https://github.com/mason-larobina/classi-cine
Build smart playlists from your video collection
https://github.com/mason-larobina/classi-cine
bayesian-inference http naive-bayes-classifier reqwest rust serde-json vlc
Last synced: 22 days ago
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Build smart playlists from your video collection
- Host: GitHub
- URL: https://github.com/mason-larobina/classi-cine
- Owner: mason-larobina
- License: mit
- Created: 2023-11-01T06:15:10.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-08-16T12:34:07.000Z (10 months ago)
- Last Synced: 2026-04-16T23:13:00.456Z (about 2 months ago)
- Topics: bayesian-inference, http, naive-bayes-classifier, reqwest, rust, serde-json, vlc
- Language: Rust
- Homepage:
- Size: 1.13 MB
- Stars: 6
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Classi-Cine
[](https://opensource.org/licenses/MIT)
[](https://github.com/mason-larobina/classi-cine/actions/workflows/rust.yml)
**Stop scrolling through thousands of videos.** Classi-Cine is like autocomplete for video selection - it predicts what you want to watch next based on your feedback, helping you rapidly build the perfect playlist for your current mood.
## The Problem
You have a large video collection but finding what matches your current mood is tedious:
- Manually browsing through hundreds or thousands of files
- Remembering which episodes or clips you enjoyed
- No quick way to find "videos like this one"
- Building playlists by hand takes forever
## The Solution
Classi-Cine plays 20-questions with your video collection to rapidly zero in on what you want:
1. **Point it at your video directories** - Let it discover your collection
1. **It suggests videos, you accept/reject** - Like/dislike using VLC controls (stop = accept, pause = reject)
1. **Watch recommendations improve in real-time** - Each decision teaches the AI what you're looking for
1. **Build your perfect playlist faster** - Find content that matches your current mood in minutes, not hours
Perfect for:
- **Large media collections** (TV series, movies, documentaries, clips)
- **Content creators** curating references or footage
- **Anyone with 100+ videos** who wants smart discovery
- **Building themed playlists** (action movies, comedy episodes, tutorial clips, etc.)
## Quick Start
```bash
# Install
cargo install classi-cine
# Start building a playlist - the AI will suggest additions
classi-cine build my-playlist.m3u ~/Videos ~/Movies
# The TUI shows AI predictions ranked by relevance
# Press Enter to preview in VLC
# Stop (s) to accept and add to playlist, pause (space) to reject
# Watch suggestions get smarter with each decision!
```
## How It Works
Like autocomplete for text, Classi-Cine learns patterns from your video collection and predicts what you want next:
1. **Analyzes your entire collection** - Understands file names, folder structure, sizes, and ages
1. **Uses machine learning** - Multiple AI classifiers work together to score every video
1. **Learns from your feedback** - Each accept/reject teaches it more about your preferences
1. **Predicts better matches** - Rankings improve in real-time as you build your playlist
**Simple VLC Integration:**
- Stop video (s key) = "Yes, add this to my playlist"
- Pause video (space) = "No, this doesn't fit"
- Standard M3U playlists work in any media player
## Installation
VLC is required for video playback.
Ensure you have Rust and Cargo installed. If not, you can install them using rustup.
### From Cargo
```bash
# Build from the cargo.io crate registry.
$ cargo install classi-cine
```
### From Source
```bash
# Clone this repository
$ git clone https://github.com/mason-larobina/classi-cine.git
# Go into the repository
$ cd classi-cine
# Build and install it locally
$ cargo install --path=.
```
## Usage
```bash
Usage: classi-cine [OPTIONS]
Commands:
build Build playlist through interactive classification
score Score files using trained classifiers without interactive classification
list-positive List classified files
list-negative
move Move playlist to a new location and rebase paths
help Print this message or the help of the given subcommand(s)
Options:
--log-level [default: info]
-h, --help Print help
```
### Build Command
Build a playlist through interactive VLC classification:
```bash
classi-cine build [OPTIONS] [DIRS]...
Arguments:
M3U playlist file
[DIRS]... Directories to scan for video files
Options:
--video-exts Video file extensions to scan for [default: avi,flv,mov,f4v,flv,m2ts,m4v,mkv,mpg,webm,wmv,mp4]
--windows [default: 5]
--file-size-bias Bias scoring based on file sizes (log base, > 1.0). Negative reverses bias
--file-size-offset Offset to add to file size before log scaling [default: 1048576]
--dir-size-bias Bias scoring based on directory sizes (log base, > 1.0). Negative reverses bias
--dir-size-offset Offset to add to directory size before log scaling [default: 0]
--file-age-bias Bias scoring based on file age (log base, > 1.0). Negative reverses bias
--file-age-offset Offset to add to file age in seconds before log scaling [default: 86400]
--fullscreen Fullscreen VLC playback
--vlc-timeout Timeout in seconds for VLC startup [default: 60]
--vlc-poll-interval Polling interval in milliseconds for VLC status checks [default: 100]
--batch Number of entries to classify in each batch iteration [default: 1]
--random-top-n Select next entry randomly from top-n scored entries
-h, --help Print help
```
### Score Command
Score files using trained classifiers without interactive classification:
```bash
classi-cine score [OPTIONS] [DIRS]...
Arguments:
M3U playlist file
[DIRS]... Directories to scan for video files
Options:
--video-exts Video file extensions to scan for [default: avi,flv,mov,f4v,flv,m2ts,m4v,mkv,mpg,webm,wmv,mp4]
--windows [default: 5]
--file-size-bias Bias scoring based on file sizes (log base, > 1.0). Negative reverses bias
--file-size-offset Offset to add to file size before log scaling [default: 1048576]
--dir-size-bias Bias scoring based on directory sizes (log base, > 1.0). Negative reverses bias
--dir-size-offset Offset to add to directory size before log scaling [default: 0]
--file-age-bias Bias scoring based on file age (log base, > 1.0). Negative reverses bias
--file-age-offset Offset to add to file age in seconds before log scaling [default: 86400]
--include-classified Include already classified files in the score listing
--no-header Skip header output for machine-readable format
--include-size Include file size in bytes in output
--json Output results in JSON format
--reverse Reverse output order (lowest scores first)
--by-dir Group results by directory and aggregate scores
--absolute Display absolute paths instead of relative to current directory
-h, --help Print help
```
### List Commands
List positively or negatively classified files:
```bash
classi-cine list-positive [OPTIONS]
classi-cine list-negative [OPTIONS]
Arguments:
M3U playlist file
Options:
--absolute Display absolute paths instead of relative to current directory
-h, --help Print help
```
### Move Command
Move playlist to a new location and rebase paths:
```bash
classi-cine move
Arguments:
Original M3U playlist file
New M3U playlist file location
Options:
-h, --help Print help
```
## Technical Details
For developers and ML enthusiasts, Classi-Cine uses several sophisticated techniques:
**Multi-Classifier Architecture:**
- **Naive Bayes Classifier**: Uses byte-pair encoding tokenization to learn patterns from file paths and folder names
- **File Size Classifier**: Logarithmic scoring based on file sizes (configurable bias for larger/smaller files)
- **Directory Size Classifier**: Scoring based on number of files in directories
- **File Age Classifier**: Scoring based on file creation time
- All classifiers use normalized scores that are combined for final ranking
**Advanced Tokenization:**
- Byte pair encoding learns frequent character sequences from your specific collection
- Language and character set agnostic - works with any naming convention
- Adaptive tokenization learns from your library's patterns
- N-gram analysis for sequence pattern recognition
**Performance Optimizations:**
- Probabilistic filters and parallel processing for efficient tokenization
- Sharded data structures for multi-core processing
- Background VLC control with multi-threaded communication
- Incremental M3U saves for long classification sessions
**VLC Integration:**
- Uses VLC's HTTP interface for status monitoring
- Multi-threaded VLC control with background processing
- Automatic process lifecycle management
- Real-time classification feedback loop
## Contributing
We're open to contributions! Enhancements, bug fixes, documentation improvements, and more are all welcome.
## License
This project is licensed under the MIT License. See LICENSE for details.
## Special Thanks
Made with ❤️ and Rust.