clemsau / whatsapp-analyzer

A Whatsapp chat visualizer built with Flask for the web interface and Pandas to handle the data

Whatsapp Analyzer

A Whatsapp chat visualizer built with Flask for the web interface and Pandas to handle the data.

A demo is available at http://whatsappanalyzer.com

Table of content

• Project structure

• Get started

• Deployment

Project structure

    /core
        /conf
            __init__.py
            secret_settings_template.py
            secret_settings.py
            settings.py
        __init__.py
        app.py
        utils.py
        views.py
    /static
        /css
        /img
        /js
        /samples
        robots.txt
        sitemap.txt
    /templates
        /errors
            404.html
            405.html
            file_processing_error.py
        analysis.html
        base.html
        faq.html
        index.html
    /uploads
    /venv
    .gitignore
    README.md
    requirements.txt
    wsgi.py

Get started

Install dependencies

Create a virtual environment with the dependencies:

Go in the root of the folder run: python -m venv venv to create the virtual environment.

Activate the virtual environment:

• On Linux: source venv/bin/activate

• On windows venv/Scripts/activate

Install the dependencies : pip install -r requirements.txt

Create your secret_settings.py

In the folder core/conf you need to create a secret_settings.py file and follow the secret_settings_template.py structure.

To get a SECRET_KEY from python, one of the way to operate it to run the following:

import secrets
secrets.token_hex() <-- your SECRET_KEY

Run development server

Make sure your virtual environment is activated with the (venv) tag, or whichever name you gave to your virtual environment.

In the root of the folder run: python wsgi.py dev

Use Docker

Run the following command from the project root to build your images

$ sudo docker-compose --file .docker/docker-compose.yml --project-directory . build --compress --force-rm

After building your images run your container by issuing the following command from the project root

$ sudo docker-compose --file .docker/docker-compose.yml --project-directory . up --detach --remove-orphans

Deployment

Deployed with the following instructions on digital ocean