yey ?

app.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: notebook.ipynb.
 
 # %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'intf', 'classify_image']
+__all__ = ['learn', 'categories', 'examples', 'image', 'label', 'intf', 'classify_image']
 
 # %% notebook.ipynb 2
 from fastai.vision.all import *
@@ -9,7 +9,7 @@ import gradio as gr
 import timm
 
 # %% notebook.ipynb 3
-learn = load_learner('cycle-model.pkl')
+learn = load_learner('model.pkl')
 
 # %% notebook.ipynb 4
 categories = learn.dls.vocab
@@ -18,7 +18,10 @@ def classify_image(img):
     pred,idx,probs = learn.predict(img)
     return dict(zip(categories, map(float,probs)))
 
-# %% notebook.ipynb 6
+# %% notebook.ipynb 5
+examples = ['images/unicycle.jpeg', 'images/bicycle.jpeg', 'images/tricycle.png']
+
+# %% notebook.ipynb 7
 image = gr.Image()
 label = gr.Label()
 

cycle-model.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:772d8b028992a9b7b0257307849a6b3823f44edefed7d7eaaf86ad42851bc70b
-size 46961195

notebook.ipynb

@@ -44,18 +44,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "id": "f0218bf1-1836-4d7a-8d47-33584471f28b",
    "metadata": {},
    "outputs": [],
    "source": [
     "#|export\n",
-    "learn = load_learner('cycle-model.pkl')"
+    "learn = load_learner('model.pkl')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "id": "168ac2e4-f83b-4ce0-8f23-00999eb5d556",
    "metadata": {},
    "outputs": [],
@@ -70,9 +70,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "id": "d343a0d3-40fd-4502-a86b-cb3bac9fdf7f",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "examples = ['images/unicycle.jpeg', 'images/bicycle.jpeg', 'images/tricycle.png']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "645eb0ee-b7e5-4ec4-a42e-9f43a163a3a5",
+   "metadata": {},
    "outputs": [
     {
      "data": {
@@ -163,60 +174,26 @@
      ]
     },
     {
-     "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": [
-      "images/tricycle.png is a tricycle\n"
+     "ename": "UnidentifiedImageError",
+     "evalue": "cannot identify image file 'images/tricycle.png'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnidentifiedImageError\u001b[0m                    Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m example \u001b[38;5;129;01min\u001b[39;00m examples:\n\u001b[0;32m----> 2\u001b[0m     image \u001b[38;5;241m=\u001b[39m PILImage\u001b[38;5;241m.\u001b[39mcreate(example)\n\u001b[1;32m      3\u001b[0m     res_dict \u001b[38;5;241m=\u001b[39m classify_image(image)\n\u001b[1;32m      4\u001b[0m     top_prob_key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(res_dict, key\u001b[38;5;241m=\u001b[39mres_dict\u001b[38;5;241m.\u001b[39mget)\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/fastai/vision/core.py:125\u001b[0m, in \u001b[0;36mPILBase.create\u001b[0;34m(cls, fn, **kwargs)\u001b[0m\n\u001b[1;32m    123\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fn,\u001b[38;5;28mbytes\u001b[39m): fn \u001b[38;5;241m=\u001b[39m io\u001b[38;5;241m.\u001b[39mBytesIO(fn)\n\u001b[1;32m    124\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(fn,Image\u001b[38;5;241m.\u001b[39mImage): \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(fn)\n\u001b[0;32m--> 125\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mcls\u001b[39m(load_image(fn, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mmerge(\u001b[38;5;28mcls\u001b[39m\u001b[38;5;241m.\u001b[39m_open_args, kwargs)))\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/fastai/vision/core.py:98\u001b[0m, in \u001b[0;36mload_image\u001b[0;34m(fn, mode)\u001b[0m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mload_image\u001b[39m(fn, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m):\n\u001b[1;32m     97\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOpen and load a `PIL.Image` and convert to `mode`\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m---> 98\u001b[0m     im \u001b[38;5;241m=\u001b[39m Image\u001b[38;5;241m.\u001b[39mopen(fn)\n\u001b[1;32m     99\u001b[0m     im\u001b[38;5;241m.\u001b[39mload()\n\u001b[1;32m    100\u001b[0m     im \u001b[38;5;241m=\u001b[39m im\u001b[38;5;241m.\u001b[39m_new(im\u001b[38;5;241m.\u001b[39mim)\n",
+      "File \u001b[0;32m~/miniconda3/lib/python3.11/site-packages/PIL/Image.py:3280\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, formats)\u001b[0m\n\u001b[1;32m   3278\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(message)\n\u001b[1;32m   3279\u001b[0m msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcannot identify image file \u001b[39m\u001b[38;5;132;01m%r\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (filename \u001b[38;5;28;01mif\u001b[39;00m filename \u001b[38;5;28;01melse\u001b[39;00m fp)\n\u001b[0;32m-> 3280\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m UnidentifiedImageError(msg)\n",
+      "\u001b[0;31mUnidentifiedImageError\u001b[0m: cannot identify image file 'images/tricycle.png'"
      ]
     }
    ],
    "source": [
-    "# Upload your own images and link them\n",
-    "examples = ['images/unicycle.jpeg', 'images/bicycle.jpeg', 'images/tricycle.png']\n",
-    "\n",
     "for example in examples:\n",
     "    image = PILImage.create(example)\n",
     "    res_dict = classify_image(image)\n",
-    "    top = max(res_dict, key=res_dict.get)\n",
-    "\n",
-    "    print(example + ' is a '+ top)"
+    "    top_prob_key = max(res_dict, key=res_dict.get)\n",
+    "    print(example + ' is a '+ top_prob_key)"
    ]
   },
   {