Update model path relative

nbs/app.ipynb

@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 12,
    "id": "39544176-83a1-4bcb-bdc3-c8acb8a2ba84",
    "metadata": {
     "scrolled": true
@@ -44,12 +44,15 @@
    "source": [
     "#|export\n",
     "from fastai.vision.all import *\n",
-    "import gradio as gr"
+    "import gradio as gr\n",
+    "\n",
+    "import pathlib\n",
+    "import os"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 13,
    "id": "38301e4e-11fc-4f1e-909f-58759b8d9e8d",
    "metadata": {
     "scrolled": true
@@ -57,18 +60,29 @@
    "outputs": [],
    "source": [
     "#|export\n",
-    "learn = load_learner('model.pkl')"
+    "\n",
+    "path = pathlib.Path('.')\n",
+    "\n",
+    "try:    \n",
+    "    path = pathlib.Path(__file__).parent.resolve()\n",
+    "except NameError:    \n",
+    "    if 'SPACE_ID' in os.environ:         \n",
+    "         path = pathlib.Path('nbs')  \n",
+    "\n",
+    "model_path = path / 'model.pkl'\n",
+    "\n",
+    "learn = load_learner(model_path)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "id": "905035bc-6dd2-45ba-aee8-b77a5b519a04",
    "metadata": {},
    "outputs": [],
    "source": [
     "#|export\n",
-    "categories = ('Black','Grizzly','Teddy')\n",
+    "categories = learn.dls.vocab\n",
     "\n",
     "def classify_image(img):\n",
     "    pred,idx,probs = learn.predict(img)\n",
@@ -77,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "id": "e77f8a97-e73f-4c3b-a73d-a958689ff1a6",
    "metadata": {},
    "outputs": [
@@ -85,7 +99,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "* Running on local URL:  http://127.0.0.1:7860\n",
+      "* Running on local URL:  http://127.0.0.1:7861\n",
       "\n",
       "To create a public link, set `share=True` in `launch()`.\n"
      ]
@@ -94,41 +108,10 @@
      "data": {
       "text/plain": []
      },
-     "execution_count": 7,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#|export\n",
-    "image = gr.Image(type=\"pil\")\n",
-    "label = gr.Label()\n",
-    "examples = ['black_bear.jpg', 'grizzly_bear.jpg', 'teddy_bear.jpg']\n",
-    "\n",
-    "intf = gr.Interface(\n",
-    "    fn=classify_image,  \n",
-    "    inputs=image,       \n",
-    "    outputs=label,      \n",
-    "    examples=examples\n",
-    ")\n",
-    "\n",
-    "intf.launch(inline=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3a379ae5-65ff-476a-ba6d-3c1edc6878b3",
-   "metadata": {},
-   "source": [
-    "## export python file from the notebook"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "af0ec285-9e53-49f0-be57-12d4e1fc1a3c",
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
       "text/html": [
@@ -204,6 +187,36 @@
      "output_type": "display_data"
     }
    ],
+   "source": [
+    "#|export\n",
+    "image = gr.Image(type=\"pil\")\n",
+    "label = gr.Label()\n",
+    "examples = ['black_bear.jpg', 'grizzly_bear.jpg', 'teddy_bear.jpg']\n",
+    "\n",
+    "intf = gr.Interface(\n",
+    "    fn=classify_image,  \n",
+    "    inputs=image,       \n",
+    "    outputs=label,      \n",
+    "    examples=examples\n",
+    ")\n",
+    "\n",
+    "intf.launch(inline=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a379ae5-65ff-476a-ba6d-3c1edc6878b3",
+   "metadata": {},
+   "source": [
+    "## export python file from the notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "af0ec285-9e53-49f0-be57-12d4e1fc1a3c",
+   "metadata": {},
+   "outputs": [],
    "source": [
     "import nbdev\n",
     "\n",

nbs/app.py

@@ -1,14 +1,29 @@
 # AUTOGENERATED! DO NOT EDIT! File to edit: app.ipynb.
 
 # %% auto 0
-__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+__all__ = ['path', 'model_path', 'learn', 'categories', 'image', 'label', 'examples', 'intf', 'classify_image']
 
 # %% app.ipynb 3
 from fastai.vision.all import *
 import gradio as gr
 
+import pathlib
+import os
+
 # %% app.ipynb 4
-learn = load_learner('model.pkl')
+path = pathlib.Path('.')
+
+try:    
+    path = pathlib.Path(__file__).parent.resolve()
+except NameError:    
+    if 'SPACE_ID' in os.environ:         
+         path = pathlib.Path('nbs')
+   
+
+# Construct the final path to the model
+model_path = path / 'model.pkl'
+
+learn = load_learner('./nbs/model.pkl')
 
 # %% app.ipynb 5
 categories = ('Black','Grizzly','Teddy')