Two small safety improvements:
1. Voice prompt cache (cache.py): add weights_only=True to torch.load()
so cached .prompt files are loaded using the safe unpickler instead of
the unrestricted pickle deserializer. This follows the PyTorch 2.6+
best practice of opting in to safe loading for all torch.load() calls.
2. SPA catch-all (app.py): replace str.startswith() path guard with
Path.is_relative_to(). The string prefix check passes for sibling
paths like /app/frontend_evil/ that share the /app/frontend prefix.
is_relative_to() correctly tests directory containment.
Replace verbose startup messages with a clean summary:
- App version, Python version, OS/arch
- Database path (fix None display), data directory
- Profile and generation counts
- Backend, GPU, model cache path
- Clean up stale loading_model status on startup
- Remove noisy progress manager log line
- Added `clear_profile_cache` function to manage cache files for specific profiles.
- Integrated cache clearing in `add_profile_sample`, `delete_profile`, and `delete_profile_sample` functions to ensure stale audio caches are invalidated after modifications.
- Enhanced `clear_voice_prompt_cache` to also delete combined audio files, improving overall cache management.
- Updated HistoryTable to implement infinite scrolling for loading history items dynamically.
- Introduced state management for accumulated history and total item count.
- Added Intersection Observer for triggering additional data fetches when scrolling.
- Implemented cache clearing functionality in the backend to manage voice prompt caches effectively.
- Improved loading indicators and user feedback for data fetching states.
- Refactored code for better readability and maintainability.
- Added support for MLX backend on Apple Silicon, enabling optimized performance for TTS and STT tasks.
- Updated release workflow to include MLX-specific dependencies and configurations for macOS platforms.
- Refactored backend code to dynamically select between MLX and PyTorch based on the runtime environment.
- Enhanced model loading and inference logic to accommodate backend-specific requirements, including updated model IDs and hidden imports.
- Improved health check and model status reporting to reflect the active backend type.
- Streamlined caching mechanisms to support both backend types, ensuring compatibility and performance.
