TikTok Scheduler

A Windows desktop application for scheduling and uploading TikTok videos in bulk
using a real Chromium browser driven by Playwright automation.

Overview

TikTok Scheduler is a PyQt6 desktop client built for creators and operators who need a repeatable workflow for preparing uploads, generating schedule windows, and managing multiple TikTok accounts. The application automates the TikTok web upload flow through Playwright while keeping account cookies, scheduled uploads, published history, and UI state organized locally.

Highlights

• Bulk upload preparation from local .mp4 folders
• Configurable scheduling windows with date and time rules
• Cookie-based multi-account management
• Published and scheduled file tracking per account
• Modern desktop UI with multiple application themes
• Rich terminal logging for execution feedback
• Optional headless browser execution
• Copyright and content quality feedback surfaced during upload

Requirements

• Python 3.10 or newer
• Windows 10 or Windows 11
• Chromium installed through Playwright

Installation

1. Clone the repository

git clone https://github.com/davins/tiktok-scheduler.git
cd tiktok-scheduler

2. Install Python dependencies

pip install -r requirements.txt

3. Install the Playwright browser runtime

python -m playwright install chromium

Tip: RUN.bat can bootstrap dependencies and launch the application automatically.

Running the application

Option A: use the Windows launcher

Double-click RUN.bat from the project root.

Option B: run from a terminal

python main.py

Account setup

TikTok Scheduler uses exported browser cookies instead of username/password login inside the app.

1. Sign in to TikTok Creator in your browser.
2. Export cookies as JSON with a browser cookie extension.
3. Open the application and go to the Accounts tab.
4. Add the TikTok username and paste the cookie JSON.
5. Save the account for future scheduling sessions.

An example payload is included in example_cookie.json.

Configuration

Runtime options are loaded from config.json.

Key	Description	Default
windowSize	Initial application window size	1200 x 800
minWindowSize	Minimum configured window size	900 x 600
theme	Initial theme name	system
primaryColor	Accent color used by the base palette	#1E66FF
headlessDefault	Default browser visibility mode	true
logLevel	Logging verbosity	INFO
scheduleRules.minOffsetMinutes	Minimum delay before a schedule can start	15
scheduleRules.maxOffsetMonths	Maximum schedule range	1
scheduleRules.minuteStep	Minute rounding step	5

Project structure

tiktok-scheduler/
|-- main.py
|-- config.json
|-- setup.py
|-- requirements.txt
|-- RUN.bat
|-- assets/
|-- core/
|   |-- browser_manager.py
|   |-- config_manager.py
|   |-- cookie_manager.py
|   |-- dom_handler.py
|   |-- logger_manager.py
|   |-- schedule_rule_engine.py
|   |-- scheduler.py
|   `-- uploader.py
|-- gui/
|   |-- controller.py
|   |-- main_window.py
|   |-- components/
|   |-- controllers/
|   |-- dialogs/
|   |-- layouts/
|   |-- pages/
|   |-- services/
|   |-- tables/
|   |-- theme/
|   `-- widgets/
|-- storage/
|   |-- publishes/
|   `-- schedules/
|-- cookies/
`-- utils/

Notes

• Storage and cookie data are intended to remain local to the workstation.
• The GUI and core upload logic are separated so UI changes do not require rewriting the automation pipeline.
• Published records are stored under storage/publishes/ and scheduled records under storage/schedules/.

License

This project is licensed under the MIT License.