dtoro
7c003bc92e
The first phase-11 file operation. Lightroom-style inline rename
of a single photo, in place, in its current directory.
Backend (PATCH /photos/{id})
- PhotoUpdate schema accepts an optional `filename`.
- When set, the handler validates: non-empty, no path separators,
no `..`/`.`, target name doesn't already exist in the directory,
source file exists on disk.
- os.renames the file inside its current directory, then updates
photo.filename + photo.filepath atomically. The DB only changes
after a successful rename — a filesystem failure leaves the
rest of the row untouched.
- Other PhotoUpdate fields still apply afterwards in the same
request.
Frontend (RightSidebar)
- Filename is now an editable monospace input above the Title
input. Same draft + commit pattern as title/notes (local draft,
resync on photo.id change, on-blur or Enter commits).
- Esc reverts to the server value.
- Client-side validation mirrors the backend (rejects path
separators and dot-segments) and shows a toast on backend
errors with the FastAPI detail message, then rolls the draft
back so the input matches the still-on-disk filename.
- Removed the old read-only Filename Field from the Basic Info
section to avoid showing the same value twice.
Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
Mulita - Self-Hosted Photo Management Application
A self-hosted, Docker-deployed photo management application inspired by Lightroom's workflow. Mulita provides a fast, keyboard-driven interface to browse, organize, tag, and manage your photo library.
Features
- Photo Organization: Browse photos in a timeline view with virtual scrolling for performance
- Thumbnail Generation: Automatic thumbnail generation for all photo formats including RAW
- Metadata Extraction: Full EXIF/XMP metadata extraction and search
- Keyboard Shortcuts: Lightroom-style keyboard navigation and actions
- File Support: JPEG, PNG, RAW formats (CR2, CR3, NEF, ARW, etc.), HEIC/HEIF, and videos
- Heaps: Temporary collections for organizing photos
- Tags & Ratings: Organize with tags, star ratings, and color labels
- Dark Mode: Photography-optimized dark interface
Tech Stack
Backend
- Python 3.12 with FastAPI
- SQLite with SQLAlchemy (async)
- Celery + Redis for background tasks
- pyvips for fast thumbnail generation
- ExifTool for metadata extraction
Frontend
- React 18 with TypeScript
- Vite for fast development
- TanStack Query for data fetching
- TanStack Virtual for virtualized scrolling
- Tailwind CSS for styling
- Zustand for state management
Quick Start
Prerequisites
- Docker and Docker Compose
- Photo directories to mount
Setup
- Clone the repository:
git clone <repository-url>
cd muleimage
- Configure your photo directories in
.env:
# Edit .env file
PHOTO_DIRS=/path/to/your/photos
- Start the application:
docker-compose up -d
- Access the application at
http://localhost:3000
Architecture
The application consists of 5 Docker services:
- frontend: React SPA served by Nginx
- backend: FastAPI REST API
- worker: Celery workers for background tasks
- redis: Message broker for Celery
- db: SQLite database (file-based)
Keyboard Shortcuts
| Key | Action |
|---|---|
← → ↑ ↓ |
Navigate photos |
Space |
Quick preview |
Enter |
Open loupe view |
P |
Pick photo |
X |
Reject photo |
1-5 |
Set star rating |
Tab |
Toggle left sidebar |
I |
Toggle metadata panel |
G |
Grid view |
E |
Loupe view |
Delete |
Move to trash |
Development
Backend Development
cd backend
pip install -r requirements.txt
uvicorn app.main:app --reload
Frontend Development
cd frontend
npm install
npm run dev
Configuration
Edit mulita.yml to configure:
- Source photo directories
- Thumbnail sizes and quality
- Scanner settings
- Performance tuning
Performance
- Handles 100,000+ photos efficiently
- Virtual scrolling for smooth timeline navigation
- Thumbnail generation at 10+ photos/second
- SQLite FTS5 for fast full-text search
Future Features (Phase 2)
- AI-powered scene classification
- Face detection and clustering
- Smart albums
- Duplicate detection
- Export presets
- Multi-user support
License
MIT