{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "from fastai import *\n",
    "from fastai.vision import *\n",
    "from fastai.widgets import DatasetFormatter, ImageCleaner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fastai offers several widgets to support the workflow of a deep learning practitioner. The purpose of the widgets are to help you organize, clean, and prepare your data for your model. Widgets are separated by data type."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DatasetFormatter\n",
    "The [`DatasetFormatter`](/widgets.image_cleaner.html#DatasetFormatter) class prepares your image dataset for widgets by returning a formatted [`DatasetTfm`](/vision.data.html#DatasetTfm) based on the [`DatasetType`](/basic_data.html#DatasetType) specified. Use `from_toplosses` to grab the most problematic images directly from your learner. Optionally, you can restrict the formatted dataset returned to `n_imgs`.\n",
    "\n",
    "Specify the [`DatasetType`](/basic_data.html#DatasetType) you'd like to process:\n",
    "- DatasetType.Train\n",
    "- DatasetType.Valid\n",
    "- DatasetType.Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = untar_data(URLs.MNIST_SAMPLE)\n",
    "data = ImageDataBunch.from_folder(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "learner = create_cnn(data, models.resnet18, metrics=[accuracy])\n",
    "ds, idxs = DatasetFormatter().from_toplosses(learner, ds_type=DatasetType.Valid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ImageCleaner"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[`ImageDeleter`](/widgets.image_cleaner.html#ImageDeleter) is for cleaning up images that don't belong in your dataset. It renders images in a row and gives you the opportunity to delete the file from your file system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe646686dd3449e38c6de102f98b04e5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(VBox(children=(Image(value=b'\\xff\\xd8\\xff\\xe0\\x00\\x10JFIF\\x00\\x01\\x01\\x01\\x00d\\x00d\\x00\\x00\\xff…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9252a3eeab4b4f0fbc8a3a5259339aae",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='primary', description='Next Batch', layout=Layout(width='auto'), style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<fastai.widgets.image_cleaner.ImageCleaner at 0x7fe7f1322ac8>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ImageCleaner(ds, idxs)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}