update from

app.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: model.ipynb.
 
 # %% auto 0
-__all__ = ['learn_inf', 'categories', 'image', 'label', 'examples', 'enable_queue', 'intf', 'classify_image']
+__all__ = ['plt', 'categories', 'image', 'label', 'examples', 'enable_queue', 'intf', 'classify_image']
 
 # %% model.ipynb 3
 #libraries
@@ -25,11 +25,11 @@ fastbook.setup_book()
 
 """from pathlib import Path"""
 """
-plt = os.name
+plt = os.platform
 print(plt)
 
 if plt == 'Posix': pathlib.PosixPath = pathlib.PosixPath
-if plt == 'Windows': pathlib.WindowsPath = pathlib.PosixPath 
+if plt == 'Windows': pathlib.PosixPath =  pathlib.WindowsPath
 posix_backup = pathlib.PurePosixPath
 try:
     pathlib.PosixPath = pathlib.WindowsPath
@@ -58,13 +58,23 @@ def set_posix_windows():
 """
 
 
-# %% model.ipynb 50
+# %% model.ipynb 51
 """with open(os.path.join('./export.pkl'), 'rb') as f:
     f.read()"""
-learn_inf = load_learner(os.path.join('./export.pkl'), 'rb')
+import pathlib
+plt = platform.system()
+print(plt)
+if plt == 'Windows':
+    pathlib.PosixPath =  pathlib.WindowsPath
+    learn_inf = load_learner(os.path.join('./export.pkl'), 'rb')
+else:
+    pathlib.PosixPath = pathlib.PosixPath
+    learn_inf = load_learner('export.pkl')
+
+
 #__model = pickle.load(open(os.path.join('./export.pkl'), 'rb'))
 
-# %% model.ipynb 54
+# %% model.ipynb 55
 categories = ('car','truck','toy car')
 
 def classify_image(img):
@@ -72,7 +82,7 @@ def classify_image(img):
     return dict(zip(categories,map(float,probs)))
 
 
-# %% model.ipynb 55
+# %% model.ipynb 56
 image = gr.inputs.Image(shape=(224,224)) #resize uploaded image -- good to align with your original resizing
 label = gr.outputs.Label(num_top_classes=3)
 examples = ['./images/car.jpg']

export.pkl

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:8786c3954eee04d919dafa21689132a2df7a017e1b53f4d6a0348fa0e32f062c
-size 46973327
+oid sha256:a55b15402361aff5d891839248cc794d1f2128a81fce8bada258f56fb757dd4c
+size 46973391

model.ipynb

@@ -71,11 +71,11 @@
     "\n",
     "\"\"\"from pathlib import Path\"\"\"\n",
     "\"\"\"\n",
-    "plt = os.name\n",
+    "plt = os.platform\n",
     "print(plt)\n",
     "\n",
     "if plt == 'Posix': pathlib.PosixPath = pathlib.PosixPath\n",
-    "if plt == 'Windows': pathlib.WindowsPath = pathlib.PosixPath \n",
+    "if plt == 'Windows': pathlib.PosixPath =  pathlib.WindowsPath\n",
     "posix_backup = pathlib.PurePosixPath\n",
     "try:\n",
     "    pathlib.PosixPath = pathlib.WindowsPath\n",
@@ -104,6 +104,24 @@
     "\"\"\"\n"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Windows\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt = platform.system()\n",
+    "print(plt)"
+   ]
+  },
   {
    "attachments": {},
    "cell_type": "markdown",
@@ -1247,7 +1265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1267,14 +1285,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 50,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Windows\n"
+     ]
+    }
+   ],
    "source": [
     "#|export \n",
     "\"\"\"with open(os.path.join('./export.pkl'), 'rb') as f:\n",
     "    f.read()\"\"\"\n",
-    "learn_inf = load_learner(os.path.join('./export.pkl'), 'rb')\n",
+    "import pathlib\n",
+    "plt = platform.system()\n",
+    "print(plt)\n",
+    "if plt == 'Windows':\n",
+    "    pathlib.PosixPath =  pathlib.WindowsPath\n",
+    "    learn_inf = load_learner(os.path.join('./export.pkl'), 'rb')\n",
+    "else:\n",
+    "    pathlib.PosixPath = pathlib.PosixPath\n",
+    "    learn_inf = load_learner('export.pkl')\n",
+    "\n",
+    "\n",
     "#__model = pickle.load(open(os.path.join('./export.pkl'), 'rb'))"
    ]
   },
@@ -1288,7 +1324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [
     {
@@ -1334,7 +1370,7 @@
        "('car', TensorBase(0), TensorBase([9.9987e-01, 3.7913e-06, 1.2237e-04]))"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1354,7 +1390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1368,7 +1404,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -1391,7 +1427,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running on local URL:  http://127.0.0.1:7860\n",
+      "Running on local URL:  http://127.0.0.1:7862\n",
       "\n",
       "Could not create share link, please check your internet connection.\n"
      ]
@@ -1400,7 +1436,7 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 36,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1418,7 +1454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {