{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6543120d",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Dash\n",
    "#### CS 66: Introduction to Computer Science II"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11cf4d01",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# References for this lecture\n",
    "\n",
    "Dash Tutorial: [https://dash.plotly.com/](https://dash.plotly.com/)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3c8aab2",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Getting Started with Dash\n",
    "\n",
    "__Dash__ is a Python framework for creating interactive web-based applications.\n",
    "* especially useful for data-centric applications\n",
    "* designed to work well with Plotly\n",
    "\n",
    "\n",
    "First, install dash (as usual, replacing `python3` with the path to your Python executable)\n",
    "\n",
    "```shell\n",
    "python3 -m pip install dash\n",
    "```\n"
   ]
  },
  {
   "attachments": {
    "dash_serve.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "405df7ef",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dash Demo\n",
    "\n",
    "Copy the following code into a file called `dash_hello.py` and run it. When it runs, it should display a message like this in your terminal:\n",
    "![dash_serve.png](attachment:dash_serve.png)\n",
    "\n",
    "\n",
    "It appears that it is stuck nothing, but it is actually running a little webserver right on your computer. You can see the web page that it is generating by opening a web browser and going to [http://127.0.0.1:8050/](http://127.0.0.1:8050/)\n",
    "\n",
    "Make sure to get this working for everyone in your group. You should all see the \"Hello there!\" message on the page.\n",
    "\n",
    "Then, add a third Markdown element with a new message that you come up with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cff215b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#these are the components we need from Sash\n",
    "from dash import Dash, html, dcc\n",
    "\n",
    "#this creates an object representing your application\n",
    "#you should have this line for all Dash apps\n",
    "app = Dash(__name__)\n",
    "\n",
    "#the layout describes all of the pieces that display on the page\n",
    "#the html.Div allows you to pass a list of things to display\n",
    "app.layout = html.Div(children = [\n",
    "    dcc.Markdown(\n",
    "        children = \n",
    "            \"\"\"\n",
    "                ## Hello there!\n",
    "\n",
    "                This is my __Dash__ web application.\n",
    "\n",
    "                Isn't it _neat_?\n",
    "\n",
    "            \"\"\"\n",
    "    ),\n",
    "\n",
    "    dcc.Markdown(\n",
    "        children = \"\"\"\n",
    "                    It can have multiple markdown cells.\n",
    "                    * with\n",
    "                    * bullets \n",
    "                    * even\n",
    "                \"\"\"\n",
    "    )\n",
    "])\n",
    "\n",
    "#this will launch your application in a web server\n",
    "#you should have this line for all Dash apps\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0527d73d",
   "metadata": {},
   "source": [
    "#### Things to notice\n",
    "\n",
    "Discuss the following with your group:\n",
    "\n",
    "* Dash is capable of displaying documents written using _html_ and _Markdown_, two popular web-based formatting languages. Markdown is what is shown here - it's really basic but gets the job done. You can check out more about writing Markdown here: [https://www.markdownguide.org/basic-syntax/](https://www.markdownguide.org/basic-syntax/)\n",
    "* Each Markdown component has a named parameter called `children` - this is where you put the text you want displayed.\n",
    "* If you want to stop your Dash app, go back to the terminal, hold down your `<control>` key on your keyboard and hit the `c` key."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218a4319",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Input and Callbacks\n",
    "\n",
    "Dash has many other components besides just `Markdown`. \n",
    "\n",
    "The following code has an `Input` component which allows users to type into an input text box.\n",
    "\n",
    "Notice that each component may also be given an `id` by assigning a value to the `id` parameter when creating that object. This will be useful for referring to this component in other parts of the code.\n",
    "\n",
    "It also has a __callback__ function which is defined to run any time the value in the `Input` textbox changes.\n",
    "\n",
    "Notice the `@app.callack` decorator which describes the inputs (i.e., parameters) and outputs (i.e., returns) of the function.\n",
    "\n",
    "Run this along with me as we discuss how it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6194e229",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, html, dcc\n",
    "from dash.dependencies import Input, Output\n",
    "\n",
    "app = Dash(__name__)\n",
    "\n",
    "app.layout = html.Div(children = [\n",
    "    dcc.Markdown(\n",
    "        id = \"name_prompt\",\n",
    "        children = \"## Enter your name\"\n",
    "    ),\n",
    "\n",
    "    dcc.Input(\n",
    "        id = \"name_input\",\n",
    "        value = \"\" #initially there is no value for the user input\n",
    "    ),\n",
    "\n",
    "    dcc.Markdown(\n",
    "        id = \"output_message\",\n",
    "        children = \"\" #initially the Markdown string is empty\n",
    "    )\n",
    "])\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"output_message\",\"children\"),\n",
    "    Input(\"name_input\",\"value\"),\n",
    ")\n",
    "def my_cool_message_generator(user_name):\n",
    "    my_message = \"Hello \"+user_name+\"!\"\n",
    "    return my_message\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0df49e1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Group Activity Problem 1\n",
    "\n",
    "The following code introduces a new kind of component - the `Radioitem`. Run this code and discuss what it is doing with your group. Answer the following questions:\n",
    "\n",
    "* What is a `Radioitem`?\n",
    "* How many callback functions does this app have?\n",
    "* What causes each of the callback functions to run? Why?\n",
    "* What inputs and outputs do each callback function use?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3efa35a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, html, dcc\n",
    "from dash.dependencies import Input, Output\n",
    "\n",
    "app = Dash(__name__)\n",
    "\n",
    "app.layout = html.Div(children = [\n",
    "    dcc.Markdown(\n",
    "        id = \"name_prompt\",\n",
    "        children = \"## Enter your name\"\n",
    "    ),\n",
    "\n",
    "    dcc.Input(\n",
    "        id = \"name_input\",\n",
    "        value = \"\" #initially there is no value for the user input\n",
    "    ),\n",
    "\n",
    "    dcc.Markdown(\n",
    "        id = \"major_prompt\",\n",
    "        children = \"What is your major?\"\n",
    "    ),\n",
    "\n",
    "    dcc.RadioItems(\n",
    "        id = \"major_radio_items\",\n",
    "        options = [\"Computer Science\",\"Data Analytics\",\"Artificial Intelligence\",\"Other\"],\n",
    "        value = \"Computer Science\"\n",
    "    ),\n",
    "\n",
    "    dcc.Markdown(\n",
    "        id = \"name_output_message\",\n",
    "        children = \"\" #initially the Markdown string is empty\n",
    "    ),\n",
    "\n",
    "    dcc.Markdown(\n",
    "        id = \"major_output_message\",\n",
    "        children = \"\" #initially the Markdown string is empty\n",
    "    )\n",
    "])\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"name_output_message\",\"children\"),\n",
    "    Input(\"name_input\",\"value\"),\n",
    ")\n",
    "def my_cool_message_generator(user_name):\n",
    "    my_message = \"Hello \"+user_name+\"!\"\n",
    "    return my_message\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"major_output_message\",\"children\"),\n",
    "    Input(\"major_radio_items\",\"value\"),\n",
    ")\n",
    "def message_for_major(user_major):\n",
    "    my_message = \"Dash is great for \"+user_major+\" applications.\"\n",
    "    return my_message\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc8bf7df",
   "metadata": {},
   "source": [
    "## Group Activity Problem 2\n",
    "\n",
    "`Dropdown` is another component that is similar to `Radioitems`. What do you think that it is supposed to do differently? In the code above, change the `Radioitems` to `Dropdown` and run it.\n",
    "\n",
    "If time, also try changing it to `Checklist`. Discuss the error messages you get and how you might fix the code to get it to work as a checklist instead of radio items.\n",
    "\n",
    "You can find documentation and examples on how to use each of these components here:\n",
    "* [https://dash.plotly.com/dash-core-components/radioitems](https://dash.plotly.com/dash-core-components/radioitems)\n",
    "* [https://dash.plotly.com/dash-core-components/dropdown](https://dash.plotly.com/dash-core-components/dropdown)\n",
    "* [https://dash.plotly.com/dash-core-components/checklist](https://dash.plotly.com/dash-core-components/checklist)\n",
    "\n",
    "Browse through the other components on the left side of the page to get an idea of what other options you have."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "971ea3cb",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Multiple Inputs affecting the same output\n",
    "\n",
    "You can make multiple inputs affect the same output by listing multiple `Input` objects with a single callback function.\n",
    "\n",
    "Notice how the inputs are related to the parameters in the example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7b27fd93",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, html, dcc\n",
    "from dash.dependencies import Input, Output\n",
    "\n",
    "app = Dash(__name__)\n",
    "\n",
    "app.layout = html.Div(children = [\n",
    "    dcc.Markdown(\n",
    "        id = \"name_prompt\",\n",
    "        children = \"## Enter your name\"\n",
    "    ),\n",
    "\n",
    "    dcc.Input(\n",
    "        id = \"name_input\",\n",
    "        value = \"\" #initially there is no value for the user input\n",
    "    ),\n",
    "\n",
    "    dcc.Markdown(\n",
    "        id = \"major_prompt\",\n",
    "        children = \"What is your major?\"\n",
    "    ),\n",
    "\n",
    "    dcc.RadioItems(\n",
    "        id = \"major_radio_items\",\n",
    "        options = [\"Computer Science\",\"Data Analytics\",\"Artificial Intelligence\",\"Other\"],\n",
    "        value = \"Computer Science\"\n",
    "    ),\n",
    "\n",
    "    dcc.Markdown(\n",
    "        id = \"output_message\",\n",
    "        children = \"\" #initially the Markdown string is empty\n",
    "    )\n",
    "])\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"output_message\",\"children\"),\n",
    "    Input(\"name_input\",\"value\"),\n",
    "    Input(\"major_radio_items\",\"value\"),\n",
    ")\n",
    "def my_cool_message_generator(user_name,user_major): #the two params come from the two Input()\n",
    "    if user_name == \"\": #the user hasn't entered a name yet\n",
    "        return \"\"  #so return blank for the output message\n",
    "    else:\n",
    "        my_message = user_name + \" is learning about \" + user_major \n",
    "        return my_message\n",
    "\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1965071",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Loading and using other data with Dash applications\n",
    "\n",
    "Even though global variables are usually a bad idea, you can use them to retain data (so you don't have to reload from a file or Web API every time the callback function runs).\n",
    "\n",
    "In this example `DATA` is a global variable with COVID country data we worked with previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c1419a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, html, dcc\n",
    "from dash.dependencies import Input, Output\n",
    "import requests\n",
    "\n",
    "#read the country-level data from the API\n",
    "response = requests.get(\"https://api.covid19api.com/summary\")\n",
    "DATA = response.json()\n",
    "\n",
    "app = Dash(__name__)\n",
    "\n",
    "app.layout = html.Div(children = [\n",
    "    dcc.Markdown(\n",
    "        id = \"country_prompt\",\n",
    "        children = \"## Enter the name of a country\"\n",
    "    ),\n",
    "\n",
    "    dcc.Input(\n",
    "        id = \"country_input\",\n",
    "        value = \"\" #initially there is no value for the user input\n",
    "    ),\n",
    "\n",
    "    dcc.Markdown(\n",
    "        id = \"output_message\",\n",
    "        children = \"\" #initially the Markdown string is empty\n",
    "    )\n",
    "])\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"output_message\",\"children\"),\n",
    "    Input(\"country_input\",\"value\"),\n",
    ")\n",
    "def display_covid_data(country_name):\n",
    "    data_display = \"\"\n",
    "    for curr_country in DATA[\"Countries\"]:\n",
    "        if curr_country[\"Country\"] == country_name:\n",
    "            data_display = \"### \"+country_name+\"\\n\\n\"\n",
    "            data_display += \"New confirmed cases: \"+str(curr_country[\"NewConfirmed\"])+\"\\n\\n\"\n",
    "            data_display += \"Total deaths: \"+str(curr_country[\"TotalDeaths\"])+\"\\n\\n\"\n",
    "            data_display += \"as of \"+DATA[\"Date\"]+\"\\n\\n\"\n",
    "\n",
    "    return data_display\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba0b8ccc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dash + Plotly\n",
    "\n",
    "One of the really cool things about Dash is that it is designed to work well with Plotly. \n",
    "\n",
    "There is a Dash component called `Graph` which expects a parameter called `figure` - you can pass that any kind of Plotly figure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd576840",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, html, dcc\n",
    "from dash.dependencies import Input, Output\n",
    "import requests\n",
    "import plotly.express as px\n",
    "\n",
    "#read the country-level data from the API\n",
    "response = requests.get(\"https://api.covid19api.com/summary\")\n",
    "DATA = response.json()\n",
    "fig = px.bar(DATA[\"Countries\"],x=\"Country\",y=\"NewConfirmed\",title=\"New Confirmed Cases by Country\")\n",
    "\n",
    "app = Dash(__name__)\n",
    "\n",
    "app.layout = html.Div(children = [\n",
    "    dcc.Markdown(\n",
    "        id = \"title\",\n",
    "        children = \"## COVID Dashboard\"\n",
    "    ),\n",
    "\n",
    "    dcc.Graph(\n",
    "        id = \"country_bar_graph\",\n",
    "        figure = fig\n",
    "    )\n",
    "])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad9db2fe",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "## Group Activity Problem 3\n",
    "\n",
    "In the previous set of notes, you made a line graph with the total COVID deaths for each state. How can we change the above code to make it work with that figure?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13dabf0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Group Activity Problem 4\n",
    "\n",
    "Now let's put it all together. Run the following code, which uses a `Dropdown` to filter out which countries to display.\n",
    "\n",
    "Discuss the following in your groups:\n",
    "* Where does it get the list of countries in the dropdown menu from?\n",
    "* Why does this `Dropdown` let you select multiple values but the one we worked with above only let you select one?\n",
    "* Instead of filtering by country name, what if you wanted to filter by number of cases like we did in the activities from the previous set of notes? Discuss ideas for how that could be accomplished (though you don't actually need to implement it in your code)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f04abbb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dash import Dash, html, dcc\n",
    "from dash.dependencies import Input, Output\n",
    "import requests\n",
    "import plotly.express as px\n",
    "\n",
    "#read the country-level data from the API\n",
    "response = requests.get(\"https://api.covid19api.com/summary\")\n",
    "DATA = response.json()\n",
    "\n",
    "#create the initial figure of all the countries \n",
    "#- this will quickly get replaced\n",
    "fig = px.bar(DATA[\"Countries\"],x=\"Country\",y=\"NewConfirmed\",title=\"New Confirmed Cases by Country\")\n",
    "\n",
    "#make a list of all the country names that appear in \n",
    "# the DATA[\"Countries\"] list of dictionaries\n",
    "country_names_list = []\n",
    "for country_data in DATA[\"Countries\"]:\n",
    "    country_names_list.append(country_data[\"Country\"])\n",
    "\n",
    "app = Dash(__name__)\n",
    "\n",
    "app.layout = html.Div(children = [\n",
    "    dcc.Markdown(\n",
    "        id = \"title\",\n",
    "        children = \"## COVID Dashboard\"\n",
    "    ),\n",
    "\n",
    "    dcc.Dropdown(\n",
    "        id = \"country_select_dropdown\",\n",
    "        options = country_names_list,\n",
    "        value = [\"United States of America\",\"Canada\",\"Mexico\"],\n",
    "        multi = True #allows us to select multiple values\n",
    "    ),\n",
    "\n",
    "    dcc.Graph(\n",
    "        id = \"country_bar_graph\",\n",
    "        figure = fig\n",
    "    )\n",
    "])\n",
    "\n",
    "@app.callback(\n",
    "    Output(\"country_bar_graph\",\"figure\"),\n",
    "    Input(\"country_select_dropdown\",\"value\"),\n",
    ")\n",
    "def update_country_graph(country_names):\n",
    "    #country_names is a list with the countries\n",
    "    #selected from the dropdown by the user\n",
    "    records_to_display = [] #for keeping only the selected countries\n",
    "    for curr_country in DATA[\"Countries\"]: #loop through all country records\n",
    "        if curr_country[\"Country\"] in country_names:\n",
    "            records_to_display.append(curr_country)\n",
    "    fig = px.bar(records_to_display,x=\"Country\",y=\"NewConfirmed\",title=\"New Confirmed Cases by Country\")\n",
    "    return fig\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    app.run_server(debug=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8219ff",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Group Activity Problem 5\n",
    "\n",
    "In the activities from the previous set of notes, you created a line graph using the `https://api.covid19api.com/live/country/united-states` endpoint and filtered out all but a few states. Create a Dash application which does something similar. It should have the following things:\n",
    "* A line graph showing the total number of COVID deaths over time for states (including other regions, cruise ships, and whatever else is listed under the `\"Province\"` key).\n",
    "* A Dropdown that allows the user to select multiple states. When the user selects a new state, the figure should update to only show the selected states."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e021d5de",
   "metadata": {},
   "source": [
    "### Plotly Maps\n",
    "\n",
    "See map examples at [https://plotly.com/python/maps/](https://plotly.com/python/maps/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61f19818",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import plotly.express as px\n",
    "import requests\n",
    "\n",
    "response = requests.get(\"https://api.covid19api.com/summary\")\n",
    "\n",
    "data = response.json()\n",
    "country_data = data[\"Countries\"]\n",
    "\n",
    "fig = px.choropleth(country_data,locations=\"Country\",locationmode=\"country names\",color=\"NewConfirmed\",range_color=(0,10000),title=\"New Confirmed Cases by Country\")\n",
    "fig.show()"
   ]
  }
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