Dashboard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "48915dfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import ALL\n",
    "from dash import Dash, html, dcc\n",
    "import jupyter_dash\n",
    "\n",
    "from matplotlib.figure import Figure\n",
    "from matplotlib.pyplot import figure\n",
    "import plotly.express as px\n",
    "import plotly.io as pio\n",
    "pio.templates\n",
    "from dash.dependencies import Input, Output, State\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import dash_bootstrap_components as dbc\n",
    "import plotly.graph_objects as go\n",
    "from itertools import chain\n",
    "import pycountry\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58d047b9",
   "metadata": {},
   "source": [
    "## Colors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "702bf84c",
   "metadata": {},
   "outputs": [],
   "source": [
    "pallete = {\n",
    "        'Confirmed': '#0091D5',\n",
    "        'Active': '#DBAE58',\n",
    "        'Deaths': '#484848',\n",
    "        'Critical': '#EA6A47',\n",
    "        'Recovered': '#6AB187',\n",
    "        'background': '#DADADA'\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5d891fa",
   "metadata": {},
   "source": [
    "## Test performed map figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "57f8a9d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('Data\\All_Continents_Union.csv')\n",
    "df1 = df.iloc[:, [0, 1, 2, 6, 7, 13]]     # Confirmed cases by country\n",
    "df2 = df.iloc[:, [0, 7, 2, 6]]            # Active vs. Recovered vs. Death\n",
    "df3 = df.iloc[:, [0, 11, 13]]             # Tests Performed by Country\n",
    "df4 = df.iloc[:, [0, 11, 12]].dropna(axis=0, how='any')\n",
    "\n",
    "\n",
    "Tests_Performed_fig =px.choropleth(df3, locations=\"iso_alpha\",\n",
    "                                    color=\"Tests Performed\", \n",
    "                                    hover_name=\"Country\",\n",
    "                                    color_continuous_scale=px.colors.sequential.Greens)\n",
    "\n",
    "Tests_Performed_fig.update_layout(title_x=0.5, \n",
    "                                  title_y=0.94, \n",
    "                                  title_text= 'Tests Performed by Country',\n",
    "                                  margin={\"r\":10,\"t\":55,\"l\":10,\"b\":10}, \n",
    "                                  coloraxis_colorbar=dict(\n",
    "                                  title=\"Tests Performed\",\n",
    "                                  thicknessmode=\"pixels\", thickness=10,\n",
    "                                  lenmode=\"pixels\", len=150,\n",
    "                                  yanchor=\"bottom\", y=0.13,\n",
    "                                  xanchor=\"left\", x=0.01\n",
    "                                  ))\n",
    "\n",
    "print(\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3c3a6994",
   "metadata": {},
   "source": [
    "## Vaccination trend line figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "96c73f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Global vaccination trend\n",
    "#_____________________________________________________________________________________________________________________________________#\n",
    "country_continent = pd.read_csv(\"Data\\Country_and_Continent.csv\")\n",
    "countries_in_each_continent = country_continent.groupby(by='Continent')['Country'].apply(list).reset_index(name='Countries')\n",
    "\n",
    "\n",
    "vaccination_trend = pd.read_csv(\"Data\\Total_Vaccinations_Trend_Administered_Globally_all.csv\").dropna(axis=0, how='any')\n",
    "indeces_to_be_dropped = vaccination_trend[vaccination_trend.location.isin(['World', 'Upper middle income', 'Lower middle income', \n",
    "                                                                            'High income', 'Low income'])].index.to_list()\n",
    "vaccination_trend = vaccination_trend.drop(index=indeces_to_be_dropped)\n",
    "\n",
    "continents = sorted(country_continent.Continent.unique().tolist())\n",
    "Any_country = sorted(list(set(vaccination_trend.location.unique().tolist()) - set(continents)))\n",
    "\n",
    "Vaccination_trend_Continents = vaccination_trend[vaccination_trend.location.isin(continents)]\n",
    "\n",
    "Vaccination_trend_Countries = vaccination_trend[vaccination_trend.location.isin(Any_country)]\n",
    "\n",
    "selected_continent = ['Oceania']\n",
    "continent = Vaccination_trend_Continents[Vaccination_trend_Continents.location.isin(selected_continent)]\n",
    "#_____________________________________________________________________________________________________________________________________#"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b41451",
   "metadata": {},
   "source": [
    "## Vaccination trend map figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6da71558",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " \n"
     ]
    }
   ],
   "source": [
    "\"\"\"Modifications\"\"\"\n",
    "total = []\n",
    "avg = []\n",
    "for i in Vaccination_trend_Countries.location:\n",
    "    vac = Vaccination_trend_Countries[np.isin(Vaccination_trend_Countries.location, i)].total_vaccinations\n",
    "    total.append(int(vac.values[0]))\n",
    "    avg.append(\"{:.2f}\".format(np.average(vac)))\n",
    "\n",
    "countries = {}\n",
    "for country in pycountry.countries:\n",
    "    countries[country.name] = country.alpha_3\n",
    "\n",
    "codes = [countries.get(country, 'Unknown code') for country in Vaccination_trend_Countries.location]\n",
    "\n",
    "dic = {'Location':Vaccination_trend_Countries.location, 'Total_Vaccinations':total,'Avg total_vaccinations':avg, 'iso_alpha':codes}\n",
    "Total_Vaccinations_Trend = pd.DataFrame(dic)\n",
    "\n",
    "Total_Vaccinations_Trend_fig =px.choropleth(Total_Vaccinations_Trend, locations=\"iso_alpha\",\n",
    "                                    color=\"Total_Vaccinations\", \n",
    "                                    hover_name=\"Location\",\n",
    "                                    hover_data=[\"Avg total_vaccinations\"],\n",
    "                                    color_continuous_scale=px.colors.sequential.GnBu)\n",
    "Total_Vaccinations_Trend_fig.update_layout(title_text='Total Vaccinations Trend Country Wise', title_x=0.5, title_y=0.94, \n",
    "                                            margin={\"r\":10,\"t\":50,\"l\":10,\"b\":0},\n",
    "                                            coloraxis_colorbar=dict(\n",
    "                                            title=\"Vaccination\",\n",
    "                                            thicknessmode=\"pixels\", thickness=10,\n",
    "                                            lenmode=\"pixels\", len=150,\n",
    "                                            yanchor=\"bottom\", y=0.13,\n",
    "                                            xanchor=\"left\", x=0.01\n",
    "                                        ))\n",
    "print(\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "521f2d38",
   "metadata": {},
   "source": [
    "## Global deaths line figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f6d55f0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Global deaths\n",
    "#_____________________________________________________________________________________________________________________________________#\n",
    "global_daily_trend_path = \"Data\\Global_Deaths_Daily_Trend-all.csv\"\n",
    "global_daily_trend = pd.read_csv(global_daily_trend_path)\n",
    "global_daily_trend = global_daily_trend[['date', 'location', 'new_deaths', 'new_deaths_smoothed', 'new_cases', 'new_cases_smoothed']].dropna(axis=0, how='any')\n",
    "indeces_to_be_dropped = global_daily_trend[global_daily_trend.location.isin(['World', 'Upper middle income', 'Lower middle income', 'High income'])].index.to_list()\n",
    "global_daily_trend = global_daily_trend.drop(index=indeces_to_be_dropped)\n",
    "\n",
    "deaths_countries = sorted(list(set(global_daily_trend.location.unique().tolist()) - set(continents)))\n",
    "\n",
    "global_daily_trend_continents = global_daily_trend[global_daily_trend.location.isin(continents)]\n",
    "\n",
    "global_daily_trend_countries = global_daily_trend[global_daily_trend.location.isin(deaths_countries)]\n",
    "#_____________________________________________________________________________________________________________________________________#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6006110c",
   "metadata": {},
   "source": [
    "## Comparison bar Figure "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2124fa70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total Active</th>\n",
       "      <th>Total Recovered</th>\n",
       "      <th>Total Recovered percent</th>\n",
       "      <th>Total Deaths</th>\n",
       "      <th>Total Deaths percent</th>\n",
       "      <th>Total confirmed</th>\n",
       "      <th>Country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9277.0</td>\n",
       "      <td>162741.0</td>\n",
       "      <td>0.905545</td>\n",
       "      <td>7698.0</td>\n",
       "      <td>0.042834</td>\n",
       "      <td>179716</td>\n",
       "      <td>Afghanistan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>242.0</td>\n",
       "      <td>272125.0</td>\n",
       "      <td>0.986446</td>\n",
       "      <td>3497.0</td>\n",
       "      <td>0.012677</td>\n",
       "      <td>275864</td>\n",
       "      <td>Albania</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>80593.0</td>\n",
       "      <td>178386.0</td>\n",
       "      <td>0.670992</td>\n",
       "      <td>6875.0</td>\n",
       "      <td>0.025860</td>\n",
       "      <td>265854</td>\n",
       "      <td>Algeria</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223</th>\n",
       "      <td>661.0</td>\n",
       "      <td>9009.0</td>\n",
       "      <td>0.762247</td>\n",
       "      <td>2149.0</td>\n",
       "      <td>0.181826</td>\n",
       "      <td>11819</td>\n",
       "      <td>Yemen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>2115.0</td>\n",
       "      <td>243033.0</td>\n",
       "      <td>0.969642</td>\n",
       "      <td>5494.0</td>\n",
       "      <td>0.021920</td>\n",
       "      <td>250642</td>\n",
       "      <td>Zimbabwe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Total Active  Total Recovered  Total Recovered percent  Total Deaths  \\\n",
       "0          9277.0         162741.0                 0.905545        7698.0   \n",
       "1           242.0         272125.0                 0.986446        3497.0   \n",
       "2         80593.0         178386.0                 0.670992        6875.0   \n",
       "223         661.0           9009.0                 0.762247        2149.0   \n",
       "225        2115.0         243033.0                 0.969642        5494.0   \n",
       "\n",
       "     Total Deaths percent  Total confirmed      Country  \n",
       "0                0.042834           179716  Afghanistan  \n",
       "1                0.012677           275864      Albania  \n",
       "2                0.025860           265854      Algeria  \n",
       "223              0.181826            11819        Yemen  \n",
       "225              0.021920           250642     Zimbabwe  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Total confirmed vs total recovered vs total deaths\n",
    "#_____________________________________________________________________________________________________________________________________#\n",
    "comparison_df_path = \"Data\\Active_Cases_vs_Recovered_vs_Death_by_Country.csv\"\n",
    "comparison_df = pd.read_csv(comparison_df_path)\n",
    "indeces_to_be_dropped = comparison_df[comparison_df.Country.isin(['World', 'Upper middle income', 'Lower middle income', 'High income'])].index.to_list()\n",
    "comparison_df = comparison_df.drop(index=indeces_to_be_dropped)\n",
    "comparison_df = comparison_df.dropna(axis=0, how='any')\n",
    "\n",
    "recovered_values_percentage = comparison_df['Total Recovered'] /  comparison_df['Total confirmed']\n",
    "deaths_values_percentage = comparison_df['Total Deaths'] /  comparison_df['Total confirmed']\n",
    "\n",
    "comparison_df.insert(2, \"Total Recovered percent\", recovered_values_percentage)\n",
    "comparison_df.insert(4, \"Total Deaths percent\", deaths_values_percentage)\n",
    "comparison_countries = sorted(comparison_df['Country'].unique().tolist())\n",
    "\n",
    "selected_countries = ['Afghanistan', 'Albania', 'Algeria', 'Yemen', 'Zimbabwe']\n",
    "confirmed_selected_countries = comparison_df[comparison_df['Country'].isin(selected_countries)]\n",
    "confirmed_selected_countries\n",
    "#_____________________________________________________________________________________________________________________________________#\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87be2c97",
   "metadata": {},
   "source": [
    "## Dashboard Layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9f6a5138",
   "metadata": {},
   "outputs": [],
   "source": [
    "app = jupyter_dash.JupyterDash(external_stylesheets=[dbc.themes.BOOTSTRAP])\n",
    "\n",
    "# Confirmed\n",
    "card_content_1 = [\n",
    "    \n",
    "    dbc.CardBody(\n",
    "        [\n",
    "            html.H5(\"Confirmed Cases\", className=\"card-title1\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'150%'}),\n",
    "            html.P(\"{:,}\".format(int(np.sum(df['Confirmed Cases']))), \n",
    "            className=\"card-text1\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'250%'})\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "\n",
    "# Active\n",
    "card_content_2 = [\n",
    "    \n",
    "    dbc.CardBody(\n",
    "        [\n",
    "            html.H5(\"Active Cases\", className=\"card-title2\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'150%'}),\n",
    "            html.P(\"{:,}\".format(int(np.sum(df['Active Cases']))), \n",
    "            className=\"card-text2\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'250%'})\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "\n",
    "# Deaths\n",
    "card_content_3 = [\n",
    "    \n",
    "    dbc.CardBody(\n",
    "        [\n",
    "            html.H5(\"Deaths\", className=\"card-title3\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'150%'}),\n",
    "            html.P(\"{:,}\".format(int(np.sum(df['Total Deaths']))), \n",
    "            className=\"card-text3\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'250%'})\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "\n",
    "# Critical\n",
    "card_content_4 = [\n",
    "    \n",
    "    dbc.CardBody(\n",
    "        [\n",
    "            html.H5(\"Critical Cases\", className=\"card-title4\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'150%'}),\n",
    "            html.P(\"{:,}\".format(int(np.sum(df['Critical Cases']))), \n",
    "            className=\"card-text4\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'250%'})\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "\n",
    "# Recovered\n",
    "card_content_5 = [\n",
    "    \n",
    "    dbc.CardBody(\n",
    "        [\n",
    "            html.H5(\"Recovered\", className=\"card-title5\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'150%'}),\n",
    "            html.P(\"{:,}\".format(int(np.sum(df['Recovered Cases']))), \n",
    "            className=\"card-text5\", style={\"text-align\": \"center\", 'font-family':'Calibri Light', 'font-size':'250%'})\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "\n",
    "# Death rate\n",
    "card_content_6 = [\n",
    "    \n",
    "    dbc.CardBody(\n",
    "        [\n",
    "            html.H5(\"Death Rate\", className=\"card-title6\", style={\"text-align\": \"center\"}),\n",
    "            html.P(str(float(\"{:.2f}\".format(np.sum(df['Total Deaths']) / np.sum(df['Confirmed Cases']) * 100)))+\"%\", \n",
    "            className=\"card-text6\", style={\"text-align\": \"center\"})\n",
    "        ]\n",
    "    )\n",
    "]\n",
    "\n",
    "# Recovery rate\n",
    "card_content_7 = [\n",
    "    \n",
    "    dbc.CardBody(\n",
    "        [\n",
    "            html.H5(\"Recovery Rate\", className=\"card-title7\", style={\"text-align\": \"center\"}),\n",
    "            html.P(str(float(\"{:.2f}\".format(np.sum(df['Recovered Cases']) / np.sum(df['Confirmed Cases']) * 100)))+\"%\", \n",
    "            className=\"card-text7\", style={\"text-align\": \"center\"})\n",
    "        ]\n",
    "    )\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02164bcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "app.layout = html.Div(\n",
    "    [   \n",
    "        # Header\n",
    "        dbc.Row(html.Img(src=app.get_asset_url(\"covid_banner.png\"), \n",
    "                style={'hieght': '30%', 'width': '30%', 'margin-left':'auto', 'margin-right':'auto', 'padding-top':'24px'})),\n",
    "\n",
    "        # Message\n",
    "        dcc.ConfirmDialog(id='warning'),\n",
    "\n",
    "\n",
    "        # BANS\n",
    "        dbc.Row([\n",
    "                # Confirmed\n",
    "                dbc.Col(dbc.Card(card_content_1, color=pallete['Confirmed'], inverse=True), style = {'margin-left' : '12px'}),\n",
    "                # Active\n",
    "                dbc.Col(dbc.Card(card_content_2, color=pallete['Active'], inverse=True)),\n",
    "                # Recovered\n",
    "                dbc.Col(dbc.Card(card_content_5, color=pallete['Recovered'], inverse=True)),\n",
    "                # Critical\n",
    "                dbc.Col(dbc.Card(card_content_4, color=pallete['Critical'], inverse=True)),\n",
    "                # Deaths\n",
    "                dbc.Col(dbc.Card(card_content_3, color=pallete['Deaths'], inverse=True), style = {'margin-right' : '12px'})\n",
    "            ],\n",
    "            className=\"mb-4\",  style={'margin-top':'25px'}\n",
    "        ),\n",
    "\n",
    "        \n",
    "        dbc.Row(\n",
    "            [\n",
    "                # Confirmed Cases Map\n",
    "                dbc.Col([\n",
    "                    dbc.Row([\n",
    "                            dbc.Col(html.Label(['Country'], style={'font-weight': 'bold',\"text-align\": \"left\", 'margin-left':'12px'}), width=1),\n",
    "                            dbc.Col(dcc.Dropdown(id='myDropdown',\n",
    "                                        options=[{'label': str(Country), 'value': str(Country)}\n",
    "                                        for Country in df1['Country'].unique()], \n",
    "                                        value=None, multi=False\n",
    "                            ), style={'margin-left':'50px'}, width=5)\n",
    "                            ], style={'background':'#ffffff', 'margin':'0px', 'padding-top':'25px'}),\n",
    "\n",
    "                    html.Div(dcc.Graph(id='Global_Map'))], width=6, style={'margin-left':\"12px\", 'padding-right':\"0px\"}\n",
    "                ),\n",
    "                \n",
    "                \n",
    "                # PiChart\n",
    "                dbc.Col(html.Div(dcc.Graph(id='Chart')),  \n",
    "                        style={'margin-left':'12px', 'padding':\"0px\", 'background':'#ffffff'}),\n",
    "                \n",
    "                # Side Cards\n",
    "                dbc.Col(\n",
    "                    [\n",
    "                        dbc.Col(html.Div(dbc.Card([\n",
    "                            dbc.CardBody(\n",
    "                                [\n",
    "                                    html.H5(\"Country With Highest Cases in This region\", style={\"padding-bottom\":\"20px\"}),\n",
    "                                    html.P(id=\"card_text_1\", style={'color': pallete['Confirmed'], 'font-family':'Calibri Light', 'font-size':'160%'}),\n",
    "                                    html.P(id=\"card_text_2\", style={'color': pallete['Active'], 'font-family':'Calibri Light', 'font-size':'160%'}),\n",
    "                                    html.P(id=\"card_text_3\", style={'color': pallete['Recovered'], 'font-family':'Calibri Light', 'font-size':'160%'}),\n",
    "                                    html.P(id=\"card_text_4\", style={'color': pallete['Critical'], 'font-family':'Calibri Light', 'font-size':'160%'}),\n",
    "                                    html.P(id=\"card_text_5\", style={'color': pallete['Deaths'], 'font-family':'Calibri Light', 'font-size':'160%'})\n",
    "                                ], style={'height':'31.8rem'}\n",
    "                            ),\n",
    "\n",
    "                        ], color=\"#ffffff\", outline=True)), style={\"text-align\": \"center\"}),\n",
    "\n",
    "                    ], style={\"margin-right\" : \"12px\", 'margin-left':\"12px\", 'padding-left': '0px'}, width=2\n",
    "                ),   \n",
    "            ], style={'margin-top':'25px'}\n",
    "        ),\n",
    "\n",
    "        \n",
    "        dbc.Row([\n",
    "            \n",
    "            # Tests Performed Map\n",
    "                dbc.Col([\n",
    "                    html.Div(dcc.Graph(figure=Tests_Performed_fig))\n",
    "                    ], width=6, style={'margin':'0px', 'margin-left':'24px', 'padding':'0px', 'background':'#ffffff'}),\n",
    "\n",
    "            # Test Performed Per Region\n",
    "                dbc.Col([\n",
    "                    dbc.Row([\n",
    "                            dbc.Col(html.Label(['Region'], style={'font-weight': 'bold',\"text-align\": \"left\", 'margin-left':'24px'}), width=1),\n",
    "                            dbc.Col(dcc.Dropdown(id='BarDropdown',\n",
    "                                        options=[{'label': str(Continent), 'value': str(Continent)}\n",
    "                                        for Continent in df4['Continent'].unique()], \n",
    "                                        value=None, multi=False\n",
    "                            ), style={'margin-left':'50px'})\n",
    "                            ], style={'background':'#ffffff', 'margin':'0px', 'padding-top':'25px'}),\n",
    "\n",
    "                    html.Div(dcc.Graph(id='Bar_Chart'))\n",
    "                \n",
    "                ], style={'margin':'0', 'margin-right':'12px'}\n",
    "                ),\n",
    "            ], style={'margin-top':'25px'}),\n",
    "        \n",
    "\n",
    "        # Global vaccination trend (Cummulated)\n",
    "        \n",
    "        dbc.Row(\n",
    "            [   \n",
    "                dbc.Col(html.Div(dcc.Graph(figure=Total_Vaccinations_Trend_fig)), width=6,\n",
    "                        style={'margin':'0px', 'margin-left':'24px', 'padding':'0px', 'background':'#ffffff'}),\n",
    "                \n",
    "                dbc.Col([\n",
    "                        dbc.Row([\n",
    "                            dbc.Col(dcc.RadioItems(options=['Countries', 'Continents'], value='Continents', id='selectOne_vac', labelStyle = {'display':'inline', 'margin-left':'12px'}), \n",
    "                                    width=4, style={\"margin-left\" : \"12px\"}), \n",
    "\n",
    "                            dbc.Col(dcc.Dropdown(multi=True, id='options_vac'))\n",
    "                        ], style={'background':'#ffffff', 'margin':'0px', 'padding-top':'25px'}),\n",
    "\n",
    "                        html.Div(dcc.Graph(id='vaccine_trend')\n",
    "                            )],\n",
    "                    style={'margin':\"0px\", 'margin-right':'12px'}\n",
    "                )\n",
    "                ], style={'margin-top':'25px'}\n",
    "            ),\n",
    "\n",
    "        # Total deaths vs recovered comparison\n",
    "        \n",
    "        dbc.Col([\n",
    "            dbc.Row([\n",
    "                dbc.Col(html.Label(['Country'], style={'font-weight': 'bold', 'margin-left':'24px', 'padding-right':'0px', 'margin-right':'0px'}), width=1),\n",
    "                dbc.Col(dcc.Dropdown(options=comparison_countries, multi=True, value=['India', 'USA'], id='options_compare', style={\"margin\" : \"0px\", \"padding-left\" : \"0px\"}), width=6),\n",
    "                dbc.Col(dbc.Button(children='Submit', id='submit', color=\"primary\"), width=1)\n",
    "            ], style={'margin-bottom':'5px'}),\n",
    "            html.Div(dcc.Graph(id='compare_trend'), style={\"margin\" : \"12px\", 'margin-top':'0px', 'margin-bottom':'0px'})\n",
    "        ], style={'background':'#ffffff', \"margin\" : \"12px\",  'margin-top':'25px','padding-top':'20px'}),   \n",
    "\n",
    "\n",
    "        # Global Deaths   &&&   # Global confirmed cases   \n",
    "        dbc.Row(\n",
    "            [\n",
    "                dbc.Col([\n",
    "                    dbc.Row([\n",
    "                    dbc.Col(dcc.RadioItems(options=['Countries', 'Continents'], value='Continents', labelStyle={'display':'inline', 'margin-left':'12px'}, id='selectOne_death')),\n",
    "                    dbc.Col(dcc.Dropdown(multi=False, id='options_death'), width=8),\n",
    "                    ], style={'background':'#ffffff', 'margin-bottom':'0px', \"margin-right\" : \"0px\", 'padding-top':'30px', \"margin-left\" : \"12px\"}),\n",
    "                    dbc.Col(html.Div(dcc.Graph(id='death_trend')), style={\"margin-top\" : \"0px\", \"margin-left\" : \"12px\"}),\n",
    "                ], style={'padding-right':'0px', 'margin-right':'6px'}),\n",
    "                 \n",
    "                 dbc.Col([\n",
    "                    dbc.Row([\n",
    "                    dbc.Col(dcc.RadioItems(options=['Countries', 'Continents'], value='Continents', id='selectOne_conf', labelStyle={'display':'inline', 'margin-left':'12px'})),    \n",
    "                    dbc.Col(dcc.Dropdown(multi=False, id='options_conf'), width=8),\n",
    "                    ], style={'background':'#ffffff', 'margin-bottom':'0px', 'margin-left':'0px', 'padding-top':'30px', \"margin-right\" : \"12px\"}),\n",
    "                    dbc.Col(html.Div(dcc.Graph(id='conf_trend')), style={\"margin-top\" : \"0px\", \"margin-right\" : \"12px\"}),\n",
    "                ], style={'padding-left':'0px', 'margin-left':'6px'})\n",
    "            ], style={'margin-top':'25px', 'padding-bottom':'25px'}\n",
    "        ), \n",
    "    ], style={'backgroundColor':pallete['background']}\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f0e8ace",
   "metadata": {},
   "source": [
    "## Callbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "dd3d5cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "@app.callback(\n",
    "    Output(component_id='Global_Map', component_property='figure'),\n",
    "    Output(component_id='Chart', component_property='figure'),\n",
    "    Output(component_id='card_text_1', component_property='children'),\n",
    "    Output(component_id='card_text_2', component_property='children'),\n",
    "    Output(component_id='card_text_3', component_property='children'),\n",
    "    Output(component_id='card_text_4', component_property='children'),\n",
    "    Output(component_id='card_text_5', component_property='children'),\n",
    "    Input(component_id='myDropdown', component_property='value')\n",
    ")\n",
    "\n",
    "def update_my_fig(country):\n",
    "\n",
    "    if country == None:\n",
    "        map_filter = df1\n",
    "        values = [np.sum(df2['Active Cases']), np.sum(df2['Recovered Cases']), np.sum(df2['Total Deaths'])]\n",
    "\n",
    "        name1 = df[df['Confirmed Cases'] == np.max(df['Confirmed Cases'])].Country.values[0]\n",
    "        num1 = \"{:,}\".format(int(np.max(df['Confirmed Cases'])))\n",
    "        name2 = df[df['Active Cases'] == np.max(df['Active Cases'])].Country.values[0]\n",
    "        num2 = \"{:,}\".format(int(np.max(df['Active Cases'])))\n",
    "        name3 = df[df['Recovered Cases'] == np.max(df['Recovered Cases'])].Country.values[0]\n",
    "        num3 = \"{:,}\".format(int(np.max(df['Recovered Cases'])))\n",
    "        name4 = df[df['Critical Cases'] == np.max(df['Critical Cases'])].Country.values[0]\n",
    "        num4 = \"{:,}\".format(int(np.max(df['Critical Cases'])))\n",
    "        name5 = df[df['Total Deaths'] == np.max(df['Total Deaths'])].Country.values[0]\n",
    "        num5 = \"{:,}\".format(int(np.max(df['Total Deaths'])))\n",
    "\n",
    "    else:\n",
    "        # map update\n",
    "        map_filter = df1[np.isin(df1.Country,country)]\n",
    "\n",
    "        # pie update\n",
    "        pie_filter = df2[np.isin(df2.Country,country)]\n",
    "        values = np.array(pie_filter.drop(['Country'], axis=1))\n",
    "        values = list(chain.from_iterable(values))\n",
    "\n",
    "        # cards update\n",
    "        text_filter = df[np.isin(df.Country,country)].Continent.values[0]\n",
    "        gk = df.groupby('Continent').get_group(text_filter)\n",
    "        name1 = gk[gk['Confirmed Cases'] == np.max(gk['Confirmed Cases'])].Country.values[0]\n",
    "        num1 = \"{:,}\".format(int(np.max(gk['Confirmed Cases'])))\n",
    "        name2 = gk[gk['Active Cases'] == np.max(gk['Active Cases'])].Country.values[0]\n",
    "        num2 = \"{:,}\".format(int(np.max(gk['Active Cases'])))\n",
    "        name3 = gk[gk['Recovered Cases'] == np.max(gk['Recovered Cases'])].Country.values[0]\n",
    "        num3 = \"{:,}\".format(int(np.max(gk['Recovered Cases'])))\n",
    "        name4 = gk[gk['Critical Cases'] == np.max(gk['Critical Cases'])].Country.values[0]\n",
    "        num4 = \"{:,}\".format(int(np.max(gk['Critical Cases'])))\n",
    "        name5 = gk[gk['Total Deaths'] == np.max(gk['Total Deaths'])].Country.values[0]\n",
    "        num5 = \"{:,}\".format(int(np.max(gk['Total Deaths'])))\n",
    "        \n",
    "    \n",
    "    fig1 = px.choropleth(map_filter, locations=\"iso_alpha\",\n",
    "                    featureidkey=\"properties.iso_alpha\",\n",
    "                    projection=\"equirectangular\",\n",
    "                    color=\"Confirmed Cases\", \n",
    "                    hover_name=\"Country\",\n",
    "                    hover_data=[\"Active Cases\", \"Total Deaths\", \"Recovered Cases\"],\n",
    "                    color_continuous_scale=px.colors.sequential.Reds)\n",
    "    \n",
    "    fig1.update_geos(fitbounds=\"locations\", visible=True)\n",
    "    \n",
    "    fig1.update_layout(title_text='Confirmed Cases by Country', title_x=0.5, title_y=0.97, margin={\"r\":10,\"t\":0,\"l\":10,\"b\":0},\n",
    "                        coloraxis_colorbar=dict(\n",
    "                        title=\"Confirmed\",\n",
    "                        thicknessmode=\"pixels\", thickness=10,\n",
    "                        lenmode=\"pixels\", len=150,\n",
    "                        yanchor=\"bottom\", y=0.14,\n",
    "                        xanchor=\"left\", x=0.01\n",
    "                    )\n",
    "                )\n",
    "\n",
    "    labels = ['Active Cases', 'Recovered Cases', 'Total Deaths']\n",
    "    fig2 = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.6)])\n",
    "    fig2.update_layout(title_text=\"Overall Active Cases vs Recovered vs Deaths\", title_x=0.5, margin=dict(t=60, b=0, l=0, r=0), legend=dict(orientation=\"h\",\n",
    "                                                                                                                                            yanchor=\"bottom\",\n",
    "                                                                                                                                            y=-0.1,\n",
    "                                                                                                                                            xanchor=\"right\",\n",
    "                                                                                                                                            x=1)\n",
    "                    )\n",
    "    fig2.update_traces(marker=dict(colors=[pallete['Active'], pallete['Recovered'], pallete['Deaths']]))\n",
    "\n",
    "    card1 = name1 + \" : \" + num1\n",
    "    card2 = name2 + \" : \" + num2\n",
    "    card3 = name3 + \" : \" + num3\n",
    "    card4 = name4 + \" : \" + num4\n",
    "    card5 = name5 + \" : \" + num5\n",
    "\n",
    "    return fig1, fig2, card1, card2, card3, card4, card5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2350d298",
   "metadata": {},
   "outputs": [],
   "source": [
    "@app.callback(\n",
    "    Output(component_id='Bar_Chart', component_property='figure'),\n",
    "    Input(component_id='BarDropdown', component_property='value')\n",
    ")\n",
    "\n",
    "def update_my_fig(continent):\n",
    "\n",
    "    if continent == None:\n",
    "        continent = 'North America'\n",
    "\n",
    "    map_filter = df4[np.isin(df4.Continent,continent)]\n",
    "        \n",
    "    fig = px.bar(map_filter, x=\"Tests Performed\", y=\"Country\", orientation='h')\n",
    "    \n",
    "    fig.update_layout(title_text='Tests Performed Per Region')\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "992ed2b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks for comparison\n",
    "@app.callback(\n",
    "              Output(component_id='compare_trend', component_property='figure'),\n",
    "              Output(component_id='options_compare', component_property='value'),\n",
    "              Output(component_id='warning', component_property='displayed'),\n",
    "              Output(component_id='warning', component_property='message'),\n",
    "              Input(component_id='submit', component_property='n_clicks'),\n",
    "              Input(component_id='compare_trend', component_property='figure'),\n",
    "              State(component_id='options_compare', component_property='value')\n",
    "              )\n",
    "\n",
    "def update_graph_comparison(button_click, input_graph, dropdown_input):\n",
    "\n",
    "    displayed = False\n",
    "    message_text = ''\n",
    "    \n",
    "    dropdown_input = sorted(dropdown_input)\n",
    "    if len(dropdown_input) > 5:\n",
    "        \n",
    "        # Prompt a message here\n",
    "        displayed = True\n",
    "        message_text = 'Maximum number of countries to be compared is 5!'\n",
    "        \n",
    "    \n",
    "    if len(dropdown_input) < 2:\n",
    "        \n",
    "        # Prompt a message here\n",
    "        displayed = True\n",
    "        message_text = 'Minimum number of countries to be compared is 2!'\n",
    "    \n",
    "    if len(dropdown_input) >= 2 and len(dropdown_input) <= 5:\n",
    "        \n",
    "        confirmed_selected_countries = comparison_df[comparison_df['Country'].isin(dropdown_input)].sort_values(by='Country')\n",
    "        # Total confirmed\n",
    "        fig = go.Figure(go.Bar(x = dropdown_input,\n",
    "                               y = confirmed_selected_countries['Total confirmed'], \n",
    "                               yaxis='y1', name='No. Total confirmed', width=0.5,\n",
    "                               text=confirmed_selected_countries['Total confirmed'], textposition='outside',\n",
    "                               hovertemplate=  \"<br>Country: %{x}</b>\" +\n",
    "                                               \"<br>Total Recovered: %{text}</br>\"))\n",
    "\n",
    "\n",
    "\n",
    "        # Total recovered\n",
    "        recovered_scatter = go.Scatter(x=dropdown_input, y=confirmed_selected_countries['Total Recovered percent'], mode=\"markers\",\n",
    "                                       marker=dict(size=70 * confirmed_selected_countries['Total Recovered percent'], color=pallete['Recovered']),\n",
    "                                       yaxis='y2', name='Total Recovered percentage', \n",
    "                                       text=confirmed_selected_countries['Total Recovered'], \n",
    "                                       hovertemplate=  \"<br>Country: %{x}</b>\" +\n",
    "                                                    \"<br>Total Recovered: %{text}</br>\")\n",
    "\n",
    "        recovered_text = [(x+\"%\") for x in (np.round(confirmed_selected_countries['Total Recovered percent']*100, 2)).astype(str).tolist()]\n",
    "        recovered_text = [('<b>' + txt + '</b>') for txt in recovered_text]\n",
    "        recovered_text_scatter = go.Scatter(x=dropdown_input, y=confirmed_selected_countries['Total Recovered percent'],\n",
    "                                            text=recovered_text, mode='text', yaxis='y2', showlegend=False,\n",
    "                                            textfont=dict(\n",
    "                                            family=\"sans serif\",\n",
    "                                            size=12,\n",
    "                                            color=\"#ffffff\"\n",
    "                                         ))\n",
    "\n",
    "        fig.add_traces([recovered_scatter, recovered_text_scatter])\n",
    "\n",
    "\n",
    "        # Total deaths\n",
    "        deaths_scatter = go.Scatter(x=dropdown_input, y=confirmed_selected_countries['Total Deaths percent'], mode='lines+markers', \n",
    "                                    line_shape='spline', line=dict(width=1, color=pallete['Deaths']),\n",
    "                                    marker=dict(size=5, color=pallete['Deaths']), \n",
    "                                    yaxis='y3', name='Total deaths percentage', \n",
    "                                    text=confirmed_selected_countries['Total Deaths'],\n",
    "                                    hovertemplate=  \"<br>Country: %{x}</b>\" +\n",
    "                                                    \"<br>Total Deaths: %{text}</br>\")\n",
    "\n",
    "        deaths_text = [(x+\"%\") for x in (np.round(confirmed_selected_countries['Total Deaths percent']*100, 2)).astype(str).tolist()]\n",
    "        deaths_text = [('<b>' + txt + '</b>') for txt in deaths_text]\n",
    "        deaths_text_scatter = go.Scatter(x=dropdown_input, y=confirmed_selected_countries['Total Deaths percent'],\n",
    "                                         text=deaths_text, mode='text', textposition='top left', yaxis='y3', showlegend=False,\n",
    "                                         textfont=dict(\n",
    "                                            family=\"sans serif\",\n",
    "                                            size=15,\n",
    "                                            color=pallete['Deaths']\n",
    "                                         ))\n",
    "\n",
    "        fig.add_traces([deaths_scatter, deaths_text_scatter])\n",
    "\n",
    "\n",
    "\n",
    "        # Create axis objects\n",
    "        fig.update_layout(\n",
    "            xaxis=dict(\n",
    "                domain=[0.2, 0.9]\n",
    "            ),\n",
    "            yaxis=dict(\n",
    "                title=\"Confirmed\",\n",
    "                titlefont=dict(\n",
    "                    color=pallete['Confirmed']\n",
    "                ),\n",
    "                tickfont=dict(\n",
    "                    color=pallete['Confirmed']\n",
    "                )\n",
    "            ),\n",
    "            yaxis2=dict(\n",
    "                title=\"Recovered\",\n",
    "                titlefont=dict(\n",
    "                    color=pallete['Recovered']\n",
    "                ),\n",
    "                tickfont=dict(\n",
    "                    color=pallete['Recovered']\n",
    "                ),\n",
    "                anchor=\"free\",\n",
    "                overlaying=\"y\",\n",
    "                side=\"left\",\n",
    "                position=0.05\n",
    "            ),\n",
    "            yaxis3=dict(\n",
    "                title=\"Deaths\",\n",
    "                titlefont=dict(\n",
    "                    color=pallete['Deaths']\n",
    "                ),\n",
    "                tickfont=dict(\n",
    "                    color=pallete['Deaths']\n",
    "                ),\n",
    "                anchor=\"x\",\n",
    "                overlaying=\"y\",\n",
    "                side=\"right\"\n",
    "            )\n",
    "        )\n",
    "\n",
    "        return fig.update_layout(title_text=\"Total recovered vs total deaths country-wise\"), dropdown_input, displayed, message_text\n",
    "    \n",
    "    else:\n",
    "        return input_graph, dropdown_input, displayed, message_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "655baabd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks for global vaccination trend (Cummulated)\n",
    "@app.callback(\n",
    "              Output(component_id='options_vac', component_property='options'), \n",
    "              Output(component_id='options_vac', component_property='value'),\n",
    "              Input(component_id='selectOne_vac', component_property='value')\n",
    "              )\n",
    "\n",
    "def update_dropdown_vaccine(radio_input):\n",
    "    if radio_input == 'Continents':\n",
    "        return list(set(continents)-set(['Middle East'])), None\n",
    "    else:\n",
    "        return Any_country, None\n",
    "\n",
    "\n",
    "@app.callback(\n",
    "              Output(component_id='vaccine_trend', component_property='figure'),\n",
    "              Input(component_id='selectOne_vac', component_property='value'),\n",
    "              Input(component_id='options_vac', component_property='value')\n",
    "              )\n",
    "\n",
    "\n",
    "def update_graph_vaccine(radio_input, dropdown_input):\n",
    "    if dropdown_input == None:\n",
    "        dropdown_input = []\n",
    "        world = Vaccination_trend_Continents.groupby(by='date', as_index=False).sum()\n",
    "        line_graph = px.line(world, x=\"date\", y='total_vaccinations', markers=True).update_layout(title_text=\"World cummulated vaccination trend\")\n",
    "        \n",
    "    elif radio_input == 'Continents':\n",
    "        \n",
    "        continent = Vaccination_trend_Continents[Vaccination_trend_Continents.location.isin(dropdown_input)]\n",
    "        line_graph = px.line(continent, x=\"date\", y='total_vaccinations', markers=True, color='location').update_layout(title_text=\"Continents cummulated vaccination trend\", legend=dict(yanchor=\"top\",\n",
    "                                                                                                                                                                                          y=0.8,\n",
    "                                                                                                                                                                                          xanchor=\"left\",\n",
    "                                                                                                                                                                                          x=0.01, \n",
    "                                                                                                                                                                                          bgcolor='rgba(255,255,255,0.6)'))\n",
    "    \n",
    "    else:\n",
    "\n",
    "        country = Vaccination_trend_Countries[Vaccination_trend_Countries.location.isin(dropdown_input)]\n",
    "        line_graph = px.line(country, x=\"date\", y='total_vaccinations', markers=True, color='location').update_layout(title_text=\"Countries cummulated vaccination trend\",legend=dict(yanchor=\"top\",\n",
    "                                                                                                                                                                                      y=0.8,\n",
    "                                                                                                                                                                                      xanchor=\"left\",\n",
    "                                                                                                                                                                                      x=0.01, \n",
    "                                                                                                                                                                                      bgcolor='rgba(255,255,255,0.6)'))\n",
    "        \n",
    "    line_graph.update_traces(mode=\"markers+lines\", hovertemplate=None)\n",
    "    line_graph.update_layout(hovermode=\"x unified\")\n",
    "\n",
    "    return line_graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4ba02e8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks for global Deaths \n",
    "@app.callback(\n",
    "              Output(component_id='options_death', component_property='options'), \n",
    "              Output(component_id='options_death', component_property='value'),\n",
    "              Input(component_id='selectOne_death', component_property='value')\n",
    "              )\n",
    "\n",
    "\n",
    "def update_dropdown_death(radio_input):\n",
    "    if radio_input == 'Continents':\n",
    "        return list(set(continents)-set(['Middle East'])), None\n",
    "    else:\n",
    "        return Any_country, None\n",
    "    \n",
    "\n",
    "@app.callback(\n",
    "              Output(component_id='death_trend', component_property='figure'),\n",
    "              Input(component_id='selectOne_death', component_property='value'),\n",
    "              Input(component_id='options_death', component_property='value')\n",
    "              )\n",
    "\n",
    "def update_graph_death(radio_input, dropdown_input):\n",
    "    if dropdown_input == None:\n",
    "        dropdown_input = \"\"\n",
    "        world = global_daily_trend_continents.groupby(by='date', as_index=False).sum()\n",
    "        \n",
    "        histo_graph = go.Figure(px.histogram(world, \n",
    "                             x='date', \n",
    "                             y='new_deaths', \n",
    "                             nbins=300, \n",
    "                             color_discrete_sequence=['lightgray'], \n",
    "                             hover_data=['date', 'new_deaths'], \n",
    "                             marginal='rug'))\n",
    "        \n",
    "        histo_graph.add_trace(go.Scatter(x=world['date'], y=world['new_deaths_smoothed'], name='World daily deaths trend line'))\n",
    "    \n",
    "    elif radio_input == 'Continents':\n",
    "        \n",
    "        continent = global_daily_trend_continents[global_daily_trend_continents.location == dropdown_input]\n",
    "        histo_graph = go.Figure(px.histogram(continent, \n",
    "                                     x='date', \n",
    "                                     y='new_deaths', \n",
    "                                     nbins=150, \n",
    "                                     color_discrete_sequence=['lightgray'], \n",
    "                                     hover_data=['date', 'new_deaths'], \n",
    "                                     marginal='rug'))\n",
    "\n",
    "        histo_graph.add_trace(go.Scatter(x=continent['date'], y=continent['new_deaths_smoothed'], name=f'{dropdown_input} daily deaths trend line'))\n",
    "    \n",
    "    else:\n",
    "\n",
    "        country = global_daily_trend_countries[global_daily_trend_countries.location == dropdown_input]\n",
    "        histo_graph = go.Figure(px.histogram(country, \n",
    "                                     x='date', \n",
    "                                     y='new_deaths', \n",
    "                                     nbins=100, \n",
    "                                     color_discrete_sequence=['lightgray'], \n",
    "                                     hover_data=['date', 'new_deaths'], \n",
    "                                     marginal='rug'))\n",
    "\n",
    "        histo_graph.add_trace(go.Scatter(x=country['date'], y=country['new_deaths_smoothed'], name=f'{dropdown_input} daily deaths trend line'))\n",
    "    \n",
    "    \n",
    "    return histo_graph.update_layout(title_text=\"Daily deaths\", legend=dict(yanchor=\"top\",\n",
    "                                                                            y=0.8,\n",
    "                                                                            xanchor=\"left\",\n",
    "                                                                            x=0.01, \n",
    "                                                                            bgcolor='rgba(255,255,255,0.6)', \n",
    "                                                                            title='Trend Line'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2bf8d689",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Callbacks for global confirmed cases\n",
    "@app.callback(\n",
    "              Output(component_id='options_conf', component_property='options'), \n",
    "              Output(component_id='options_conf', component_property='value'),\n",
    "              Input(component_id='selectOne_conf', component_property='value')\n",
    "              )\n",
    "\n",
    "def update_dropdown_conf(radio_input):\n",
    "    if radio_input == 'Continents':\n",
    "        return list(set(continents)-set(['Middle East'])), None\n",
    "    else:\n",
    "        return Any_country, None\n",
    "    \n",
    "\n",
    "@app.callback(\n",
    "              Output(component_id='conf_trend', component_property='figure'),\n",
    "              Input(component_id='selectOne_conf', component_property='value'),\n",
    "              Input(component_id='options_conf', component_property='value')\n",
    "              )\n",
    "\n",
    "\n",
    "\n",
    "def update_graph_conf(radio_input, dropdown_input):\n",
    "    if dropdown_input == None:\n",
    "        dropdown_input = \"\"\n",
    "        world = global_daily_trend_continents.groupby(by='date', as_index=False).sum()\n",
    "        \n",
    "        confirmed_trend_line = go.Figure(go.Scatter(x=world['date'], y=world['new_cases'], name='World New Cases'))\n",
    "        world_mean = np.average(world['new_cases'], axis=0)\n",
    "        world_max = np.max(world['new_cases'], axis=0)\n",
    "        confirmed_trend_line.update_traces(mode=\"markers+lines\", hovertemplate=None)\n",
    "\n",
    "\n",
    "        # Trend line\n",
    "        confirmed_trend_line.add_trace(go.Scatter(x=world['date'], y=world['new_cases_smoothed'], name='Trend Line', line_width=1))\n",
    "        \n",
    "        confirmed_trend_line.add_hline(y=world_mean, line_width=1, line_color='green')\n",
    "        confirmed_trend_line.add_annotation(text='Avg = {}'.format(int(world_mean)), x='2022-05-24', y=world_mean, arrowcolor=\"green\", font=dict(family=\"Serif, monospace\",\n",
    "                                                                                                                                                 size=14,\n",
    "                                                                                                                                                 color=\"green\"))\n",
    "\n",
    "        confirmed_trend_line.add_hline(y=world_max, line_width=1, line_color='green')\n",
    "        confirmed_trend_line.add_annotation(text='Max = {}'.format(int(world_max)), x='2022-05-24', y=world_max, arrowcolor=\"green\", font=dict(family=\"Serif, monospace\",\n",
    "                                                                                                                                                size=14,\n",
    "                                                                                                                                                 color=\"green\"))\n",
    "\n",
    "    \n",
    "    elif radio_input == 'Continents':\n",
    "        continent = global_daily_trend_continents[global_daily_trend_continents.location == dropdown_input]\n",
    "        confirmed_trend_line = go.Figure(go.Scatter(x=continent['date'], y=continent['new_cases'], name='New cases'))\n",
    "        confirmed_trend_line.update_traces(mode=\"markers+lines\", hovertemplate=None)\n",
    "\n",
    "        continent_mean = np.average(continent['new_cases'], axis=0)\n",
    "        continent_max = np.max(continent['new_cases'], axis=0)\n",
    "\n",
    "\n",
    "        # Trend line\n",
    "        confirmed_trend_line.add_trace(go.Scatter(x=continent['date'], y=continent['new_cases_smoothed'], name='Trend_line', line_width=1))\n",
    "        confirmed_trend_line.add_hline(y=continent_mean, line_width=1, line_color='green')\n",
    "        confirmed_trend_line.add_annotation(text='Avg = {}'.format(int(continent_mean)), x='2022-05-24', y=continent_mean, arrowcolor=\"green\", font=dict(family=\"Serif, monospace\",\n",
    "                                                                                                                                                 size=14,\n",
    "                                                                                                                                                 color=\"green\"))\n",
    "\n",
    "        confirmed_trend_line.add_hline(y=continent_max, line_width=1, line_color='green')\n",
    "        confirmed_trend_line.add_annotation(text='Max = {}'.format(int(continent_max)), x='2022-05-24', y=continent_max, arrowcolor=\"green\", font=dict(family=\"Serif, monospace\",\n",
    "                                                                                                                                                size=14,\n",
    "                                                                                                                                                 color=\"green\"))\n",
    "\n",
    "        \n",
    "    \n",
    "    else:\n",
    "\n",
    "        country = global_daily_trend_countries[global_daily_trend_countries.location == dropdown_input]\n",
    "        confirmed_trend_line = go.Figure(go.Scatter(x=country['date'], y=country['new_cases'], name='New cases'))\n",
    "        confirmed_trend_line.update_traces(mode=\"markers+lines\", hovertemplate=None)\n",
    "        country_mean = np.average(country['new_cases'], axis=0)\n",
    "        country_max = np.max(country['new_cases'], axis=0)        \n",
    "        \n",
    "        # Trend line\n",
    "        confirmed_trend_line.add_trace(go.Scatter(x=country['date'], y=country['new_cases_smoothed'], name='Trend_line', line_width=1))\n",
    "        confirmed_trend_line.add_hline(y=country_mean, line_width=1, line_color='green')\n",
    "        confirmed_trend_line.add_annotation(text='Avg = {}'.format(int(country_mean)), x='2022-05-24', y=country_mean, arrowcolor=\"green\", font=dict(family=\"Serif, monospace\",\n",
    "                                                                                                                                                 size=14,\n",
    "                                                                                                                                                 color=\"green\"))\n",
    "\n",
    "        confirmed_trend_line.add_hline(y=country_max, line_width=1, line_color='green')\n",
    "        confirmed_trend_line.add_annotation(text='Max = {}'.format(int(country_max)), x='2022-05-24', y=country_max, arrowcolor=\"green\", font=dict(family=\"Serif, monospace\",\n",
    "                                                                                                                                                size=14,\n",
    "                                                                                                                                                 color=\"green\"))\n",
    "\n",
    "    \n",
    "    return confirmed_trend_line.update_layout(title_text=\"Daily confirmed cases\", hovermode=\"x unified\", \n",
    "                                              legend=dict(yanchor=\"top\",\n",
    "                                                          y=0.85,\n",
    "                                                          xanchor=\"left\",\n",
    "                                                          x=0.01, \n",
    "                                                          bgcolor='rgba(255,255,255,0.6)'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cf3352f",
   "metadata": {},
   "source": [
    "## Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c4110cb2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:8050/\n"
     ]
    }
   ],
   "source": [
    "app.run_server()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29f49733",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c346838c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e7a80a10",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4ef53579",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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