{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|default_exp app \n",
    "\n",
    "# This notebook uses nbdev (https://github.com/fastai/nbdev/) to use these \n",
    "# special comments starting with `#|` and create the `app.py` output."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tree leaf classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "from fastai.vision.all import *\n",
    "import gradio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "learn = load_learner('model.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = PILImage.create('images/ash.jpg')\n",
    "im.thumbnail((224, 224))\n",
    "im"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%time learn.predict(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learn.dls.vocab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "\n",
    "categories = ('ash', 'chestnut', 'ginkgo biloba', 'silver maple', 'willow oak')\n",
    "\n",
    "def classify_image(img):\n",
    "  pred, idx, probs = learn.predict(img)\n",
    "  # Change each probability to a float, since Gradio doesn't support Tensors or NumPy\n",
    "  return dict(zip(categories, map(float, probs)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "classify_image(im)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# gradio interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#|export\n",
    "image = gradio.Image(shape=(192, 192))\n",
    "label = gradio.Label()\n",
    "examples = ['images/ash.jpg', 'images/chestnut.jpg', 'images/ginkgo_biloba.jpg',\n",
    "            'images/silver_maple.jpg', 'images/willow_oak.jpg']\n",
    "# More useful args\n",
    "title = \"Tree leaf classifier demo\"\n",
    "description = \"A tree leaf classifier demo, trained on images downloaded from DuckDuckGo. Created as a demo of HuggingFace Spaces and Gradio.\"\n",
    "article = \"<p>From this blog post: <a href='https://briansigafoos.com/ml-quick-start' target='_blank'>Machine Learning quick start by Brian Sigafoos</a></p>\"\n",
    "interpretation = 'default'\n",
    "\n",
    "interface = gradio.Interface(fn=classify_image, inputs=image, outputs=label,\n",
    "                             examples=examples, title=title, description=description,\n",
    "                             article=article, interpretation=interpretation)\n",
    "interface.launch(inline=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# export"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nbdev.export import nb_export\n",
    "\n",
    "nb_export('app.ipynb', './')\n",
    "print('Export successful')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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