jwallace145 / bartensor

We gon' make some drinks bruh.

Bartensor

Bartensor is community-driven, intelligent platform that connects alcohol consumers around the world. This Django web application allows people to easily search for their favorite drink's recipe or discover new similar drinks from our SQLite database and much more. Bartensor allows registered users to create custom drinks and add their recipes to our database and vote on other drink recipes. Bartensor aims to connect communities through alcohol so registered users can friend and comment with other users.

Getting Started

This set of instructions will help you locally install the required python dependencies and run the Bartensor Django server for development purposes.

Python Application Dependencies

Bartensor uses Django3.0.3 - a python framework - so there are required python dependencies listed in the requirements.txt file of this repository.

appdirs==1.4.3
asgiref==3.2.3
astroid==2.3.3
atomicwrites==1.3.0
attrs==19.3.0
autopep8==1.5
awsebcli==3.17.1
bcrypt==3.1.7
beautifulsoup4==4.8.2
blessed==1.17.4
botocore==1.14.17
bs4==0.0.1
cached-property==1.5.1
cachetools==4.0.0
cement==2.8.2
certifi==2019.11.28
cffi==1.14.0
chardet==3.0.4
colorama==0.3.9
coverage==5.0.3
cryptography==2.9
distlib==0.3.0
Django==3.0.3
django-crispy-forms==1.8.1
django-jenkins==0.110.0
docker==4.2.0
docker-compose==1.25.4
dockerpty==0.4.1
docopt==0.6.2
docutils==0.15.2
entrypoints==0.3
filelock==3.0.12
flake8==3.7.9
future==0.16.0
google-api-core==1.16.0
google-auth==1.11.2
google-cloud-speech==1.3.2
googleapis-common-protos==1.51.0
grpcio==1.27.2
ibm-cloud-sdk-core==1.5.1
ibm-watson==4.2.1
idna==2.7
isort==4.3.21
jmespath==0.9.5
jsonschema==3.2.0
lazy-object-proxy==1.4.3
lxml==4.5.0
mccabe==0.6.1
more-itertools==8.2.0
nltk==3.4.5
packaging==20.1
paramiko==2.7.1
pathspec==0.5.9
pep8==1.7.1
Pillow==7.0.0
pluggy==0.13.1
protobuf==3.11.3
psycopg2-binary==2.8.4
py==1.8.1
pyasn1==0.4.8
pyasn1-modules==0.2.8
pycodestyle==2.5.0
pycparser==2.20
pyflakes==2.1.1
PyJWT==1.7.1
pylint==2.4.4
pylint-django==2.0.14
pylint-plugin-utils==0.6
PyNaCl==1.3.0
pyparsing==2.4.6
pyrsistent==0.16.0
pytest==5.3.5
pytest-cov==2.8.1
python-dateutil==2.8.0
pytz==2019.3
PyYAML==5.2
requests==2.20.1
rsa==4.0
selenium==3.141.0
semantic-version==2.5.0
six==1.11.0
soupsieve==2.0
sqlparse==0.3.0
termcolor==1.1.0
texttable==1.6.2
urllib3==1.24.3
virtualenv==16.7.9
wcwidth==0.1.8
websocket-client==0.48.0
wrapt==1.11.2

Install Python Application Dependencies

First, clone this repository to your local machine.

git clone https://github.com/jwallace317/Gin-and-Tensor.git

Next, navigate into the cloned directory and create a python3 virtual environment with virtualenv.

python3 -m virtualenv <virtual environment name>

Now with the virtual environment activated, install the python dependencies with pip and the requirements.txt file.

source <virtual environment name>/bin/activate
pip install -r requirements.txt

Run Database Migrations

Django creates migrations to record changes in the database design. The migrations must be applied prior to running the server to ensure correct functionality.

To make migrations, run the following command..

python manage.py makemigrations

To run migrations, run the following command.

python manage.py migrate

Run Development Server

After the python dependencies have been installed and the database migrations have been applied, we can run the development server with the following command.

python manage.py runserver

If everything was installed and configured correctly then Django will start a development server at the following url.

http://127.0.0.1:8000/

Run Django in Docker Container with Apache2

Instead of running the server on your local machine, this repository features a docker-compose file. So, with the given command, docker-compose will run the service outlined in the docker-compose.yml file. This command will containerize Django, Apache2, and SQLite.

docker-compose up

Use the following command to force a new build and run the docker container listed in the docker-compose file.

docker-compose up --build

Management Methods

These custom management methods are useful for development.

To auto-populate the SQLite database with test users and custom drinks, run the following command.

python manage.py db-populate

When new drinks are added run the following command.

python manage.py get_images

Only drinks where their static image folder is nonexistent will be searched for or downloaded.

For any drinks where the image field in discovery is equal to "images/placeholder.jpg": The bot will search google for the name of the respective drink and download the first page of results into the folder at '.../static/data/drink_images/00000000from_google' within a folder named the drink's discovery ID. Enter this folder and delete all but the desired image. Remove the __# from the end of that image's name. Move the folder into the drink_images folder when done.

All other drinks where an img src address is specified in the discovery result will be directly downloaded into a folder named their discovery ID and saved within the drink_images folder.

Testing

To run tests enter in the following command

python manage.py test

To run tests with test coverage enabled, enter in the following command

coverage run manage.py test

To generate the coverage html report, enter in the following command after the tests have been run with coverage

coverage html

The generated coverage reports are located in the coverage_reports directory. To view the coverage report, open the index.html file with a web browser or html parser.

Deployment

This repository features a CircleCI CICD pipeline that pushes new master branch images to an AWS Elastic Container Registry and updates a service running on an AWS Elastic Container Service Cluster. The update to the service forces a new deployment of the updated task definition. This pipeline will push master code base changes to the designated AWS resources within five minutes. The details of the deployment process can be seen in the CircleCI config.yml file.

To deploy this Django project, our team decided to serve our project with Apache2. We decided to configure Apache2 to serve static requests and application requests for ease of use. The Apache2 configurations that we decided to use are listed in the bartensor.conf file.