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Open-Source Tools for solo-devs, students and companies - Generate Digital Products, Update legacy code by chat, Inject new modules, Software Auto-healing, AI, Deployment automation (any provider), Docker, K8s.


Features


Stack

  • Python/Django
  • React
  • Docker
  • CI/CD - LIVE Deploy on Digital Ocean

Manual Build

Step #1 - Download the code

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

Step #2 - Install modules

$ virtualenv env
$ source env/bin/activate
$ pip install -r requirements.txt

Step #3 - Set Up Database

$ python manage.py makemigrations
$ python manage.py migrate

Step #4 - Create the ENV file

The .env can be created from the env.sample file, provided in the root of the project

$ cp env.sample .env

Step #5 - Compile UI (that uses Tailwind)

$ pnpm i
$ pnpm run build

Step #5 - Execute collectstatic for Django statics

$ python manage.py collectstatic
$ python manage.py migrate

Start the APP

$ python manage.py createsuperuser # create the admin
$ python manage.py runserver       # start the project

At this point, the app runs at http://127.0.0.1:8000/.


The Documentation being generated by , the compilation requires a linux box

$ cd docs && rm -rf build && make html
# Or via a while loop
$ cd docs ; while true ; do rm -rf build/ ; make html ; sleep 10 ; done

The output is saved on docs/build and rendered on /docs/ URI


CLI Interface

All CLI tools are listed by dev_tools subcommand:

$ python manage.py dev_tools

Below sections explains each dev_tool added to the codebase - If somethings is not working in your environment, please contact support.

Generate Code

For now, only Django code is supported.

$ python manage.py tool_generator -i # Print HELP 
$ python manage.py tool_generator -f sources/input-template-volt.json

The generated code is saved in generated_code DIR. Open the sources using your favorite editor and start the project. The easier way is to use Docker:


Generate Project Template

Using this tool, we can generate a JSON template used later by the generator

$ python manage.py tool_generator_interactive -i # Print HELP 
$ python manage.py tool_generator_interactive    # Generate JSON File  
...
# (Truncated Output)
$ python manage.py tool_generator_interactive

[?] Project Friendly Name: Some Django project
[?] The Backend Framework:                                                                                                               
 > django
   flask (soon)
   nodejs (soon)

[?] The UI Kit:                                                                                                                          
 > datta
   volt
   soft-dashboard

[?] The Database:                                                                                                                        
 > sqlite
   mysql
   pgsql
...
# (Truncated Output)
...
> File saved = sources\Nt5QWHGI_django_template.json
> HOW to generate code:
    |-- python manage.py tool_generator -f sources/Nt5QWHGI_django_template.json   

By running the sugegsted command, we should be able to generate a valid Django Project.


Upload to GitHub

Note: For having SUCCESS on this operation, a GITHUB_KEY is required in .env - read more.

$ python manage.py tool_github_uploader -i # Print HELP 
$ python manage.py tool_github_uploader -d generated_code/GENERATED_PROJECT -k GITHUB_KEY 

Once the operation is finished, the generated project should be saved under the account associated with the GITHUB_KEY.


Inspect DB (sqlite, MySql, PgSql)

# SQLite scan 
$ python manage.py tool_db_processor -f media/tools/db_inspect/db_sqlite.json
# OR
# MySql scan
$ python manage.py tool_db_processor -f media/tools/db_inspect/db_mysql.json
...
# (Truncated Output)
> Processing media/tools/db_inspect/db_sqlite.json
    |-- type      : db
    |-- DB driver : SQLITE
    |-- DB name   : media/tools/common/sample.sqlite3
    |-- DB host   : None
    |-- DB port   : None
    |-- DB user   : None
    |-- DB pass   : None

 > Dump data for [api_user_user]
 > Dump data for [api_authentication_activesession]
 > Dump data for [auth_group]
 > Dump data for [api_user_user_groups]
 > Dump data for [django_content_type]
 > Dump data for [auth_permission]
 > Dump data for [api_user_user_user_permissions]
 > Dump data for [auth_group_permissions]
 > Dump data for [django_admin_log]
 > Dump data for [django_migrations]
 > Dump data for [django_session]

The SQL dump is done in the tmp DIRECTORY

ROOT
  |-- tmp
      |-- 05_27_58_SQLITE.sql
      |-- 05_28_04_SQLITE_api_user_user 

Migrate DB

Note: the feature works with SQLite, MySql and PostgreSQL.

The source and target DB credentials should be provided in JSON format. Here are the provided samples

How to use it:

$ python manage.py tool_db_migrator -s DB_SOURCE.json -t DB_TARGET.json           # Perform Checks
$ python manage.py tool_db_migrator -s DB_SOURCE.json -t DB_TARGET.json --migrate # Migrate the data 

CSV processor

Authenticated users can process CSV files and apply transformers.

DEMO: https://app-generator.dev/tools/csv-processor/


Inspect CSV Files

$ python manage.py tool_inspect_source -f media/tools/csv/csv_inspect.json    # Truncated output
$ python manage.py tool_inspect_source -f media/tools/csv/csv_inspect.json -k # Print all rows 
# OR for distant CSV files
$ python manage.py tool_inspect_source -f media/tools/csv/csv_inspect_distant.json
...
# (Truncated Output)
 > Processing media/tools/csv/csv_inspect.json
       |-- file: media/tools/csv/titanic.csv
       |-- type: csv
{'PassengerId': {'type': 'int64'}, 'Survived': {'type': 'int64'}, 'Pclass': {'type': 'int64'}, 'Name': {'type': 'object'}, 'Sex': {'type': 'object'}, 'Age': {'type': 'float64'}, 'SibSp': {'type': 'int64'}, 'Parch': {'type': 'int64'}, 'Ticket': {'type': 'object'}, 'Fare': {'type': 'float64'}, 'Cabin': {'type': 'object'}, 'Embarked': {'type': 'object'}}
[1] - PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
[2] - 1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/5 21171,7.25,,S
[3] - 2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38,1,0,PC 17599,71.2833,C85,C
[4] - 3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,STON/O2. 3101282,7.925,,S
[5] - 4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35,1,0,113803,53.1,C123,S
[6] - 5,0,3,"Allen, Mr. William Henry",male,35,0,0,373450,8.05,,S
[7] - 6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
[8] - 7,0,1,"McCarthy, Mr. Timothy J",male,54,0,0,17463,51.8625,E46,S
[9] - 8,0,3,"Palsson, Master. Gosta Leonard",male,2,3,1,349909,21.075,,S
[10] - 9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27,0,2,347742,11.1333,,S
...

List available commands

$ python manage.py help 
# (Truncated Output)
Type 'manage.py help <subcommand>' for help on a specific subcommand.
Available subcommands:
...
[cli]
    help_print_apps
    help_print_cfg
    help_print_models
...

List Registered Apps

$ python manage.py help_print_apps
# (Truncated Output)
 APP -> Webpack Loader
 APP -> Administration
 APP -> Authentication and Authorization
 ...

List Registered Models

$ python manage.py help_print_models
# (Truncated Output)
APP -> Github
APP -> Google
APP -> Django_Quill
APP -> Celery Results
        |--> django_celery_results.models.TaskResult
          |--> id: AutoField
          |--> task_id: CharField
          |--> periodic_task_name: CharField
          |--> task_name: CharField
          |--> task_args: TextField
          |--> task_kwargs: TextField
          |--> status: CharField
          |--> worker: CharField
          |--> content_type: CharField
          |--> content_encoding: CharField
          |--> result: TextField
          |--> date_created: DateTimeField
          |--> date_done: DateTimeField
          |--> traceback: TextField
          |--> meta: TextField
        |--> django_celery_results.models.ChordCounter
          |--> id: AutoField
          |--> group_id: CharField
          |--> sub_tasks: TextField
          |--> count: PositiveIntegerField
        |--> django_celery_results.models.GroupResult
          |--> id: AutoField
          |--> group_id: CharField
          |--> date_created: DateTimeField
          |--> date_done: DateTimeField
          |--> content_type: CharField
          |--> content_encoding: CharField
          |--> result: TextField

Celery (async tasks)

  • Make sure you have a Redis Server running: redis://localhost:6379
    • $ redis-cli and type ping
  • In the base directory inside tasks_scripts folder you need to write your scripts file.
  • Run the celery command from the CLI.
$ export DJANGO_SETTINGS_MODULE="core.settings"  
$ celery -A core worker -l info -B

Executed Tasks, tasks_scripts DIR as defined in the EXEC Schedule

The output for each task can be found in the LOGS Directory.

Here is a LOG sample generated by a critical task that runs at every 5min:


Team

Core

Developers/Contractors


LICENSE

@EULA



Crafted and actively supported by AppSeed - [email protected]

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