TikLocal 0.8.26

A local media server that combines the features of TikTok and Pinterest

TikLocal

TikLocal is a mobile and tablet web application built on Flask. It allows you to browse and manage your local videos and images in a way similar to TikTok and Pinterest.

中文

Introduction

TikLocal's main features include:

• A TikTok-like swipe-up browsing experience with a mixed feed of local videos and images.
• A file manager-like directory browsing feature that allows you to easily find and manage local video files.
• A Pinterest-like grid layout feature that allows you to enjoy local images.
• Support for light and dark modes to suit your personal preferences.

Use cases

TikLocal is suitable for the following use cases:

• You don't trust TikTok's teen mode and want to provide your child with completely controllable video content.
• You want to browse and manage your local videos and images locally, but don't want to use third-party cloud services.
• You want to use a TikTok-style mixed media browsing experience on your phone or tablet.
• You want to use a Pinterest-style image browsing experience on your phone or tablet.

How to use

Installation

TikLocal is a Python application that you can install using the following command:

pip install tiklocal

Usage

Starting TikLocal is very simple, just run the following command:

tiklocal ~/Videos/

You can specify any media folder.

To close, press Ctrl + C.

CLI Commands

TikLocal provides several CLI commands:

Start the server:

tiklocal /path/to/media           # Start with media directory
tiklocal --port 9000              # Use custom port
tiklocal --media-source photos=~/Pictures/AI  # Add a media source, repeatable

Generate video thumbnails:

tiklocal thumbs /path/to/media    # Generate thumbnails
tiklocal thumbs /path --overwrite # Regenerate existing thumbnails

Find and remove duplicate files:

tiklocal dedupe /path/to/media              # Find duplicates (dry-run mode)
tiklocal dedupe /path --type video          # Check video files only
tiklocal dedupe /path --execute             # Execute deletion
tiklocal dedupe /path --keep newest         # Keep newest files

Options for dedupe:

• --type: File type (video, image, all)
• --algorithm: Hash algorithm (sha256, md5)
• --keep: Keep strategy (oldest, newest, shortest_path)
• --dry-run: Preview mode (default)
• --execute: Execute actual deletion
• --auto-confirm: Skip confirmation prompt

Build image vector index:

tiklocal vectorize /path/to/media --dry-run
tiklocal vectorize /path/to/media --limit 200 --order latest
tiklocal vectorize /path/to/media --source photos --limit 200
tiklocal vectorize /path/to/media --cleanup
tiklocal vectorize /path/to/media --max-size 512 --quality 82
tiklocal analyze-similar /path/to/media --limit 500 --yes
tiklocal analyze-similar /path/to/media --profile --dry-run

Recommended workflow:

• Run --dry-run first to inspect total images, already-indexed images, missing vectors, stale vectors, and selected items.
• Use --limit 200 --order latest for the first low-cost batch.
• Use --source <id> to index one media source from media_sources.
• Use --cleanup to remove vectors for files that no longer exist.
• Use --force only when intentionally rebuilding existing vectors.
• Use --yes to skip the confirmation prompt in scripts.

vectorize only uploads images that are missing or stale. A vector becomes stale when file size, mtime, model, dimensions, image_max_size, or image_quality changes. Images are EXIF-transposed, resized, re-encoded as JPEG, and sent without original EXIF/ICC/XMP/IPTC metadata.

After vectors are built, run analyze-similar to precompute visual similarity groups into SQLite. The image detail page can query similar images directly from local vectors, while the Library Similar Images mode only reads precomputed groups for fast loading.

URL Download (Web)

TikLocal includes a /download page where you can paste a media URL and enqueue a background download job.

Requirements:

• yt-dlp (required)
• gallery-dl (recommended for image/gallery posts)
• ffmpeg (recommended for format merge)

Download engine:

• yt-dlp: video-oriented sites and links
• gallery-dl: image posts/albums (Instagram/X/Pinterest, etc.)
• Download form allows manual engine selection per task (default: yt-dlp)

Cookie for login-only content (optional):

• Put exported cookie files in ~/.tiklocal/cookies
• Filename should include domain, e.g. x.com.txt, youtube.com.cookies
• The download page supports Auto match or manual file selection per task
• The download page also supports cookie file upload/replace, history delete/clear, and retry for failed tasks

Example installs:

# macOS (Homebrew)
brew install yt-dlp gallery-dl ffmpeg

# Ubuntu / Debian
sudo apt install yt-dlp gallery-dl ffmpeg

Home Mixed Feed

The home page (/) now uses a mixed immersive feed:

• Videos and images are mixed in one swipe flow (video-first density, randomized order)
• Image cards support in-feed AI caption/tags panel
• Image cards support circular magnifier (2.5x / 5x)
• Image cards do not auto-advance; swipe manually to move next/previous

Configuration

TikLocal provides some configuration options that you can adjust to your needs.

You can configure one or more media directories in ~/.config/tiklocal/config.yaml:

media_sources:
  - id: default
    name: Main Library
    path: ~/Videos/TikLocal
  - id: photos
    name: Photos
    path: ~/Pictures/AI
download_source: default

vision:
  enabled: true
  base_url: https://openrouter.ai/api/v1
  model_name: google/gemini-2.5-flash
  temperature: 0.6
  tags_limit: 5
  system_prompt: |
    You are an image analysis assistant. Return JSON only.
  user_prompt: |
    Analyze this image and return a short Chinese title plus up to {tags_limit} Chinese tags.
    Output JSON: {"title":"...","tags":["..."]}

embedding:
  enabled: true
  base_url: https://openrouter.ai/api/v1
  model_name: google/gemini-embedding-2
  dimensions: 768
  image_max_size: 512
  image_quality: 82

The legacy single-directory configuration still works:

media_root: ~/Videos/TikLocal

Multiple media sources are merged into one unified library. Internal media URIs use the @source_id/path format, while old bare-path links and favorites remain compatible through the default source.

Image recognition uses vision; image vectorization uses embedding and stores image vectors in the local SQLite application database (~/.tiklocal/tiklocal.sqlite3 by default). The image detail page only reads the local index for similar-image recommendations. Use the CLI to build or update vectors:

tiklocal vectorize ~/Videos/TikLocal --limit 200 --order latest
tiklocal vectorize ~/Videos/TikLocal --dry-run
tiklocal analyze-similar ~/Videos/TikLocal --limit 500 --yes

API keys are read from environment variables, preferring TIKLOCAL_VISION_API_KEY for vision, TIKLOCAL_EMBEDDING_API_KEY for embedding, then falling back to TIKLOCAL_AI_API_KEY, OPENAI_API_KEY, or OPENROUTER_API_KEY.

• Light and dark modes: You can choose to use light or dark mode.
• Video playback speed: You can adjust the video playback speed.

Documentation

• Docs index: docs/README.md
• Flow interaction unification: docs/flow-interaction-unification.md
• OpenRouter image-to-video research: docs/openrouter-image-to-video-research.md
• Release notes: docs/release_notes.md

TODO

• Add search
• Add more management operations, such as moving files and creating folders
• Add basic login control
• Add a bookmarking feature
• Add a Docker image
• Add a tagging feature
• Use recommendation algorithms

Contribution

TikLocal is an open source project that you can contribute to in the following ways:

• Submit code or documentation improvements.
• Report bugs.
• Suggest new features.

Contact us

If you have any questions or suggestions, you can contact us in the following ways:

• GitHub project page: https://github.com/ChanMo/TikLocal/
• Email: [[e-mail]]