final script

app.py

@@ -1,7 +1,23 @@
+#|export
+from fastai.vision.all import *
 import gradio as gr
+from fastbook import *
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_cat(x): return x[0].isupper() 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+#|export
+learn = load_learner('model.pkl')
+
+#|export
+categories = ('Dog', 'Cat')
+
+def classify_image(im):
+  pred, idx, probs = learn.predict(im)
+  return dict(zip(categories, map(float, probs)))
+
+#|export
+categories = ('Dog', 'Cat')
+
+def classify_image(im):
+  pred, idx, probs = learn.predict(im)
+  return dict(zip(categories, map(float, probs)))

cat_vs_dog_app.ipynb

@@ -0,0 +1,62 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "663730d7",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'gradio'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Input \u001b[1;32mIn [3]\u001b[0m, in \u001b[0;36m<cell line: 3>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m#|\u001b[39;00m\n\u001b[0;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastai\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvision\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mall\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;241m*\u001b[39m\n\u001b[1;32m----> 3\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mgradio\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mgr\u001b[39;00m\n",
+      "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'gradio'"
+     ]
+    }
+   ],
+   "source": [
+    "#|\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr\n",
+    "\n",
+    "def is_cat(x): return x[0].isupper() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "25d8b577",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "img = PILImage.create('dog.jpg')\n",
+    "img"
+   ]
+  }
+ ],
+ "metadata": {
+  "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
+}

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1c09d8f778f89c9eb588eefd9a5ba094eb163cd3ea708a4ea567a83e924f149f
+size 47059947