adding model

app.ipynb

@@ -0,0 +1,157 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "0fa79b4a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "489a7e57",
+   "metadata": {},
+   "source": [
+    "# Dogs v Cats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f0ff3631",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\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": "84312fd2",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im = PILImage.create('dog.jpg')\n",
+    "im.thumbnail((192, 192))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c0e23187",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "learn = load_learner('model.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8c19e825",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d678926b",
+   "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": null,
+   "id": "13fbf93e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a76c0b35",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "image = gr.inputs.Image(shape=(192, 192))\n",
+    "label = gr.outputs.Label()\n",
+    "examples = ['/kaggle/input/dogvcat/dog.jpeg', '/kaggle/input/dogvcat/cat.webp', '/kaggle/input/dogvcat/cat1.jpeg']\n",
+    "\n",
+    "# create gradio interface\n",
+    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "intf.launch(inline=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "465ea051",
+   "metadata": {},
+   "source": [
+    "# export"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5809c2f8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import a package to convert notebook into py file\n",
+    "import nbdev\n",
+    "nbdev.export.nb_export('app.ipynb', 'app')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.x",
+   "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"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "31f2aee4e71d21fbe5cf8b01ff0e069b9275f58929596ceb00d14d90e3e16cd6"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

app.py

@@ -1,7 +1,20 @@
+from fastai.vision.all import *
 import gradio as gr
 
-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()
+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)))
+
+image = gr.inputs.Image(shape=(192, 192))
+label = gr.outputs.Label()
+examples = ['dog.jpeg', 'cat.webp', 'cat1.jpeg']
+
+# create gradio interface
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)