Welcome to the Content Suggestion Team Project! This is a copy of the original repo (which is private and owned by Minerva University). The project was co-created with couple other students and is an Instagram suggestion tool that provides account analytics and recommendations for future content with an automated CI/CD pipeline (on the original repo through TravisCI and Github Actions). It uses Jinja2, Bootstrap and basic CSS/HTML for the front-end, while making use of Flask, SQLAlchemy, PostgreSQL, BeautifulSoup and instagram-scraper for the back-end. There are also multiple unit and integration tests spanning various features.

Below are some screenshots from the website:

You have two options to access this project: through Heroku and by manually downloading and running locally.

The link to the Heroku website: https://content-suggestion.herokuapp.com/

The initial Figma prototype can be accessed here: https://www.figma.com/file/vrpi5Gtfw80gj8RYzmrxMT/CS162?node-id=8%3A162

Virtual environment is a key component in ensuring that the application is configured in the right environment. To run the project locally, make sure you follow the next steps:

• Python 3
• Pip 3

$ brew install python3

Pip3 is installed with Python3

To install virtualenv via pip run:

$ pip3 install virtualenv

Creation of virtualenv:

$ virtualenv -p python3 venv

If the above code does not work, you could also do

$ python3 -m venv venv

To activate the virtualenv:

$ source venv/bin/activate

Or, if you are using Windows - reference source:

$ venv\Scripts\activate

To deactivate the virtualenv (after you finished working):

$ deactivate

Install dependencies in virtual environment:

$ pip3 install -r requirements.txt

All environment variables are stored within the .env file and loaded with dotenv package.

Never commit your local settings to the Github repository!

Start the server by running:

$ export FLASK_ENV=dev
$ export FLASK_APP=web
$ python3 -m flask run

If using Windows, use 'set' instead of 'export'

To run the unit tests use the following commands:

$ python3 -m venv venv_unit
$ source venv_unit/bin/activate
$ pip install -r requirements-unit.txt
$ export FLASK_ENV=test
$ export DATABASE_URL='sqlite:///web.db'
$ export SECRET_KEY='testing_key'
$ pytest unit_test

To run integration tests you need to start your containers and then run tests:

$ python3 -m venv venv_integration
$ source venv_integration/bin/activate
$ pip3 install -r requirements-integration.txt
$ export FLASK_ENV=test
$ export DATABASE_URL="mysql+pymysql://root:test_password@127.0.0.1:3306/main"
$ docker-compose build --no-cache
$ docker-compose up -d --force-recreate
$ pytest integration_test
$ docker-compose down

Flask-Migrations is an extensions that manages DB migrations, common commands:

flask db migrate (create migration) flask db upgrade (implement migration in the DB)

The app can send emails using the following template:

from .mail import mail

msg = Message(subject="email_subject", 
sender="no-reply@suggestme.com", 
recipients=[recipient_email@gmail.com])
msg.html = render_template("html_file_name_here.html")
mail.send(msg)

Standardized form template that can be used in any view:

First import render_field, it's a template that will render form fields and show their respective errors in case there is any.

{% from "_formhelpers.html" import render_field %}

Then you can create forms by calling render_field(form_data, placeholder="test"):

<form method=post>
    {{ form.hidden_tag() }}
    {{ render_field(form.data1, placeholder="Data 1") }}
    {{ render_field(form.data2, placeholder="Data 2") }}
    <p><input type=submit value="Submit">
</form>

There's also an optional extra line before submit, if you want to send 'flash' alerts to your form.

{% include "_messages.html" %}