{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "659038cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setup the Jupyter version of Dash\n",
    "from jupyter_dash import JupyterDash"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1653e615",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure the necessary Python module imports\n",
    "import dash_leaflet as dl\n",
    "from dash import dcc\n",
    "from dash import html\n",
    "import plotly.express as px\n",
    "from dash import dash_table\n",
    "from dash.dependencies import Input, Output, State\n",
    "import base64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b86503df",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure OS routines\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f21674e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configure the plotting routines\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47fcde56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the CRUD module\n",
    "from AnimalShelter import AnimalShelter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f0ff9f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Instantiate the CRUD module\n",
    "username = 'aacuser'\n",
    "password = '1B@nana4U$'\n",
    "hostname = 'nv-desktop-services.apporto.com'\n",
    "port = 30909\n",
    "database = 'AAC'\n",
    "collection = 'animals'\n",
    "db = AnimalShelter(username, password, hostname, port, database, collection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "03f59f24",
   "metadata": {},
   "outputs": [],
   "source": [
    "# populate df with a dataframe of the items from the CRUD module\n",
    "df = pd.DataFrame.from_records(db.read({}))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "49395346",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop _id; but I actually already do this in the CRUD so I will ignore it\n",
    "\n",
    "#df.drop(columns=['_id'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4159b76b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000\n",
      "Index(['rec_num', 'age_upon_outcome', 'animal_id', 'animal_type', 'breed',\n",
      "       'color', 'date_of_birth', 'datetime', 'monthyear', 'name',\n",
      "       'outcome_subtype', 'outcome_type', 'sex_upon_outcome', 'location_lat',\n",
      "       'location_long', 'age_upon_outcome_in_weeks'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "# Debugging to confirm records pulled\n",
    "print(len(df.to_dict(orient='records')))\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3ed24c36",
   "metadata": {},
   "outputs": [],
   "source": [
    "# build the layout\n",
    "app = JupyterDash(__name__)\n",
    "image_filename = 'logo.png' \n",
    "header1 = \"CS-340 Dashboard by Cody Cook\"\n",
    "encoded_image = base64.b64encode(open(image_filename, 'rb').read())\n",
    "\n",
    "# define filter options\n",
    "filter_options = [\n",
    "    {'label': 'Water Rescue', 'value': 'water'},\n",
    "    {'label': 'Mountain or Wilderness Rescue', 'value': 'mountain'},\n",
    "    {'label': 'Disaster or Individual Tracking', 'value': 'disaster'},\n",
    "    {'label': 'Reset', 'value': 'reset'}\n",
    "]\n",
    "\n",
    "app.layout = html.Div([\n",
    "    html.Div([\n",
    "        html.Img(\n",
    "            # show image and set size to 100x100\n",
    "            src='data:image/png;base64,{}'.format(encoded_image.decode()),\n",
    "            style={'height': '100px', 'width': '100px', 'display': 'block', 'margin-left': 'auto', 'margin-right': 'auto'}\n",
    "        ),\n",
    "        # show header1\n",
    "        html.H1(header1, style={'textAlign': 'center'}),\n",
    "        ],\n",
    "        style={'text-align': 'center'}\n",
    "    ),\n",
    "    html.Hr(),\n",
    "    html.Div(\n",
    "        dcc.Dropdown(\n",
    "            id='filter-type',\n",
    "            options=[\n",
    "                # show the filter options\n",
    "                {'label': 'Water Rescue', 'value': 'water'},\n",
    "                {'label': 'Mountain or Wilderness Rescue', 'value': 'mountain'},\n",
    "                {'label': 'Disaster or Individual Tracking', 'value': 'disaster'},\n",
    "                {'label': 'Reset', 'value': 'reset'}\n",
    "            ],\n",
    "            value='reset',  \n",
    "            clearable=False \n",
    "        )),\n",
    "    html.Hr(),\n",
    "    dash_table.DataTable(\n",
    "        id='datatable-id',\n",
    "        columns=[{\n",
    "            \"name\": i, \n",
    "            \"id\": i, \n",
    "            \"deletable\": False, \n",
    "            \"selectable\": True\n",
    "        } for i in df.columns],\n",
    "        data=df.to_dict('records'),\n",
    "        editable=False,\n",
    "        filter_action=\"native\",\n",
    "        sort_action=\"native\",\n",
    "        sort_mode=\"multi\",\n",
    "        column_selectable=\"single\",\n",
    "        row_selectable=\"single\",\n",
    "        page_action=\"native\",\n",
    "        page_current=0,\n",
    "        page_size=10,\n",
    "        selected_columns=[df.columns[0]]\n",
    "    ),\n",
    "    html.Br(),\n",
    "    html.Hr(),\n",
    "    html.Div(className='row',\n",
    "         style={'display' : 'flex'},\n",
    "             children=[\n",
    "        html.Div(\n",
    "            # show the graph\n",
    "            id='graph-id',\n",
    "            className='col s12 m6',\n",
    "\n",
    "            ),\n",
    "        html.Div(\n",
    "            # show the map\n",
    "            id='map-id',\n",
    "            className='col s12 m6',\n",
    "            )\n",
    "        ])\n",
    "])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b29d287",
   "metadata": {},
   "outputs": [],
   "source": [
    "@app.callback(\n",
    "    Output('datatable-id', 'data'),\n",
    "    [Input('filter-type', 'value')]\n",
    ")\n",
    "\n",
    "def update_dashboard(filter_type):\n",
    "    # define what happens when the filter_type changes\n",
    "    if filter_type == 'water':\n",
    "        query = {\n",
    "        \"breed\": {\"$in\": [\n",
    "            \"Labrador Retriever Mix\", \n",
    "            \"Chesapeake Bay Retriever\", \n",
    "            \"Newfoundland\"]},\n",
    "        \"sex_upon_outcome\": \"Intact Female\",\n",
    "        \"age_upon_outcome_in_weeks\": {\n",
    "            \"$gte\": 26, \n",
    "            \"$lte\": 156}\n",
    "    }\n",
    "    elif filter_type == 'mountain':\n",
    "        query = {\n",
    "        \"breed\": {\"$in\": [\n",
    "            \"German Shepherd\", \n",
    "            \"Alaskan Malamute\", \n",
    "            \"Old English Sheepdog\", \n",
    "            \"Siberian Husky\", \n",
    "            \"Rottweiler\"]},\n",
    "        \"sex_upon_outcome\": \"Intact Male\",\n",
    "        \"age_upon_outcome_in_weeks\": {\n",
    "            \"$gte\": 26, \n",
    "            \"$lte\": 156}\n",
    "    }\n",
    "    elif filter_type == 'disaster':\n",
    "        query = {\n",
    "        \"breed\": {\"$in\": [\n",
    "            \"Doberman Pinscher\", \n",
    "            \"German Shepherd\", \n",
    "            \"Golden Retriever\", \n",
    "            \"Bloodhound\", \n",
    "            \"Rottweiler\"]},\n",
    "        \"sex_upon_outcome\": \"Intact Male\",\n",
    "        \"age_upon_outcome_in_weeks\": {\n",
    "            \"$gte\": 20, \n",
    "            \"$lte\": 300}\n",
    "    }\n",
    "    else:\n",
    "        # leave it unfiltered\n",
    "        query = {}\n",
    "\n",
    "    # Fetch data using CRUD module and query\n",
    "    filtered_df = pd.DataFrame.from_records(db.read(query))\n",
    "    \n",
    "    # Ensure the '_id' column is not passed to the DataTable to prevent rendering issues\n",
    "    # I readded this and manually drop the _id without errors in case it already doesn't exist\n",
    "    filtered_df.drop(columns=['_id'], inplace=True, errors='ignore')\n",
    "\n",
    "    # Convert the df to a compatible dictionary format\n",
    "    return filtered_df.to_dict('records')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f91a318d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Display the breed of animal based on quantity represented in the data table\n",
    "@app.callback(\n",
    "    Output('graph-id', \"children\"),\n",
    "    [Input('datatable-id', \"derived_virtual_data\")]\n",
    ")\n",
    "\n",
    "def update_graphs(viewData):\n",
    "    # return nothing if the data is empty\n",
    "    if not viewData:\n",
    "        return None\n",
    "    \n",
    "    # if there are too many breeds, limit with \"other\"\n",
    "    dff = pd.DataFrame.from_dict(viewData)\n",
    "    breed_counts = dff['breed'].value_counts()\n",
    "    top_breeds = breed_counts.head(10)\n",
    "    other_count = breed_counts.iloc[10:].sum()\n",
    "    pie_title = \"Top 10 Breeds\"\n",
    "\n",
    "    # Check if other_count is greater than 0\n",
    "    if other_count > 0:\n",
    "        # Create a Series for Other to concatenate with top_breeds\n",
    "        other_series = pd.Series({'Other': other_count})\n",
    "        # Use concat to merge the top breeds with the 'Other' series\n",
    "        chart_data = pd.concat([top_breeds, other_series], axis=0)\n",
    "        pie_title = \"Top 10 Breeds + Other\"\n",
    "    else:\n",
    "        # If 'other_count' is 0 or less, only use top_breeds\n",
    "        chart_data = top_breeds\n",
    "\n",
    "\n",
    "    chart_data = chart_data.reset_index()\n",
    "    chart_data.columns = ['Breed', 'Count']\n",
    "    \n",
    "    # Generate a pie chart\n",
    "    fig = px.pie(chart_data, values='Count', names='Breed', title=pie_title)\n",
    "    \n",
    "    return dcc.Graph(figure=fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4c3537de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Highlight a selected column\n",
    "@app.callback(\n",
    "    Output('datatable-id', 'style_data_conditional'),\n",
    "    [Input('datatable-id', 'selected_columns')]\n",
    ")\n",
    "\n",
    "def update_styles(selected_columns):\n",
    "    return [{\n",
    "        'if': { 'column_id': i },\n",
    "        'background_color': '#D2F3FF'\n",
    "    } for i in selected_columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1c440a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Update the geo-location chart with the selected item\n",
    "@app.callback(\n",
    "    Output('map-id', \"children\"),\n",
    "    [Input('datatable-id', \"derived_virtual_data\"),\n",
    "     Input('datatable-id', \"derived_virtual_selected_rows\")]\n",
    ")\n",
    "\n",
    "def update_map(viewData, index):\n",
    "    # If viewData is empty or index is not set \n",
    "    if viewData is None or not index:\n",
    "        # Center on Austin, TX\n",
    "        return [\n",
    "            dl.Map(\n",
    "                style={\n",
    "                    'width': '1000px', \n",
    "                    'height': '500px'\n",
    "                },\n",
    "                center=[30.66, -97.40], \n",
    "                zoom=10, \n",
    "                children=[dl.TileLayer(id=\"base-layer-id\")])\n",
    "        ]\n",
    "    \n",
    "    # Convert viewData to DataFrame\n",
    "    dff = pd.DataFrame.from_dict(viewData)\n",
    "    row = index[0]\n",
    "\n",
    "    # Latitude and longitude for the selected item\n",
    "    lat = dff.iloc[row, dff.columns.get_loc('location_lat')]\n",
    "    lon = dff.iloc[row, dff.columns.get_loc('location_long')]\n",
    "\n",
    "    # Update map centering dynamically based on the selected item's location\n",
    "    return [\n",
    "        dl.Map(\n",
    "            style={\n",
    "                'width': '1000px', \n",
    "                'height': '500px'\n",
    "            }, \n",
    "            # reposition the map\n",
    "            center=[lat, lon], \n",
    "            zoom=10, \n",
    "            children=[\n",
    "                dl.TileLayer(id=\"base-layer-id\"),\n",
    "                dl.Marker(\n",
    "                    # drop a point on the map\n",
    "                    position=[lat, lon], \n",
    "                    children=[\n",
    "                        dl.Tooltip(\n",
    "                            # show a tooltip on mouseover\n",
    "                            dff.iloc[row, dff.columns.get_loc('breed')]\n",
    "                        ),\n",
    "                        dl.Popup([\n",
    "                            # show a popup of the animal's name\n",
    "                            html.H1(\"Animal Name\"),\n",
    "                            html.P(dff.iloc[row, dff.columns.get_loc('name')]) \n",
    "                        ])  \n",
    "                    ])\n",
    "            ])\n",
    "    ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eefb074c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dash app running on http://127.0.0.1:30088/\n"
     ]
    }
   ],
   "source": [
    "app.run_server(debug=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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