This document provides a comprehensive overview of the Video Library Manager (VLM) project, intended to be used as instructional context for Gemini.
## Project Overview
The Video Library Manager (VLM) is a Python-based CLI tool designed for managing personal video collections. It emphasizes a "safety-first" and "human-in-the-loop" approach, ensuring that no file operations are performed without explicit user confirmation and that all actions are reversible through a rollback mechanism.
**Core Functionality:**
***Scanning & Parsing:** Discovers video files, extracts metadata (file info, video properties via `ffprobe`), and parses filenames to identify titles, years, seasons, and episodes.
***Metadata Enrichment:** Augments local data with information from TMDB, including bilingual titles and reputation scores. It uses a local SQLite cache to improve performance.
***Analysis:** Detects duplicate files (with quality comparisons) and identifies gaps in TV series episodes.
***Planning & Execution:** Generates a reviewable JSON-based execution plan for file operations (move, rename, quarantine). The plan is executed only upon user confirmation.
***Reporting:** Creates reports for inventory, duplicate files, and series completeness.
***State Management:** Tracks the status of files throughout the organization workflow.
**Technologies:**
***Language:** Python 3.10+
***CLI Framework:** Click
***Configuration:** YAML
***Dependencies:**`pyyaml`, `click`
***Development:**`pytest` for testing, `hypothesis` for property-based testing.
***Package Management:**`uv` is mentioned in the documentation.
**Architecture:**
The project follows a modular structure located in the `src/vlm` directory. Key modules include:
*`cli.py`: The main entry point for the CLI, using Click.
*`commands/*.py`: Implementation of the individual CLI commands (scan, parse, enrich, etc.).
*`scanner.py`, `parser.py`, `enrichment.py`, `analysis.py`, `planner.py`, `executor.py`: Core logic for the different stages of the workflow.
*`providers/tmdb.py`: Client for interacting with the TMDB API.
*`models.py`: Defines the data structures used throughout the application.
*`config.py`: Manages application configuration from a YAML file.
## Building and Running
The project uses `uv` for dependency management.
**Installation:**
* Install dependencies: `uv pip install -e .`
* Install development dependencies: `uv pip install -e ".[dev]"`
***Code Style:** Adheres to PEP 8, uses 4-space indentation, and includes type hints for public functions.
***Testing:** Tests are located in the `tests/` directory and written using `pytest`. The project also uses `hypothesis` for property-based testing. New features should be accompanied by tests.
***Commits:** Commit messages should be clear and descriptive.
***Documentation:** The `README.md` file is very comprehensive and should be kept up-to-date.
***Safety:** A core principle is safety. Changes to the filesystem should be gated behind user confirmation (`--confirm`) and be reversible. The tool should never permanently delete files.
