create gradio with cat model

app.py

@@ -1,9 +1,31 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: test.ipynb.
+
+# %% auto 0
+__all__ = ['learn', 'categories', 'image', 'label', 'examples', 'intf', 'is_cat', 'classify_image']
+
+# %% test.ipynb 1
+from fastai.vision.all import *
 import gradio as gr
 
 
-def greet(name):
-    return "Hello " + name + "!!"
+def is_cat(x): return x[0].isupper()
+
+
+# %% test.ipynb 3
+learn = load_learner('model.pkl')
+
+# %% test.ipynb 5
+categories = 'Dog', 'Cat'
+
+
+def classify_image(img):
+    pred, idx, probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
 
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# %% test.ipynb 7
+image = gr.components.Image(shape=(192, 192))
+label = gr.components.Label()
+examples = ['dog.jpeg', 'cat.jpeg', 'dunno.jpeg']
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

app/app.py

@@ -0,0 +1,17 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../test.ipynb.
+
+# %% auto 0
+__all__ = ['categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% ../test.ipynb 5
+categories = 'Dog', 'Cat'
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% ../test.ipynb 7
+image = gr.components.Image(shape=(192,192))
+label = gr.components.Label()
+examples = ['dog.jpeg', 'cat.jpeg', 'dunno.jpeg']
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

ch2/app.py

@@ -0,0 +1,17 @@
+# AUTOGENERATED! DO NOT EDIT! File to edit: ../test.ipynb.
+
+# %% auto 0
+__all__ = ['categories', 'image', 'label', 'examples', 'intf', 'classify_image']
+
+# %% ../test.ipynb 5
+categories = 'Dog', 'Cat'
+def classify_image(img):
+    pred,idx,probs = learn.predict(img)
+    return dict(zip(categories, map(float, probs)))
+
+# %% ../test.ipynb 7
+image = gr.components.Image(shape=(192,192))
+label = gr.components.Label()
+examples = ['dog.jpeg', 'cat.jpeg', 'dunno.jpeg']
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)

test.ipynb

@@ -0,0 +1,194 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "outputs": [],
+   "source": [
+    "#|default_exp app"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-24T10:37:08.913579Z",
+     "end_time": "2023-04-24T10:37:08.916434Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "from fastai.vision.all import *\n",
+    "import gradio as gr\n",
+    "\n",
+    "def is_cat(x): return x[0].isupper()\n"
+   ],
+   "metadata": {
+    "collapsed": false
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "im = PILImage.create('dog.jpeg')\n",
+    "im.thumbnail((192,192))\n",
+    "im\n"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-24T10:17:24.640902Z",
+     "end_time": "2023-04-24T10:17:24.720177Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'load_learner' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
+      "\u001B[0;31mNameError\u001B[0m                                 Traceback (most recent call last)",
+      "Cell \u001B[0;32mIn[15], line 2\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[38;5;66;03m#|export\u001B[39;00m\n\u001B[0;32m----> 2\u001B[0m learn \u001B[38;5;241m=\u001B[39m \u001B[43mload_learner\u001B[49m(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mmodel.pkl\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
+      "\u001B[0;31mNameError\u001B[0m: name 'load_learner' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "#|export\n",
+    "learn = load_learner('model.pkl')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-24T10:17:40.853831Z",
+     "end_time": "2023-04-24T10:17:40.916389Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "%time learn.predict(im)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-24T10:18:41.130215Z",
+     "end_time": "2023-04-24T10:18:41.263864Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "categories = 'Dog', 'Cat'\n",
+    "def classify_image(img):\n",
+    "    pred,idx,probs = learn.predict(img)\n",
+    "    return dict(zip(categories, map(float, probs)))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-24T10:24:12.712802Z",
+     "end_time": "2023-04-24T10:24:12.716475Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "classify_image(im)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-24T10:24:13.986686Z",
+     "end_time": "2023-04-24T10:24:14.038054Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [
+    "#|export\n",
+    "image = gr.components.Image(shape=(192,192))\n",
+    "label = gr.components.Label()\n",
+    "examples = ['dog.jpeg', 'cat.jpeg', 'dunno.jpeg']\n",
+    "intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)\n",
+    "intf.launch(inline=False)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-24T10:29:07.009173Z",
+     "end_time": "2023-04-24T10:29:07.451301Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "outputs": [],
+   "source": [
+    "import nbdev\n",
+    "nbdev.export.nb_export('test.ipynb', '')"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "ExecuteTime": {
+     "start_time": "2023-04-24T10:40:21.436209Z",
+     "end_time": "2023-04-24T10:40:21.493462Z"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "outputs": [],
+   "source": [],
+   "metadata": {
+    "collapsed": false
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}