Popular Places

A Powerful Tool for Live Crowd and Traffic Data.

This application gives you the ability to search Google's popular times information, particularly popularity and crowd traffic information. Any type of place can be used including shops, establishments and prominent points of interest.

It utilizes a set of Geocoder APIs in order to perform the search, while being able to provide both place and time-based live crowd data.

Example: Find out how busy your favorite bars are...

Features

DBMS: PostgreSQL, SQLite
APIs: OSM, Overpass, Nominatim (Geocoder)
DB Tools: SQLAlchemy ORM, Migrations
Maps: Leaflet, Wicket
UI: Boostrap, PerfectScrollbar
Visualization: ChartJS
Timezones: Luxon
Session-Based authentication and mailing system Forms validation
Mobile-focused UX Design
Advanced Data Analysis
Deployment Scripts: Docker, Gunicorn/Nginx, Heroku

Showcase

Basic Modules

Flask Black Dashboard is an open-source Flask Dashboard initially designed by AppSeed on top of a modern dark-themed UI. Features a beautiful Bootstrap Dashboard with a huge number of components built to fit together and look amazing. It combines colors that are easy on the eye, spacious cards, beautiful typography, and graphics.

Flask is a lightweight web application framework written in Python that provides a lightweight codebase and libraries that can be easily extended to complex projects.

Popular Times is a Google's Places API-related Service that provides information about popular times, wait times, and visit duration. The initial idea by m-wrzr was to query and scrape Google Search itself.

As Google Maps is constantly updated this application can be unstable.

Official Definition: Popular times graph ~ This graph shows how busy your location typically is during different times of the day. Popular times are based on average popularity over the last few months. Popularity for any given hour is shown relative to the typical peak popularity for the business for the week. For example, in the image below, 8 PM–9 PM on Saturday is one of the more popular times of the week for this business.

Data is acquired from Google popular times, updated every 15 minutes. Google uses aggregated and anonymised data from users who have opted in to Google location history to make a reasonable estimate of how many users are in a given location at any given time of the day.

Ideas:
High crowd - places that have more than 50% crowd
Gaining crowd - places that have more than 10% gain in crowd for the past 15 minutes
Usual crowd - shows how active a location typically is currently, based on average popularity over the past few months
Current crowd - shows how active a location is right now as a percentage of its estimated maximum capacity
Crowd ratio - shows how active a location is right now compared to its usual level of activity
Crowd changes - shows the percentage change in crowd in the last 15 minutes

Places API Documentation:
https://developers.google.com/places/web-service/usage-and-billing
https://developers.google.com/maps/documentation/places/web-service/usage-and-billing
https://cloud.google.com/maps-platform/pricing/sheet/#places.

Google's Nearby Search Documentation:
https://developers.google.com/maps/documentation/places/web-service/search-nearby
https://developers.google.com/maps/documentation/places/web-service/search?hl=en#PlaceSearchRequests
https://developers.google.com/maps/documentation/places/web-service/supported_types

Alternative Implementations: https://besttime.app
https://github.com/philshem/gmaps_popular_times_scraper
https://populartimes.herokuapp.com
https://github.com/bluemelodybox/flask-popularplaces

Overpass and Nominatim are APIs based on OpenStreetMaps with the latter also being a Geocoder/Reverse Geocoder. A scientific comparison on the performance of various Geocoders: https://www.thinkmind.org/articles/soft_v8_n34_2015_7.pdf.

Option 1: Pelias (Uses ElasticSearch) (Self-Hosted)

Option 2: Nominatim (Only usable with Text Query, Reverse Geocoder returns just 1 Result) (Hosted Online)

Option 3: Overpass and its online hosted version Overpass Turbo. They both use Nominatim for Geocoding.

OSM Documentation:
https://taginfo.openstreetmap.org/
https://wiki.openstreetmap.org/wiki/Overpass_API/Language_Guide
https://wiki.openstreetmap.org/wiki/Overpass_turbo/Wizard
https://boundingbox.klokantech.com/
https://github.com/wiktorn/Overpass-API

How to Use

$ # Get the code
$ git clone https://github.com/app-generator/flask-black-dashboard.git
$ cd flask-black-dashboard
$
$ # Virtualenv modules installation (Unix based systems)
$ virtualenv env
$ source env/bin/activate
$
$ # Virtualenv modules installation (Windows based systems)
$ # virtualenv env
$ # .\env\Scripts\activate
$
$ # Install modules - SQLite Database
$ pip3 install -r requirements.txt
$
$ # OR with PostgreSQL connector
$ # pip install -r requirements-pgsql.txt
$
$ # Set the FLASK_APP environment variable
$ (Unix/Mac) export FLASK_APP=run.py
$ (Windows) set FLASK_APP=run.py
$ (Powershell) $env:FLASK_APP = ".\run.py"
$
$ # Set up the DEBUG environment
$ # (Unix/Mac) export FLASK_ENV=development
$ # (Windows) set FLASK_ENV=development
$ # (Powershell) $env:FLASK_ENV = "development"
$
$ # Start the application (development mode)
$ # --host=0.0.0.0 - expose the app on all network interfaces (default 127.0.0.1)
$ # --port=5000    - specify the app port (default 5000)  
$ flask run --host=0.0.0.0 --port=5000
$
$ # Access the dashboard in browser: http://127.0.0.1:5000/

Note: To use the app, please access the registration page and create a new user.

Codebase Structure

The project is coded using blueprints, app factory pattern, dual configuration profile (development and production) and an intuitive structure presented bellow:

Simplified version

< PROJECT ROOT >
   |
   |-- app/                      # Implements app logic
   |    |-- base/                # Base Blueprint - handles the authentication
   |    |-- home/                # Home Blueprint - serve UI Kit pages
   |    |
   |   __init__.py               # Initialize the app
   |
   |-- requirements.txt          # Development modules - SQLite storage
   |-- requirements-mysql.txt    # Production modules  - Mysql DMBS
   |-- requirements-pqsql.txt    # Production modules  - PostgreSql DMBS
   |
   |-- .env                      # Inject Configuration via Environment
   |-- config.py                 # Set up the app
   |-- run.py                    # Start the app - WSGI gateway
   |
   |-- ************************************************************************

The bootstrap flow

run.py loads the .env file
Initialize the app using the specified profile: Debug or Production
- If env.DEBUG is set to True the SQLite storage is used
- If env.DEBUG is set to False the specified DB driver is used (MySql, PostgreSQL)
Call the app factory method create_app defined in app/init.py
Redirect the guest users to Login page
Unlock the pages served by home blueprint for authenticated users

App / Base Blueprint

The Base blueprint handles the authentication (routes and forms) and assets management. The structure is presented below:

< PROJECT ROOT >
   |
   |-- app/
   |    |-- home/                                # Home Blueprint - serve app pages (private area)
   |    |-- base/                                # Base Blueprint - handles the authentication
   |         |-- static/
   |         |    |-- <css, JS, images>          # CSS files, Javascripts files
   |         |
   |         |-- templates/                      # Templates used to render pages
   |              |
   |              |-- includes/                  #
   |              |    |-- navigation.html       # Top menu component
   |              |    |-- sidebar.html          # Sidebar component
   |              |    |-- footer.html           # App Footer
   |              |    |-- scripts.html          # Scripts common to all pages
   |              |
   |              |-- layouts/                   # Master pages
   |              |    |-- base-fullscreen.html  # Used by Authentication pages
   |              |    |-- base.html             # Used by common pages
   |              |
   |              |-- accounts/                  # Authentication pages
   |                   |-- login.html            # Login page
   |                   |-- register.html         # Registration page
   |
   |-- requirements.txt                          # Development modules - SQLite storage
   |-- requirements-mysql.txt                    # Production modules  - Mysql DMBS
   |-- requirements-pqsql.txt                    # Production modules  - PostgreSql DMBS
   |
   |-- .env                                      # Inject Configuration via Environment
   |-- config.py                                 # Set up the app
   |-- run.py                                    # Start the app - WSGI gateway
   |
   |-- ************************************************************************

App / Home Blueprint

The Home blueprint handles UI Kit pages for authenticated users. This is the private zone of the app - the structure is presented below:

< PROJECT ROOT >
   |
   |-- app/
   |    |-- base/                     # Base Blueprint - handles the authentication
   |    |-- home/                     # Home Blueprint - serve app pages (private area)
   |         |
   |         |-- templates/           # UI Kit Pages
   |              |
   |              |-- index.html      # Default page
   |              |-- page-404.html   # Error 404 - mandatory page
   |              |-- page-500.html   # Error 500 - mandatory page
   |              |-- page-403.html   # Error 403 - mandatory page
   |              |-- *.html          # All other HTML pages
   |
   |-- requirements.txt               # Development modules - SQLite storage
   |-- requirements-mysql.txt         # Production modules  - Mysql DMBS
   |-- requirements-pqsql.txt         # Production modules  - PostgreSql DMBS
   |
   |-- .env                           # Inject Configuration via Environment
   |-- config.py                      # Set up the app
   |-- run.py                         # Start the app - WSGI gateway
   |
   |-- ************************************************************************

Deployment

The app is provided with a basic configuration to be executed in Docker, Heroku, Gunicorn, and Waitress.

Development inside Docker

Feel free to perform development inside a Docker container instead of the host machine.

Using multiple compose files

$ sudo docker-compose build && sudo docker-compose -f docker-compose.yml -f docker-compose-dev.yml up -d

Docker

The application can be easily executed in a docker container. The steps:

Get the code

$ git clone https://github.com/app-generator/flask-black-dashboard.git
$ cd flask-black-dashboard

Start the app in Docker

$ sudo docker-compose pull && sudo docker-compose build && sudo docker-compose up -d

Visit http://localhost:5005 in your browser. The app should be up & running.

Heroku

https://devcenter.heroku.com/articles/python-gunicorn

Steps to deploy on Heroku

Create a FREE account on Heroku platform
Install the Heroku CLI that match your OS: Mac, Unix or Windows
Open a terminal window and authenticate via heroku login command
Clone the sources and push the project for LIVE deployment

$ # Clone the source code:
$ git clone https://github.com/app-generator/flask-black-dashboard.git
$ cd flask-black-dashboard
$
$ # Check Heroku CLI is installed
$ heroku -v
heroku/7.25.0 win32-x64 node-v12.13.0 # <-- All good
$
$ # Check Heroku CLI is installed
$ heroku login
$ # this commaond will open a browser window - click the login button (in browser)
$
$ # Create the Heroku project
$ heroku create
$
$ # Trigger the LIVE deploy
$ git push heroku master
$
$ # Open the LIVE app in browser
$ heroku open

Gunicorn

Gunicorn 'Green Unicorn' is a Python WSGI HTTP Server for UNIX.

Install using pip

$ pip install gunicorn

Start the app using gunicorn binary

$ gunicorn --bind 0.0.0.0:8001 run:app
Serving on http://localhost:8001

Visit http://localhost:8001 in your browser. The app should be up & running.

Waitress

Waitress (Gunicorn equivalent for Windows) is meant to be a production-quality pure-Python WSGI server with very acceptable performance. It has no dependencies except ones that live in the Python standard library.

Install using pip

$ pip install waitress

Start the app using waitress-serve

$ waitress-serve --port=8001 run:app
Serving on http://localhost:8001

Visit http://localhost:8001 in your browser. The app should be up & running.

Links

Deployment:

Crawling

Important Thread on Google Search Crawling

Name		Name	Last commit message	Last commit date
Latest commit History 161 Commits
.idea		.idea
.vscode		.vscode
app		app
cache		cache
db		db
media		media
migrations		migrations
nginx		nginx
ssh		ssh
ssl		ssl
test		test
.dockerignore		.dockerignore
.env-template		.env-template
.gitignore		.gitignore
.travis.yml		.travis.yml
CHANGELOG.md		CHANGELOG.md
Dockerfile		Dockerfile
LICENSE.md		LICENSE.md
Procfile		Procfile
README.md		README.md
config.py		config.py
docker-compose-dev.yml		docker-compose-dev.yml
docker-compose.yml		docker-compose.yml
gunicorn-cfg.py		gunicorn-cfg.py
package.json		package.json
requirements-mysql.txt		requirements-mysql.txt
requirements.txt		requirements.txt
run.py		run.py
runtime.txt		runtime.txt

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Repository files navigation

Popular Places

Features

Getting Started

Showcase

Basic Modules

How to Use

Codebase Structure

Simplified version

The bootstrap flow

App / Base Blueprint

App / Home Blueprint

Deployment

Development inside Docker

Docker

Heroku

Gunicorn

Waitress

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spykard/popularplaces-app

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Popular Places

Features

Getting Started

Showcase

Basic Modules

How to Use

Codebase Structure

Simplified version

The bootstrap flow

App / Base Blueprint

App / Home Blueprint

Deployment

Development inside Docker

Docker

Heroku

Gunicorn

Waitress

Links

Credits

About

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