dog-cat model

app.ipynb

@@ -0,0 +1,133 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Dogs v Cats"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "im = PILImage.create('dog.jpg')\n",
+    "im.thumbnail((192,192))\n",
+    "im"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "import pathlib\n",
+    "temp = pathlib.PosixPath\n",
+    "pathlib.PosixPath = pathlib.WindowsPath\n",
+    "learn = load_learner('model.pkl')\n",
+    "pathlib.PosixPath = temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "learn.predict(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "classify_image(im)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "image = gr.inputs.Image(shape=(192,192))\n",
+    "label = gr.outputs.Label()\n",
+    "examples = ['dog.jpg', 'cat.jpg', 'dunno.jpg']\n",
+    "\n",
+    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "intf.launch(inline=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.8.8"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "793ac646cb1bdfbbf9b49ec8438cf418377ae0464a060bb58045a63a1c103122"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

app.py

@@ -1,7 +1,21 @@
+
+from fastai.vision.all import *
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_cat(x): return x[0].isupper()
+import pathlib
+temp = pathlib.PosixPath
+pathlib.PosixPath = pathlib.WindowsPath
+learn = load_learner('model.pkl')
+pathlib.PosixPath = temp
+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.jpg', 'cat.jpg', 'dunno.jpg']
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)
+
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

app_old.py

@@ -0,0 +1,7 @@
+import gradio as gr
+
+def greet(name):
+    return "Hello " + name + "!!"
+
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:973902c0345a943c47acdfc4b5db1e51c338c0cd314d59f9cd910451841bfe7e
+size 47061483