try autogen app.py with nbdev

app-manual.py

@@ -0,0 +1,23 @@
+from fastai.vision.all import *
+import gradio as gr
+
+# necessary for load_learner not to complain about missing functions
+def is_cat(x): return x[0].isupper() 
+
+learn = load_learner('model.pkl')
+
+categories = ('Dog ', 'Cat')
+
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float,probs)))
+
+inp_img = gr.inputs.Image(shape=(200,200))
+out_label = gr.outputs.Label()
+
+iface = gr.Interface(fn=classify_image,
+                     inputs=inp_img,
+                     outputs=out_label,
+                     title="Pet classifier")
+
+iface.launch(inline=False)

app-nbdev.ipynb

@@ -0,0 +1,197 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "8ceccd3e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp app-nbdev"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "9cc2c7e8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "6cb828e7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "def is_cat(x): return x[0].isupper() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "70a94ed8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "learn = load_learner('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "2856326d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = ('Dog', 'Cat')\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": 15,
+   "id": "a11b5552",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/homebrew/lib/python3.9/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",
+      "/opt/homebrew/lib/python3.9/site-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
+      "  warnings.warn(value)\n",
+      "/opt/homebrew/lib/python3.9/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",
+      "/opt/homebrew/lib/python3.9/site-packages/gradio/deprecation.py:40: UserWarning: The 'type' parameter has been deprecated. Use the Number component instead.\n",
+      "  warnings.warn(value)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "inp_img = gr.inputs.Image(shape=(200,200))\n",
+    "out_label = gr.outputs.Label()\n",
+    "\n",
+    "iface = gr.Interface(fn=classify_image,\n",
+    "                     inputs=inp_img,\n",
+    "                     outputs=out_label,\n",
+    "                     title=\"Pet classifier\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "32e6f831",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7861/\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(<gradio.routes.App at 0x1688dc0d0>, 'http://127.0.0.1:7861/', None)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "iface.launch(inline=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "01c53c53",
+   "metadata": {},
+   "source": [
+    "export"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "40731685",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from nbdev.export import notebook2script"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "ea42582a",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Converted app-nbdev.ipynb.\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = 2\n",
+    "notebook2script('app-nbdev.ipynb')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "78b0ea16",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "2bfab3daf39c717d5b0b70976ea3368fa383d7e036680b30bd721c6f21472435"
+  },
+  "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": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -1,17 +1,25 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: . (unless otherwise specified).
+
+__all__ = ['is_cat', 'learn', 'classify_image', 'categories', 'inp_img', 'out_label', 'iface']
+
+# Cell
 from fastai.vision.all import *
 import gradio as gr
 
-# necessary for load_learner not to complain about missing functions
-def is_cat(x): return x[0].isupper() 
+# Cell
+def is_cat(x): return x[0].isupper()
 
+# Cell
 learn = load_learner('model.pkl')
 
-categories = ('Dog ', 'Cat')
+# Cell
+categories = ('Dog', 'Cat')
 
 def classify_image(img):
     pred,idx,probs = learn.predict(img)
     return dict(zip(categories, map(float,probs)))
 
+# Cell
 inp_img = gr.inputs.Image(shape=(200,200))
 out_label = gr.outputs.Label()
 
@@ -20,4 +28,5 @@ iface = gr.Interface(fn=classify_image,
                      outputs=out_label,
                      title="Pet classifier")
 
-iface.launch(inline=False)
+# Cell
+iface.launch(inline=False)