Add files via upload

AnimalShelter.py

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+# Cody Cook
+# CS-340 Client/Server Development
+# SNHU
+# 2024/04/02
+# Project One
+
+# Import necessary libraries
+from pymongo import MongoClient
+from pymongo.errors import ConnectionFailure, OperationFailure
+from urllib.parse import quote_plus
+from bson.objectid import ObjectId
+
+# Define the AnimalShelter class
+class AnimalShelter():
+    """ CRUD operations for Animal collection in MongoDB """
+
+    # Initialize the MongoClient and the database
+    def __init__(self, username, password, hostname, port, database, collection):
+        """ Initialize the MongoClient and the database """
+
+        connection = "mongodb://%s:%s@%s:%d" % (quote_plus(username), quote_plus(password), hostname, port)
+
+        # Try to connect to the database
+        try:
+            self.client = MongoClient(connection) # The client object is an instance of the MongoClient class
+            self.database = self.client['%s' % (database)] # The database object is an instance of the database class
+            self.collection = self.database['%s' % (collection)] # The collection object is an instance of the collection class
+
+        # If the connection fails, print an error message
+        except ConnectionFailure as e: 
+            # If the connection fails, print an error message
+            print("Could not connect to MongoDB: %s" % e)
+
+# Create method
+    def create(self, data):
+        """ Create a new animal in the database """
+
+        if data is not None:
+            # Try to insert the data into the database
+            try:
+                self.collection.insert_one(data) # Insert the data into the collection
+                print("Animal added successfully") # Print a success message
+                return True # Return True if successful
+            
+            # If the operation fails, print an error message
+            except OperationFailure as e:
+                # If the operation fails, print an error message
+                print("Could not add animal: %s" % e)
+                # Return False if the operation fails
+                return False
+        else:
+            # If the data is empty, print an error message
+            print("Data is empty, nothing to add")
+            # Return False if the data is empty
+            return False
+
+# Read method
+    def read(self, data):
+        """ Read an animal from the database """
+
+        if data is not None:
+            # Try to read the data from the database
+            try: 
+                # Read the data from the collection
+                documents = list(self.collection.find(data, {"_id": False}))
+                # Return the documents
+                return documents
+            
+            # If the operation fails, print an error message
+            except OperationFailure as e:
+                # Print an error message
+                print("Could not read animal: %s" % e)
+                # Return an empty list
+                return []
+        else:
+            # If the data is empty, print an error message
+            print("Data is empty, nothing to read")
+            # Return an empty list
+            return []
+
+# Update method
+    def update(self, query, data):
+        """ Update an animal in the database """
+        
+        # Check if the query and data are not empty
+        if query is not None and data is not None:
+            # Try to update the data in the database
+            try:
+                # Update the data in the collection
+                result = self.collection.update_many(query, {'$set': data})
+                # Print a success message
+                print(f"Animals updated: {result.modified_count}")
+                # Return the number of animals updated
+                return result.modified_count
+            except OperationFailure as e:
+                # If the operation fails, print an error message
+                print(f"Could not update animal: {e}")
+                return 0
+        else:
+            # If the data or query is empty, print an error message and return 0
+            print("Data and/or query is empty, nothing to update")
+            return 0
+        
+# Delete method
+    def delete(self, data):
+        """ Delete an animal from the database """
+        # Check if the data is not empty
+        if data is not None:
+            # Try to delete the data from the database
+            try:
+                # Delete the data from the collection
+                result = self.collection.delete_many(data)
+                # Print a success message
+                print(f"Animals deleted: {result.deleted_count}")
+                # Return the number of animals deleted
+                return result.deleted_count
+            except OperationFailure as e:
+                # If the operation fails, print an error message and return 0
+                print(f"Could not delete animal: {e}")
+                return 0
+        else:
+            # If the data is empty, print an error message and return 0
+            print("Data is empty, nothing to delete")
+            return 0
+            

AnimalShelterTest.ipynb

@@ -0,0 +1,292 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f9cc3d73",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Import the AnimalShelter class\n",
+    "from AnimalShelter import AnimalShelter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fe96019a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Configure the connection details\n",
+    "username = 'aacuser'\n",
+    "password = '1B@nana4U$'\n",
+    "hostname = 'nv-desktop-services.apporto.com'\n",
+    "port = 30909\n",
+    "database = 'AAC'\n",
+    "collection = 'animals'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "5685f758",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create test data\n",
+    "test_file= {\n",
+    "    '':\"10001\",\n",
+    "    'age_upon_outcome':\"2 weeks\", \n",
+    "    'animal_id':\"TEST123\",\n",
+    "    'animal_type': \"Dog\", \n",
+    "    'breed':\"Hawaiian Labrador\", \n",
+    "    'color':\"Black\",\n",
+    "    'date_of_birth':\"2024-03-12\", \n",
+    "    'monthyear': \"2024-03-26 21:18:00\", \n",
+    "    'outcome_subtype':'Transfer',\n",
+    "    'outcome_type':\"GOAT\", \n",
+    "    'sex_upon_outcome':\"Intact Male\", \n",
+    "    'location_long':\"-97.4033754809296\",\n",
+    "    'location_lat':\"30.6664774192579\",\n",
+    "    'age_upon_outcome_in_weeks':\"50 years\", \n",
+    "    \"name\": \"Bark Obama\",\n",
+    "    \"datetime\": \"2024-03-26 21:18:00\",\n",
+    "    \"status\": \"Available\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5bd5672a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a sample query\n",
+    "query = {\"breed\":\"Hawaiian Labrador\"}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "e43f344a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initiate a failed connection to the MongoDB\n",
+    "shelter = AnimalShelter(\"username\", \"password\", hostname, port, database, collection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "4d770845",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Could not read animal: Authentication failed., full error: {'ok': 0.0, 'errmsg': 'Authentication failed.', 'code': 18, 'codeName': 'AuthenticationFailed'}\n",
+      "Check existing sample: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check to see if the entry exists already \n",
+    "# This assumes this hasn't already been run before.\n",
+    "precheck = shelter.read(query)\n",
+    "print(\"Check existing sample:\", precheck)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "15b3d1bb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initiate the connection to the MongoDB\n",
+    "shelter = AnimalShelter(username, password, hostname, port, database, collection);"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "7f73158c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Check existing sample: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check to see if the entry exists already \n",
+    "# This assumes this hasn't already been run before.\n",
+    "precheck = shelter.read(query)\n",
+    "print(\"Check existing sample:\", precheck)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "38e43a66",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Animal added successfully\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Add the animal to the database\n",
+    "shelter.create(test_file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bb4ebdd0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Check post-add sample: [{'': '10001', 'age_upon_outcome': '2 weeks', 'animal_id': 'TEST123', 'animal_type': 'Dog', 'breed': 'Hawaiian Labrador', 'color': 'Black', 'date_of_birth': '2024-03-12', 'monthyear': '2024-03-26 21:18:00', 'outcome_subtype': 'Transfer', 'outcome_type': 'GOAT', 'sex_upon_outcome': 'Intact Male', 'location_long': '-97.4033754809296', 'location_lat': '30.6664774192579', 'age_upon_outcome_in_weeks': '50 years', 'name': 'Bark Obama', 'datetime': '2024-03-26 21:18:00', 'status': 'Available'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Confirm the entry was added\n",
+    "postcheck = shelter.read(query)\n",
+    "print(\"Check post-add sample:\", postcheck)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "a822e964",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Animals updated: 1\n",
+      "Updated items: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Update the test animal to an Adopted status\n",
+    "query = {\"animal_id\": \"TEST123\"}\n",
+    "updates = {\"status\": \"Adopted\"}\n",
+    "result = shelter.update(query, updates)\n",
+    "print(f\"Updated items: {result}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "5f5300c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Result: [{'': '10001', 'age_upon_outcome': '2 weeks', 'animal_id': 'TEST123', 'animal_type': 'Dog', 'breed': 'Hawaiian Labrador', 'color': 'Black', 'date_of_birth': '2024-03-12', 'monthyear': '2024-03-26 21:18:00', 'outcome_subtype': 'Transfer', 'outcome_type': 'GOAT', 'sex_upon_outcome': 'Intact Male', 'location_long': '-97.4033754809296', 'location_lat': '30.6664774192579', 'age_upon_outcome_in_weeks': '50 years', 'name': 'Bark Obama', 'datetime': '2024-03-26 21:18:00', 'status': 'Adopted'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Confirm the update took place\n",
+    "check = shelter.read(query)\n",
+    "print(f\"Result: {check}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "974092b3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Animals deleted: 1\n",
+      "Deleted result: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Delete animals matching the query\n",
+    "result = shelter.delete(query)\n",
+    "print(f\"Deleted result: {result}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "aae82505",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Check for records:  []\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Check for the deleted animal\n",
+    "check = shelter.read(query)\n",
+    "print(\"Check for records: \", check)\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Dashboard.ipynb

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+{
+ "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"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}