update:

.ipynb_checkpoints/app-checkpoint.ipynb

@@ -109,7 +109,8 @@
    },
    "outputs": [],
    "source": [
-    "learna = load_learner(filename[0].name)\n"
+    "#|export\n",
+    "learn = load_learner(filename[0].name)"
    ]
   },
   {
@@ -146,23 +147,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "634686dd-4306-4888-8792-699d95f2782c",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'learn' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "File \u001b[0;32m<timed eval>:1\u001b[0m\n",
-      "Cell \u001b[0;32mIn[37], line 6\u001b[0m, in \u001b[0;36mclassify_image\u001b[0;34m(img)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclassify_image\u001b[39m(img):\n\u001b[0;32m----> 6\u001b[0m     pred,idx,probs \u001b[38;5;241m=\u001b[39m \u001b[43mlearn\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(img)\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(searches, \u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28mfloat\u001b[39m,probs)))\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'learn' is not defined"
-     ]
-    }
-   ],
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "%time classify_image(img)"
    ]
@@ -211,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 41,
    "id": "5f14dab3-e6c3-4768-a8e0-8b7355c50929",
    "metadata": {},
    "outputs": [],
@@ -221,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 42,
    "id": "1998fe76-7137-4f3a-b1b2-69f202bc0f49",
    "metadata": {},
    "outputs": [],

app.ipynb

@@ -109,7 +109,8 @@
    },
    "outputs": [],
    "source": [
-    "learna = load_learner(filename[0].name)\n"
+    "#|export\n",
+    "learn = load_learner(filename[0].name)"
    ]
   },
   {
@@ -146,23 +147,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": null,
    "id": "634686dd-4306-4888-8792-699d95f2782c",
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name 'learn' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "File \u001b[0;32m<timed eval>:1\u001b[0m\n",
-      "Cell \u001b[0;32mIn[37], line 6\u001b[0m, in \u001b[0;36mclassify_image\u001b[0;34m(img)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mclassify_image\u001b[39m(img):\n\u001b[0;32m----> 6\u001b[0m     pred,idx,probs \u001b[38;5;241m=\u001b[39m \u001b[43mlearn\u001b[49m\u001b[38;5;241m.\u001b[39mpredict(img)\n\u001b[1;32m      7\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(searches, \u001b[38;5;28mmap\u001b[39m(\u001b[38;5;28mfloat\u001b[39m,probs)))\n",
-      "\u001b[0;31mNameError\u001b[0m: name 'learn' is not defined"
-     ]
-    }
-   ],
+   "metadata": {
+    "editable": true,
+    "slideshow": {
+     "slide_type": ""
+    },
+    "tags": []
+   },
+   "outputs": [],
    "source": [
     "%time classify_image(img)"
    ]

app.py

@@ -1,7 +1,7 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['img', 'path', 'filename', 'searches', 'title', 'description', 'examples', 'demo', 'classify_image']
+__all__ = ['img', 'path', 'filename', 'learn', 'searches', 'title', 'description', 'examples', 'demo', 'classify_image']
 
 # %% app.ipynb 1
 from fastbook import search_images_ddg
@@ -20,6 +20,9 @@ path = Path()
 filename = path.ls(file_exts='.pkl')
 filename[0].name
 
+# %% app.ipynb 6
+learn = load_learner(filename[0].name)
+
 # %% app.ipynb 8
 searches = ("grizzly bears","black bears","teddy bears")