DTTD_Tutorial_Widgets-D2LAPITeam.ipynb

{
 "cells": [
  {
   "attachments": {
    "IPyWidgetsTutorialImage.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div style=\"max-width:875px; text-align:center;\">\n",
    "    <img src=\"attachment:IPyWidgetsTutorialImage.png\" />\n",
    "</div>\n",
    "\n",
    "<!-- Reference:  https://stackoverflow.com/questions/57930004/jupyter-notebook-position-embedded-image-in-markdown --->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Welcome to IPyWidgets!\n",
    "\n",
    "This DTTD tutorial was created by the MSU D2L Instructor API Team for CMSE 495 Spring 2023.\n",
    "\n",
    "IPyWidgets is a package for Python that allows you to bring interactivity to your Jupyter notebooks!\n",
    "\n",
    "For further reading, the documentation can be found here. \n",
    "https://ipywidgets.readthedocs.io/en/stable/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing IPyWidgets for Use in Jupyter Notebooks\n",
    "\n",
    "IPyWidgets can be imported just like the most common Pyton Libraries that you may familiar with. Check out the cell below for the typical method to import the library. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the ipywidgets packages\n",
    "# we will be able to use \"widgets\" to access it later in the notebook. \n",
    "import ipywidgets as widgets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Importing Other Libraries for the Tutorial\n",
    "\n",
    "We will use a variety of other Python libraries to enhance the functionality of our widgets. These are imported in the cell below. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing common Python libraries for Data Science \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# importing the io package\n",
    "# this will be helpful for our work with the \n",
    "import io"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using IPyWidgets with JupyterLite\n",
    "\n",
    "To use widgets with JupyterLite, you will need to implement the following. \n",
    "\n",
    "(From: https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20List.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Imports for JupyterLite\n",
    "try:\n",
    "    import piplite\n",
    "    await piplite.install(['ipywidgets'])\n",
    "except ImportError:\n",
    "    pass\n",
    "\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Further Installation Instructions\n",
    "\n",
    "Further installation instructions, including integration with JupyterLab, can be found here: https://ipywidgets.readthedocs.io/en/stable/user_install.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Types of Widgets\n",
    "\n",
    "We will cover a variety of widgets in this tutorial. Specifically: Buttons, Sliders, and File Uploads.\n",
    "\n",
    "Here's all of the widgets you can explore!\n",
    "\n",
    "'Accordion',\n",
    " 'BoundedFloatText',\n",
    " 'BoundedIntText',\n",
    " 'Box',\n",
    " 'Button',\n",
    " 'Checkbox',\n",
    " 'ColorPicker',\n",
    " 'Controller',\n",
    " 'ControllerAxis',\n",
    " 'ControllerButton',\n",
    " 'DatePicker',\n",
    " 'Dropdown',\n",
    " 'FloatProgress',\n",
    " 'FloatRangeSlider',\n",
    " 'FloatSlider',\n",
    " 'FloatText',\n",
    " 'HBox',\n",
    " 'HTML',\n",
    " 'HTMLMath',\n",
    " 'Image',\n",
    " 'IntProgress',\n",
    " 'IntRangeSlider',\n",
    " 'IntSlider',\n",
    " 'IntText',\n",
    " 'Label',\n",
    " 'PlaceProxy',\n",
    " 'Play',\n",
    " 'Proxy',\n",
    " 'RadioButtons',\n",
    " 'Select',\n",
    " 'SelectMultiple',\n",
    " 'SelectionSlider',\n",
    " 'Tab',\n",
    " 'Text',\n",
    " 'Textarea',\n",
    " 'ToggleButton',\n",
    " 'ToggleButtons',\n",
    " 'VBox',\n",
    " 'Valid',\n",
    " 'DirectionalLink',\n",
    " and 'Link.'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The full list of widgets with descriptions can also be found here:\n",
    "https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20List.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Button"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Very Basic Button\n",
    "\n",
    "Here's the most simple version of the ipywidget button. You have to add an \"on_click\" action (see below) to add functionality to the button. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80a8b62b6ca1485b9d49e6b70fe6d209",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Button Text', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code to create and save button\n",
    "basic_button = widgets.Button(\n",
    "    description='Button Text') # text on button\n",
    "\n",
    "# code to display button\n",
    "display(basic_button)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More Complex Button\n",
    "\n",
    "Here's where the button becomes more useful. In the following code, we customize the look and feel of the button, as well as add a function associated with the button. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "button = widgets.Button(\n",
    "    # text on button\n",
    "    description='Button Text',\n",
    "    disabled=False,\n",
    "    # style\n",
    "    button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    # button size\n",
    "    layout=widgets.Layout(width='50%', height='100%'),\n",
    "    # what is shown if you hover over the button with your cursor but don't click\n",
    "    tooltip='Description Of What The Button Does',\n",
    "    # little symbol on the button\n",
    "    icon='check' # (FontAwesome names without the `fa-` prefix)\n",
    ")\n",
    "\n",
    "# reference for these kind of nested functions below:\n",
    "#https://github.com/jupyter-widgets/ipywidgets/issues/2103\n",
    "\n",
    "# function to be associated with the button\n",
    "def f(self):\n",
    "    return_funct()\n",
    "# NOTE: this function cannot take inputs: see application example below\n",
    "\n",
    "# assigning our function to the click of the button\n",
    "button.on_click(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80a8b62b6ca1485b9d49e6b70fe6d209",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Button Text', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(basic_button)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sliders\n",
    "\n",
    "Sliders are great for allowing users to adjust input, and select values from a range. IPyWidgets has a variety of slider options, although they are primarily numeric. Below, we will cover how to create a basic float-vbased slider, but an integer-based slider can be created very similarly. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e09b94d916ed4623902047a58651b683",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FloatSlider(value=8.0, description='Try It!', max=15.0, step=0.5)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code to create the widget\n",
    "slider_float = widgets.FloatSlider(\n",
    "    value=8, # starting value of the slider\n",
    "    min=0, # minimum value for slider\n",
    "    max=15.0, # maximum value for the slider\n",
    "    step=0.5, # value the slider can be moved by\n",
    "    description='Try It!', # text accompanying slider\n",
    ")\n",
    "\n",
    "# code to display widget\n",
    "display(slider_float)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## File Upload\n",
    "\n",
    "This widget is particularly useful for data science applications. No more hunting for the paths to your CSVs!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Basic File Upload\n",
    "\n",
    "Here's the most simple version of the ipywidget file upload. This allows you to bring in a local file from your computer. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c70220d0b994e19b3e29523d326f07a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUpload(value={}, accept='.csv', description='File Upload Text', layout=Layout(height='100%', width='50%'),…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# File upload widget\n",
    "input_file_widget = widgets.FileUpload(\n",
    "    # will only accept csv input\n",
    "    accept='.csv',  # Accepted file extension e.g. '.txt', '.pdf', 'image/*', 'image/*,.pdf'\n",
    "    # description on button\n",
    "    description='File Upload Text',\n",
    "    # height and width of button\n",
    "    layout=widgets.Layout(width='50%', height='100%'),\n",
    "    multiple=True  # True to accept multiple files upload else False\n",
    ")\n",
    "\n",
    "# make the widget actually show up\n",
    "display(input_file_widget)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting File Content\n",
    "\n",
    "Setting up the file upload widget is fairly simple, but accessing the uploaded content can be a bit more difficult. The following example will walk through how to read in file content from a .csv as a .csv. \n",
    "\n",
    "In this example, we use the actual file content to read in the csv. You could also use the file name, which can be accessed similarly to the content, with read_csv, but this can get complicated with directories if you aren't careful. \n",
    "\n",
    "*Warning* this example will not run without a sample file uploaded above. You will get a list index out of range error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-fe37948ab844>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# Gets in to the nested dictionary that is returned by the .value call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0minput_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_file_widget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;31m# now that we have the dictionary we want, we can access the item that holds the content\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0minput_file_content\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput_file\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'content'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# reference for accessing file contents\n",
    "# https://stackoverflow.com/questions/67434906/ipywidgets-widgets-fileupload-updated-csv-file-read-the-csv-file\n",
    "\n",
    "# Gets in to the nested dictionary that is returned by the .value call\n",
    "input_file = list(input_file_widget.value.values())[0]\n",
    "# now that we have the dictionary we want, we can access the item that holds the content\n",
    "input_file_content = input_file['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating our dataframe from the csv contents\n",
    "# reference: https://stackoverflow.com/questions/67434906/ipywidgets-widgets-fileupload-updated-csv-file-read-the-csv-file\n",
    "\n",
    "# have to use bytes io to parse\n",
    "# reference for using BytesIO to do this:\n",
    "# https://stackoverflow.com/questions/44888689/stringio-initial-value-must-be-str-not-bytes\n",
    "# https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-read-csv-table\n",
    "\n",
    "df = pd.read_csv(io.BytesIO(input_file_content), index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Styling and Layout\n",
    "\n",
    "Now it's time to make our widgets visually appealing and engaging!\n",
    "\n",
    "Style refers to visual characteristics not related to layout, like color. More information on style can be found here: https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Styling.html\n",
    "\n",
    "More information on \"layout,\" which refers to positioning, size, etc, can be found here: https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Layout.html\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Style\n",
    "\n",
    "To get the style options for a widget you can use the command .style.keys. So for a  button titled 'button', it would be button.style.keys. \n",
    "\n",
    "You can change all kinds of attributes from colors to fonts, so make sure to explore when customizing widgets!\n",
    "\n",
    "To set style, use the widget name, style, and the attribute, then set it equal to the value you'd like to set for that style attribute. You can also include it in the creation of the button using a style dictionary.\n",
    "\n",
    "NOTE: some widgets (like buttons) even have style presets to explore like the \"Danger Button\" that will quickly customize multiple style attributes. \n",
    "\n",
    "See the code examples below. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b065f86a72174e98baccd855433b50ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(style=ButtonStyle(button_color='green'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a new button to experiment with\n",
    "# here's an example of setting a style attribute in the creation of the button\n",
    "\n",
    "basic_button2 = widgets.Button(style = dict(button_color = 'green')) # text on button\n",
    "\n",
    "# code to display button\n",
    "display(basic_button2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# changing the color of the button \n",
    "# This is an example of setting a style attribute after the creation of the button\n",
    "\n",
    "basic_button2.style.button_color = 'blue'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "56be41bf608b412ca8059c33860aa819",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(button_style='danger', description='Danger Button', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# the DANGER button\n",
    "# Here's an example of using the button style presets\n",
    "\n",
    "danger_button = widgets.Button(description='Danger Button', button_style='danger')\n",
    "\n",
    "# code to display button\n",
    "display(danger_button)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Layout\n",
    "\n",
    "Layout is crucial for sizing to make sure that your widgets are appropriate for the desired purpose. Customizing the layout can be as simple or as complex as you'd like! We'll keep it fairly simple in this tutorial with width and height, but you should know that there's more out there to consider. \n",
    "\n",
    "Layout attributes are typically set in the creation of the widget. You can use different units to do so. Setting a value with a % symbol requests that the widget use that percent of the availiable space. On the other hand, values set with 'px' denote that number of pixels. \n",
    "\n",
    "In the following example, we will create a button with a custom size. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "10436df5024f45728c4df35bc30a5cdb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(layout=Layout(height='50%', width='50px'), style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#code to create a button using 50% of availiable height space and 50 pixels in width\n",
    "layout_button = widgets.Button(layout = widgets.Layout(height = '50%', width = '50px' ))\n",
    "\n",
    "# code to display button\n",
    "display(layout_button)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Application Example\n",
    "\n",
    "Data scientists often need to read in a .csv, manipulate it, and export it. This example walks through how you might use widgets to add interactivity to this application. \n",
    "\n",
    "*Warning* this example will not run without a sample csv file. You will get a list index out of range error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We need to define this function for use with widgets\n",
    "def f(x):\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1352a6918dd4adfad4459cffd40e5bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='Put Desired Name of Output Here Including \".csv\" (Example.csv)', description='New file name:', lay…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reference for layout \n",
    "# https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Layout.html\n",
    "\n",
    "# Text input widget\n",
    "# this is for inputting the new desired file name\n",
    "file_name = widgets.Text(\n",
    "    # what appears in the box initially\n",
    "    value='Put Desired Name of Output Here Including \".csv\" (Example.csv)',\n",
    "    # what appears in the box when all text is deleted\n",
    "    placeholder='Put Desired Name of Output Here',\n",
    "    # what the box is labeled on the left\n",
    "    description='New file name:',\n",
    "    # width of secription\n",
    "     style={'description_width': 'initial'},\n",
    "    # text box size \n",
    "   layout=widgets.Layout(width='60%', height='80%'),\n",
    "    disabled=False)\n",
    "\n",
    "# make the widget actually show up\n",
    "display(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving widget input into usable variable\n",
    "file_name_value = file_name.value\n",
    "# this has to be in a different cell than the actual widget"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "363227393d13465a852126ee08af540c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "FileUpload(value={}, accept='.csv', description='Upload File to be Converted:', layout=Layout(height='100%', w…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# File upload widget\n",
    "input_file_widget = widgets.FileUpload(\n",
    "    # will only accept csv input\n",
    "    accept='.csv',  # Accepted file extension e.g. '.txt', '.pdf', 'image/*', 'image/*,.pdf'\n",
    "    # description on button\n",
    "    description='Upload File to be Converted:',\n",
    "    # height and width of button\n",
    "    layout=widgets.Layout(width='50%', height='100%'),\n",
    "    multiple=True  # True to accept multiple files upload else False\n",
    ")\n",
    "\n",
    "# make the widget actually show up\n",
    "display(input_file_widget)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-51-fe37948ab844>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m# Gets in to the nested dictionary that is returned by the .value call\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0minput_file\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_file_widget\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      6\u001b[0m \u001b[1;31m# now that we have the dictionary we want, we can access the item that holds the content\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[0minput_file_content\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minput_file\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'content'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: list index out of range"
     ]
    }
   ],
   "source": [
    "# reference for accessing file contents\n",
    "# #https://stackoverflow.com/questions/67434906/ipywidgets-widgets-fileupload-updated-csv-file-read-the-csv-file\n",
    "\n",
    "# Gets in to the nested dictionary that is returned by the .value call\n",
    "input_file = list(input_file_widget.value.values())[0]\n",
    "# now that we have the dictionary we want, we can access the item that holds the content\n",
    "input_file_content = input_file['content']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'input_file_content' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-7182409c956e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;31m# https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-read-csv-table\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mio\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mBytesIO\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minput_file_content\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex_col\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     10\u001b[0m \u001b[0mdf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'input_file_content' is not defined"
     ]
    }
   ],
   "source": [
    "# Creating our dataframe from the csv contents\n",
    "# reference: https://stackoverflow.com/questions/67434906/ipywidgets-widgets-fileupload-updated-csv-file-read-the-csv-file\n",
    "\n",
    "# have to use bytes io to parse\n",
    "# reference for using BytesIO to do this:\n",
    "# https://stackoverflow.com/questions/44888689/stringio-initial-value-must-be-str-not-bytes\n",
    "# https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-read-csv-table\n",
    "\n",
    "df = pd.read_csv(io.BytesIO(input_file_content), index_col=0)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "button = widgets.Button(\n",
    "    # text on button\n",
    "    description='Click to Convert and Download New File',\n",
    "    disabled=False,\n",
    "    # style\n",
    "    button_style='', # 'success', 'info', 'warning', 'danger' or ''\n",
    "    # button size\n",
    "    layout=widgets.Layout(width='50%', height='100%'),\n",
    "    # what is shown if you hover over the button with your cursor but don't click\n",
    "    tooltip='Convert',\n",
    "    # little symbol on the button\n",
    "    icon='check' # (FontAwesome names without the `fa-` prefix)\n",
    ")\n",
    "\n",
    "# reference for these kind of nested functions below:\n",
    "#https://github.com/jupyter-widgets/ipywidgets/issues/2103\n",
    "\n",
    "# function to be associated with the button\n",
    "def return_funct(df, file_name_value):\n",
    "    return df.to_csv(file_name_value)\n",
    "\n",
    "# button can't really take input so we have to nest the functions like this\n",
    "def f2(self):\n",
    "    return_funct(df, file_name_value)\n",
    "\n",
    "# assigning our function to the click of the button\n",
    "button.on_click(f2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# click button that is displayed to download file\n",
    "display(button)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ipywidgets Documentation\n",
    "https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20List.html\n",
    "\n",
    "https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Styling.html\n",
    "\n",
    "https://ipywidgets.readthedocs.io/en/stable/examples/Widget%20Layout.html\n",
    "\n",
    "### Functions for Widgets\n",
    "https://github.com/jupyter-widgets/ipywidgets/issues/2103\n",
    "\n",
    "### Reading File Content\n",
    "https://stackoverflow.com/questions/67434906/ipywidgets-widgets-fileupload-updated-csv-file-read-the-csv-file\n",
    "\n",
    "https://stackoverflow.com/questions/44888689/stringio-initial-value-must-be-str-not-bytes\n",
    "\n",
    "https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#io-read-csv-table\n",
    "\n",
    "### Embedding image:\n",
    "https://stackoverflow.com/questions/57930004/jupyter-notebook-position-embedded-image-in-markdown"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}