{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0eee85c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| default_exp app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "598c11f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install gradio > /dev/null"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f836217",
   "metadata": {},
   "source": [
    "### Ecommerece Image classification app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76333c52",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "from fastai.vision.all import *\n",
    "import gradio as gr\n",
    "# ['jeans', 'sofa', 'tshirt', 'tv']\n",
    "\n",
    "def is_jeans(x): return x[0].isupper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "09b9fe0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "im = PILImage.create('jeans.jpeg')\n",
    "im.thumbnail((192,192))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ab0e0e6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('export.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "97559e61",
   "metadata": {},
   "outputs": [],
   "source": [
    "%time\n",
    "learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4e112978",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "categories = ('Jeans', 'Sofa', 'Tshirt', 'Tv')\n",
    "\n",
    "def classify_images(img):\n",
    "    pred, idx, probs = learn.predict(img)\n",
    "    return dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d8d09015",
   "metadata": {},
   "outputs": [],
   "source": [
    "classify_images(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8530628e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "image = gr.components.Image(shape=(192,192))\n",
    "label = gr.components.Label()\n",
    "examples = ['jeans.jpeg', 'sofa.jpeg', 'tshirt.jpg', 'tv.jpeg']\n",
    "\n",
    "intf = gr.Interface(fn=classify_images, inputs=image, outputs=label, examples=examples)\n",
    "intf.launch(inline=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9487e868",
   "metadata": {},
   "outputs": [],
   "source": [
    "import nbdev\n",
    "nbdev.export.nb_export('app.ipynb', './')\n",
    "print('Export successful')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a97c4bb5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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