App Generator app-generator

App Generator

Dev Tools for solo developers, students, and companies - Generate Digital Products. Registered users can download full-stack starters built with Flask, Django, or generate new starters.

👉 LIVE Demo: https://app-generator.dev
👉 Release v1.0.7 - 2026-06-30

LICENSE

The product codebase is available on demand. For more information, feel free to contact us anytime:

Email: < support @ appseed.us >
Live Support Server: https://discord.gg/fZC6hup

Products

👉 All products and support included in the App-Gen Subscription

Rocket Django - Tailwind/Flowbite design, bundled with API, powerful server-side DataTables, Charts, React Integration
Dynamic Django - Simplify the data processing and consolidation via generated APIs, DataTables, Charts and CLI tools.
Django AdminLTE - AdminLTE design, Dynamic DataTables, Dynamic APIs, React Integration and Docker Support
Django Berry - Berry Bootstrap design, bundled with API, powerful server-side DataTables, Charts, React Integration
Django Datta Able - Bootstrap design, bundled with API, powerful server-side DataTables, Charts, React Integration

Services

👉 Services are provided on demand, deployed exclusively to the App-Generator Cloud infrastructure.

Documentation Manager, Electronic Archive - In-browser Documents Editing, Electronic Archive, AI content parsing
Rocket Website Template - Site Template with blog section, projects section and AI Integration
Website/Feed Crawler - Fully managed engine to monitor homepages and deeply scrape articles.
X (Twitter) Crawler - Fully managed engine that monitors targeted X accounts via official API v2. It uses since_id state tracking to capture new tweets (text, links, media, tags)
Secure IMAP Email Crawler & Parser - Fully managed IMAP crawler that processes mailboxes (Gmail, Outlook, custom) via TLS. It normalizes fields (sender, subject, date, body), converts HTML to plain text, downloads attachments locally, and outputs structured, clean CSV logs.
Live Video & Audio Stream Transcription Service - Fully managed engine that processes live and recorded video/audio streams. It segments incoming media into optimized chunks and delivers highly accurate text transcriptions with a near-real-time delay of just a few seconds.

App-Generator Tech Stack

Python/Django
React
Docker
CI/CD - LIVE Deploy

Manual Build

Step #1 - Download the code

The product codebase is available on demand. For more information, contact support via
Email < support @ appseed.us > or Discord.

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/.

Compile Documentation

The Documentation being generated by Sphinx, 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

PowerShell Build

$ cd docs ; while(1){ rm -r -fo .\build ; make html ; sleep 5; }

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

The sections explain each dev_tool added to the codebase. If something is not working in your environment, please contact support.

Generate Code

Django Starters

$ python manage.py tool_generator -f sources/input-django-template-soft.json # Generate a new Django Starter, Soft Design

Flask Starters

$ python manage.py tool_generator -f sources/input-flask-template-datta.json # Generate a new Flask Starter, Datta Able Design

The generated code is saved in generated_code DIR. Open the sources using your favorite editor and start the project.

Generate Project Template

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

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

[?] 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 suggested 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 every 5 minutes:

tasks_scripts/logs/2024-05-20-16-30-critical_task.log

Team

Sm0ke - PM, Tech Lead, Automation, Design Patterns
Mominur - Django/React Developer
Sugeng - CI/CD, Deployment, Docker

App Generator - Dev Tools for solo developers, students, and companies.

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