRJEGF30iC5AQERgRh5HU4I9cDKxPrDwYbe0I17UQPFYtICMsfMIhuUESFFFxyxu3CPBvdC9OTVzo2VJCAzTNDhom/N1ERL4beF6neOS9t+vbq7jw2QFLKacjgJgibYwJoccVKFhMWF0QsPqcUr3xlr1lpT7MBEEaSAteAE8mFFEhIDEJHkmFhe/QcdoNPhVuUIKVwC104Z4xetblAmRzpQVqXt/E8imLnhcBu15eq5iSapmtM03VDF4KBYIwSDCAJQgBAMKIYU0o0XcNURX5egZpGdEWtESU5N6wnpYoml5QBQAhTgQFzKRBCISHq4qPTNtgt7YJSIWWM2tU0m7QCGX8j9ke1UJ1ES+dHK8wMnh+35u2JjWviS1Y2UmGJyaEF2WhWduXTscnphXzU8a1t8UdilUfHVu3/7quXGn+C6vzLty8J1EZTqOpsXl9+RTR3/1ffGisd31HtNFWrFmdXcp7fdB0SQm5IeI2GuCIuAWmPZSmZX8WSEpBYCtXBjkFyKRFIIglIxAUgkBxc6pNzpqhP7tYKMst2mOPQa3ZKKRtcRg6VGxUFerFUGSBCqa7pXPgMEWKSAAR0XTBBCXLrlRBCGFGM3CoYTcNksQ+QEEI1ggkiGBPiJRXdpnfPAiFAgI2w3ra0VubmZoamHMdXKeyGhfNTE8IMVdRGm0IyWOWLh2KXakR0Mlt9YcoasaboTH01MmoaePJSunfgWMX88VVta4iYGj38zItHm2D9p9d2PBBuhls217YMvTpxeQpm4jOnTyPn4iebNvxuZc2y8BXpHz/66tHq4Piy9UZ4r/no4IrX29p3taff2zRjEF7KkoMH8z2Push3lmsw8pTDK5S5tlS1JGG3aUFIyQUT0mHY5NitQlA19CplxBzH4cxhtsNsm1sOYyVjW4r8SzsJFoFcme66AnY9OdGoIRU1iAmlQb/BmeqOAkKAKL/mVg0ir6Flsd9duVFSKovB2Ls3KRDCgWAg4osEIDmbDIX1hpZme36m2H8gt8D1uvbcto0TZl3CqGhEzU1QS/PtMKLj0InGkV4YWTFVSE5FizsfXrZsOQRbr9D+lsZQd/LchZBPTs2vqJ4f7bmytzuir/FPhyYRGnuDPXZm3keX/eGGf1nSEkwtM+ylUD8lmjZWX8dOvFm89NgpJ/PAf9/6vr0DI68de+nmm52aCHYdkCfM8iVz19JrpFEidoGkUglPTdwFVahaCkcwwh1QnQ3MYUyYjGdslGNISFBxvG1zhzm2zSymwkPpcG47bu0gdZFYmbSkFOiqUKw80Pa+ugqHCaEaAMKIEF3TGFO2HEAIBIIQL8xW0lKOFKNSddtVFTEIMAYCQBAShlHRs762ZU0QmUHzUjrRb06LsdH5HKycrG0zxeWGNjHXuqsu0F6fnUqkF07kE+Ti+JoRxLtWVsQa2jb3LDt3fO+Vl3ctjKwM7l7TNrcq1GPQRpGKAqFbaq/s7HkUFffLN47YBWtmAtWuZqEKuLBk66qbPscKgcHzJ+dOv7b0TrN+5d3L7vydqqaqb33pUXNyZCzaW5wcapNjSbtVs5EPWcQNG1BpfVApxEHI/QuUqpIXl1L5PzcFqBLXnAvBTWZZ3CLMdCzbtljR4lmbpywoMiQBHFWKzQVToSAXqvyaC+CSqzZMWibFReFd4+pLtrdEPyLpTcAhmADSARFMNN21E1JwUGUfpVwuAoIwYIQw9srQkUveKzSIECCUCLSNF4yecHDlkuujWEcLJ+35fi07HKoJn5unh3HPaGX0ypTvrSu8zar4WPW5SOG5orbKqbrpe7m3L1dW3HHjA+HmxjlMa69rfujysqn82YWBd5JnjgdyvGFJhI/N5yYqJgrpU5a2Y3TZEr7qyOm3T1bNfaQTVe0h10Xai71zL77d+asXxm/1v7i5Kjkxfcpee2+wMPMnNcWRUwf+9qnJM5UkHPX3DlVt7Okq2gspIweG8FGCvGJHVKof8Jo7Pf8kXPAipfRmgyyOllBcjMOZY0vHwrbJTKtoOTmLpy0ocMQBCSEdLpkQXAD30n8eryjVVAKCMRVCeFpSHv9eFWMtetayoEpVEqlbwNRF85wLhJAEDKCmF7iQDHnd84BV/a/XXC2lREB91NCkLfzPhLb+ZJpcHx/5j7XZqCxMXn5jy1wmPTRfnL1o5fAMO9u7eumrF0a5ees/0+pIZkpMtJKeFV3ByOaKlpF4Yr6Yick66g9FQm276hozC6v6+t94/NnX7+x+rHFrix2Zms/nhpfd+mLLDYWfD3bjlZsaZlekZkI+jlf6K/VxOP3Wlf0RFI+vXcWHrfedPLdi6K19H9RO2UdsEB3/2bD5UOuZX86OLhmvWbd5Jzdap6bGXpkdHsfJjS3NQZ0QmZeSgwpsQLgpRAWT3NYx7rJmXr08c8lq5tjcth3bYY7tOJZjO8x0uM2ExbEjkEq9CwlchZHK8yKgCFOMNBUmaFTXiDdX6KruSa8h0kt/el7/WpRdhoddnkFtC6wanQABSC9vj5Bbk68wjpsBkkIaQd+yDd0BUpjrn+hy5v5kW9P7OjaubWwbH+l78fnhhQKtXb/58sxgdRaHsyMb4mJgXBoN4jYrD/lqnLpeP+LMPv68HBqINoYLmRyzbOFzEEaE6uGqet5xw+Ytc0a4FzrvDjZMRipe6svrtXX1r/D9N4jc8h00+bq2d3rNpivvJWOy/3Jdtjh4XTWxt//h3JqPiEvJ+wOpJeMXDow7S1du9xV8504maqrtj4cHxp/+Abnp4ZatOx84Hv7C3sffmefv6ejoDoFuCAmq9VK6GXghvEpPx2FCMMdxuG0zh3HbZrbDHCZthzlMOIy7/3JhO8LhksFi/TogwAgRTAnGlIBK1OqUGDrRderTqaFRTdcWp3791p+SwN4N1aRnfl3lQ1wiwBgJgVW0K4XwWrMBuZaWKHAEbhwupSBRH6sMMmxUNkdH/rBiJLh0Kfi7nEKhcWr+i2bbq5EF+8bOfDE4mCpMpxo2TtIfV0ZXRIKVg8MwnJi6PDmVSiTmJ0Zai+/oYibbfPliYnnOt6GhzampoJUxIxTu2Lg+PZmdmVxIYHwxE7Gs1Pu1VP42Mzp6uTjhP3PH7b+IrDDH9sjDpH9iZcKZGsgfb8vWd0yMP/r68/7awytiDl65qmb9+gNPPfNoPk4K+HQowUaGaqzRhvyO6rm5uxf6P5uNO/Hk+5c2djXoGDkOV5WfTDDhcM5sZjsOc7jtMMtyLIfZNrO5dLhwGGcCcSa4kEwgziWXnAvpcKkKplT7CSGYENAo9SmWTCMapbqmaZRoOtY1qgrSqEbpNcK7SkFLsixJ9Oqu5tK7SrZUSomQ9HKqoFCVC3YwxsoOK7yAMQD4MW/SknhhBCZziYPT/KHPBgPdtskLfb3RomyqXnLPubxGckaTOYDnTy7od7H2Gg7F147PjgwWMzO/scw5QB0bWqbvX9p75txaK//iq8fyl9F9d7xvZOSdU22+jpt31HasGEL5z548OTg99AdL6353dSPiEwNVgR+eh4vaHbRnbU3fBDl06IUL08uuu34NWfvmMfvYkf50y7S25lSTcUFYhealLbiz7qI/29HlX9UReHI2VwgFvhZNsbefyE1DdhKEljOwlu1qPmv6620riIscuG1hq1A0C3nLYZbNbYfZTNiOq4VMABOgMkil/JqaMiUxNgjSCKGE6JToOtU0rFOq61RTfUiUKNZMhe+EuuVphBBaYmhLQv2tbKTn4EsvuNatei9SAytKPVtYghTuKASvnkFKwEgCilLZ0zBn2EX7TLHw+P6LNSs6Gq4HrqfS47l02pcVOtaqaBSNJ+zcIMRWT9f7Th0bva3Q+NzpN1dU92eRk6zRe3Z04ptqVrXH1o+13fOCffDSpRihuStTS3D1sW89993LZz/yuU+K5vrzJ5JLBlO7btgSjsQOpZyv4+DLq7rs2J0wWowVWk+syoxEQtUNUTGZG6Szozf7z1ZN/qXRvzObH3m5KpvQXz76lLjPeO5TD2iJ3ov7L538ufPlvXIqUDmhVw80tuSoJOu1d9YEhs9Nrjw/1VQpm5rbZu3NiflEwDqj8SnbYY4A5kpRcu4Wg6ncgwq6iQrQXXaMGBqlGtFVAZPmiUuVnnmFooQSTBACrLAQwYRKWeo9XkRi18jVC04AAcYqbkaoHLwpzsiTFWCMBCAQro1WA6pUnCXdOAxjKVpCcwHfldw4HT5Tl7OqZx75SFukcXhmvCYUiGzcnj1xMCRPyraWwNHG4UO9T38wFb5hx6sdE5vPmIa0kZ9lgQQ3hD78pU3gr0ilJqzNhYVM+KbNnTWzJ37w+Hevr924kJ56/bljN4YKG9bUfnlsnK69Vau+OQ6I+Qq1+vntgdTt3ZOH+vKX5mD7pnBrrO7Uqz+9Qb+844GOqvtuXTHZf6s+OfYWjMvuHes2PvfiU5VLa6pi1eeOjr51uLilAKMrlj299Y+vj9T/eZimxkZH4vsW9k9smyoULs6+pZuZs1lBAlLG/LRiZUU2SBJMpYK5G7cRjClBukZ1jeiUUo3ohGgapZqq/vSIM6ry8bjUxOJNrHMTSOCFeWr9qZRcZetck7jIHmAAgRFerB6UoIpz3FDfk7fbJCQBEGCMQUguBUIYkBAgkcQSQAhBkJSESoEAI6lR26BzscZpZrzUN3zxSF9NS1s/3fCVV2d9RevvdjXe0xaRy1umruTnfvqLJQfafzk8+Uq478FNPaMV+O97f/SxjviZCbq3tr6jqn5i2tT8k9H6TbOdKb52cyj6qdf/61+zJ5/VJl/1cdgSpHv6DjQG9fru4F6UKMqqyljDWmBdiQAxD7TUn0klXrKNYm1TY845Yt01VZVxhmPWYXvPrqYlMFzra5hc1nx6PJ1e//XPBltC//D/vv/ioWhqyGzb6PzpJxq18fSDvsb17RT8YUDXcQyj6YsvZNLBgHnMTA06L/lodYJUaRLW1GqESgRADKwRrPTPr1NN13TVgaRpmldAjwkq1c6rrk53xAgG7E4MQWWgxYt6EQJApTFRXtjraiTGGCRX6sRL/AUACFQaKVfKb4M7mAQ8ZIuwVJOUvGmFgvpsvYXyZHXQlCYf0Yzx1qazhhaZnz85cfHk+JSviC4dNEUhB6b1oRNvfm138dO7mhrbUq3LBicn+nc+QH73xqrjz/zorVPaQ9cba8Lo8PecOONLW7e9PeLrn+j7/T+8r7W1uuiYGibNj3zWNzw5+eqxdzAS2cr4+draz3VUb+ux/98Tv/8nV9579907Vq5aO2aRhXlx6ZlY//ligc6cq6hd6ujU99J+mAiPdyw3teW3mKi+puYpNHzA1/nRHevfe/DY6W9P7o53rf188DvvXX5o4KWDn9W6Gutj8vD43IWRucRkbevyA6ODr/Op5dnK9+gNp3zpg2yConxTTWd1RZggQYnKNBDlC93cg+b2IbnDB6kasesOykIAiLjmszQNyOOaShU/SsNALtIIZXFIyTUCAq/+cvFVHs1xFYJyP8LL77npQ4QwwkJyiZHlW31mfEmVPrFriy3PPkrznVtWfZYyh4bGb7qt+N2BVCFq/LF2YjBcPRHyT08Nzg7PkN13yq6Pst95xH9XfKV1PDp8cuvTTt0lTasJTP7xyp4/1bRXJm68887acNXIr585cnxsz/WVtZGKvGU3trQM3HXHYwPD58fTX2XtdaavILQI9XeGrd0jb4cfv3jYt2bVde9H5hjSe197Wr5V6P69PQ9DNjSitb4iLhbt85sjTYH6pXb1MrHQimO3GUs+PDwz86M3z8SjW8hM5viIeBr83d2hxhpMuJk8eGn/1LEBmLdmzr+ArX5aGEPWOlbRygIV2Gf42cqgqMRBvYoSQyMEEUKpRlxd1LCqpVDjkhU1plgml0dzy7KUXrkOz2MXS3jGLccBBFRlvSSUDS4pRzcYqyGtUritnCo2VpTdIlYCEKULIYwQlJo4AaQ0quftVcdOTHYZMtkdSR7vtbbuaIg2mGaxSPyplQxqDmmbb7kcafl84YlYBKWWbyLBpsLADF1+n6z1B7qK2dPt2Rebw1pALsMnM/u36OaO67pDbRXVAT4fjyf6Z75/7OlfXLryoTuv39PZUl/XcssHPykaG1v+6gekv3iqkAq+lggUhoYvm392XUfAmtt3ZrA4lw7FNFzfLbS+cwkcijXITDSV79i9ef1ccfLEmym9eq69q57XbTOja2kilTx1LFHd4WNR/3RfuurmN3yhzdsLsRTKf+/48elTia7xqjrt4HSub9zhEq7I/BQUsa1bVIYKbPpYok5WhW7SA6uiatigx16rpBQutbQqN7hY51eOQMuxKri59qv1SgJIFXe6YvIsLYIyfg4hLBVHKzwPKr1ZpWo6a1n+ryRfjLEQAkAiQp3Ayolh3+zs4ah18ds/bjxsrbnt5g3rJNcQHO4//+O3nx/a2HGhZcPfbu4wcsVLP/9KYWr/ts4OLZdi2wdR9/XWubm5Zw+fPHGOr1iz9JG/kme3vvH8f1y+9Fz31iXB1PMz56ybTk6Nrr3hN/PLnvvh2O6WwX+8oWFdd8f2FatP91RfuHIhSEXunewrb7Bczneg2gnqAX7T8gPW6Y+v3NXR3rNj63fenkzmilYokXjt10/0da2/acvN86n2H33r3Jrrju65fm0FhP2H9y35xb/Urvwbp77STo6bRuDsXPc7T852oqrj/a+c7koXm7ARdRIZwQBRjUofsZn89Aq7meIfXuCn89OIZXrO1SxfXqtVBZFUle/ITWFi4lnERWLV9WIlf+YKSaqKEE9G4BlD8HQXaFkl4CI/4A40KCVwlLTxYnOmlO4geFXXUZbEVRDJ3QhCQMTvq21ctm/GzNwQvL9+6xY9Zp0J/vszQ0enX7y50Zm1RtGWpv6zZ7bab326Z0l2asm5yB3x+DheEFqf1SPP1ARvGPvWqy+NHd4XMz/3vuazHfHeyZrC+YcDb4qXe08uv7en0SoOZPvGzvkg4Tiphfa1TfVycv7Vtx4z5ve3yoloenWd9fcfqRh5mb1yKfD4vNa8vfmhD9zz7IuvBfP8z/TuULO/O5SfTyZjli8RKhydfu3M0YXcDQ+vzcUib7IB++wyMUEpjrRUdZ4/GLx+Y8bQt65dUl9R/eSz364IPN/6yPzW+i194XW9/ReG2TkG45E1nZEHNhePXVzHz7cXCk4E9/mdhQxMLGjLC5a/qYJLLkEVLytb6mJL6f0gr/UFlQTj0a5lZI6QZRyiW6AggUq37dyrjCrrbi9tGUUClJXeuHqrhF96pYTyJxXzLOp9qAZPNvqMCv/O1uZYcwv7KsI3j+ZPXHg+Pzv6eo44uzfPv37yg/fzUP6V+NkjO7t2n+Cr+nKc3rlt4Nxvbnv06empwx0wo4Xr/n28abLON+kv+khifUGz+vGPfh2u9IU/sgZvKB6OH9vfBfC7K9/TOtv5/UefeKKpGOqurAW2ZlVk886NK6+cfpzcKSPLSfuFTLEw0T9RtTKaZpUpuXp6WTK3erhQ1fHeO/8xvD+RQsxkmaw1u/36HfXOoUr71/L+H9n3fmDNd38sn/4xTYz7/KuqO9r3+bH/zpHVd7SN7e+bjt22+45lW2+86/j5s69PDfT1D39me2xJ9a4zb59bmJpryDrrb2iOdS2PtlcTTcNAhBQgFiny8sgQAZTbOlQu56tYHcWaS3c8o4tUEF3ESd77ryUHEFLVJQhhxUW6gYo3ncfja68qnFG/YIzzeYQPvfxg4fqG0aUzk2f4hgGtdsWtHcs2Hzg3saKuvem2bKjyjodCYZw8vPeZ5IVLvp6PvT66cProG5uvr6+pXn7q7OViRINCMLfy4WPsDvj+s6GNS83d608fmsB9Z2H6QqJqhdVWfW+TTSYy0TH7yFO//naotk2f//0xM39ppMNiFTXVRrS6qiYMCxGI1F9JOJPfPFg4eOkx9tTUR2/t2vzZ6jUjq1rIXCaGJsStnfx0rra//7VH9pzsiV6Mp8XhmXXbzk/ndjQtu/fWj53+xpkTZ+fekhMTfbOZycPnuZwe+OU5X/XO07c9eF9V/ZK1m9ffMjHxylsv3ayPtbf5Vn3uxqFbRt96bcwx6js3LXEkEpxjiYWUAnllcHDVj/Ry3SXLW3pCybVU9q5ykW4jimJu1Gj/ctktXhEhISVWMxQQRiCF6vdDGK6x3e5nX2Xq1Q8leCA+c/S8vq5u5ZmLRz7/3q/zoYGTT+9Y9b7fOUMj0Yru+yu7p2cmRntWDw+OBHJHt+/OxMe/Kgf8d9ixM4kLvTe/73jzCt+rv/rowjN/Wuj/+ZGZ1zKX/67m1AV64y/qNhSyRZg/AYmRNy/r22pI83J4JQ5nUXG6MPmFFVWFcfTcfHYIoL033/r2xfG+TGT29XzEsLrusQ5JYp45eexI+L3LJ0003z/Q4Cxds+KGFdk5/4HDyYn4hrpjG8/M50Ze+N6mf/uVdedf/Pilad943JLLV60/dXDaSWSMvnFk5r55wtgbKN6ymetTBxf6OiurYj5/RU9Xd0vDJ0cHTv50369r9NGd69tuurXj+bcs2xGaz5AYcy4Ury3cOvMyAk7K0lEXUCrN9Wb7qdJI4bamesqqAhYJICUiaLGS7xq9FEIgjIVYnMAEni0VZS4WSqoKbk675MnV30cnZqdy1Rs3ipabEhXLfYUz+hO9Q/viZ86salr91EsbYr8Y2nDLH8T5wK9f+5Av9eX7qmvoALrzkz/rbTbNqdTZmcpiqj2Yq08mD518tQLhra3bz7/tm2EXVwt0XiArsgJyIzBbiF+JdtRZ3a1zlRl4f5gdn87+bJ5IGhlDzv1Hay5fwOMO6b6BLuC+qYF6Z8UWnkvke3+69tErkeiwPnEkeu8Hm6Kv476DuYErm/3ZGj4093jhDafiSWs8bk1+eXTv3akHA1XRHx88UrRqK2iQ5WlXbUuU6ksrxv7mQZYYnD/95r+PnN1rrbqhbumaQFVNTds60Oue+c2jP3thb2PQP5OsXLM0XxXyM+aWzquzVt6llG5m2atX8WCpULPeXUAkVfetO5u6RMIiJCUFQEJ4WgiAvDJOFX0izxyU5O11gsnSH0t1C1dvCCQBHOaEO3tuuWHdsjXGttjDgbn6iZP/+KbIRY+9+nB97cras3nL/8ILF1fCvctk4a5KNH+Un1xunKndNDNmAeHhbOqPHllvDkbJL0++Z+3pJbetmD/MnnzaftZIhSvmekhwMjNgWuM1EmXON4SX6K0N2Tfipk+HYdu5YtNvipWAtf328E+d3ARY40cHER34w0jfhQ2feLFtM8/O1Z+a/9TugHb/JjAv8DMn2NgJWBPkxrLX87t9oVOzM+ZM71FsJ1YZVzY9/0qmpRUuXUpZjBoV/lrfd39XXzo//tkfZh/r63z4Y3cFf/FTGLp49lj6B0/u3bF108pNWyrqm+97zyO/No1XXn61KobGFnL1TbUcSySuArFXpadKoYOKNMViZxBSBbZuD7yrM7jUzuBtiWsP3ijPk5S0rcwCX7WbRNm0bQCQQpTxvEJgHOretGfFjkikChCWQGEiWSfHv7Bd33W31l6fh5g88uL0yOFNH25fdXx8fOvGaJDtL4jaT9QVf7Cr9mJvxaZEssvnu1RZ99RcLDdv/OENwYIzfCQ9IqIriokByZPE7o9ScwzId1OTy6dCN7frSb/4UdEnKMVBEc/h22XLBTR6hkwNSViY41EEibkUXfgvWPlRWb/mnHNYZkLJaS7XbA6cfUqcKqZvfPjNW37/b8eicOXf7fwZgYq3143++EN1NXNnTx27YHL9rBPc3FS/647r18TOBWuufHhTzu65HrXf3HTj9GjLldu10C//+sC5S0PrTp6/6fY7Wpcuu/3OO+OW/mZ463O2s97JYYyuSml4yuCZTYCrni3LcQFgF86WJ0sUaFkUgSdO8J5Qg/6Q6vVTzIKExbEzgABJEOWAq/TNwK1kAyRBSO5r7GpetkMLRIFqPi2AZ4+R819HlnzkMxtTLPTx5O/wmWTv4f93b33DvTdl1xYSOPLLgBxJPaG13609IZM/mF+TJdt+9dOjI0E0VHPbFL355VfssJytfSC3TQQunDw9kZzGMr3Rji6VVXv99GwiWYGsMMUjNgZN10PkB4HZM8n5Xmde43wpJlo0YADfm3esheFbzv1o7dLtf7x1ueavkg3B7MZ1Np6vm+nNv/Hm6emmpRUrvnLbzsH/uTJAL/7uPZnKSNHeuodd94Gbv/uru99YaI1X1r+eHvjp6Y6vdt61y97raHMpp2X1rhdfPnH0eH/RBNuxj504MzM7f+c997YvX7Fqzep3jk+8MmTeGNRvWhouukyd6ykXKQKlDFdrTKnJVCoPiZE7lvEq/OupealdF3kSVbUmbqMLUoSBxIKrgxyYVENWZYmPKDewJR+sZpkZoUqGUUg3CODCzPno2b+0T06w7iWGY/9mf+7RrR3gi4nIqtVjF+L9q332eG13avBE2+sS3cWLPaT1r+Qv32oNjDfd8P9++KtGs2ByX+v6NWtv2f7g9bUHxwq9/xX4QP/4xqh9bPxEOFPcZEROcnt4iu/aUmXM5mfGE3MieNFfGIniDWGcThAOldva20fnxychsayIH6bF5H31A3ahfmyf7/mg5ssYD60rTFaPvT5y/tQv7tj44OYlTU3mxZbU1Os/wrH1xbW1nZP+Oy72FI4f/JIRH2ibDXXfvrk4T6QdObb0k08+c/bjxs8tA5pWdNRODuYmc5Ti8ampF46+fYvfqAtV3szPv9Q39Exs5bbWZUTTlD5hKAOS5Zkr8Io0vYC0hDkRuCW4Krpw36PiFc6FkFSqs5W8yUBQVsOHAKl+UcoZFQy4cCQUgQiEpHueyVVFYljxDC4IFpIaAV+wkE5Mjg77CuMVOevgZTwdaL171Q0td3WsFftOJy9XbZg8+tTI7/3qv0JaeufQlscqPkpuOfO77T3Wk3MHe9nLl58o3FeRujETfeLknQ3L1m+tEWbtP/3L6/uK2HRq7unKvke2PBa/suKObZVa5ZnJiQv8xIf+ZGcDzSf3H/rRs+mv9XIZpbs3+vtG7JNztEWjzT7thkd8XVOF4mTnVzKbXpqa+t65wfhCikxHN7MdM8vfe+X9Kz4y/9PNl05P/OQs+UBgif2+uq4sXpvw15kNzlD+lpsPXDn5woUrq7vI7X/+yWN9ky9PBa+cT28eeCZ2u/aFP/gc2BMrK4LfeC4+NHhFhP2XmoKBsfN3tq7fsXnj/PhIbWEcy2UuclSGzUM3boqqVObp9caUCxypWgCPTHBtpnD7+DgXknFaknzpRYtmXQoiBBFc4w52bMk5AawhwjF11Lw+5FlwUNYCAQAIKaQIdaytb189fvns0TfeGhwaW7JurdnwHvs9t3WtvC7V3EHiY9Vvv9p64VJgNAOUn6/INhnhf5y644M34S/f2MbP9skqdHrW93hyoHb48eLN7Y2HrN1OeubQiYZ77/vQTc4j/lm/X19XpQ2/cIX0WnxgAJHa+3aE72kpTp7dj1dWN+257m+aXt/wSv57F0P9pNKgdqiQXmOO3/ShtljzXBD3vTPXf2zAzi3k36Di9i9vrt+9fvrkY2ntYW3Ve3LnhnzdT/bFe8j52KqOYMOtGwPLw/nJvprk24NwwyqcM0LJ1Xd9LF9gb8wa+zNR+tZzq1r21Wpts2d/c/Tk4P5j+sc+8LEf/vpX5/t6Ky+OztlizA6tXrEqGokkk7OMOYauudbNlWVJWuUkTTkdK0o11yDBHaULpVnvUgguHSYYdxxOVTfZNXYYuXVbEguOOQNmA7OFEAgQRhSBqtVD0qURQFHvKh4VgmuVda1rb0jHZ5/8fz84eupcz7ZNLUs6fW1dHYTMLcQPnzkCjv2xhs2yfpNOESa0ePLi9x/7zWZneo2+IZMVVRtE0TjXHjAa93Z+7WITDLPTiYZD88MHJvrvXWf8/RdrITGVnzxoaS0dX9jVn7+Qqd+dzuZC1neLg8yoNfjzE5emLjZuC951C97akPzwKUpzVR8j0V3bzPoVI+mnLKPnNs0/Vz98ePfy6O5dq+YmBsjeX+bSM1Vr+jpqC/tSm8+tndz1hZbhN58bTT4XOnSsa7aLtrV0tLDq6kPrP2Bd3JeezY0ePOef1dpb5gY6zWePn0+8dWFhPu+fTmrSclpXTH78I49895vf4hO5uF04bB22stmmmtqxiUQul/f5A6DGSgvhxotqzT0MUxKmdPvnJbjHHgkkBEgBqoNP0QjSPe5BjZ1T/RauWgrpChZhLLnba8E5o2rOklTtkAJJgdzJad4OUPOh3cZmUdG6FHP0+jNPj2Zzn/jLP1m3eh0Qouk6SOIPxFYvqQwFg4bfhwiRTFbmmHPIPt9U0bS5ncnOv/7qqY/dlNp6x45Hvly7ZMNb49895BwP3Y+a62t3jrLswfPH3nwStVN+7mhxKtD7yY1oXW3U94lPTJ498/zXnh4bKnzqunqkIV9gIXUKvXrZaQjFPjPR/GImmQuGo9yQiZm+vpTTdc/ltcu3P/e9vwive+fxi/04vyycDVqW78Qv2OZd9Q1r3nrjUd/or41sNp0L3Ky1yh8fvJxKjH/wfRt3rl1y/faaRt8//f3T+4sr7OU33+07+dV/aXvlBxc/+ij+on/N6ah9gA+/9MqLf/hnX3rgnvuefvwJXnQGRoZaaqIP3nlvRzUxEAjOJOcghBpkpgTm1edKBMDd/kwom1/oVhOBlFhKpZgutQpSIsCYECwkFhS8rn7vTQhKR18hYEIqZk9BKQbAkeTgkYaeOiPXJQsESCLsC1fNDPcNXD734HsfbOleceztg9MTU6Yjw3pxe+XM48eK4wVcHQt1VNY8tOMmPGxNJg7//eO/d+H0sS++rR+s25R+5fwn4wuddzdsf+/NncuOP/PzgX84U1gRn9q8QCJXgs0XbqqJVexIz/34qW89/9w74WVfvDjYd8g+PhL76Lc27B1+7UIvlTu+3IKHZ97aJ2OZyO0Q2b6iE963+hRhjXjP3D2JlOPX65ajtge++ZtvNAWno7ube3UrYImJd4ZD6eeX3rL97YH6t944/ef3fyF46wYKvjO/+M/XIwMfuOvvFhLj//Q3f/eebv6fq4wnf/r6qbGzNR2OnN9975bAJ05adR3LdzZXHH05eXl0cv+rr9xy172njh0dPxWvDvtXtzc5diGTzlhmVUgKhISacVKi171xIlIKid2mMyRAYtdKgnKw7mAqQoRQaAoBAOYCYQ6SSCGodKdBusJRQ7JADYIE4BLZCEtMKBYgBceIYyzAzXZK7yTHEguPEQJMAKHMgl4HD55+2X700W9MTfbnU9mobv/L/ZnVw8V/fNs474RjOlq/897gK6OvDO/d+e09BZb9xtzaAxUr/F21T89M/uq5iVXnc1+5w75726bPfKm9+fjFx59PZdJNG1auiaBQMj0nNlTOnl+prfjc6BXt9TceTX18a+XajU0Hn5udL1wCDN8d69kRvu7OFq1xQzIXqqraNZYZHGkLah07l93Qmhq6ONJ7qWX3hozd9dnw7IXTU99NW6c1nUfW6WZ2/9CT8fV5u6rq4PjlzhctEal7qWLFb1Bq+9RYZC556XjmpSkWLcZtq7jGnowW6U//5qlPfrb6Dx5ivZ3LOnbdsz85+fYLLx05/M62bTu2rF9z6vxph/P49LQh2fzseCFXWy0YCAGSuyNGJEIA2E1CqtmRi+ks5CUgvTyzEBhJISQQ4Z7aKJGUQlUFckbduvXSPgFQB35yRdAK4BKYal1QczQwlhhj9QBcjlGWByogx/v7Ro51bep4/xSfe/zVt3C4UFdd+ec7RzdD8Ultg/jUksBvTtiZwrPnTryeePmLP/sg1DZ87FD1s32N+OwRqXWT0fSNscaty5c8/bNHk2+du+nBZXft3LW1ZfD7rxemVrWeIVUbdt2nhQLbG9qc/rZfv/VvY/wF/cjMyPGjT+qjH7yepi8xtnJl81+8/wOsgAyfBA3BzMyC/fXz1sFXnrt75031lRVQnZiYnT4yxZuqtc9d333n3isvxgOJNm3l+zePXTplVuHAw3f/S8qe+JfHr+96+KFH/uLKK499/uN//LNb3v/T3Z86ePKdHw0ez/fUTU6PsWxuZNjYUoyuf+h6euRAKnPD+nWrD715IJmzhy4PgFUgtDpEa/oGRoqpubrKmFkoCMcB12UuIlj3/EqEyFUUvBKqNyVRHQjBJefAHS64O8ucM+bYzLJs27K9liPs4ikP2Hh+2HWmhCOEkCrowohgVZ+geoyF9/EYQEoZlPbBl5995Z18d/0ehCIpfyEXW/aptedub83+6CfwxvuXiq2r5MGzEC/2sulYAArJ2R8PbH32ZD289HPR9+PW8RV7rv8rnwjAmbF/iK7FT03+8XNP3PK+lo9vre6JBP/zyv5AfDqom1vufHD91u5/fua/WeWZ1mB4/srJdU2hj391a4wdrbmSnpwfnHn7GaOpbiaewfSmybGm2eorI23LLz2xPzA517NxD/UvGb64f4EM/cP5+BsTqUlT5AoJfYZ0r6zrWVr/0j/+8PSzB2lz3fLKuqH4lZSc/fCHP8x/kPnVEy/+/obbH/7Epx9aeN/5i+eeXTvy47dfsizxnUPRPwgN58dsY2Wmpa6SIuHTpJManZhZcLivMbRhWQS1tEBtUzem0o0AMXInwyuJqdO+1HkMLpngcmtusCC4ZFwKIRzhqP4HxmyH2w63bOY4jmU5pmlRrk4ZA6wiDm+0nIeR1fQHRd0idy6yG9gqG6H8JQBIySWEDbYpMnBifFRn1rGhK8mcLXquEy1bOqoGe0dX/nByMPvsCXbw0t/FzO1bA4fH7bdx7dP75lJ1B1cuJNZfZzU+8LGItW5uEj398r+urs879/9+b7hybB4t788RPbzlpXN/vsy5Z4f9o59OZo2K2269ZfsN7SePPXt/3bIxO/vmXZ/8+OvxG/uPbKmFFRWpQlaSQPTY24H48I7esaGTqbMfvfOlf7+1IO3i8Yv+qvfd2/rJrjffbEv88mcDNTntT28R3/qNPD3Y+8S+2Ja1PJ8PDc4EJuZy/jqtjj73xqP37fmQJNq3Emde2XvuU+MnP/x7n1235aEl75yORgL//dxje0/OHz4rAkHjg5XvHDlzPpVNR9buHss5EYIjfpws5gI1TY2RjBYIWjwLEpTo1Aq7/R6qgg8TjAkgNeLVrccVgjHH4d74LNt2bMuxTNuyLNN2ihYvWnbRZgWTFUyTCsEQwiBVygbAnczpJsigTPNLPfllU8URQlh4b5BMNKLZsH9wlZE6wo0lMeOKzxgcPqkPHT/C/qDQ/P7a5p/NTD0emclXbY92rmnujMYTR9KZji17tmx9iErMazTinxhqT0GchVInDL7T35Lb8BCgW146/53gXN8b47l7H4qhNDtzcebxf/96Q0Pd2OTMqmDDH/fc+28DT6Q07ZkVD795dHT1xafvXB5d2169gTdHfbe/MRg/Mv1okp+rHuNVt20Vc+aZ0weOGmc/deu6rZv3vPbCIZ2fybRV5FY39IzTJecbRy+fCk9ad9/bfd+W8LcfHdt37sz4zHgmP1hIj8RigWlA/z55+MTXJj7/yc/b/Ve6dq7eU1x47JkXzUCF32L/8o9fj5t5EqhMr7lvKKrdNfta1cDsbCqRLlSk5scqKyzQiXTnHrlQwzt0lrq1OrDYIs8dW43VcizbcRzbsh3LMS3btOxi0S6adtF0cqaTt5yC7eRNXrQZ5YJjJBAgoQ6ecucg4xIli71D/Mr6j1zqQNl8j46XzSLfjSgU7HVtsGPWwdKJhipyNqv0ySVLNBzyh0RPISsDVfo/H144emy4ipAfxW35i0cjEXrHQx+cmZycGB/TA5HujR1Vp+7rP/cdOLeP0eZwdcuZOz8SbzQmnj5w/cEz35zLPDNq08SFP/jIJ6ampnWh3fPc16pWNN++cOa1rtXmtrvj+y6Nt26o1qvQeAOrCZ+b/35anKgM+wqdf3/g6K7k0JWD1sj+sB5PWcGjF+Kzx8zm1ANVzVNdbZVVsQ21d+ozz93zyW0Lw6cee/nN5s7abz+w0UxPW87w9HxlWBeD8VTeLOxLDe375z9CVH6683duv/f+N45eHIrt8KH8ismDq8PwxkI2MTwwumr90GzGZ+eETMXNOsZ8fp1U1VRiTZOAQGL3EFAhheTluRXBheCcOTZzbGY5pmXZpm2aVqFgFkw7X7QLpl20nILpFB1mObzIhM2Ew6UQQN3ZqhhjIQG7A4SgpJcAQgqCiPSIxDLq3/1BGAshCPBOarHecbO+UNWE718ln5iquDhFgxq//6OP7Lp39/ED79T1HL+xolqamZl5eMKMOcLh4YBOYs8891ZjW9fajZt6enoKRefK+fRDm0L740YwvV888Pvj4B8oLrswOPFgsL4h9MDLh385v2W3kSV8+AWKwJIWr6pZctvfXR6/VHPk+VG+um3dZ/qnElMzB977/rbWLcVP/V7LoRea+idnXz3ykv/GFbXdm2MX8x2HXlpx++cn2zdMtf6X8Zv/vLLvcHRw9sLlwxfPvdlWS2956K+37/j83qer77sO71qRn7kyWSho//ro0PnR3MbVO5c2rP/Vvp8siGnExavPvdzZuezGW+++Ml5bqOrq8fNvhk59bP/k3olTlQ0V+WwyYnDDTM4VRNEKYmEGI53I0Fk+KRwGbnmIwh8SpOQgJRecMcd2bMu2imbRtIqFYrZgFYp2vmDnLce0maVmogtFDgFG2K/RkI8ShKgazSCEAKzmHmHkMQpu/IqgdGKX9Kr9VAm9YiAUkcgxuVLEdhGbqZpbmhKdN69vfXF+9lLGH/bV1FSPzyWaVkVXr705XPOJfGr+yuE3Xn/6udP9s6s/9CfpmuuPvfLqz3749KpjJ1etXtHS0dbSXd/UtfvmO1on4unXctHMr3/5OefSby6MDVRF9Ls/ev99dz386Y/MnbtUkc6np/r+54mRIiPdgeYDAxf6D//jth3v277zS796+tHpiTemJgd+57OfuPvDDzWg9DsH45cn7b/9zsc3LNt916fu+MjKPefN2VyoJhrTrGQi8+gUTRSrwgGrmJ6bT/e/uXftrl0f/p3fmT77wuf+7NvziXTB9vdNk/mMc9N1jyyJbd/79lvx7IS/Ihp9eOeb833bVvS8c/jX1XMdqwrxc0Pjn9TpxPiJdKYfKklHdVAg53JqwgYOToEjCpE6O5ezcinBuXTH7wqujk/l0nEc27LMopUrmoWinS9aRdPKW9xmwuGceyNTdUr8Pl2jVCNEo+6pOoqxU7ZSCgCMFscnl5XllgrAFutOrsrOKKsr5aSvAtbGpicSwYmTO64vBDLTzZTmLJ7sO9IZcSLLd/sb12LNCNW11y/fvPz629/4wX9OUEG4U50YC1q9vlyABiux5pOADMOoW7rKXxV/6R++8ZnMvvem8gtZsS+iTW9tvXPrPcmFiXT98HX37Jg+N/WrvfLS1Mz+fY+h+ZlwMLyrY8/FixMzs4eZyPcPjfzbP/z768+9cPuePe954AuvPxs/MfjIyWg8Egw9Emv8YEP4hlT6+YVjbwTTq5esq62qef3lV+fmzD2rN3w8sKHwo1eKD+/R6rdHuuK/2v8LmzmUitpIc1ft0tHeiaSZAoAcZ5GNKwoh38SBqVon/WktR3Ds6FRjU60dIKmmMEESpGDXNTstlQMd1WEsw5LopLLByWUTU1PmwrxUEyYZszm3HW45rGjZBcsxbcc0bZsLhwkp1WGAxG9QXaO6RjWq6bpGKdEp0TSNqsMa1aFVSudUukVNRweEAAiUSCI3/w2lROvVZb5SwTNFEGKCExnz+VFnR/elZAZVxapy6VS8v//p4clC8KhDY9TwR2PRDTfu3n79rQ995buP/cvf50ZmO6IXvvMp/fLRty9Pb6xct6IiHFERbaGy4fKK229Lnh8rjh2i/A///R9qrt/ae/zI8bfe6D1zuL1ife/ZvukExxjvPfo/WIqaSLiiueHI5ATB09HqjkJmtmjmjh4+Mnyp//c+vGnFijWVR9eSjdf/qnLji+/MXV+8/Omt+ntu3b62reKH3/6fIXapqTJGHfv9m27qqelOPHPkN+i52j3b99xxm+TyR794zLLtntaNAb36zMUnTZLWNEPmzfjJy+1rup5/6qmRoaF/1yZX2cE/Rpv2p8dEI18aC4wnc4zadVVGe5sPqI8jIRE1ItV6rcWmZsb7hvKptHsOD2cWE7YA5p68AxQTjeKQT/dp1PAZhqbpOtENnXjnNKrzICgmGsGYuEdauXEnUiyvkG6rrZtuWzzspsQyIO/IqVILPoCX18YISWk5xaEJMTRENmyNTk+120Ozj5+YqfQlK4z5Jds2t67Y3Lli6dKVa7htZYvOhtvuPv2f3+7prArXGuFk7/4nfsmBrd+6JVZZFfL7Z2x01rfyT0bavnDD+rncQR2hs4cO7X/5pWhl5d0f/J3LieKZhFkRO1GMxzUidUJ0O5M88TU5xh+8/4/i2fA7b/xd0Z72ETKTnOsfOXTXndff8dyWD8joz95483+Cxpsmvq5vfPOqtjXrN/z1V7783//2X/OT4+3tbd976elUx9qsyP762TOf2by0rnvpAx94bypb+OUTj0/MjTz3wnMHRp8gPuqzNY2j3p/vG8dv5KbnDZ8xwHJSmifp0Bkj3W7QqF/HOBSlBSPoJ6GgkJQhBMGgPxgNVYlg21LrysjIlQlmWapWGbmy0YIGNTRKNc1nUEOjhqFruttapk5/QAQTjCWAGrKMcUkIkkpvFJ1UeEgIwOBGRV6dyVVtKqWQ1BuqsMgJgRRS1tXUQEHMksTa65o+MMfPr7s+sPfI/OjoTJ71SLbrxu2SBMYH+7Op5MWTZ6Ynp2ZjTeOD5wF36T566Njp5072bl2/tqejtaW+9uzym8yFimQqOc1D7/vwI3/5B39q2/k77739urvvj8SiUsAN7duXbR898PrBA2+9NTo68YWbtS+uPvRfuTXxys2HTu1LZdMECTBwAOl5OTk9c27jam37+qXpf/2fpyb2feRP/3Tbhk1BDYNgnUuX/sVX/ubFXz+ZS2SOj099beDIbCpRtNIzfRe3XL/HKeY+/ukPT0xP7j9w8IdvXgQqAsFoZaSiUMxzUy8W0gahphABrMV96B/llWq/tpVGBOMG4lIAR7qh+bgDWm1j48r1vkAwJGV1Y3vr+i2puURy5Ipf13yapulU13RDnXxLsTrVSg1Zxt5AO0zcUz2uqtHzZqCAgPLh4eCVFiGBBMZ4sf5agjtjGS3qKy47Xkl61RJIIsuyNi/T12xaroUDDUFm52oShd2vWAdDTCzZefO//tXfrt60uaWzs2gxxlGcRMa2PRA68QpyrkhTcA6W5GNjk7RonT3Ze6zqDnG5T8r5pxfWvCdm17U0Xzp9vEJHlVXVBCMpJMH+1p6uB1ubd9606+grb25qPoQCuYblD56bLkT5qb/8aOvQeP7FAxNCx0dPHY5GUWWn75e/7Ht17jxn6Zg5vWRJj7CKC7MTacdqbOt48MMP//ff/4NZzIPu14PBbDF/8p3jd3wwG4pW0Fb8+c9/cmJsbHxqEktq5fP+5mps2wtOkUtRpxuObYU13ZHc5KwqGEEE5jO5qUyhvSJUVRCBCiQwql62LhyIOaapIVxVUdWzbBVh9uhRXaQWdNcrahqhCLtHH6tzIiihiGDvnEAkEZZSSC7dQyg5V5GqkjCGUgmugs0AXOXCvDO2lOJKr+CzNGFVqNO7+OIMXYwQRpDNpHLZpOoopRqpq/atadMbYgiSc6++/JpTUT+bzDa1d2y/4Yb3fOj9yz/2ezlRR6YGkGDFgjARBiGLlrlx57br7r+XZggc+rmxZuXC6g995yfPEsGCkeqzx06KbCIYiWoa1aiua0YgGOjo6Xzwkx8ILP/IW7m7Cqtvy8xfiBnn//qvP/6PX9hQHeR5JoamJg+fPcei5Clr4KQvoWvavhdfG+k9H6usrq1vys7N9p89VdXa/uHPfGxJXQRxplFq+IyzfUMvP/m4lMLwh5avWfXJT37U0HQMEoBPJxZMALAKmWJREC1gGHXRiEGIEDxezGhEEtAx1WyBLFtIxjKp9MHjvQfefPvs24cTY5MGoTW19Z2r1i7ZuqOyuTUci4Wi0UAkYoQjvlDYFwz7giE9EMS6DzRDUp0BsRiYjrRsbjnCcphl26bJLEuYFrdt4XDJuMDCOzBIqjk3TEgB7olBi0UQLiwSslRkpAImlSIvY40RACZHJnjG1gEE0+okrVhaPfvxu1s3LWscPH2iqas5trTnhaeeTSzMkYqaK6hy9O2TS+gQQjie575Y9Zc+s727Uew9eGT3jo1fqh793J2NN9x4CznxaoM/ly3kbadw/PLIT3/4k8TMtOHz64ahzvkyDH+gIkaW3iB3fS5cY9bEhvqujJw8m2vdvPNDN9VzLm3gl/v62Pal+u3rcqZDEZ2ajf/TX/39YO/5cLSytWvZwNkzh155sWvzjgcfvreKCmHbJrfzTHznuz868NLzlBAjEL7htj1333Gb4NxHKGHizxp3fqv2pr9f43xqbTHqC9hOsbOutj4cLTA7bZqm49QG/eGg4adSmvn0QuHt1w5+/5vfe/Tvvnnmlbcd0wz6A7U1Da0r1jasXW9UVRmBoOELEMOHDB9ouoOILaTFZdFhBcspmHbBcvKmnTdt03QchzMm3WEnXDJ1RIUA6lUGSQCEAHEMpXGrJVq+vDp+sUYTlXRXylLJNsj2lrrJi9aVBVxZG7Orb6SJYaoVl/dULL8QP3oJv/OLx2o7lwxe7hsZ7K/58OdfTK6E8+9UruGQnDk2Je99/wOaPz2fODU00/fjH/30wfvuWnLrjbaE++oa2N1fKBTMZx79Wd/w6M9+8XTfhSt333/X5t07YzXVlGrq5GlMNGoY0epiXe22XVu1/MwsRHZ97JFNr5w40Bd35tfe+tJkxfsevrNx3wv+uHgygfoGx//pr7/y5X/+u/r2zu233vLL73xfM4Ib7rz3sxPz9rTv0pGTF3nykp37l6/9WyQWWbP9+lCs6uEPvf/C+QuXBgaXYvzB2tXm5Mmbd/tnouabQ7FwqCKPjeaq6sScNDDlVAaCvlBIx7hom/ZIFhD4dImydvHC+QvdExubDMNH9VAwVlnXZufz2alJEIKDKHUTgZAcXLIBvOYFhBEGj/AFUFMoMIAQILGkjDFCiAdPJQgECBGEOOfSnWSAVV68fIqXdIObRUBU+nusIrh7VVsq6xRwM0iDmuOIGoDJxq0rCwVxZmjmQv/FKgKDJ04e2ZDI20mYP3V0jOw171z+Jx957rX9A9PJqTjza/Tll9+4cP7yBx66f9Pm9cFwhDksOZe44/736s+/1Dc+crH/yvQ3v/fWi3tvuef29bt2VDU0CI44YxgjSpBPq6+t2kWtZCHttG2//b07L/5bfBd/318eP3q09fJvvnxfoMGcS/4SnpsJn7zQ/89f/uqffeWvq+sbb73/7l//8OcdXX92x8e/qF1MQGwN658eGxt/iox945/+84//VmvqXlbb3Pje++796n/8ZzcOBvVgvDhdExLxeTabzi04xZQpNi1fFjQMJz/bEItoAR9BAiTKWfrR6fRUYSZISAxrYnQwsPfVVVs3hgKx8ank/JUpYZoUEMG2kiN2j+xeLDYBUEllwIgAIInU8cCqLVqVnyDwptPLEsuuRCLUlCqXSnJ/PJMsSpUs5dopvdI0CUL3EdPmjlYjE72oOCTspJ2drFl785677vzD969cv6xO2CxQVx9u32SePLmp1bn5+u2X4syoqNm88v6xIbJm3YbVq1cGdTo0OPhf//2946fPabrPF/D5Q8FwKNxcX706ppm2gwzf1NTsT/77B//6p39z6PmXkOT+gF/TNEqophmEGKSiuVjUIbTynrtXhxeG7LPHHrz8/YfGfja9IAK3rXtkEzeYIwg6drbv+1//diGf6VyxYuOmdUfffCsdhqMHfvhG/1vTMF/rozt8dVeGpn/yne8lpoaZZJu2bexpa9sYbkYmv0ySs1OZ/Ue1eYtNpHKc2wRsbBhZKZhjE0wwCOD8zKyTYroBeDadGEjGL0yNvfj0Cz/+5ve//e2f/Md3fv3CG5dyKBZo7JBGqGDZtm1b6uQD4fYXKWmpblkp1RgE4jI7yJ1dqcoZsBdkKEoACYkAkJBScPe8RE+WpYrOsoYX9zdRUk23nR4hTHQpANk5VpjLjx0rzA5zR/povqG9bl2jFiqkP3bz8hucwV3oyJ++Z9UnPvOJ2++6MQSkmO/hxubx2WxDZWRNY01dQCsWCt/45ndee+mVscHhdGJeCFlZEfyLB6K3NRTi2ZzQfZrPNzg48o2v/fsvv/Etu5DzB4KUUoQJ1gNUDzASEqiybeWGpbmz6Lufu7ny1N1fXFmfGjWzPe1NpEoD03RyOPDci6++/NivCMUbdm1Pz06nmHA2d3z13K/v7f3Ve1Mv//7UvqxTOHbizMzYeCQSrW6o3rB65aHU8KMXXvgbc+a5E+s+0PDZLf46jkTBsY5duHBpaKBos3TRIZgEdNQfLzzTO245VkU4GvGHuBCJfGE0sXCpr7//9NHcQp8ZSjY0+tdu3dCwYiX2B4QAQAQBRnKxTEBFHF71rar7QxKwRFhI5I5pZLJUZu11QHhmV3gHrytFBelSQsKdc7wo0ZKCuiBZCoQQ0XRhJVihUMg48xM8ma+X+bkAnya6L+w37lhbe/P2zq919/7zPczEfmQEqztWdqxf39Hj3HNrcHVLuL214cH7b1zbFAnpetFiP/rJz/Y99dzUlaH+S5cimr39xu57G6xkMjE0O2sEQ12dXTZHTz72mx9+7V/tQt4fCFFNF4gKiQXohRwrsMo19TWdjcYgbeD62s7rGnxvv1xM2AvZZKJ1Z3znH6WE/tijv5geGgxXVSOMDr22t7JrdVNDRVGafeZCgmWDmmBW8Z23DxmGEQyF2lvrL0XtfzBPT2umP9zY3da+Pe/HmPg0I2VxizEEQDHxGT6fzxjjkbQFmWzS5qw6XFETjgQM3WYsUcxn7AKjbMLIvDPbK4vFpoaWmtYOMHRFhoN7YIvbZO81FpXMqKodk5yLUj6Uesi01Opw1Wnc0h05RABJcMuIpJASvKEoJUEu/kiQIB1bOmaCYsfBDSxESc8DRHDpr7yYrjubHGuorC2mU8H6luUrltgXJ9789Y/rlm1taG3ackvdyu23FpObic/IJdPHjp/RJs5oCBfzuXO9F+anpyZm5u/aGIRCfTpDhETZQjZtmo5pEkIEhueee0XTjd/5q780/CHJbLOYFUICijmRHXd9aGFbe/sxPHfs9P7tza2nju97ejI859i6My9QKhyJxGeGD+59+c6HP1hdVXHqjde6Gt5LdE3wAgborvRHfcAK5szpg3PjYzV1tfHZeM4salRHujY9OQHOQJNFIyFfopBL5fMUg5RYgmAOc6LVdSuWd+e1gcFLC+mFhqq6ZY0tuWJmfGE+WTTzdhHSXIzjQW5elKipMhBEmlVbn52ZUgAHPOmU+viElMSN/t3Jf0IILtx+FTdBBmVwxpPiYl8KSOGe1ISx9Ir2AEDNShFCqHqWUj8iAuk4LJMvVre2MGYGtaCI1OPUOwkegPbdFW3ZJ7//diGVu+nO+bb1qzes64n2j3zrG/9qVDY1tNTX1VRsvOkm3fAjPh8KhyoDRjFTsJltOvb07DRI0tVeB8XMQlFITQcpiqZJ1EQODkTTn336hWgs+vDvf94XCDlWYX52MmzbFXUttZt2VDTVfvetx9/+zvFbqwo/PxnICKiKhLS5C2LfmSqd4wCZH7hoF9INLQ29h8yCI0ORqIAMgBb1aW2VRIaxYxVe/Z9/Q5XNh/cf0IlWtO0sM0cCOagJ1I/67ELB4Tzk8wU0qoGTzhULpjVTiBSo0VLfNDU1nitk0rq/o6a6pb6qNhaL58y+mYlcLqNPJ3jemXSKHVt76ro3+kOVY8zJzcwQjKQEJNySAYzUWHWpaPWyFAkCjIUQQiBvxKIUZU0TXsOgK1uBgChsi4R75qMXf4J7JqsiHcrCGQk4nbWEk+EYB6tq+s6/HalOzs3JvuHX/FHKGjseOz6SMXOfau8M10e6umq7a8mvX31zY6wYXRKyGmTVjR/hNXU7dl03MDBUEDSRy1dEK2t8WjyeCOhF4PZ8QTKrYAQDs/ML3K9rIDFCEoEtnJ987wcA4uE/+L1AuLKQTb399JOtqzYt37n1jDM3NzicnMx9/bKpaVgIhhGlBNX48brmYIRYdb4s0fTG7p6utgYJdPmmLS8du1xAocmk1VYdbli6uqZzzeDBl8++dpCYOEj9gGjesYbBdOojTUY44uAFKYNUi2qoYFtZITsRHZlI7x88EjJwc2MLk1wCSTHWEoo0+PTuUGVbftn5SxdmZqfjqUyCttdf90CgsyuRK1iCm7m8k00rMCulRBgkSCylEEhg6R59AVJwNWXchapYqZpS2UWA6spdukcDlBXhI+nNQFHzPbk6aMxFYRzryB/B0Xq9tr1gNNs243YOGcFQrMIKL0mfOzzz8o9mxy4+9Pufr1i55uXe9IG9h4XlSEQium9Ps/nIbdV3vOfmltZ6gnEwEmlcvvqOu+/btOMGADSfTNiFAmbmOweGj70zdJH5AxpCdrFoFdKFTNEqFM18NpfJm4W0XfjOt7798s8e9fkD4WiFj/LvfvlvBvt7x2Temk/5EWqNkLW1/taIz7SdhWxOAqmJ0PoqXxSn2dwIZOeWrVtjzU00di9b0lDjC/gm8+LSjFWzeueKux5efsPdkYDhIwgJQQnSMJl3ijPIzkQpx1KntCLgX1Pr66iLYAIFIZc3GIX09OTshMVFXW1TV2e3LxDOWhwhAtjZtn7tg/c+cPee29rbus/PZk70jiwMjRYvn4kiUt3ZLTTDsbnNmMMF55ILqY7Z447kTHImuXA7G0C6Q4RLNIKqLsOlI9JLUchVXZ4gpXBPKUQIuEQYE6ERrPux7sd6kPjDmj9EfUGiB4iuF/C4hqaTw5fsRMGsfw8Nte1eOqUt1x899sZH//yPvv4nf/HEq+dXbV7d0NFdzSZvfn9bdOnNxOeXxJGUEOyLVddW+FBlrJJzZ2Z2am1t56ollYnZuf9+Q885rC2qT6dyOUuYQCoCGCPkMJMCaAhrwPf94Fvbd2wMNdStWNp5mmTP731GfuZDfC7tFIpN9ZGldf6Uxeb755jE83lWtJymSj2oFayB/e3v+VL98o3nX3wiHo93NNVNFObTEp+ayPT0Dq6/XWvbsK3uuV+nc/GswDZHOsEZzD5+8PsO4zkKUaTVUrm8PqL7tMHEZDyVDKghk7Y5NnJ5anosEKnQERnWyMraukCaVNY0dnT2tLc2r+jqPnjgwA9+8tNNddoWLRFavbtm+23Z5vT8lQHEBABICgQBByy5VHQ6AlAzUFzYgxEC7BVmcokxllhKsTiFRvWWYeK2lCIMLv8DGBGCNB/xBYkvTAMR6g9Tf4jofqr7ieGnmoY1DRMjb8uGquSRQ6+OHD55S3Xduvvfkznxk+oWsmVs/vibL9x0990v/uSHLz5z6OYtQxtvWB1rW4H9FRKELCZFbgZCzaGKCmbnKv1418YPTY321Ufn7rl3KxI5Jmgxk/nV630Z2rakU3/x1QsVwJuievOK+uowQcyq8IuaoC36nvF1/kl1a/fuXRum5sZJJrdWOleQM52226u1ppjWEDGG044t8EJOLKcoEAlpPE6AC+KvX7ZibvjZ9WtXXxzei32V08Xs/lf33XnvXdGQf+mSepFPsSQqFkWAGpqG47Lo84ebSAEJXoHo4Ex22pZn5nJX0szw5yjWbGRJQJzLXCqpXFEuk2qN1Jk8mTUzK1ZXtPd0CLv48r75sxcvrdnY2rlsqaittTAUU+nM9BTC6mQkJLGQGHMuCQbhzvVCgNQZ8CCFpOVED5JClh1SrWJYIjkgSSQhWAPNoIaf+MLUH6GBCA1ENCNAfQGq+4luYKphqhGiqcAWMLW1Zk6nl1235+LFSauQ8nfv1GEu2//mPTvb+L4zB0Zre1avf+346U3Lokt270EIY4oFMhAr2HMXQasGgjs3bDn7s6dv3/nVk87Zvv4vvf38b7beuKuuq0dWaQ/ft2rN7bdD6vWJvgvJadi6LLpqaayqEmsUY+6As6Bnj+JCIljd1HPdDc4Lv1hphXfddut/v330QFL2ThWu74l0VusTKYsJNpWmAEA1CsU5nhgxWjbFmrsr6msIjm9d1rD3/EwsVoGlSI4O0cpoZU1lW3MoZRUtiYH685aZNVmqkFjbFKjSIZ62h+eLxxfMhaJNkFXJSCwQioVjFHiFbY8W8jmEKdFSxaLgLFXMLaTPVoS72zqaWjrabtp1/fETmrHnrvpd9xYRYYTmunss03TSKeS2/7ljLRiTUk3pc42tgroSq+BGIuASuAA17lhwyRjj6gw/oqFAlFY26HVdodbV0Y4Nsc4N0fa10dbl0YbOSF1bqKohUFETCFf5QzGfP2z4AppmEKITjIUezkNVe3vNJ77wYH17M8c+3LjLhJjk1kO3L1lZEXcyiTwOnBnKW7msBAbAsWTIV0GcNE5ckMVcfccSHzYHBp+N1jTMi67zQ9nZqbiVmeOO3dro97FLr71y6OgAcoQQwmF21jazwPNIZhGS2JwUU4d9oVistb2uvoaN9jdvvW3L8oYoZvGsjGft2ogRoGA75mTSzuQZBk6BO7PnMdWNQLhl3VY2P7RmaWc1KRTSiUgsYmjUSc9pFDd1tnU2RfcsDe3u8jX4QRNmslgsIrmkOVARhMtZs8CZppLLBCqra7rqm26LRN8DZDXViR6QUiDJMtbMbO6diyOvvbLv5Uunz3PLbm1taWzuSAQriD/s8/nDkUhtc2tNWwc2fJwLLgUX3GbMLh0drfo6GZfcRacq3+nSOYK7Q6MQpUgPaL4QDUb1UIUvGNWDEeoPaL4QNXxUM4imE6qr82rd8Y6LLcNqDpEECQJwTquLFEcq65o5EMdJQ6A6tmSbtCYFtj5035KhiXND48XXj/RtXkZX7doGGINwAOnEqMD5YeYwo6Jz7arur3//25b2fLIwGgFfIm3anIcCKDmT/tYPDr14et5BoVh1VEjOkc4BW4UMRbahYc5tPvKysfROX2VD3cqNqaHT8rp7Vu66rvr4r+JO5ViCbWvVa4JkPMsTRWc+UeDVSUZ1c+iNyPoPaEYgVN8eq6mcHx9d19U0cWY0FNB05AgrqesCULShPh8IBmemZn2SrQji7qrA+tZwomi+Nl0YyRSoAiKAi2YxXchz2xpLL8zhaF/N5oK/Ljx/FNKDGhES5/PMOXX5fDqZ3LN129IlbcFgwAHBpUCU6IY/Gq2sbe0oZjLJkWHuOBK5h8djKTEqnWO9yLVSl4NFAFjDhoZ1PwlE9FBUD8b0QFTzhzR/UPf7NcNHNINSHVONYAKEIES8AW7YKwor8VEu+YtAWHpNIR8l1hz4aomTBlLHIVBMZiNNLUHDft/9cLx3Zigl3jlyuacLoVCIaj4pqbAjQHRdDPCpfasa7I2txjPnznDA4wJfjNfK3rtY5tX66qQv2l1bbXz6i59du3ndS//516fPXly6pK42iisqqyXFiMREdlrEL2nVK4NdqwvnjhSTs017Hl7zxEuX+4pz2SAV+bqAmC0Qm4ukKf2+oNQDxcxCYfxUaNnN1PCH6xuOH3jjykJVbU1de01IFhI6FShS4QNDR4A1HWG5iUE+k6+LaVOZwhO980NpO2AYjDE1U9a0rImpUUKNMQw8UJtyNCMMrS3VeXNqR66yL8qHjGKxkB+amYSjhykIopHaWBUgBAhTzfD5Q5WVtcW2TjOfy89MIc5LnbRclRiUCB8AkIIKXceaT/OF9FBMD8X0YIwGwpovoPmC1PBpukGorqZXY0QJxtIld7G3K9S4TdWFhqQKZVQqBgABCKyljdZAfpz4pDRzkF8QufHRU6dW1DbLQP3SVZFNqy+9dKDv/KRMF0M1NbWSCCw54hlZLGAzC5xX+4OfvK9uNmseH8tH/Vpt9Y4w3do/U3Xs1G8m0ZmccCKVlW0rVuz61B+9+b1/OXvuwvLuEKJaZXU18CwvzMHgq5HWHb7azlDdpDU/Hlmyadvtd7w68Hja8hs+smN51cxZK10Utc31weZaIQTTAvMX9pHqbnBskHZChBKWAKoHfZQSjpwUBYGwTTAwK2P4KcOivj60bzDz+pVkxnT8ulYZimJMMoWcADCLBSSBS1nLjNut0Ev53orK4hf21PTWN4y+lpMIuuqaJuZn0unE+Hz8xLnzDc21KwwqVHcKAqJpuj9QUVNfbM3ZxUJxYYECVy1mEiPJZTn9gwBopH2dEYzowYgWCGu+gO7zU91HNUOdH62OH1bnLHgy8pq+S5lP97hlbwqR9Mqu3a5EUfA15wo1oXzcSqV94XBFY3vL5t1crwV/ne43t+9c99bxoYmENT7LK1b0kIAPWBo5OSgmCUjkWJKZzQ3kzz+05Pxzs2MT+QuXX+zvnd68/JGqqt/lFw5O5Z790l985R+kc8Oddxj+v3vzv786MHgcOLNzRcyyhubEfKeDO7JaRZ3e1OkwEwB37Ly15zevnJoXkZr69cvqYo1WImuv6/InM0wIR4oMXphPzX+ZhOq4mUd6KIgLg7Ozw2F7V9yPwOaCgUCIGJI5rJDL5J3X+5JvjKakBB0hzsVCNmPoBpdCCEaw1DSjuq5514Jxi1VxUUwNLcwWMtadu5u+Ec+PXnZurFtdHYucvHDSstl4fBYHiEaw4JwJIaREGGu64Q9FK+qaCrmsU7SsfJZgcGfvgZTS848ISQBa27OeGn7N8FFNJ1Sjmo4xxkRDCABhN6npHail6PWSXS1xtF6yRl1dIrfr152cIYmeDKxE2QtmLqcbBRSqq2pbwjkXwhF6dPXGNS3NB68Mjp+6OL3seqppEYIpED9oIWTEiLkAglkyhkT0ev3VJ6bipzOZsHaRFQ9sWvbZnWtv6EmuPTf5+j9++XsS4Ia77sg+9OnX/+Py4JiZnM9VhFEsqqHZkcrZy762bXq0zo6PCisfbl9RVd8UzU/X11aatqzzZWIinZySmhHwBfyEACEiP32Jy37GdbuQEEIHQJl0Mj91GQOzTK5pAdBjCzOTCzn22mDxwqwdoLoqziHUBwgzCYYeyOeTUkiL2+G0ud1qsU2+iVQPz+Uf3Z/9uMFDFQ0OH0xmE+uXrUzNTYxOzyaL+VA2W0xnTMuSlEoJQgAmVNd9oUhFdWOrWcgnRofBshS/gzACwKU8NEJAo7XN7pGnRFMH1ILXfyKvkVvZBKNrCN4ywbqO2DtxDQAAhCj6GrNWNlxTZJABKy2LSTBnsZ0Qwa6Gusiq1V2XhiaO9C3cnuFGfQ2SeWynCPEBt4BQJNl0ZMf3U5uztc29TS8mU+ctK9sm+jn+j2hVnGh7QuE7Rw73/uEX/uyLA4MNAQcIOEJKGsSRMKmu4iF/fm7E37WLBCrMude4ZNJfE4lFdvekZXZiLqMzIbAkhqEbgaAvHApGKogR4HZOcG4VrJrwzMVZEvAF26uwY5lIo1UNrZYFQ31D8znx3Cj0xU2KIKAZVeGI5vc7QCRANBrxG8Ghkb74/BwAFpaVs1NR8O3m0RM0cGTMhNdRNFYdC8yMT09sXrmmu62DF/JjmdzMzMyRw++0dPeEYxXCtYQSU6L7/OFYTVVj0S4UM9PjUnAi3SnxyMuFACDqC4QxQlB2/JvHvF81pe/atMlv+0vJJ0vVQ+ieBIsAAGGUD7VqiWM6SgiCQTDBOeQTglOK4fbrWve+HZ2cyyyM9Ve1L0WBSqLFBMtjHEA0DHYyLKbq6Jy959YNK66LnXzn/Ds/MmFAp6Y/ejhR7H3zhDaZOJyxMl//1/+6dXl0SVM0ZISDlVG9Isr9oZyVnZ+4EnNsgXFy8Ezu7V9go2JDbcrfFgRqaKFKQDSdyApWCITCeiAQjFYBoShSgTC2i8XN23Tpm84kM+uaabCuLhjSfQhmRgezFrwyATaJVIVFJpdi3EEY1i1dGqyuRVT3B0MOlz6dmIWjeQZjyP52cKxKYoOjS6RApTY+Gc9kTACcsQtZp9jY2Dw7MTpXyCXyhZnxyWIubwTD7jnXAoTEWNN1fyBSWWs2FqyiaSbjGAQC7B4y5C6zpJiSkhg8grZ0jPq1B2lfI8tr5O1VW7uwFhYPywIAyfRQOrRKm3k+YDCJNexrAOqXQlr5+KqOwN/87i2Xz/cFedyZ7tVa1qBgFUKGQBrCPtAqY7z4ydbTF/u0vti69ttvnxnpXUgOOgylEoXWjtA2e2JovmBzTWiagzWkh2iQcqIXbcFYmrIMGjppFZJ2dgHZaV2jEgqxqgowooQaQnIrXxhP0KHL83fdEgtXBThzCEGaHkbAtcqanvrumoaRFx57aXjCvq4rGI2Gz53qP9ifPjZVGEk7rXX+5pq6EbtoM5bMZhaSidaeHiNShSh1hOi0e2YmBkenp03LGdf0aaoRjTJHEIACt9bW83RRO1LQzvLMHZVtkXBITktHCIGw4JIxzt1uaJUOQ1jTjUAoWtNgm/kFx+T5THkBtCKO6KIYPMm8WymvLv26SqjvfsFVL5Nu9acUAktggdr5ypsq4q8G6TzXI4AQ0vxasAYR4+Ybu3dtXVmYG5KFGWc+IK0MMkLAHYT9SPdz7hgEtp7/4eiT3xjYcQ8unJ1Oi9Gk0bSuTRqRtStjoxPZF4+NOpwVmBSAOBC7WCCchevqQ6E2vz/AsgtccASc+MNcIkB+IaRdzGq+ICDUe+7iyRGruXnu+qooEEMPGAhTAALERw2/xdC5actkfJdjnTg5+aPDCzNpNJ9xiBSpTLoqEquOVU4vxDkXY+OjHV1dtdEYpQZGWk19U2dHTyIxz03GuQTgwUDQIVq+kHNQccdqO+jzn3hh4fDM5OrKmvpYjBKEMOVYYxJMx3ajd3fqJRKAqeELhCOx2iZmmqmpYW4WsFQHn0vOhXu67jWqdo3YSqPZ/jcdfbdcS8rq1mZLQO5gK+5E2xbQbdbUvog1QwK2NBFQP5BaANCj9TRUK7klkcbycZGbk1ZCEg3RANYDYASLlU1LV+baduu9l9PTlrg8ld8mZDBWy6z8xhVNZ64sjMynJZeIM8E0pEO0ui5W36yB7fMHMdW0cA2JNUtHczIJVpzDEkCqmWgoy1CC8/OjCxuX+pGTk9k5EqxEgBhzEKZDvZNECzrCfOXkxJMnxqazPOzzaRhzAXnTnE8n62JViUxSCLGQSo0NDVY1Nuv+AKYkEAl1LF05PNKfnZymhBBqMNuUCBu6j/EcwnZLXSDsFLMz8eHWxHKNGgRjRBxCuQDbUUQQqMPI3BnjmFDdF4hUxupsXiwkJ0esQp5LaTHmOMy2GH23bH6rzMqN8P/9Ynn14PFrLoQkF9GWtP5gcXhvLNXvC/qBczCzQP1ADRRqxcQA3Uf9nZLZ5lzejp/HUuBYm4w0OTdsa735dgcFuttPXx4ezxWtQsEMVdUV4iN1FVpXrX88kQsSi+cWuNRjXe21bT2I51kh5WiUBqqEbfsaltkmzhf781MD1BeguqYbhmPbAESAGJy3Lcsx0mmhGXw+AY5jA8k5ZPDybJ7R8ax45+0xyRmSPGcWQE0fBDGXTkQCwUgovJBKMYyujAx29PSEKqo0TQPAlfX1zU0tU7NxTBCo+eusiBBlDIYn7SDYoogawepmyLLyYR1hzMaGh8dGJlq6OyRxpzwJl2lXBx1R6gsEY1WF6qa5hcTs9JxZLDqM2w6zS2dfv1v/3i2w/83klp5dzKNd/RrpnaetBhiB4NIIpev2TJwvNCaHqis0wAxRS0pMGefUh4guAZNApVHdRTBxMpMciJOdY4xzScFXt6qn/rVD2pV568pkZlU+rrGkhoUfWcAc26YE6TUNDdUN9VhYhfikU0g6jAvhSISNpnUynSLJTCJRWMhnlrRHKWIGIat7GvqSUBWhBmdmXs5mzZHpTH/cHEiYcznhMJm0GCAcpBpoOpNSqiOsMSYYO0KMzi/4iEowkmw2Ozo82NDZ7SMhRGgwEu7oWjp45UomX4wGAlnbsUwHpENw+PDpyshQbYUoWkLy+EIhE++pCs6bYmJ4aO+Tj9597/1VS5Zzr2cE1AxEdVQV0akR9EdrfJWNxbGpqcSM4zhMABfcawh8F5T938T8f4v8t73EjXZK5+kghCRjqUxmb59dXLDfv6OxOWwVp4ZfeLVvWXd0y66NeiAkOWP5WUSD3CwIMOwiE3bWLmZBWJEWY/OKaE1lZH587tz5gZs2NgY00yCytSboJwsDcWd5c6yypsHJZybGh4vZVCCg67VhQAQRLKnhSAmhGguFTwyMdzVVgJCaoW3uEJqoChB4szf91lB2KOHkbWFziQA0jVJCDJ9PcC6BGwRrCNtcMs4QCIywTghHOMc4EE1gzKUcGx9fnVwIVVSqQ6iaOjt7urvPXuiN+P0CgDsUS2iIbq/ROouJyyus0AWSmp4aqgswYWg2wtFAIDp6hh/VeE29FYmp8ZglcRJCCUKg6b5wNFLTGGlom4zP54tzBBDBmKoxYgDwruNQrpLo/yG5a2IbKDO5SiORdzqp0lOMMSsWR0dHRuemly1Zx7t3FnVnaOhXvzr8TsvF2bqqcMuypcCymCCihaQkUgoigQZCWqgK6T5AuCroLG+tvDi2kEwnZnoPEOojvnBTbbA6EhxP5CcWCm3jE3YhaRczoVAgEA4LEgJMkZRcyEIuy4kRq2vf2JomFhN5FE/yyXkzkedP9M2PJk0hgWpGSNctyZEAQoBxLrCm+8NmMQec18Uihs8nEWKWlcokbYlBckP324zajokQmU+lpsbGqppbqc+vaTRaU9PT0zM4eIWArI1GTMsOYLS6lUzOX5YLk58X3a+TsUE2y7g2k7Ed4rth5561ei5ETMe2HDWECAAQxkSdN8Q4IRQT6veHK6sbWrsL+dzMsATL1Kk6v1MuVsmXhPdbg87FupOyZ8sONHqXykq3jMhx7GKxaFuW7jN8RJuPzyWs4patmzctW15dGXYYC6295673ETJz9uK5IS5JXWOFRhiGJNAgon4QDAC4yIKJEQiq6/fctHQqXbxzU1VXmxOPm1q0oqazpepk+spCzhY4FZ9izAwGfQiRQiqN56Y4swRgwRlnDGFKJFT6xOWJ7Nk5O57HoXAVIv4CS+kYKEEBg7TVN5oIdN2Ymo3PJBYkd4QQfn+kWMwkC/m1rR1Llyx3rFzvuVNTyVzGsiynoFEfwQHLNi3bGhgZ7Vi+mvqCVNMMv6+xo31ZR8fU7FxzZWxhIV4f9C3vCIQiRbEg/VlagYmu4ZzNLcvWSbC2uyfW1cZt0/SHBC9NxgMkJMISE8ASJMVUo+FItLGlRceiIkBz0+NYMAout+rlt9y5ULJchCVRvRv9lgQvpcSKklfo2iNtEWAhWbFQnJudTaQTldFYQDfyjtnZ1lIfrQgHA7lEYnDg8lQm9eCH3tvIts9e3JtOZbJ5hoQJTk7X84RqwrYAsAQAjKjh0/zRtV3Rb/3FzVKAw6UMzBbzOWSn17b4wapc3xHShUm1ANV9jsO5zfzSxyVitsmKBQJoYmrqqcMXp+JmwhRz2ULEb2yvD9bV1PsMcv7KFdOyQbDm+pr2VWspNXrPnjp86nSBI8vKOZgG/GHLKpw6fyq5MNXd1mlounBMyZnk3HYyRDP8RoAJ/fLIWOjw0XXr1zS1dfojAX802tXRMTuXaKiqDOrdhrAqKgKxuuoD+eKX+i7NW/OrpeFHIkRkbawC+X2mP+z4AlIIJCRXh564hy0KDBhLIRGSmGiGL1YR0wkEdZwI+sxUnEIZxkGqpV4NUL3a3v5vpvVaJmExRJEgJAIQUlimmZmJj/X1FYXjR8RXY1RVVfo0nXAxNTZ29uzZ8xcuoZB/45o1LZXR7iXNBDFOAo6gBVNODo1O9V2urIk6rAiOGQj4ZCrnC1iaP0eoBoRajrTTScd0smZyeYXVvSGCMC7mqeDcsZAUlIEvn0Qrc1nM7X173zp88MiliZmpmfmgz6gJhoIGJ5j4AqHKxsaKqop8NjswNSk5N81cdVUsUFGtEW4WClemF+YScSksx3T8/qhl40vDI8NjowghR4DEVMMEEYokR7yoaQG/cBK9x18YGdy5adv2pWt4jGqaJjn3BX2rN+5JzwwHQkYqkx2fHe/NTVPgzTns84uqqmj39psrG5uYFG7tqwTh1tlKKQEhxAUHwCAwgMAYU58RQDENg6Gh9LRG32VUr4VC13jK8ojlGk0tl7RLyktpW2ZiIXFlcHBmeqa2rjYajVVUVWsU29ncwOCVC/39yXS2uaE1Fgk5NjcZwibTUZZojs/nD9XWFLLV+48H73nfF0ONHYW5ifmzr2rByOz0bOryxaZlS8EqFjK5Qk4rZIvFTDZn4WzB0g2f1KqidY0Hzl4eXchML2R33rX8TsFPHT/xnZ89kU2mBUiCEBNC17SqSLRQNB0hwlVVoaDfwFweQVPxeCGX9weMWENtKBp0TDNz4IRdzK3vWTIxMzE4NRXyVwqtumgWJMYa0Th3bGZpCEcCYYOSyXTixqbof61r/PN3xl578ZUtx5L6/WsXEguZbBIBC0WChFVZuWx6ok8z53XgPt2vU6pporKxuaZrKQR8XAg3q4m83KOaV+PWC0nEuQRgKhTVDT0YCYt6EA69WmASkFfY9y5L+3/ra6lBsIy7lyClbVlj4+MX+/tChl7d3FBZW21QLZ1Y6B/oH5uaDPsjXTXNgIAbVOq6A4gLLJwssnPADS5FQ41WFxFj/b3re9aFKhtFIR2oqg20moV0rnnnIyhSk0vN24WcKGSHTh/KDFzBQWlU1DIaGJnPDM+Zs0nTofqNe24khJy5cGlhIWkYuuEzmGUxh02lUz5N54LPJxd8fl9lU1PUgGIm4+QKrFAQZi4ciUA00rm0o/dc78IcWbZm9c5bb7p44u2FkcGB2Vw6FMW+MOfccayClbPM/Fw2jUFGfMYCBH49mswifR6ZL/gmt2RrUHp2VWOsrbnOTkwkhs7buRRkE52V/pQIWQ4PBfyhMI02deNASCBEEBZIDR6Wi5wpAilcHleVyAokGEJAMDF8OkRDkl91ypFKObtV9G7O5SqAUxJVyV9eXWB9laTVHxljpmn6NaNr6ZK2jg5d0xMLicHx0WQ2Wx+KBamRZ7b06Q2xmM54kdEAijCrCFgghDhKYRL0k+Clvc90dHeHl2zmgOdHL1e2rfcFgkTDdW1LnMbWYj6bz2UiDW3L80nOgDEBINLxuOakzpy6MJPNP/a975jzs/Gxcb/hb2xq2n3bLe/sPzQ+NGAxXrAyCOT0QjKTSDd2dUW7lqzjdnxkdGZuPjs7ZehU8/v1zo51K5ZMjo1jYTWvWLHk/9f1X8F2XleaILjWdr87/nqLC28IeopepAwlpTKlNJ2ZXSYrJ6u7ciImOqKiZjpqzHPPy0R0zNN0dHRUTPfMRHWZrLRSZspLpCR6AgRAeI/r7bnH/nabNQ//uReUKhsRJMCLS+Ccs/699jKfee6Z/urdD/7+r97/+LO9YZI6sI4iv4JEST6caTz93OQrt9t/+//Y6cbWSiFv9ffmV+9OhDBbbcFwZ+X2g53VB5VAVmt+oxJ6A1boQb3aqs9UvbmToAIEKA1tCTiiwxKrM+rZSzYnOSqzMAFnmgiYECr0KvSr4SSA0QLk19NsqYLxD85ygYCVAprwuTuXyrUKAYEQcvHMqXPnz9Ur9UE83I8HHuPzUaM/HDzY2oiHyUS9vp/q+3eHm9O1l8bUhKg61zN5zEkTJrV6tbub5Xc/lr7HdBqv3a6OHTOFaS/fjmZPKj+q1lq1eittTSbDbp7GpQqhX2v89j//r156/VZ3b5+4CGwe7Tx6Zqa2Ndh//8ffJ23r1UquyZEzVu/147/70Q+TIn7ta1+bOX322Rcffv+7P9xZW0FnvSAQjebpJ05c+fRy2tlVEirjY42pqckjx06f/96FDz5a3o07w7STxMQZ+pFQcq/or+7vjQUi8FQ3idtDs9/tLB2fa+9sXP/o5/1E56TCWrVAcWNnsLybtjwhJYUzS2xszjGOQMjQOVdaXTtnSsrRCLqOZUjJgQNgQMgcGAbABfOCx+E8oJIdjBX+s9T6nzcwv3qwEX4tbwM4cjrLHNDswlytWrXoDLiK9FRYGQL0drd2ltf77e4jcDFYJ/nTT51dqMzXwvEAGek+Gc24PTbLHt5ln37/z57PHtbmz6w+urm9dc+1B71H48OT2yKqSc/3/CAIIz+IsjROk36RZUYXKqxUZ44YrV2Rmd6e3b3/6usv9ViICC7Lr9xc+ckHHxaWgAngeH9tc/0//rlO49/7J//kld/9g5v3VjdWVvPeXnN2Hnh9Zmnx6ML8oLPPilRyJpX05pe+8Af/9ZNf/s3+7tbDD9793g9+cb+fFabY6Vzb6t5gaKfrVSY962yuC23M5sb29vYGkmk2a7MzE8pnK9v99U6apMl0EHk+ipnjENaAlaRbQsYYMOcc5xxGwEsAInAHgGfnENE5C8AcECFKLsU/kDnx14/mYSB/7TtHYX6cWQ+jT4DgrIv7w42dXYPUCHxijJApLo0ntZFmJx2u762srW/3u/XpifPnzj154thEtZLprJMFfrWGQC7vki1aoRV8770r+3NTF+uRKeLuzp1HYW384YfveL6qLp7xGq3KzKnCj6RSUnqs2hAi0XlmjHbOWmOMViSwOtb0JyZPP/sVhm6wtRL380+Vr10+Nj6RWlje2My1+8EPfnxkZuKLX/riC6+8+rf/8d93N1dnzj4jlB9UovGqv/Fwr+js0NwCSsk5F74vp+eDZpO1V2fezv2pRp95q3vtvHDV8alT8+NJf3e7J5phpYj7W1lfef7sZHV+fgLAdDq9YZyRtYhQD8PW0hk2sUBc8MOWEMABMQalmyqQswTgSk1NgLIhHKE+AB0BQ4v46xuVg8vyH9iufD7B/kp0P1fi4khXl4AgT5LVtbUb9+9N1KsSkUuFABkRaT3Y3Ll29cbFW9esL770ylsvPv3k3HhLZ8nmztby/p6pUSOQoao5m0I+4ByfOsp//pHc2u6fTjafeab5fm7e+WSjiivjbDt87hVdj7Lh83LqvInGddQUUjHGhZCMMWst45Jx7sBNnH5SRHUvCgGhNjE5VvdanmhUgq9++fXtAv/dn/2nuodYuD//t/9h/daVxSNPaJI7K6undC5lVVbqkedMPCh6e5TGTgoBChHAGkWOinQ88l966hwfm3q01nn70+vjzTGZrTepf7KCjdCN+SwIq82xeqXCgEM8iLMk1dpqIk/5zel5f/EJ50UH/nCjtTF7PKIBhljaOx5wjQgJyTk3cpFH65xD/JWNykGoAP6BYvYfCGp5ax72NofpGhGLvNjf79y4efvBnTvNE6dAKd/zhoPB2urK7p1H1+7fvb+zfea5p199/tmjM7NIZmtzfW17yzlX9wJt5c6wP9ewXDWJ0OlibLw63vDTONZZVm2KmbFwKtJ39rz5zb0lvVUNz4r+bZZvaG/SzbzomvOjfgqgNCNmIK0Xjp97CZAz6RFZHkTN8VrIXJ7G8fZypw8hgy8cm0PS8SD+6btX8eJDbtzqw+W0uyeUz6Sq1CvNSEUC8s07EI9pzpPdZbB5VGlu3b2rizzfu5z3on6b5Xm6urnSqGZV3z/WhEZNSOkLLwx84aw2WWLyrNQilsKbaE5MHH3SRGNgHUPDOUPOS4PqEtU+MjQqP2M2spACGHmKESFaIlZKHv+qf+f/1qX4D87ly8kgHpz70XMAhIjkbJrEqyvr1z67lg/6+ExQqdbAuq3Nre9/7wcbqxuLT5z8p1/6w9NHFpWg9tbmrbt39vv96fr4VLPJGFkOA6+VuNUINcgqMOPX+XhD6WyQJwO/vzPs7p9fwPpYZbCvwRbcl9KPwKaYLOftRpb1SUbAJEqPybDURUehGOMIAIw5Y62jSq3iKdzvZ3tbGw/XumRNWKt+9auvb9699uhHFx922xz5zVt3X776iXpeUp6G1fD08Yl865beeD+IAqvNYPORs2xidj7e3uRYTDeD2KTrg34+NN3CyfHmWMOTUkgpHApHOs+dYOhswRnzlK8UDyueNz5rVHWrO1CF9X3fj0LP9xjHgwUUHAhCI0Ow9BhG6ciRLVnZUA7+LB1wVH4tkdLjP+YfaFdgxM9/HN/D67b8RqP1/t7+9es3NlfXTp0/ferJJ6ph2O/3rl64eOfe3Ze//Oa3v/LWRLOR9ju3rl29/NnVItFzY5M+F51+P+l3JhcXvNZSWjAetxVnKEIWqbHxGm23AcCZJBT9nqZnjoaf9jv5YICOkHsoFJrMS5Zt75ZRE2AS65yuLVE0Q0ELkY8oyWSs0XkSg81qoewNqSaKiNmK5919sPzkM2dOvvb1l3eK/idXMmtx0Lvzw78ert5/tLr14Nq1uVDX+PbckelIoNZ7QVPG3Rzi+0fn1Hi9WanLdD/PHC+cQwa+J4JAMsEQGFlrtUFyxC2QUUIBlwUwGXrIxMONze3+MIzC5vjYzOx0vdlQvoclM3p0okYDHjyEwZYdCAE4R4yVg108TLafDxiNsi3CASn0Vw7lgWHWoeFn+ZtwqClOlMbxo0crt27eDCvBG19988zSMaOLh/fu3r1370vf/Ppvf/Xr9Wp1f3vto/ffv3DxUmBVo17LBba7nfWHD4eueGVuDrgcUEQxq/KYhxXkqtqobGygQ0miUq8PNpY7jAbayZ3VztjCQyw6QsrSulA4J80+ygC4pPSWTh6krpZnKUVzhWNFb9MVMQ+nWN574czYjLc7Fy6nLQ8oBHT71348jY+en863Z5v9OD8dDIdr9967de/Ovmacz8wzj0mkRPl+beykS3PBbxFTSoSqCmlOqdZ9jRYxsy41BjgQOaN1nmsgLF2hlOcJ6RstO5ZQBp1er7u9zRH8MFw6fS6qhJVqFejXUuUosgwZgQU4kIo+WCETUYn9Eodx+rXs+jm69sHXAeDAvuEQRfufR9oUer/dvXvvQXdn+8U3X33yzFnJ2Mrm2tULn84eXfj6V7/WqNfWlh+89847lz682OnEE1OTE8ePTNYa6aBbnRk/Oze/ODVphr3OxtbOZmepvhc0Ck9VAMAJZUAZ8AvnGyJPUsHU3QfdqemrY3PjUnEuJaoKCoFmCLbCvAYxqUDD8B60lzPNqdCUdBkijyYiJs4exaWoRvleNcTFVoFgmi2usjsLY80/+ZKXpWy419/eSrNUfOnFL8yMh/nOTSYRmbOmINKERoR1TZ4xzrlC68I6Mm60enCOmDXWWWNGHkOMCz8IlO8jF51hvp9hpovdTidNY4bOmHGji9LG6vADRwQ7Eu1CRLD2sUjM48ARlNIjo9P5D7aSZbPxeAx0+ESMxC/Afa50evw0EGVZtrm9s7m6qhBnpielde3drYvvvbu5s/lbb/7BdKvV2dv74J13fvyDn/ngj83PHj9xfGlikvlsbGLh6fFWJYwGvc7tG1euXf6svbv620+IU0tpR3fWVrtAmBkVsaBgIVNM1av+uPzkSnJ8vVutM1DgvAhyjZy4CJlKKW6DCPUwzfbu6yLXFLgiYQCOBc4OrdXobKUhinQsM3Z2KkTMuF8EtWq9NTblC5O09wMpqGAqkhUR+txr1KTMyGpb5HkSkzUWuC2GVluT5Saz4Fzdd6FSlSicqAXG2jLVMQZcyrBWCwKfSZ6n9v5e0kmonaZpOgTnVBCNj09N1puBlOSc0QUyxjknRIBRQXQ4UcdRTz9y4AU40EH4NSvzXzmLOKIqHPSaj2Gbo29grDzpn5/WOufiJGl39inTofI6u+3Vu3d3tjcvvvfh3Imjc61m3OvcuHz50keXRKP++htvHpkYJ3KiXmk16y1PuXz44PbNTz+9fO3ajc29dpzrIo3+KehKgDOVouC8PzANjCyrqWoIXiUMbUpYALPIHWnKM4AcIDcsRB4gOGvSpD/UvW7hpCFUQQCkHQIZRyiUAEKMxia8sG/SXJs8ak2qsApgC8sIVFhvTh8JvCDv9j9yeTWqB6GSzGVUgGVgtWaWbDrMc11ocoYJwZcm/E7uJqpeMyQqxS8ZY1z6UTUIA8YQnF1rJw/appe5fpoCgZAyjKJqpcIYi9PMdrqyz4VSQRh4voecQWnC+9hbAQ4vQToYLyDjVJolf37P9Wsn9XNfL62Y8SDSDhHQETrrsJwuAgDpPE+T2Glbr1SyYf/BzTs8STfX1x+urC0dO1Ls7l1dfvS9v//xkPF//s/++NTiwnZ7G6NwdnIqYNjbWrnwwYdv//LDh5s7jSAMvTDOeh89ikmIP3xx6vRZruNuQa63uzfoDmr1BqpartuBYF61RsEksRTMEKkgU5isD4TOMGcLR6KwaFlgisyYIZPIJViTB7Ua0jBN+1wIRONMx1MVhmSLARUWeGgsupyAV4JKJgQ3uu/JxJchENmssEWKKIy1WmsCRC4YCgEsqsJiIw984oIhU9wLuFLKCzhnyAiB2r3s4nJvd2D7hXHOMSG4UMhlqovN3v5ekQtkkvFavTo9NdMcb3lBUIKi8cC6ajQVGEVuFJfSpExYa0tnlrKqOYTzAHz+coTPfbHUfkNODnVqi8IBOhVwLqjIYGeLbyyLbscWthenwzh2w6zdacfDdGdza2N5/er9tct3Hz55/uxMpdZNY2+8tTR7RAnW21q7/Mknf/PDd3b72dOnzp8en7h+9+Z+PDyzMH/u2WdwcSbHfcXvBDTQ+aDI41rLSxK9tpf5Pgb1JosmmbCUd8HlaFIuY7IGdG41OO24zxAMCqETgxaQZaaXGCrSImNSJt3dojfY3SMUdmo69isKrcbGmeHYb7z/nX97ZrxXqWSVaiT1EGEITnMRGmsIOFeeLhLgChgH4mA4oeWcAp9LKZhQTPkyjIQXcrBkMg6QxPmnj/oPO3ZYWG0tMmRcMiZ0YXY77Z2478ihttLB1Nw85zKqRMrzSo5CObf9HOkSD6pWJAQkQnAiTlMphBClyw4gALKR1Ax8vrz6XIULhMgYd4bpHEyqSTgmEBD7vcry9fjaxeHKYL1rMkSW6ZjiLM+cc/eW19f3+y8ePbf81Obqgzvf/f73nn/rtVdPvhRFlW57587Vaz/6xUckK9989QsVyR+sr6z2utVa/ezpU2eWFnVevPew09nun5vInljwl442CSAeDM4vevLUfG1iDGQInkQZOZNym6BNiRgjQmtZkckitc6SQy06WZw4OxCBSLKBRQxU4IwxBLv71OkV6IqJac0FDLa2375x6ZdXB+xY98yphierUhFSACp0pYWNqBITxC0BGgPGMVtS2REZJ64kSIVSMSEZgCsSRlonxZ1H3YuPkk7Oc8dGukyERKR14XqZds7ZAgrNGIRRaIoCyrVH6ZtRKq0d7LHLnw4zLiESkej0BoGnPK/U/mdccITSWBCISgmwssRCh4cVFgICMAaMAXKGHBknQGEs7e2sfXbp+kDOLz13emr+wfqjtN8xlriQWzvt737wi2994Y0GF9e7vdWttd8NKnl/sLqycu+z6+999EFUm3zh9OIwHrx75dJ2e/fs0rGTZ05PT05ce7B+5epna5tb/ST7WQC/82T9N56tBdUAuXhuUnpRzQmPcSIgJkLOFBVgCZEJRIXARAjOGUFAxqG/I/KUyJYv1jpA4SEAr6Rzpt2Ki8qYIk+QkKanr33y7lZSdLBOqpW5yDChPMWUj5wBEw6l0ZlhoUNBAJactmAZWLSadCBEibljYEjnaIs8SS+vDD5ezbcTMiWLB/HQ14iMdWTJarKaiKLq+Nj4ZCWKuBTluAY+V/H+Ssr8/J2IKNqdbuB7gef5vvKU9FR5VDkypo1Js1RrzRGV9KSUnPPS8wwAHZPgVYBJBORcAnKq1HbHjn7EZm521l6YLnjIAaw2mhBqUb0wevn6nf+w2Qatwdka4MaNW2+/+/7V6ze1pfOnzhxpNK/fufvx1SvDXn9yYuLM088++eSZvNu7fvnyw5X1NM/Bub2u+18/bm8N9e+8PDszWUWX6WwAIgHrgeCAEhCcyZ3NkQiFR1wQcOC+IyLmBJszfo5A1hTcERAjJhljqoaz1RmTJ86Uwju2wvUXjnuFzOcXJ0S1YtARkwACUDFkgIqYVyB3wjcOLZBxppSi1GZARIIRZ8SRXJEwa4rc3N0yd3shelUZJDrN+CiW5MiARXCWnCFnOedRrTV/5OSRxeONZlMoBQClugzBiJBZrsgOY3mYNBFBJHFWFEWiMj/1fF/5nvQ9TymPMRzqeLXX6Q/ihvCm6s1apeoxXmqmOkAHwKQHXB4OeV0YZbNHWidfOHM3fnDrpg5ZkcRG25zspBd+yVesiH+0vd5BLoS4ff/Rxm57d68btZrf/s3fPDI2/t7HH//y4ieD/uDYsWMvPf/8wszU2sPlC5cu3r510+hCIhgkzjGqNfvVE1vyhMxTnqxI2+Wsp4KA+xHnggmJyMkBkAGmS5/LMh9Za8BaR66k8wCiA146DiNw4gH4Hlhb+tPkQj93zj97SgZRRAIZByGAcW45s4TGgCNjLXcOHaG22hAah9boIjdKIueMnLVZYh0VVN0Yij2rT81U2xntrqwlaUqEQA6RnLPkHDgLCFKF9bHJ+cWjS0ePz8zPRbVquWb+XLuIUNLbR9XpQaFThoBQWGuttVrbPCuSVHi+CgM/CLWnMMv2d/Yf3NvrHq8sNoKKIzgEcNJoM45wOIRHIGQ8qjw1NT/Gpn68/+BeHhsiQ+ArT+fx884t9ns3ATuKAbnBMBkMkqDV/O3f+90nl5beee+9X3z0YZJm55975q3XXqspdfnq1ctXP9vd3eHOSY6EUGk2n3vy3GvPPjM/Nd4fDj68fWd1vZiuRGfGo0k7CPKhkCikYtIHYACaGUQBhAwQnHPOaGsMWWutNtoAl4BIgA6RnCEaxdoSGe0KjSS80JckQHpKSo4IzjmjtbVgHJB1zpFz5AiNsdYaUxitNWMQKAZWO2MSKzNv1obzlrk664ZK5rHxlXDOMACPceEotTojJ4Ss1Fvjk3Mzc0fm5+fGp6Yq9QoX3CEcxKwsaN3j5DpKtjgqZwAJncBS+d84Y5zVRmutC0OgfXCVfPu422KoG4r7ypNcMsZKMDYccK4P9BEASs1UsD1R7LTAgBSO52DPnT07U2ncuXfrk/2dnoNEAidAIrIGlHrtrS+9+PQzn1377L33P9DOfeO3v/XVV18pBoPv/eSnly5dzJOUIzqGmmhhZuq//PY3Th1byNP04fLDoSFoTR2dOdoKQxS0P1gXw7sN26+AcZRbB85axgvkKTCJQgIwcFYbTdY6Y7TOAS1yckySQ2udteRKCW4Aa50z1qEDzgT3iGyRG2sNOGe10dYRMGddSdkjQmMcEFijszixRcqR9WJ4uK93dLU+5sYnskoURc0x35MisFN7g+12X1lTQ8ZJK6mCIKw0J6Zn56dn5scnJ2vNuh8FTDACR5YYcgBHpcnfKGyEVKKAShQslB1L2cJy65Csc5aAyGiDCCZ3nGW+jWUQVKJ5UZ2KKpFS8kBm/LBWPgjlgVAfatum/H4rywqQmZybmH7lqedMlt98eO9HBO9H/r5x0vfqrXFr7Oz5M1/74pv94eD9Dz7qDeJv/v7v/e43vt7b2/3Oz3/5ycVLNs85F0DOOtLGyjCQiO99fOHjKzcHXP2T//IfvfjEE4g03N/ZXl1d6bG9zbos8ifGi4WWFp5wZE1REKSEggkPGEdER0jO2cI4S5Y0cgABRpOxDggdIgBzREVeOEsgEJ3TaWKsIUfkbDnj1A6MI2cckXW2dJknJDJZlg0GeW5j7X3W5jf2ck355F6y2N4+sbAwN79Qa7WqIBe79sFmwpMsFKCka1UDNT7Rmpkfm5yoNxt+JeJKImOWCEu8ASvLpgMJy9Gk9vHWxNHhfAGFkJyXw0XuiMg5Z7XJExNDoQSX/kTNnxHVpvI9LtjhWvsw5eIBRBrAgTXcuckKLk4zKuBodWFh6SQ4ff3BvX6va5DtEalK+OZvfOP8yTM7/e4TTz9ZCyo/+vFPHt15cPTcqd/62teI6Ac/e/vCJxetNowLImKIxpqxyclzx869d+nOxzevtqZn3/rmN88983QYRsNOe3t948JHF2/ce7TT7adZ/gOen5mAV86MnZmrSq6BjANjjSUUyBgw7qw9uGnIZKnDXDtpHCIwS0SOHJF1hMiZI1sU1llnrXPkLBhjHZEt4VTInLNkiQCsod6w2OqkOwPoFn5ig71EG4NEJo37Xcj2uB2vV+TMdBTWxxuxH7ZyE7NQNpt+dbJZm5psTEwEtQr3JGMcoDTVZQdK/ORGoLyDOhbKryOVshWHWrdAQkphrUUCx9CVgnzOFYXrIDdeNQqjSEUetwILILIk6HMuAQd5FgEBHADDelU0j6kJQ9d9EdTnwtr4ja3dTtLnnDnGOLmFY0d/462vBYHfSAezrbHLV69+/P4HhtOLLzzv0uztjz7++L1PTFYwhuCIiAxRc2LqjRdfnTsyn+143z7/xMsvvzy/OA8A3fbuw2vX3n3nFx9eud4bDMlqa+1AqFxOws7cjlZKb455+UTFRvWGF1Y4WnK6nLxZV5B1Re6MLQpH7uAaElwIxoGMcdpq7RwV2loL1qLWxjogYMaSIdCWhrmLCxfntNHP1/tFJ3UOZCCl4IVxBpACzloKKlDk/d3O9srE/FxUa4RVFdUCQOS1UE7Vm9PjtfFWUImEEq5U17NAiOgcIGcMHLFSyB8RAUdk9gMQHh4YVgERMASBiJxzIALHAIicI4ZEPM3RIYoA627oFUNjQ81qJCJk6rGl3K+WyUJIr1oJ2FxFn6/aR/sad1l+fHF+qtJ6//KlLI5BWyHk5qNHQ7TjrbFH3fs//v4PN7c3v/G73372zBOfXrz0y5/9Uqd54AeOrDXaAs3OzZ0/enL25ImlM8eX3JmxmZl6q1Xk+d766vULF37xy3dv3bufZxlzDoScP3L0mbPnpmutze2tv710a3Nvs171X3vq9MvNE6q7295Z1nkSSPQEGWPIIWPoABnLBYNyklKQzos8y622UBhICzvMzFBTWrhYO+1YbqGf6cQ4bWhY2NQ448BYAkABjDOdEnDGAMFjMOZhy3N1Th4URX837+3BxIzv84mmqvgQVjxV82UtlIEPHC1BSR+mkbN8WWPS6CsMeWn6dgDeIYDSspyglBlGAhCIDJGAC4bOMucsAGG5cTEGXZE5ljqtnahZTzAeUMlPK2OIgKPRPAAyEpJE3dojnm9n6g3R3+yYjFgDGDVazfrUXL/Xv/Ho7n/8j38WNmtvPPvi2tbGrWvXz77wzNe++KWdnd2PP7lo8vz0yZPVRiNJ8/3u/tETSyenZm9vrPitmmRqZbBbB4wHw7s3brz3ztuXL1/a220z6wRgY3r6hedffOr4qWHc/+Dip5/dulU4e/rME8+cOz83MXl3f+/Cp5v3ltet0ZKBFCgQFUfJERAb1Wh+cmKsFjnn0lxr7dLCdofp3iDuJmmcF6mxzpChEqxTzmAOBjKI5XCHM1YWKY4cQ+YLHJM0rlxVuEjySAkF2hWx04UXiLmpVncw7LsiRZM4E4HjQGgKYTQjS8CAccc4cea4ZCSAYYlnL6frCMARHLJSxR1G4yVARFGy9rhAcgwduLJodUQOnKNhTh6gURxAMCUYMsEFQ3S/ykkqf2IICIJYjfwZv5WOYdzsJh/cvXnl7mojqMzMN3Y7Ok6HezuDYC98uxfvdPa9WvSNr33dGvOzd97eWH40NT7xzDNPnX7qvGVifWd3ohKura34YZjvdH/Z3j13+rQEvH75yl/85V/dun7d5RkScE+dO3/+N7761litceGzK2+/++7O7vb8wpE3X371+Pzc9t7euxc/unX37n6nA8YAUAZADoicJgiqlSfOnDt7+uRYo2m17nS73Z3OZm9vo93bH3TzLC2rWTxYAB5cXVjmOAIgZIjIEctRmhBSCikZNIRtSQo5BQJDj4dKKF96nBOQ8MNqo9bR2W63w4fOC8NaWBEEPI/toA3DHgdE5QsvcFFNB5FVIXBOCIIx5Fw5G5qMIxQqyIRHxEf2fwiIIBBH/AcuGCfmyIEjcGQtWmvTQrSJpaBCVYt4gDByqMMRVxNHkIfRmyVCQslUDT0AlHwaRWjj7c3NJKgK5N1et+FXhIVhnt1fXXZEX/7yt44vLn38ycd3rl63hc6ybJAMVSU4duz4CXvy4cpqPYuz3uDC8v2vfOVLp+YXr9+4/hd/+dfXPruK2nDGW3PTX3z1tZeee04X+u9++pP3Pv449INvfPXrz55/qtDFzz/5+PK1q+29PbSWIwIHIuQElpFj4vTx41/+4htPPXHOD7x+r7f6cG2n3bux/Gh7ZztLY3CGE/CR4DbQ44a75I0wKhuEEjBFyIRQUkrGlaAKc1VuPXAcQDEWSiYF+mGkwsgpD6wHyoud2+53VDyYDSNTCXIt0/19vfMIOptYFJxL7kdsbBbG5ky1SVIBkBAiEuBnfT/eJRXQ2LyOWq4UtwRgJbjkc5vnkuHLy8a6jLa1mGjnGLcFYuFQOLROCI6lf+fnQH/lTwyBeUxwKaWygjc0PjFfuzpZv7a224v7RBSpoFFrsDRO08SvR88/9XQcDy9euJD0B1Lw3qB39dIVw8AJOHfyzMmjiwG53c7+F59+4ukTJ2/euf3dv/n72zfvgCYehc+/+OI3vvylucnJG7fvfPeHP3r06NG506ffeu2L9Wr1yq0bH168sLW+bo1mI3Wscu0A1jmvVnnxpZe//uWvLBxZsM60d3bv3n3w8aef3X60PBwMyeQM6NDL8mBXf3g8P9/Aj+oTxpkUQnHwsQgZ1AR5aDmC5NxTQkkuPakqNV5pOBU5zQ3wJNNS61mlF/h+NbfDodre6fTWN3R/i/KUO2JMyN19MT6A2gRJCYie4HMsr2S7Nt0zzUkb1MmvoRRQrkQYIqAoe6nHa04qy2PiB8JR1pI2juc6FZkQgpVQe3ZYM9OBOJwrIQsaKGeOcQeCKx+XZqO3np7NdfZoJyl0AUDj1bon5EZe+MpThb159eqDu/e0KQCks25jY73d3V9dX/vW7//ekbm53BUvPP1UFEaXPvvsJz/66er9ZUXcmxr/9u//3ldffUUivf/eh3/+3b/Pnf393/ndF544t7mz8xff/96t2zd1knAstevKPRE6Qgs0ubjwja9/7bWXX642GnE8XHu0/MmFSx9furqxs+Os5QCci9IFqjx8ow0jHcT2c6CaUefHGBfSF6zKjc+sx1CCE0CSoy+Y4sg5qiAMG+MsqOXE+okeDGKdJEu+O1ezM7Cetrd3evJRW+/1kjxzpIEZZM7wtM0HFr0dyxgAhQwTWVgWNxVX1VkJsoQW4Gi7ifwQXHJ4Rsszx5ARkRCsnBM7cs6YvNAyLwTngnFEcSAAhda4PNPGailQedyAiQ2qTHgpE8irrerzZ6d9t//+nfz6umgnxWZnzzmX5HEtMZ3bv0z6wxDyHMEa4xgDgLSTuTv3th6sdNa3usnwxJFjn3x04eqnV3Y3d8DYqFn/7T/+R9/+2tezdPjuO7/4T3/3g0qz9aff/tbC1ORHly//4Kc/21xdRSJENlLkGc2sgTg/++S5b33zG+fPP4ECdra2r1+78cGFT2/evTccDBmAYJyzUcCohJuW16wjAAuHfQKNaFylmIiQ0hMs5NZnzmOkmJMcQs4iKULFlUAupay2VGOK/Kgbp2vbnf32Lsa9Fssjkwy72XoP7rflZh9ibYxz4DgnJogxgyoZUppm1hHZvjWZgkFVzi8sTdVm6zJUgByRAXIGDIjzz8GmSwn4snPH0X86BOeQO4NAztmiyHlxYBBa3qKWYDg0G1v9pEiaDTHWipQvjPMHWSUciCaQ8FR1fPyZs5MLlb1Pm/pnt93ddifTdtzHl+foXLDsN8VRWf3prfSzzdTYshRzJ0+daoWVv3/7p2PNlp/DL372Tmd3V1tTFMXR2RMvPvEEA3vtypW/+fvvN5qtP/2n/5Sj+86Pf/zeBx8N9ruMiXKw4fAAHOWI+d7rr7/yrd/4xtzCXJany7cffPDhxQ8vXdlrt9E4NTJ+xlLh7hA0DAc9AJQLXUQAtMYgjtSBBWMex4iZEMljpDj5DCPJQskjyXzFlGRMBao2IaotYGI46A92l3FrTXW2Byy9l+jU2tUe9VJnLEdnkRwQAQIh1CXMSItod4j2cp1pvUs8q9ewMlWrjtWUj5wzRM6Ac+ScCc4EHvw4xHQdrqoZQ0LGODpmiAjJGV3kuRBCMMFLDIzRJo715m7ei4ee8sfHKjU/AoaJEzwugGesFoqo5k/Mz6tBq7VxdMJ9fBe2h/LEtPfy6frMlC8Fzo41jkz4f3+F/eJ+3EtM2Kg//8LzwzR58OB+7YRc1Q82t9bzNCnHwqsry9/5y79qNOrvf/LJ8oOHrz3/4oM7t3958eObN26Rtpwzj3MHoO3Iy4cc+PXaW1/78re+/rX6WGN/d+/SpSvvvPfRnfsPizTl5RD6czCcUX9ejkMRlFAEZA9tMckxhoILBsQYBAwj4QJOPiPFqCx8AoGBQF+i4oAM0Yt41OJhDZBh0q+0V8TOw8EwXTcmR2aIZ0YQMsbYAXaLgByCC5ydBxrjru2xu+S2GPBa0Jierk1M+dUqU5IJzjkTgnPBhOCCMXEIqCyHuOXE6OA6BABkHBgK5ywyBHC6jKgUnpCcIefc8+XshD83BuPjfi0MQ99HdIqcG7SF6kIUotdg0TjypUa1/uxEb2mh2+tlUSWotyqSE5gcJCzOef84bIyF9J3PBhNHl04cPfrehx8Oe72PL3/CGNNpNtrPIia9/vf/9u+AoTUWAS9++sknVy6kaYqWkCERSi6qftBJs6Rwzrmo0fzt3/32W1/6ovLVozsP3nnv/Xc/vri/20YiwR7bgMMBmHW0TkAKg3CsVvOY2O13O3F/5AcGxBgoDj6SxyHg5DOnGChGkqHk4AlQAkV5XCQKIYQXsbCG0ktzY+IhDHtUxFqbTqILVEJ6THiCCUckOfnEgGxhXW5sz5od1FWwMxxrNZE3PDbW8ubnw9nZSrWqlJRSSMGVFJwzIRhn7Ndlog42XiPGS7lLQ6QStocIzhmtc10oqyyQkII166ri1zirSJ9HkkuTuyxmWcx7beb3TKVCvMG4Qr+FYVXVTMvf89vrSIXySod1Sya2BK3x4NvPVyZDvV6rFYPeyvKKM6bQpR03P3hdQIjOWrAlGNHlWVzSbsoBJmjKMI88n5xz1gaN5u/+/u++9cXXclNcePfiz9/78NbtO1ma8lJBi0b1/AHYHKicp3O2MDt7enahP+g/3FxPipwBjkggDBSDSFDE0eNOAEgGHieByBkKxhRnUjAhUEnm+UJ5Pgsrwg8tsEGhY8cLWc+jIrMxmlQQShVUZdAi7qxBcjUy0rl9svvSFQxWHMtzOOHh0SkYP4o0PVZMzutKkylfKCEVV0JIwaXgXHDOUBzWQaNjeph0HhOJRu8ZgJAcIjirdZHnmeSMCcE8n/m+zwAYGT/r+0U/29/JttYgRaVCVxRYdIEp5A5FCMB46FQ2oGyXgUUmUAihBOqcXBxW8PWn6u1kffnOD9zufZ9jYdkhcOlxO3Tg9HLY1z+mYQBkeb6h246siKrf/J1vf/2NL25sb/74Zz//8OKnvXaXEUnGD8q4x4iNEofjrGWCv/zC82+++NKDhw8+uX2122kzQMnR4xhyiAQEHAQ6yVEgcCCJJFj5dCBnyBA4ohLM90UUheRXXFABIS2gYRKjZrRwmo/NT+zuZ3u7cZpw5FPgz+ZUGDckp2yhTNFQMD3Go4qXpyLtUl6gBzzyQltpYtTgQchLj2gllRRCcFFmXc4/R2o4lKE+BMsffFil9dHIhmx0ieaFFqJgDD0uRimLaavSnt++ZjZXkg7ZcKFSYYLnpGPOC844M1gkqctSISLyc3AJWYeATHjIhC0y54zw+DiLA7z7x0/qz2r+lU33oGu6mXPWlujycnTpiKy1ZW0CcEAiPlCLs9YA42+8/sXfeO2Vuw/u/+V3/vb69ZtOG8XFYZ+BMAKPU7m0doRAlshX3vnjx8eblR/9/EHSa9cEBAJDhTXJQomKkSgfewKOJEatTGnMgIKBZMiZk5IFURA0mjmrWRlpx4AYKlEdmwirdZNrEWwlwMz+HhXWFKZnTUFFm6wDQo4VX0xE4kTF87jf64BLKOhL3K9RpQJVpSqKKymVVEoIIZQsLew5ZweVLR1gcg9wXaPPaLQVPSj3yzaGyDmrdZEVgnOOjnjqrDGFb7XKMpljis1+s2K9hooYw11WPEK0QgTCpmLvs72dPTF+JKhGZHJ0RVlpA5NMAKAmR+Cs55ulaTtThS8sskdteXULbuzZrYFNjEPggEBGY/lifgVj+Pi0McGqUl2+eOlvf/aTe3fucQLOGTlT3oxApdB5WcvTwcoJBENusk8uvrf18Ea69fBYlUUSQik8CZKB4ijKTpvIjaRiyNGBCwYC5yA4eRKDUAW1qqw0u0Wtl3s6Bc8DJ2VQVcZaPUytVJrI5to6u2t1l2nHIAfPOg5AKTIvxcjxxcxNFNoTupEIueqTKbDmWAtRSuUpKbhSQpank3Fkh33ngZbC4fCDPS4ToBx1jay0ywW4NUbnuuCMY65pP2uzZKvlLFkRi7F0bB55IJXnmHMZR7PnqEsopMu8+GF/dTcB69fOo6ygi0fO6Y4O/Hk4Y5Lz3EHGpR5rsFaNn5llu325si8edNz6gPZi6GSQaLKOwDkHDg6vCUQgYODIuJ/+/B1C6Pf7gogxBo4sGoDSRhEAGAJyBhwZBxCMQo5VBRUFXnd5OFxd9Kwfcs5QchQceWlSgcCBnIO8oLwgCwyg1PAGzjDwWOjzKPQq9ZpfrRv0V3pwb5DW48G0CVtjMow8iayb99b3dra3VrPBAIVEghxLuT3FARDRIqymRTbIIiyOeV2lUiJMt61Tc8KS4hylKEshJbkoj6bgeMi+RjgEFoxupHLE/ngXNqorS3NHKrELmnPgPIc0Klaj9JO6aSuxmMDx1GswirK+0cx6BquFjMCYIskNkAh5AAItoCQuEBRzKenYudyZDJy1BomQMSGUj4i60M7pCudRhAvT7IWU9RNqD2GnTxsDttzD3dj2NaSaCkcHFoej15gkMZGTjB9ufhkgY8gRJQPFQSEEwoUcFINAYigxUswTKDkwIEQmOXBEZMAYKI6E5awWraG+Bc1YKTZRmmUGHqtV/TAKokoUVGpCyCTO13aTi7sY9Omc9J+qREGEgJDrojfodva3KM+4CgQXnCtEBc4gckDmCAvifYJ9yavCT53RIEStNjbZnG5EIvKkp5QSQoqyuOWcl42jOGShjDLtY+j7AWqkHFoeoj1H1ymRtUbnxMhncb3YrbjNUCagTZHbvg4GQ9xY6XeL/elxWqjJOaVC7KdAWXTSzIMMm4jkmA/CcwU5GoLNkApyjkwBwIErLhVjHJi21jrjDvR5SXquWaOlSZfnNEhcN8V2KtoJ9jIaFhTnLjHOOFZYlluyltxBgyEZSoY+B49RICCSGCgUjEkGHIkzkBw9hYwhA1fqpAnOEIEYYww8zgDJaNCFMQQIZeEjGDkA8iSvVoN6s+5XKkp5iFgkw8GATK4azGOUkynIGQbIhAzDaKw5Vq01uzvrNus7oRiTyAQXijGBnAOgc5Qyf4NaXWNXXd6V4Xhz4bUjJ2YnWyLyPU9KJYXkUsgSHT0K52P+wq+Dcg/jSqOg0miODUjAGDjrsYTyTjfZSTr3ISFbfX6Mcr+3Zbdv3nzUubnc5+F+8OLc1NRMIjxhHkrXxrDG2RmkjJwmjJwIyFmSOUMBGLtk6KxBBEQN4CxZcNoWhqgcZHFEQuYQ0JOCCyeVbTW9RQe5dtqgtSwrXJxZYzEvMM6csVRud4lIlrr3o2fW+RKV4kRIZLHcCGLJY2clxoqcw4NLVXDOELS2xlpjXWHAEXpKEjAmUAnhh15Ur4fVmlQeGZMnA6fzLI+qlehso+FVGmOtqOJLzkBKPj7WPHPqDDl3987V9saKKRIGGjjn1lR5mKGOyRAgE5VVO1bwypoQWnnt1sxLszO8XlG+Jz1R4twPwlnejY+HfOWCYPRmf40feLCqfWyhi0SS2wk1EOnqx2u3f/bh3Uf65IY//5tj2b+ga3M7dz96dFRreexEa34yqDRaIBpF7HAYC0q4p8iANQWxzMhAqhpwgc6BGBTpqs67QpXDU2RgjEmSXm4M8yvEFJQuhkJKzjjjFtABSgFcSO0cWcAa+bpg1lprXZ6jsaM2zBEwUc4mEZCsdZwzxhmgsNqQM45KJiUjZICMARhtSmkJJAtAxkKaF4V2xgAQKaWkH3LJpRRCCaG8UjDc6sLkiSkS7oAz0ahVGtVWVG9VmtUoEIIjZxhWgoXFGc+T9WrjQePW9vqDfq9NxkwYnCmwo2AFdA6gKenIuMMj7VdgZuLEianF6UYU+corL04uBJeiHA1xZPg4nA7oc6iTQ9bRYXsNCOC0sc4iEIITCMKzkcrrtDvhHt26u/fR7gR4m2+P5d+s7zbj25kesuZiUG8INAiWgpphS2ATGtyUECPjQAg6calzfsBEQETOoHYqy/JAoFANhgiaKd/4vk1jE3djFLkKfO6hkD6XilkHyFwJx7KGSlNnRC4QGRdSCGmsgXI/a8v1n2CeEojkrAPGABki0wUZbYjQWrBkyzTKkBnJnUGjdW4KY7FwQjvmGGOKhYEQfqTCGjIOAI4sINmisMYgOeZ0wAUKlAX3PCkqQbUWBFEghSwhP8jQC7zZ+el6rTY9PbG2sri68mjQ3q3FuYwdQkbIBQvmGa+w4UajOZiNjp0cf+tEY2E8DKPA81RZAUkpBWNSSETGEQFcOSOkkcXfAaFzFNTSDYUAAaw1VuuiyJ01CM6TTDiXeSZwIJ2p8fx4dVlLt7XS/Q/YmcTwSjVPar3x4V5zN/SiQEa+V5tA8YQhgN4VYWMmq8h9MKmJExGMMSZMkhfD3FkEURWVKYao8z4Ck0HhnEVOhMYLmQoirhQAAjnOAYDAFOgMOAJ0zpEz2hh3WKgjAFiHBFJyJaUUDgGIESBDLpABp6JwhSMSjDsHgITSY4gaQJPR2hAwxyRwz5MKOedCMCmFCrkIiiwfDvrGZFxw30dP+gyRGYfkrENthXHoIZOCS8Y4Y5wxZOiMKXTBEOv1SiU60oyi8Wq9vbOTdXppZ28YJzFrLWDlmxktklubrW0eq8wcCU6MB9VK4PueklIKUcpgCy4444yBcIbFHUFEjKBEcdJo0EWHG7NyuO+cM9rkWa7T1OgcyBkpwKNdsrGrrPYnT5/K/vlCAdmN/99P85/etRUujNWMdW4aapCYnJ4FAOb53FsARK2Hdv+Ssl0MpxkP0fRsssdtld0v9FaPNa3wWyKaQqkg7ZMDy3Ydc35diaCuwjHhhcC4K4YIDpmHhhlnkDuO6IisMc7kzlgABuhKQiQieBJ9D6QkhhkAAhMoJZc+E0KKQjCwjjHkxlpHQAwtcg7MgmGkuBTEfMECxxVXSgjJhECUrjBx1k3jvmToB34l8JVSOkuLIiOjO6n3MIeCILAOyXG0DC0ygUDOmiSNdZKBJWOh1x8wZifrlR7jhcKK5baYpFgeEeapQTIpg6uo0eUMSXGupFBKqIN6VnDGEBkZ1d+orn4oDouewyL2sMod5V5HRusiz4ssN1lOzjIoQY2sY7Fn6wUce+Fc67VjeyK5vroyuLOidnPwhhrSbL2TdMammfSVFwopfYGKRdmgPtj1bbolrRWVGSGqSCl2nVgO+dCDcQ+lcACIEmQkosmgpYW/x2SkKhPcryJXgODygPuZtcDyGBiXPODEnLNFGgsqaVHs0A9RBn4Q+r5kDApCh4DIfRY0VDTJBdPxXtLfNxqIwFhjiBmsOPIQODckcwIrGAmH0iFDzgBKb2mr86HJBgEzlaji+VKCNnGaJkmeFZkW94dy2YqGR5QPZaZ9byD9aQMKAaUUHpdx2n3w6NHK+kY973+hZhu+f7fwxo9GRxZbX4irH17UNx4MlyCdyofb++1rNFQBb9XC0PeYlByBIzIsLXM0xp3K6kfi5r8Th23TAYMFDsRMy3RFzpgiz7M0s1lGxgIRcSoZG1oDw7A5fSyS/VxECDsvhlurQv8wEwOLTBc5EPqBX6+rwOdCKplXKeEV0/dreZpiGgPfh2BCqjGoI53qe31mwgjI2bRHRQrOIDlVneR+E4XH/Dr3A+QcCJlfJ1cwbTAbWB4IqR2A0xkxRFk4C0xIwcv7UvnVZlCpCWaZSwEsIkcVieq8DFtgYzPwgYs8y4zlSJyhD1ghkNyhGyZOD3RmCp1bZMgEODJZQs4FnnTGeJQzxQQzpJMsozQpksxkzu+4egfrvqeqvJDFpj8cBqoJkU9+1THBuaxW6yYttLHrK3ebxfbpGa242FWtp2cWv3AK+kXsd/Cne8HpmbGv1y++xts7m0sX43iYJqeOL0yNtWqRL6Uoq1OrC3/zbvHZ983AiJJiCHDoT4WHXg9AQNaawhS5yZMsHw6KOCWynKEveOgxUpw1p7XXANfb6NtWe24h3fhaunfX8YsAgvP5xYXjZ85Um02pPM45IBrGjdfEySfBO2qSFUw3nNtzrkFBgx3zo+G4tQFwYdKOMxmQRS658BEYouJcMO6j9FCoEW1VGxRdTj4XKbPWSh+Fz7UmAMYVl5JxLryKX295YU0wizZBMoxL9Kq8MovCp6JLQNygUAWQZKCApCSFjplCu94g7e1nSW6cE56v/AoXLEejTQookUhyYgjgrM0pzd0whZ72B1DLIGoIz3GyeTfpDGPblYJEJYeAgCEhMMkrrcaxYyd7na7ahA3TjgfFDdtpLNPJ2UGR6sIeuVE7/aNG7SvzO2foyhvr+spy9mF/6/rtqYmx8VqjwgVHcjbPk8GA1u75D2/NHD16OBUapdxSDxdL5pm1VpuiKIzWOs131jdWl1f7SWYcSQ5Vn9ea9dq5yb4fWMbP6OzY+vrM3pgN0rqSi5X60sljr7/24hNnz4VRFRknIA3K4WQR+d4cqAlN3UW39gkN77gic6YQKmJ+3ZdNLoI8HWbdVZO1kUtUVQIAPUQAYMQoRHLEPUIBUpKxqLQA5pwD7iH3mNQEiEJx4QkhuFTSqwgvZGCQAYJlMhDRFAYNQO4QKc/B04iOAScQ3KAtDHOOmZyyPjOJxyD0lAp86UlkIpTM5IqB1bnOTMoIOOBQu+4QOrnXNYGzrO5M1cVbzmxglqs4qpIcDMPhqlyqwtgk+QEwrnz/6NEjtdDfW1tY2V3rP1hrrnWLdX3t3s6d9eRHdzwnsrc3/AvVaEmypWr7v/pio+/hT65s//zjwrgSY+3QWmeMzVJfFH/4bPJYbfqgpj3Y4Jblv9amKKyxCJCn2frq2upOe5Cn2hScQVCJvE3Wm302A/VktvVkZ7u1nrSF6npqaaL1yvknzh45GvpekWdMKkJFwB2rgh9I5QgAhE/d/by/o9N9a3asCpmUyOugWkK2PPRZPzBFG0BzHmhti7ivGAcxICZARcADQo+KzBSxLRJyzlkD1pDRRMTAIQJRBkSUE0pkaEAPOToupeAIJiYsR+l6NFC3moyj3Jp0aK0WjDcqSlIzzyxyJaQkRCLHJCOuyBmBgFYigTG8n8JOJjpFgCk2ktyPk0FBD0W+JfMaN3aLEjHUjT72tTv5BGuNR5XID/xKvdKoh4vzE8P94/7CzpHtnenj7b7/6NZqdybZm6tcvNqp/OWNzWPB9NSSfOV86Dy3001/eWmYxnZkwgrEyBVFfvJE9atPgoARuXdU0x6WQuSo3EKRdUgkpKrVmlEQoDW2KKxOOMNE59nln9q7l5lXXSPTSTeljsuVU9jd++z+HayHx8SSnw+rztRqTaZ84gjIkXMAQL/CJk8Kxs1wQw+XzXCXczJFwQstvJAxD6MZLn3BnRA+t844R8YURYLgUKYgAod+Puxngx1bpOVFj86aIgXnpB+woIoSBYE0wK3kpCHf42CkAuHqYAunM9JE3bbtdXRmizx1xjhgaJ0npBIR9708za3QUA77ELBUQmOIJAWXUiidm27ftTXvYeikiArj8vRhmi5jspFntjAbXDrnO5fp4YZHnutkjdmpU4sLk/Pzgd8MwqBeDafGm978XCXps6Av6cnjbPuk+mxs79PvDYJ7cXO4fXYhd/R0rxd29/ZMURREeCRi49I+GJi9zGijiyJN7NKBUwMeLqtHKkJA4Bw560pQNQpeqVdbrZZcWVMMvt2snwL7Tl7ccwkbphCXfA+XIwIgGZPt733w0XtjR2aee/J8YXVvb58D+pUaF7IcLyIieB5NzGB9TGWx6azq3dt55z709tigI/2ASR+YlCIiFYIfSkYCgKzVaa9IujbrE8uMw6TfywZ7zmjkgjMEkxk95Aw9jzxV84NASicFeIpzYLZwzBVSSCEEmcTle8ne9nB3N03JknDOMaF8v+pIFoVJ4izLtNEWgazRjqwUEiUnAucQgROhdtjPcTtjXRaxSjXkSoQ2FoN1qR/2ulpnHLHjnQi9J/fNXtveht12Kx580eFZdazrvYzBSR6OMeEJT/Iw0FDvo7Gkj6j+kfHG1K6rLa9/dJfNr1dhPfsR7H9U3X33vujHsODj/+loeq6W/cUD+f9+hGmudweNh/Di52DTnx/aEjjnjLWGyFKpxgFhrbKwuLixtU1r2dcbldeKgUnTLcEzhoIxhkDEnBstkZErYMIXvpCVewX/eGX3yV735GReqwR+GEjPF0IwximooA+iUmfVBjRmoX0y375r9m/xfodjjGCZCnlQ8ytWBb70KzzwBa+kuYx7m4wNc+2KPANUTHoMBbgCmQ0qE36lVmlNRc1JJSW4IZeeV51kzuS6ixDxygSTgXOJLbJ00M0zy1VDKZ+QETFrMUuLZBjrwjhiSBwsmcJoUxTccCGtc9qQcSrOoJNQ38o+1qhSj4JIeb5kQk0XY6tqZznv7GcALPDm54IXQxN34sEguf2Ppop/3EptvnVpPc9ER7MndGVW+QHzPOFx5EwhhH61WnHVRvp65eOjuDO4t3Fln/3tNbwPQSMTC6A9lSxWkqfnqR+bv1/lSa357Ctfmz3yBcEYM8YAQun1wA71NZ0z1lkLJTuCo3WBN3fsyEvGXnD2l53t9kBfMi5FRoiHFG/uCessQdStP51A8J3L+yvy+uVk9taamQyT3z/V+9IR1RxrRLWGH4ZSSsYQCZExYBXPC0RljDXm890lvXOnaN+DZJP6e8ZuiqAa1ptBpYmqOiwqm+usv63Hqjqoel444QceZ0wncZF2pKpW62NBva6iqh9GDB0Y4F4IXDpyqKpceChCXSQ2TdIcNFWYqqKqAoLRmc51GudpmjntnCVtjLVkLGW5LaxxBAR2WEA/w04atodBz0gR+NWxqBZFUS0KwtDzQ8F5pVHXzt1I4yTp9/TGllzPCqdNfr6m//hEOlmDC7vFve5qIFjFa3iyJf0AhQAukSEyQj+McbrHn6miaA4vX30q/Xi69ma48N9CWF1NPl59+D/1bv90Hac92Ldha3Z66omXvvzGF6cnp4SU0pWaGyO08OePKAEAZ1wgcQQHDqrR0bMnFYMLH3/4s26/Y5EheCNMp1NVb+rZ41zJ23eSHXMEoHHr0b27D/+OggmC6W30LiTstPWlnqjkPRlVqTpmg2jkrYWMGLAw9D1P1JpmfCnfO5Nt3y127sZbd017y293lbepQW30a+8/yJd32lNVemap8tQJj3PhIMuSHthChB7jQC6nAixmDpkQHHSiezEAQ1LOybSzl6d9k2bpIIuHvCiQ8sxYWyRpUbg8K4pcO0OFtmlh0oJyh9qBIZYTT0ilLugUwSCZHKRN58JIyqYwtbqoT1QrtaofBJzLamW20xnfaPu5/jDOH9y0f4cgE7d1riGbY0v3MfwPXXnbV0t5cDLB+YIEMI4HiB5CYixRFUPz3WaUn1hK1W6zB8/p6LyWciEpbnlP9NlHKqvyhjsx8coLR2ePHZ+dGjdAwg88a22h9agGGq03LTlHB/qM9vEwHkXkHz1z0lMyqtdvPri/2+2m2gpGEslamB6vHa/Xkkc3ezsfD1zN5l2Vb0h3zYnQqVZCwX2cmqJjk8O44cW7lfOdsVOs3vK8UAgJJd6GcSmkCCLRGPemjmV752D1Vn/9XtLbHvSG6JLAJIse7gp3fceu9bu9QXFuVlW83OO553ErIWeGjO+UMJJzrgSXzmprCuSeEL61Lo8HaZzqApLYxqm1DhGcNqS1K4zLM5OkRZzoNDe5w4SEFYEI6jyo56isFyq/OiUqE7ZZ5FVygR+I8SkzMcsr9TAMQ6EkEu/xcOHIkVPp7BbW0t51IfRYhRbZmDe/eGn6Kx8lrX9feRjObQatPMpTf2+Pc8aoFvmBRDLOWQJUwsgoCYVltbFoMUgynerVjHDJDJ5svKrPDhSvVbywEi36Fe77GrAfxyL0/SIvrLXWlU4so4AeKHM4IHLkGCNGgEjIkFeD+TMnGuNjx5aO3Lpz+/bKyn6/nzjbHCSvvXf5eE0ky53a0N2XlU0D2hRjJE5DJVeznV37bvZQDzpjc/3T6tG+v7c5k1aOnWzOzLOoipxZNxIEAM5lVGG+L2pNNbEUHdvL2ltJezvrbvN0/4nGcG4iX25nl7fin95NlreT52bFbNNWAkumY/JBoaRQ8sC4AkxhgED6EWOySPM0HhaFzQtIUpYZDsAJMDeUayiMS3I7iF0vIUhZYP24WcPGZHV2vj4+gZ4vPM8PQ+X7nHnAPM59pYQXoAq48oTgwtfWHxbdkPdnap2B6g3f8qsnFsP282OD35naPdV0/Sj5bnesOlX9v76Unw52h731rLvv3R0HMU0iSJ3f7cGQdPV4WJ2KPF8wzlXoo1SuQr0R+JAWiRxHyQEAMuK5Q0eOnBFhEOjCGGOd1ocSfqOjWEI13IGBB8MDtCsxX1VnJs40q3MLcycePrh1996j9TXf6MEubKwMAfjxVmtibGLd8dX93iuc/nfTrU5w8oON/Afba3/zyc0rt9wzY1Pj1blsZXdif3j0bDE5P89Db9fEJs8nRRgFEZcKkbEgCJSv6s3KzJEiTfNhPx10ikHXG/Za8XBqd/Dhvc0si/OqikVcJO0k6YVeIiVyIREZYEkgY1wpZQpCbXWByITkhSFARsSTApKC4gJjywz4GpUJFQ+9c1t8ftM8bAQ703MTx5cmpqe8KPCkOoBbSSEk44yzchDOShG8sXY8tdPfdeyOCXcJZsPxWSLMku5gdWZ29Th/sLN8Y/7Rmd+cnP2dNKwr3Q7WdUh2P1p5W9y+VonV2ftF8yFunnvBPfXSzMTchF+tofSAMcehYIwxIAdky+WeI0B0KAnAoQQSvu9rY/KisNZqa0s0OAMGjJfAYnpM3EZkI9ucMi3zIGjOz1bHWotHjmytPEp6cR9VVhSeyasVtdBqngBvvzN4wdv5wvQ9obZf7Kg372/99YPiJ225lrOjwbD34L66sfncjYvjx0/D4twtng7b7df98bNzxxqT01Gt7nk+cs4UopTMD1WtHuhpUxS6yE1RNLLs6ItJlmbcFZQnNNxj/a0s3kzTfZblAiwiSMk93yMuteNEBCCZlMZizlkisOfkEHgqpakH6IVRWA8qdT8IKyCf3DNT9wb+uKufna4tzdebTS/wR1uMA3AOMmQHfISSYckzB5mRfaoVeK7Te7hxY7d7ezC81q+uP3jCVYR7+97Wz2/rY2uZ2zuXBvLRETnxfBA049vs4Q/3ZBBVdGDMGInxmogilJ5lEkpcOIBGBwS5dcYhElpCArQjqB6CQxF4vtE6C/28KIwxbkTGQMYOmduPR0UHGKoDtzMiQi6iYNyfa4y3KC+0ccYYtIVAEoJbZAtzEy03ue83Jv3V+cne3BQ/Oi4nN4/eU7PoSdULX2D4dfPRuz+/8O+L6U6tFlD21HMtqK7m7Dg3R11lgqmqUnJkE8wEE0J6vl+qrDgL5Jy11lhyloxxReaSgU26Lh06naPJwOZW51ma5FlRGG0J0SkjvKLm2VYgZNQMoskgUmHkBaEXBoEf7OzDo63hQiMLTppKwz8+P+7Xq0p5XJY0CDz890FFcbBVJOgL+qy/fuPj9jKbfY7deUn8/C8GW72h/daLR557upbl9y/Y9s1e0Tc735+cD9jYn91/WWH1GXb1k631OKSzyXJI7cqJM7Nn57xWLRMBWKaQFJIrQcGA2pC1iAwLBEQ3UsxEtMSFktzz/MDLM98zxhqjwTlbTv4YA4bgDgfyQMAcAD8AKOABDZIY42HIQl85C9aSMSVjXUIpmBuuy+kkeLEZUDjZrk+nX6X5l5vzyg9tUSzFD07ufk9cfnjxbu1RXx5bwn/ycrS0YHJ6VJjdtS15absh/enxsWa1Vg39UCjJBGeMExcM6PPV+ONfOwe21HfIiyLP0yFLEkxyrgsHjAnBlCeUL5Qn/UB4SkjFueBCCCGGGXt7ufe3V9b+65ns5ZONsfnxxtSkF/jIGALjjEHJyEYEYFTKNT02IXKptW9v3/uPH/31sWr0r77YO3mif6oC7z048acTb7S6+Z2V5cUNs6DTddf9N+H6XoVtbM+c+pSfSvpqB17Uk1/pi6u63Xf9octIex6XCtEa0EiWnHEwMrh1jBwS0OGxI8YcgECb+Z7K/SDIizwvrDUlV5VGvF0c4XARCNhomV2Cvw6fTCjTr3OAjHEAJERyFp0DIuNcxnGoAqqO5fW673uOywnlTamAcY5AXjrfbi9UZnf+WRb0NXDex7HhAAdo9lixiqur7/7N8HYnnJ2dmp6Znpqfm5+fnZudbY6NqSBkvGxy4PNw+BK9UUL3iUg6Gxhbs9bZUtMLABmWSCHGSk4d46PtIRH/aF9/94HOt3ptP9l30ZjvS+Up6THGCDDO7WpPS3DTISpV/iHlLA3K+ZlleGzC+6PFjPf3ySAfx6+cpaSbbj/qBettd2X3zB6d4Xa531u5dDuVK8w+esH33urdaUq+HmY3Ws301Hx4aq5aDQOpSgE668ABWXJuhPIHVzrmEDEiYIwYA+cAQAz2lmvTpwLfK3SQFbkxxjjCkYh5yS0e1UcOAJxjDLDEkJcntvwoieEIGk4ACMgBwCESWUSWOyJtbJFbIutFKqgoKZGzEpJlwnpfnRMTp04AApLROiuGD9O9rL8J7c2Jm41/dPXRctJjd9p9tveT6L31KfHEubNvffUr584/GVVrDoCsJWMQkQmJokRBcUBW8jEEAqgDufrR04eEjxV5GJZ+MOjIDnrx5Tu7vevXJ9qXk4osiinJlSlwUBilUHlcm+J+u/vdh/mcgDem7akJv1KpKCkEs04XjlibMjEFf/QE17dw7SoIBeM1rLW2Plj/4cCEfn22Px02Pf+5En7OuQgqzXq1V31zPqr79YloanpqrCmiAKR0WMpUOBqlQgasvEbLeI4Y4Yd0GwAQW1d/FDRmfFXLVREGflFoYwzYEQUHRgIArBQTNOSYA8ZLOukITA2IxJDcQYE0ogVxcOU3kHHOGefSjIYxeAEo3xOSA8apvrDaVVScGfOUJ7nknHPhB+CioDqu60d52J/e2Vg4f+e5tT3meCePN83ta9uPPtXXT509d+L0OZ/Iab2xsnLn/j0kmp2dnZ6artXryg9GMLcyKx4ULQf55vF0+iBJIxHlWfLp5U8u/sV3qzceYbHz6b5PMg+8xkLlfNqHjGf1pfRIa/NNvPf2dvXfXhb6xHD+lGlXPR5U55VLhzvLSXhD0C9NrPnkm4NhvAL3FsX0ov2tl9xzu7tr8al284V6M3g1nHrZHyMpQEnuRZ7yqwpqHKeEElwapNyScW50GA/w6lBKz44eS+IcqER3I9LBexHdmx+2jzw7c+oN3/cC7edZrvOiMLac85XfxBAtlEgca7Nkv9ct0gQRo9ALQ98PwogDmiITPkmBBzN9LI1DHABjjkjnRRLHzA+5FzIuksJ991bvf/j+jbN+5//w+szs7FSt3vD9EpDJuUKpFPOjYVh1x8ZNZ+i0HWbZ08lTtaJfrVdPnDwWVCqM8TSPt/f2Ln786cXLl6vV8Ozp08dOHJ+dW5ianBxvNSu1mh+ETEpkUILgy/Ad0lmQ0MHodsmznEA/OVVJ7va6cdzrxD9/50ePlvdenPu9CVrK0/SJ49sTJ6+v9T9xF4KX+k98wT2xv+niKxcGnfWaN0EPh7ur+fS5qbln/Vs08bzZoFohT1T2onywlU7XoFVfvevsjazapfOOR0F9ot5oKs8HxhzDsrTTRNY548ABjIAieMhbHP0DoyPKRhXN50CXAtJ497Mft+bO+cFY7vlBkGeFttrYg7azfJIBwZGzhVm9d++zy5c297uFMb7i9WrwhaXWv3yK8vb2R+65dPacH/lC8nL+i44Bd+CgFJGFNE+HQ+GHXKi7ffG/XOum7e7SUQPIhFRCyDLpEQAROiAmmG5VoRYa68haTu6cc2eIGGeMcyY4IkrfP3f67JiqhEF45eKnH/3yw0sfrTYap1ilNzbrHz1x6syR2amJyWq93qrWVBhwz2OMIZSsOEZIQK6U1VG+/8qzXzg9vujJ4KP33h/GwySL79++sPpgVcm5V9nUv7o1viM2/mdc30zV/+2FL10RZ//vN/q1rfxl6b/VfFTsxF/ITvXW/aS5M7e7fzHFt50Yv92oP8LO3ZWvTdqvnkxr4uFn6eK7w9vTgTl39klPBUIIhkLbsi5mdAAmADgkv8CBWD9AKSlDpYbyCO3uSl9WAHAkwOi9axfl3DtnX/kvfM/LPN/3tc4LrQ2MLJHKtzsCVudxsru+ttOLHXJH+r62eof9SY12VpLvruzC3PLSkYXZ2dl6o6l8jyPPCszz3JPEBddFAYOYeQPknnLhH5yoNI6cenJCTU/UwygSgsPBtrWsF235uiVHycBxAcAPl+2jM0ZKebKlFqLoD6fG3vzim+vrm73NyXR57Gf33vlwbx/ubE/mPzpS0f/iqcbk0sTd6tnxuYWpejWvz1FY44ILrf1+agCKQIEUufY6g/qsfP6Nsw0t9rYH3V5/Nu03zJDmhWjMh4MbO52+252M/rx6mlPzOWvf2ZM7LYnBg50Xly6Nf+N92e9Xrs3Hmyuarqbe89e/1ZBjd4Z/+eHO9V/ssIkjEz9eXLzaM6+mZpCZ1JiQgB+S9kbodUAA9jneLRzQ4cvi+WCZCVTO7JwrGbCMofjw5v56J6Xd//VfHX1mbvp47vlFoIu8MIXOGCcwI4P0klupvPlTTzw/GPLLF7Z7XWIirFXWU/Mvf1aomcU7RYJXLt2+d2t8bGxuZnZhZkYFY73h9Ga3/fQpOTNVBWtzRyAYcFmN+G8fDwNV8T0upCgfyDIdHF5vIymYXxVXftzojW4VQEDpqam5+cmp2VPni+62uH3F3PafXqHYEpjV1ZPm2jf1TXMZ0r0fTZ+per7/c/aSfPr5V4/5Ym9Qu+5XKhNp099Bc3Nf/vLDbPUOBP7c4snakaOhHpy/fd/urO1qkQyfto356BufwQ01dOzGc2y8MJTk/nA5/+9Wi94pb72a3+isQKXgc1OV8BGPaSyYfX76xfba9sWte2uPiupAu7z7heNPvXby6YXZ6Ua9opQqh3GPmQcMENhhOA9pyaOPwZUC/WVvNKpWRmRyhuKvr7bT3GZ3L04+/ef/+k//deD7hc6LINB5oVRutHZ2NIFHxhChOt469/qbzenZzy68vxbm+rWnk6vLt649olMnRKPHPr4zHGZxsrG2sX0tuOG4Z2G2XmmcGp/PIgSGwJi1GggjIL/e4DzA0rivXKA/fgbpcUT/N/x8R9OMgwgzZCCZJ2RzDo4r/MMji18yCWNQz//wefP0QvKXGzfWXp/KokH2o4vwl4OtP5n+bGwiuHRp4/5Pj78++dJ8syE21zY2Hz3ay+/lUAC71cknJmpAK+s73V5nZVcmf0K1596w/5fTset3CP/H9c73/l+rT6bJfFbA25VxXdf9O3/Ort1S854IPSyMEOl9+5MmU9RIjvhL9VBOz8weP3f23FNPj42NI+flDssBjYQCDhIpjIpteEyJc6Ugzii1IiBHRMZHZVI5SsmN6CUaCJ2hv/6zv/r6m1976swLeeEXvi58rwj8Is+1tc5R2csBAiHzKtHCk0/Vpic/o+3V5xfzhbnBVjt+/7ZivFQYt8aZzPbSnMARbEPefPSwL2ApqleZVHmWal1YpxkVITEoZC4nGBPcGeISpCznip87o6M9D43uDDqQecSDZglx5FxKAExKnJqWk5OTDKiw+cM+yzppb/N0a2qr6JtP7sOPe97rXxr76hna3Xz0n97v/921wQJ78N8szL5lOr+1t/Z04v4NsR8w1tuArW0keA8BgMy2of/lU1lAZVx53AVxjDe2Ny8FC1eOnon66ydmJ+tnj/Y2OhPibCsKojBQx56t+GFUadXq8sngBc9/MQhUUIm8qIJSlEErKcOP6+2DArwcrFrr6OCHIyIEhuxA66nUfrCF1kVe5HmWJWmSxIIJEXpiLIjGKX7w/t+eP3428P28KPzAz3Ode1kJABtlahyxxoFjdX7upG7Zfppb3MmL+kbbISMGUmDhXKy1QPYmLAHChe7aLz+7trqzc2ZpaWFuNqxVDEIusFAU+sOa3no0WAy1t2gfDjHYqz+VVcel50spR7O0A1u1w0N5IISAAMAFDzyVpXG31yEHikue5IWxEAWeJ/fi7v/02XuTKyv/ewNHUo+c11GNb431njiNjKL8syPTD8SxeVuZazaOTuX7j+Jd3lD1p02QhJVKEHgMhODK86WQylORUpershkFge9ZJgnl76i5o3ljQh8/29CVZkjPMQVCeJKxsk9ERA4ggZEFTQcFiBtJFhCwx7wgzrkrqzJH5dN56CVXMv3KqFpndZHpQud5liZpGsfxcJgMesVwkPR74rfOjc20gqmqbEU86lxvL1+aPPl64Pm5X3hB4ec+6YKctY4OjkhJDwEC1/TCM0M0D5PcyB5nBAgMBcOax5gIAqf+j8NnuMv/pdx+aIfxw9W13f0TW9unjx49dnymGQpJvnEYio63dy/ernvqbtPdDuH09eAr7fmn65NTtTA0xC05VWL3Dw8rIJX6f4xLKcIwNEV2/dqV2zfvHVWNpzfYz7vLa3PKj8LOoLd8/fLa3v7/KPFYo/rtf/xHJ0Uw9/f/3+5P1q89HIoLL/wX3uJTvz0NS1MhE48GT+UMo2bzubB63gs4R0RgJZyeMcaZRAgZMc4coCMwwGZQPKkRaJZzxzgZCwRADB05MO7gzjCHktxEh4BmKk3jRiO6g4umDCIREDDOsJwJOeuM0UVR6EKX5zAeDpP+IB/2skF/2O30uvv7+939Xk9865U5JQQyALDodPuzHzbmnvD9ml94ZU2k81wbY52h0RQNGEO04BxAYSfR+8qR08/9ln/pw/eu3749zLMUWUXK8+MTM9HM/X3X7/aGVplCJtomnf72ML6+uvkVffTc3JFhnHc7Y15YN/leH8JHlbeOymO1vU/53Z9tdDE5/dQF1/zhnb7E+F88NzHeiDzfV1KO2tqyaGCMIVrrqDB3rl7/zl/9zbyrtuPJt2H9nuyWDUkUYFG4j9Jiu3bqD556w/e9vU9/WfvxWvfT3p8ln8rxvLI9NqW3TjfdzMLc2OQCj0KPs4K4I0fg8JCODmCAckREMAQOnKVy4lmSa60rB9UAYEdtwyFF9mBsg+5AlJKo3FBhqfDgiJx1cDCFcQ6cc87qUS7NsjRJhsNBkSR5Guf9Ydzr9Dp7+/v7O539/V5/ECdFoZ1zIqqGQGCsscZy5Pn6rfbd92ee+s3AD4q8yHwv8/0iK4x15Us/2JchA0BHSoCIvMbpExPjY/MLn31y8cLa1kbHZHp/yxl6p6arc8Gb8tmNwXC9vTfo7p1J4yc73fd/ce2/W1urh7WzJ48eObt0f0eEnMbOP7MWvISVV/Nof6Iyu+eq/89LRX99+M+ekZ39XZMOJiYnsFoVQhwudwjIWLvfbl+6cnllY98x717afsS7TolKYzqJk6U5+9/+Vu3OSvfP3obOfmd5fXVhfCnvPZHkW37ycLU28dHE63SZ15OLf3x+72x/3tt+trW46Ddq3AtKcQhCoLK2R7AECRBSKXZBUMpzlZ8/jQQXymceyB22/AfT+VFNTnAwxCCy1pX9YzlHdUROG2tMUZg0zZI4ToaDdBhnSZwN+2m/F/d73W5nb6+92+v2hnGS5c5YIscYQwAhhCiNVxEAgVlnwaZ7V35YX3o2rM4WRZEVRRGGRmtjLRysz0bFF5YvHEoOU3Vi7IU3Xj+ydPTTCxcuX/9sr9/5LF6+IzcalXBhfGxmbOpEffHV5eS55W7icCvjf3W1g3bn4qPdaYH3NvZaew/Gw5nTx09xb5qdODpbqwfafzOJz7/a+Menaw8e3F5e3uRkGVEQhoxx5IJxckYTsDzN4iT5vZdfm1DB937xi0olf+utN77y1d/f7A0wufr84sOzS9dsZv/tL7N//2/+P+eOf+3ZwcLbwxP/fZBdOf0nxJfg/t/32tf+ordf/eQG4x8cWTry5W9886lnn1W+d7CkOdgoHUhGHP76sA6nz/WOh9U4jq750R1oR/8DIsNyZUoAbiRgaXShsywvK5pkMBz0u2m/r5Nh0ut1e93ddnt3f787GAyzzGjjHHFEhig4Q8YZjmQfhHMOyGkzUu1hKG17vX3jpwsv/1EQBllR5IXWua/zoiAiAOfs55Vpyma+fCKYp6aOH/nK5PjxEyc+uXjh9oO7wyTOtN7pD+TKWqQU5ukuwP70xMzZJ7+c5fdW13eT/r1P7hXd+Ijp5T/+n7/z7pKaP/PMCy8sHJfNqvqX52u1WiAjwYTMl+94lz8AwVB5+fQEe+EFCFv7W+z2w/jUgnmd7yftT7vZ4Mq5t/6bbw+/dEbFLfHizJeN/eLe6pXlO/+mn99qBv7D27f/+1udp/XUdbd5tVJJ/QnYuIR774Rmm+e8MMjZYNDey5P48DwdTizoYOL9+Lf+we7pccV24Ht6kKvLTq8UpyYiY4wxJi+KNE3j4TAeDOLhMO/38+EwGfT6++3d/c5uZ7/d6w3ipNCarCuVZyVjyLEM4+iPPMjnIs/yz13Lpbwy9W6/2zj+YmXiXObneZYZ3y+KwliLzpUXwMhCixyNkCej102IXjU6+fST04sLZ27cvHDxk5X11dRoclAU9j8BCc7qVr+A+RdOLz1x7NiNR6vXHj5M+umfPmNerG3/ny/sfPDxzYufXTl+8kRzZrrSaJ4+e/L5k6d7+932Zn+87a1depf2lo//4dSzr6T3dxuf/HzmJz8enp6/988m3/3w0p0N9rsvnPnW3OStwL/981/+u17twRuvvuXUwncuRu++O0BKnTXvwcqHsMYj1qJauPHvQyzGT41NtI5PTIyPt5qticmZ+cWx2Wnp+Ycb+9EBK0+kg9H+gQAZHp5VIkBgZUNR9kujcDMcDVuRUTmPNcZonRVFEifJoB8P43jQz/q9pN8d9jqd/c7u/v5et9cbDJM8t9YCAAfkjJWuRAcRHIEf2CHGoBQPLxtwzhkBkANXCvF1d7cvfz/6ylLo+0UYFEUeaN/owloDAOXfcfA20BEhjZqh8m4gwaoTrWdeffnI0rErn1789OqlnU4HAYGxQuDOYPjzixdWNlfPnDo3ceaFZ8ZO+hsXzs7d9J12IPpJfu3ards37zKGwHB8dvILL35hd3M7tidvzLx1s55BVv2j4PjJwbDbPnLrEd9I+jtX7t3A9U7OF+bHX7Pz1y9X2+0X3v/op9/76H/4myd/Mt4cu3V7bXLmWLMW1ar1eq3Sqtcb9WalXg8aLa/SCALfC/ygXPMIVoaGCPDAV3E0EhsZRT0eaZQd4eP+eDSiQeccHCxcAZgjZ11ZmuZZlsXDJO734kE/HQyyYS/p93ud9s7e3tZep93rJ1lqjCkXeIIxzjlnI0WH0dx2lA7LQMJhOEv0jwjDoGxJjbFUDnMZkqPOzQ87J15pHX0lCII8z4vC+L5X5NpYY8vSrWzqgdxolX3QRxBiKX8oobkw/WrjS7Nzcx9++P691YfaWcnRWDbM7J2VjZ3ecHi8+frC7KsnFqPGrhusTTUap/2GH1VrtZrg2O91dtt7P/7OjzKiU2+8ue1X77PWN2f/ZPPm4r/efGd+adx6jTzc6BqZyNnp6QAmh256oO3snbtmZ3hkevpMRTTmp46++dIbE81GEPqe78lS8UMIx9Ais8SJCKHUfCjfggNyWFrhHrpEsYPzeRDhUcYd5bnyYR5lX8alAyijqHVW5EWcpMlwOBz0kn4/jQf5oJ8Nur39zk67vbbb3u71sywnS4IBZ1xywRB4WZKMjmD5NzwulMtlVRlHPqqwCJH+/7vBfFyChvXqAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "PILImage mode=RGB size=154x192"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "im=PILImage.create('donut.jpg')\n",
+    "im.thumbnail((192,192))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/.ipynb_checkpoints/model-checkpoint.ipynb b/.ipynb_checkpoints/model-checkpoint.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..d1cfa06565314aa23939e718b33e4b28aad43534
--- /dev/null
+++ b/.ipynb_checkpoints/model-checkpoint.ipynb
@@ -0,0 +1,73 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f86c8e41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from duckduckgo_search import ddg_images\n",
+    "from fastcore.all import *\n",
+    "def search_images(term, max_images=30):\n",
+    "    print(f\"Searching for '{term}'\")\n",
+    "    return L(ddg_images(term, max_results=max_images)).itemgot('image')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ae55d0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastdownload import download_url\n",
+    "#from fastai.vision.all import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1efed2ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "searches = 'donut','scone'\n",
+    "path = Path('donut_or_not')\n",
+    "from time import sleep\n",
+    "\n",
+    "for o in searches:\n",
+    "    dest = (path/o)\n",
+    "    dest.mkdir(exist_ok=True, parents=True)\n",
+    "    download_images(dest, urls=search_images(f'{o} photo'))\n",
+    "    sleep(10)  # Pause between searches to avoid over-loading server\n",
+    "    download_images(dest, urls=search_images(f'{o} sun photo'))\n",
+    "    sleep(10)\n",
+    "    download_images(dest, urls=search_images(f'{o} shade photo'))\n",
+    "    sleep(10)\n",
+    "    resize_images(path/o, max_size=400, dest=path/o)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/app.ipynb b/app.ipynb
index c4c7a2d904977fd69f7d54dd1cb172817dfbbd8e..aa6bdfd2561f236f1c514e37d1b88514eb5f59bd 100644
--- a/app.ipynb
+++ b/app.ipynb
@@ -4,158 +4,418 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Searching for 'donut photos'\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'http://images.unsplash.com/photo-1551024601-bec78aea704b?ixlib=rb-1.2.1&q=80&fm=jpg&crop=entropy&cs=tinysrgb&w=1080&fit=max'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from duckduckgo_search import ddg_images\n",
+    "#from fastcore.all import *\n",
+    "def search_images(term, max_images=30):\n",
+    "    print(f\"Searching for '{term}'\")\n",
+    "    return L(ddg_images(term, max_results=max_images)).itemgot('image')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Path('donut.jpg')"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from fastdownload import download_url\n",
+    "dest = 'donut.jpg'\n",
+    "urls = search_images('donut photos', max_images=1)\n",
+    "download_url(urls[0], dest, show_progress=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Collecting gradio\n",
-      "  Downloading gradio-3.6-py3-none-any.whl (5.3 MB)\n",
-      "\u001b[K     |████████████████████████████████| 5.3 MB 3.8 MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: pandas in /Users/dinglab/opt/anaconda3/lib/python3.9/site-packages (from gradio) (1.4.2)\n",
-      "Collecting paramiko\n",
-      "  Downloading paramiko-2.11.0-py2.py3-none-any.whl (212 kB)\n",
-      "\u001b[K     |████████████████████████████████| 212 kB 25.4 MB/s eta 0:00:01\n",
-      "\u001b[?25hRequirement already satisfied: pyyaml in /Users/dinglab/opt/anaconda3/lib/python3.9/site-packages (from gradio) (6.0)\n",
-      "Collecting uvicorn\n",
-      "  Downloading uvicorn-0.19.0-py3-none-any.whl (56 kB)\n",
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-      "  Downloading pycryptodome-3.15.0-cp35-abi3-macosx_10_9_x86_64.whl (1.6 MB)\n",
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-      "  Downloading rfc3986-1.5.0-py2.py3-none-any.whl (31 kB)\n",
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-      "\u001b[?25hCollecting linkify-it-py~=1.0\n",
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-      "Collecting uc-micro-py\n",
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-      "  Downloading PyNaCl-1.5.0-cp36-abi3-macosx_10_10_universal2.whl (349 kB)\n",
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-      "Building wheels for collected packages: ffmpy, python-multipart\n",
-      "  Building wheel for ffmpy (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4712 sha256=51dd2d770517fb8d6fbde09ce138e4f739e1b5cde0ceaf79360170d436984e38\n",
-      "  Stored in directory: /Users/dinglab/Library/Caches/pip/wheels/91/e2/96/f676aa08bfd789328c6576cd0f1fde4a3d686703bb0c247697\n",
-      "  Building wheel for python-multipart (setup.py) ... \u001b[?25ldone\n",
-      "\u001b[?25h  Created wheel for python-multipart: filename=python_multipart-0.0.5-py3-none-any.whl size=31678 sha256=1003fb9bd410d535b246e156b16f4cd2f0ff15995dbf4d175865d59765170d7f\n",
-      "  Stored in directory: /Users/dinglab/Library/Caches/pip/wheels/fe/04/d1/a10661cc45f03c3cecda50deb2d2c22f57b4e84a75b2a5987e\n",
-      "Successfully built ffmpy python-multipart\n",
-      "Installing collected packages: mdurl, uc-micro-py, rfc3986, markdown-it-py, h11, starlette, pynacl, pydantic, mdit-py-plugins, linkify-it-py, httpcore, websockets, uvicorn, python-multipart, pydub, pycryptodome, paramiko, orjson, httpx, ffmpy, fastapi, gradio\n",
-      "Successfully installed fastapi-0.85.1 ffmpy-0.3.0 gradio-3.6 h11-0.12.0 httpcore-0.15.0 httpx-0.23.0 linkify-it-py-1.0.3 markdown-it-py-2.1.0 mdit-py-plugins-0.3.1 mdurl-0.1.2 orjson-3.8.0 paramiko-2.11.0 pycryptodome-3.15.0 pydantic-1.10.2 pydub-0.25.1 pynacl-1.5.0 python-multipart-0.0.5 rfc3986-1.5.0 starlette-0.20.4 uc-micro-py-1.0.1 uvicorn-0.19.0 websockets-10.3\n"
+      "Searching for 'scone photos'\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'https://www.janespatisserie.com/wp-content/uploads/2015/02/IMG_9481.jpg'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "urls = search_images('scone photos', max_images=1)\n",
+    "urls[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Path('scone.jpg')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "#|default_exp app\n",
-    "!pip install gradio"
+    "dest = 'scone.jpg'\n",
+    "download_url(urls[0], dest, show_progress=False)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
-     "ename": "ModuleNotFoundError",
-     "evalue": "No module named 'fastai'",
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "PILImage mode=RGB size=154x192"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "im=PILImage.create('donut.jpg')\n",
+    "im.thumbnail((192,192))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastcore.all import *\n",
+    "learn = load_learner('donut_model2.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "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": [
+       "('donut', TensorBase(0), TensorBase([9.9993e-01, 6.8589e-05]))"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = ('donut', 'scone')\n",
+    "\n",
+    "def classify_image(img):\n",
+    "    pred,idx,probs = learn.predict(img)\n",
+    "    return dict(zip(categories, map(float, probs)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "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": [
+       "{'donut': 0.9999314546585083, 'scone': 6.85889390297234e-05}"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/gradio/inputs.py:256: 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",
+      "/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
+      "  warnings.warn(value)\n",
+      "/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/gradio/outputs.py:196: 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",
+      "/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
+      "  warnings.warn(value)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861\n",
+      "Running on public URL: https://dba9753ffbaa6b77.gradio.app\n",
+      "\n",
+      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<gradio.routes.App at 0x7fb527720100>,\n",
+       " 'http://127.0.0.1:7861/',\n",
+       " 'https://dba9753ffbaa6b77.gradio.app')"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "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"
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "image = gr.inputs.Image(shape = (192,192))\n",
+    "label = gr.outputs.Label()\n",
+    "examples = ['donut.jpg', 'scone.jpg']\n",
+    "\n",
+    "intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)\n",
+    "intf.launch(inline=False, share=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# *export*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'notebook2script' from 'nbdev.export' (/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/nbdev/export.py)",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [2], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[39m#|export\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mfastai\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mvision\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mall\u001b[39;00m \u001b[39mimport\u001b[39;00m \u001b[39m*\u001b[39m\n\u001b[1;32m      3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mgradio\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mgr\u001b[39;00m\n",
-      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fastai'"
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mnbdev\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexport\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m notebook2script\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'notebook2script' from 'nbdev.export' (/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/nbdev/export.py)"
      ]
     }
    ],
    "source": [
-    "#|export\n",
-    "from fastai.vision.all import *\n",
-    "import gradio as gr"
+    "from nbdev.export import notebook2script"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import nbdev\n",
+    "nbdev.export.nb_export('app.ipynb', 'app')"
+   ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3.9.6 64-bit",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
@@ -169,9 +429,8 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.6"
+   "version": "3.10.6"
   },
-  "orig_nbformat": 4,
   "vscode": {
    "interpreter": {
     "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
diff --git a/app/app.py b/app/app.py
new file mode 100644
index 0000000000000000000000000000000000000000..a48de7f9788a230abd4aa6421ae2dc479bb15bfb
--- /dev/null
+++ b/app/app.py
@@ -0,0 +1,27 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../app.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% ../app.ipynb 1
+from fastai.vision.all import *
+import gradio as gr
+
+# %% ../app.ipynb 7
+from fastcore.all import *
+learn = load_learner('donut_model2.pkl')
+
+# %% ../app.ipynb 9
+categories = ('donut', 'scone')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% ../app.ipynb 11
+image = gr.inputs.Image(shape = (192,192))
+label = gr.outputs.Label()
+examples = ['donut.jpg', 'scone.jpg']
+
+intf = gr.Interface(fn = classify_image, inputs = image, outputs = label, examples = examples)
+intf.launch(inline=False, share=True)
diff --git a/donut.jpg b/donut.jpg
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diff --git a/model.ipynb b/model.ipynb
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--- /dev/null
+++ b/model.ipynb
@@ -0,0 +1,571 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "87d59ec1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from duckduckgo_search import ddg_images\n",
+    "from fastcore.all import *\n",
+    "def search_images(term, max_images=30):\n",
+    "    print(f\"Searching for '{term}'\")\n",
+    "    return L(ddg_images(term, max_results=max_images)).itemgot('image')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "6ac770e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fastdownload import download_url\n",
+    "#from fastai.vision.all import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "4e36b9c9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Searching for 'donut photo'\n",
+      "Searching for 'donut sun photo'\n",
+      "Searching for 'donut shade photo'\n",
+      "Searching for 'scone photo'\n",
+      "Searching for 'scone sun photo'\n",
+      "Searching for 'scone shade photo'\n"
+     ]
+    }
+   ],
+   "source": [
+    "searches = 'donut','scone'\n",
+    "path = Path('donut_or_not')\n",
+    "from time import sleep\n",
+    "\n",
+    "for o in searches:\n",
+    "    dest = (path/o)\n",
+    "    dest.mkdir(exist_ok=True, parents=True)\n",
+    "    download_images(dest, urls=search_images(f'{o} photo'))\n",
+    "    sleep(10)  # Pause between searches to avoid over-loading server\n",
+    "    download_images(dest, urls=search_images(f'{o} sun photo'))\n",
+    "    sleep(10)\n",
+    "    download_images(dest, urls=search_images(f'{o} shade photo'))\n",
+    "    sleep(10)\n",
+    "    resize_images(path/o, max_size=400, dest=path/o)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "d51b4218",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "failed = verify_images(get_image_files(path))\n",
+    "failed\n",
+    "failed.map(Path.unlink)\n",
+    "len(failed)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "0cfa1c6d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dls = DataBlock(\n",
+    "    blocks=(ImageBlock, CategoryBlock), \n",
+    "    get_items=get_image_files, \n",
+    "    splitter=RandomSplitter(valid_pct=0.2, seed=42),\n",
+    "    get_y=parent_label,\n",
+    "    item_tfms=[Resize(192, method='squish')]\n",
+    ").dataloaders(path, bs=32)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "f6828f44",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.\n",
+      "  warnings.warn(\n",
+      "/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n",
+      "  warnings.warn(msg)\n",
+      "Downloading: \"https://download.pytorch.org/models/resnet18-f37072fd.pth\" to /home/yigewu/.cache/torch/hub/checkpoints/resnet18-f37072fd.pth\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d149e2258a546869d7c7c9a06442933",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0.00/44.7M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>1.105229</td>\n",
+       "      <td>0.340821</td>\n",
+       "      <td>0.178571</td>\n",
+       "      <td>00:23</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "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": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>0</td>\n",
+       "      <td>0.291610</td>\n",
+       "      <td>0.079422</td>\n",
+       "      <td>0.035714</td>\n",
+       "      <td>00:31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>0.199587</td>\n",
+       "      <td>0.040430</td>\n",
+       "      <td>0.017857</td>\n",
+       "      <td>00:34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <td>2</td>\n",
+       "      <td>0.141043</td>\n",
+       "      <td>0.037486</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>00:32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "learn = vision_learner(dls, resnet18, metrics=error_rate)\n",
+    "learn.fine_tune(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "b80d9503",
+   "metadata": {},
+   "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/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": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16920438763447d7babcf088ec67ce8e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "VBox(children=(Dropdown(options=('donut', 'scone'), value='donut'), Dropdown(options=('Train', 'Valid'), value…"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from fastai.vision.widgets import *\n",
+    "cleaner = ImageClassifierCleaner(learn)\n",
+    "cleaner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "f4f497c3",
+   "metadata": {},
+   "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": [
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: left;\">\n",
+       "      <th>epoch</th>\n",
+       "      <th>train_loss</th>\n",
+       "      <th>valid_loss</th>\n",
+       "      <th>error_rate</th>\n",
+       "      <th>time</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "Caught FileNotFoundError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 302, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 39, in fetch\n    data = next(self.dataset_iter)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 140, in create_batches\n    yield from map(self.do_batch, self.chunkify(res))\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/basics.py\", line 230, in chunked\n    res = list(itertools.islice(it, chunk_sz))\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 155, in do_item\n    try: return self.after_item(self.create_item(s))\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 162, in create_item\n    if self.indexed: return self.dataset[s or 0]\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 455, in __getitem__\n    res = tuple([tl[it] for tl in self.tls])\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 455, in <listcomp>\n    res = tuple([tl[it] for tl in self.tls])\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 414, in __getitem__\n    return self._after_item(res) if is_indexer(idx) else res.map(self._after_item)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 374, in _after_item\n    def _after_item(self, o): return self.tfms(o)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 208, in __call__\n    def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 158, in compose_tfms\n    x = f(x, **kwargs)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 81, in __call__\n    def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 91, in _call\n    return self._do_call(getattr(self, fn), x, **kwargs)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 97, in _do_call\n    return retain_type(f(x, **kwargs), x, ret)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/dispatch.py\", line 120, in __call__\n    return f(*args, **kwargs)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/vision/core.py\", line 123, in create\n    return cls(load_image(fn, **merge(cls._open_args, kwargs)))\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/vision/core.py\", line 98, in load_image\n    im = Image.open(fn)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/PIL/Image.py\", line 3092, in open\n    fp = builtins.open(filename, \"rb\")\nFileNotFoundError: [Errno 2] No such file or directory: '/diskmnt/Projects/Users/yigewu/Deep_Learning2022/pet_test/donut_or_not/scone/24f75cc7-6e8a-44e3-80bc-8c5132d2ec30.jpg'\n",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [15], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m cleaner\u001b[38;5;241m.\u001b[39mdelete(): cleaner\u001b[38;5;241m.\u001b[39mfns[idx]\u001b[38;5;241m.\u001b[39munlink()\n\u001b[0;32m----> 2\u001b[0m \u001b[43mlearn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfine_tune\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/callback/schedule.py:165\u001b[0m, in \u001b[0;36mfine_tune\u001b[0;34m(self, epochs, base_lr, freeze_epochs, lr_mult, pct_start, div, **kwargs)\u001b[0m\n\u001b[1;32m    163\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFine tune with `Learner.freeze` for `freeze_epochs`, then with `Learner.unfreeze` for `epochs`, using discriminative LR.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    164\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfreeze()\n\u001b[0;32m--> 165\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_one_cycle\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfreeze_epochs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mslice\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mbase_lr\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpct_start\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.99\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    166\u001b[0m base_lr \u001b[38;5;241m/\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m2\u001b[39m\n\u001b[1;32m    167\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munfreeze()\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/callback/schedule.py:119\u001b[0m, in \u001b[0;36mfit_one_cycle\u001b[0;34m(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt, start_epoch)\u001b[0m\n\u001b[1;32m    116\u001b[0m lr_max \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([h[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m h \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mopt\u001b[38;5;241m.\u001b[39mhypers])\n\u001b[1;32m    117\u001b[0m scheds \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlr\u001b[39m\u001b[38;5;124m'\u001b[39m: combined_cos(pct_start, lr_max\u001b[38;5;241m/\u001b[39mdiv, lr_max, lr_max\u001b[38;5;241m/\u001b[39mdiv_final),\n\u001b[1;32m    118\u001b[0m           \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmom\u001b[39m\u001b[38;5;124m'\u001b[39m: combined_cos(pct_start, \u001b[38;5;241m*\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mmoms \u001b[38;5;28;01mif\u001b[39;00m moms \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m moms))}\n\u001b[0;32m--> 119\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn_epoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcbs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mParamScheduler\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscheds\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43mL\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcbs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreset_opt\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mreset_opt\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwd\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwd\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstart_epoch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstart_epoch\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:256\u001b[0m, in \u001b[0;36mLearner.fit\u001b[0;34m(self, n_epoch, lr, wd, cbs, reset_opt, start_epoch)\u001b[0m\n\u001b[1;32m    254\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mopt\u001b[38;5;241m.\u001b[39mset_hypers(lr\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlr \u001b[38;5;28;01mif\u001b[39;00m lr \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m lr)\n\u001b[1;32m    255\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_epoch \u001b[38;5;241m=\u001b[39m n_epoch\n\u001b[0;32m--> 256\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_events\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_fit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfit\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelFitException\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_end_cleanup\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:193\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_with_events\u001b[39m(\u001b[38;5;28mself\u001b[39m, f, event_type, ex, final\u001b[38;5;241m=\u001b[39mnoop):\n\u001b[0;32m--> 193\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m);  \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    194\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    195\u001b[0m     \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m);  final()\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:245\u001b[0m, in \u001b[0;36mLearner._do_fit\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    243\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m epoch \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_epoch):\n\u001b[1;32m    244\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mepoch\u001b[38;5;241m=\u001b[39mepoch\n\u001b[0;32m--> 245\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_events\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_epoch\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mepoch\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelEpochException\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:193\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_with_events\u001b[39m(\u001b[38;5;28mself\u001b[39m, f, event_type, ex, final\u001b[38;5;241m=\u001b[39mnoop):\n\u001b[0;32m--> 193\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m);  \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    194\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    195\u001b[0m     \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m);  final()\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:240\u001b[0m, in \u001b[0;36mLearner._do_epoch\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    238\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_do_epoch\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    239\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_do_epoch_train()\n\u001b[0;32m--> 240\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_do_epoch_validate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:236\u001b[0m, in \u001b[0;36mLearner._do_epoch_validate\u001b[0;34m(self, ds_idx, dl)\u001b[0m\n\u001b[1;32m    234\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m dl \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: dl \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdls[ds_idx]\n\u001b[1;32m    235\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl \u001b[38;5;241m=\u001b[39m dl\n\u001b[0;32m--> 236\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m torch\u001b[38;5;241m.\u001b[39mno_grad(): \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_with_events\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mall_batches\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mvalidate\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mCancelValidException\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:193\u001b[0m, in \u001b[0;36mLearner._with_events\u001b[0;34m(self, f, event_type, ex, final)\u001b[0m\n\u001b[1;32m    192\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_with_events\u001b[39m(\u001b[38;5;28mself\u001b[39m, f, event_type, ex, final\u001b[38;5;241m=\u001b[39mnoop):\n\u001b[0;32m--> 193\u001b[0m     \u001b[38;5;28;01mtry\u001b[39;00m: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbefore_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m);  \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    194\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m ex: \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_cancel_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m    195\u001b[0m     \u001b[38;5;28mself\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mafter_\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mevent_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m);  final()\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/learner.py:199\u001b[0m, in \u001b[0;36mLearner.all_batches\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mall_batches\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m    198\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_iter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl)\n\u001b[0;32m--> 199\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m o \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdl): \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mone_batch(\u001b[38;5;241m*\u001b[39mo)\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py:129\u001b[0m, in \u001b[0;36mDataLoader.__iter__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    127\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbefore_iter()\n\u001b[1;32m    128\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m__idxs\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_idxs() \u001b[38;5;66;03m# called in context of main process (not workers/subprocesses)\u001b[39;00m\n\u001b[0;32m--> 129\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m b \u001b[38;5;129;01min\u001b[39;00m _loaders[\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfake_l\u001b[38;5;241m.\u001b[39mnum_workers\u001b[38;5;241m==\u001b[39m\u001b[38;5;241m0\u001b[39m](\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfake_l):\n\u001b[1;32m    130\u001b[0m     \u001b[38;5;66;03m# pin_memory causes tuples to be converted to lists, so convert them back to tuples\u001b[39;00m\n\u001b[1;32m    131\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpin_memory \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mtype\u001b[39m(b) \u001b[38;5;241m==\u001b[39m \u001b[38;5;28mlist\u001b[39m: b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtuple\u001b[39m(b)\n\u001b[1;32m    132\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m: b \u001b[38;5;241m=\u001b[39m to_device(b, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdevice)\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/dataloader.py:681\u001b[0m, in \u001b[0;36m_BaseDataLoaderIter.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    678\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_sampler_iter \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    679\u001b[0m     \u001b[38;5;66;03m# TODO(https://github.com/pytorch/pytorch/issues/76750)\u001b[39;00m\n\u001b[1;32m    680\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reset()  \u001b[38;5;66;03m# type: ignore[call-arg]\u001b[39;00m\n\u001b[0;32m--> 681\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_next_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    682\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m    683\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_kind \u001b[38;5;241m==\u001b[39m _DatasetKind\u001b[38;5;241m.\u001b[39mIterable \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    684\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \\\n\u001b[1;32m    685\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_num_yielded \u001b[38;5;241m>\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_IterableDataset_len_called:\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1376\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._next_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1374\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1375\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_task_info[idx]\n\u001b[0;32m-> 1376\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_process_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdata\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1402\u001b[0m, in \u001b[0;36m_MultiProcessingDataLoaderIter._process_data\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m   1400\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_try_put_index()\n\u001b[1;32m   1401\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(data, ExceptionWrapper):\n\u001b[0;32m-> 1402\u001b[0m     \u001b[43mdata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1403\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m data\n",
+      "File \u001b[0;32m/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/_utils.py:461\u001b[0m, in \u001b[0;36mExceptionWrapper.reraise\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    457\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m:\n\u001b[1;32m    458\u001b[0m     \u001b[38;5;66;03m# If the exception takes multiple arguments, don't try to\u001b[39;00m\n\u001b[1;32m    459\u001b[0m     \u001b[38;5;66;03m# instantiate since we don't know how to\u001b[39;00m\n\u001b[1;32m    460\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(msg) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n\u001b[0;32m--> 461\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception\n",
+      "\u001b[0;31mFileNotFoundError\u001b[0m: Caught FileNotFoundError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 302, in _worker_loop\n    data = fetcher.fetch(index)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 39, in fetch\n    data = next(self.dataset_iter)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 140, in create_batches\n    yield from map(self.do_batch, self.chunkify(res))\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/basics.py\", line 230, in chunked\n    res = list(itertools.islice(it, chunk_sz))\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 155, in do_item\n    try: return self.after_item(self.create_item(s))\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/load.py\", line 162, in create_item\n    if self.indexed: return self.dataset[s or 0]\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 455, in __getitem__\n    res = tuple([tl[it] for tl in self.tls])\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 455, in <listcomp>\n    res = tuple([tl[it] for tl in self.tls])\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 414, in __getitem__\n    return self._after_item(res) if is_indexer(idx) else res.map(self._after_item)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/data/core.py\", line 374, in _after_item\n    def _after_item(self, o): return self.tfms(o)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 208, in __call__\n    def __call__(self, o): return compose_tfms(o, tfms=self.fs, split_idx=self.split_idx)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 158, in compose_tfms\n    x = f(x, **kwargs)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 81, in __call__\n    def __call__(self, x, **kwargs): return self._call('encodes', x, **kwargs)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 91, in _call\n    return self._do_call(getattr(self, fn), x, **kwargs)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/transform.py\", line 97, in _do_call\n    return retain_type(f(x, **kwargs), x, ret)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastcore/dispatch.py\", line 120, in __call__\n    return f(*args, **kwargs)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/vision/core.py\", line 123, in create\n    return cls(load_image(fn, **merge(cls._open_args, kwargs)))\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/fastai/vision/core.py\", line 98, in load_image\n    im = Image.open(fn)\n  File \"/diskmnt/Projects/Users/yigewu/tools/miniconda3/envs/deep_learning/lib/python3.10/site-packages/PIL/Image.py\", line 3092, in open\n    fp = builtins.open(filename, \"rb\")\nFileNotFoundError: [Errno 2] No such file or directory: '/diskmnt/Projects/Users/yigewu/Deep_Learning2022/pet_test/donut_or_not/scone/24f75cc7-6e8a-44e3-80bc-8c5132d2ec30.jpg'\n"
+     ]
+    }
+   ],
+   "source": [
+    "for idx in cleaner.delete(): cleaner.fns[idx].unlink()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "6cf656c1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a: donut.\n",
+      "Probability it's a donut: 0.9999\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_donut,_,probs = learn.predict(PILImage.create('donut.jpg'))\n",
+    "print(f\"This is a: {is_donut}.\")\n",
+    "print(f\"Probability it's a donut: {probs[0]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c6e7eb32",
+   "metadata": {},
+   "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"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a: scone.\n",
+      "Probability it's a donut: 0.0000\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_donut,_,probs = learn.predict(PILImage.create('scone.jpg'))\n",
+    "print(f\"This is a: {is_donut}.\")\n",
+    "print(f\"Probability it's a donut: {probs[0]:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "f6e944f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.export('donut_model2.pkl')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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