Chore: Stable release V1.0.0 #31
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…p cloning for catalog data in the frontend, removing conditional name handling.
…imilarity methods
…and apply them during catalog generation (#21) * feat: Add genre exclusion UI, store excluded genres in user settings, and apply them during catalog generation. * refactor: simplify filtering recommendations by excluded genres using list comprehension * refactor: streamline genre exclusion logic for similarity recommendations
…t and section navigation (#22) * feat: Rework UI into a multi-step setup wizard with Watchly account management and section navigation * critical: check if user already exists * refactor: remove disabled class from source code button
… fetching, replacing credential-derived tokens and direct login. (#25)
* feat: add option to rename, enable disable catalogs * feat: add option to rename, enable disable catalogs * feat: Implement user ID-based token management and Stremio user info fetching, replacing credential-derived tokens and direct login. * feat: Implement user ID-based token management and Stremio user info fetching, replacing credential-derived tokens and direct login. * feat: Refactor token management to use a dedicated token model, remove token salt, and streamline catalog refresh logic. * feat: Encrypt auth keys in token store and enforce 401 for invalid tokens across endpoints. * feat: Introduce token redaction utility and apply it to log messages for enhanced security. * feat: Introduce token redaction utility and apply it to log messages for enhanced security.
* refactor: fetch loved and liked items at once and use that * Refactor services for improved async handling and error resilience
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Summary of ChangesHello @TimilsinaBimal, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed! This pull request delivers a comprehensive upgrade to the Watchly addon, transforming its recommendation capabilities and user experience. The core recommendation logic has been advanced to a hybrid model, leveraging user taste profiles and AI for more intelligent content discovery. Users now benefit from a modern, interactive web interface for configuring their preferences, including language, genre exclusions, and personalized catalog management. Under the hood, the system boasts improved security for user data, robust API communication with retry mechanisms, and a streamlined development and deployment process through the adoption of 'uv' for dependency management. Highlights
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Code Review
This pull request introduces a major refactor and feature enhancement to the Watchly addon, primarily focusing on improved user experience, customization, and backend efficiency. Key changes include migrating the Docker build process to use uv for faster dependency management, a complete overhaul of the README.md to reflect new features and installation methods, and significant updates to the API endpoints.
The catalog generation logic (app/api/endpoints/catalogs.py, app/api/endpoints/manifest.py) now supports dynamic catalog types (e.g., theme-based, item-based) and integrates user-specific settings like language and genre exclusions. The token management (app/api/endpoints/tokens.py) has been redesigned to use Stremio authKey exclusively, removing username/password fields, and now stores user settings directly with the token, including new options for RPDB API keys and genre exclusions. A new /api/languages endpoint was added for language selection in the UI.
Backend services have been expanded with new modules for DiscoveryEngine, RPDBService, ScoringService, TranslationService, and UserProfileService, enabling more sophisticated recommendation algorithms and internationalization. The TokenStore was refactored to use user_id as the primary key for storing credentials, and a migration script was added to handle existing tokens. The frontend UI (app/static/index.html, app/static/script.js) received a complete redesign with a multi-step configuration flow, dynamic catalog reordering, genre exclusion options, and improved feedback mechanisms.
Review comments highlighted concerns about the removal of Python environment variables from the Dockerfile (suggesting they be re-added for consistency), the inefficiency of creating new TMDBService instances per request in app/api/endpoints/meta.py (recommending FastAPI's dependency injection), and a potential reduction in recommendation availability due to the strict requirement for imdb_id in app/services/recommendation_service.py.
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Dockerfile (8-11)
These environment variables (PYTHONDONTWRITEBYTECODE, PYTHONUNBUFFERED, etc.) are generally good practice for Python applications in Docker to optimize performance and logging. While uv might handle some of these implicitly, it's safer to set them explicitly to ensure consistent behavior. Consider re-adding them to the new Dockerfile.
ENV PYTHONDONTWRITEBYTECODE=1 \
PYTHONUNBUFFERED=1
app/api/endpoints/meta.py (10-24)
Creating a new TMDBService instance on every request to this endpoint is inefficient, as it creates a new httpx.AsyncClient each time, preventing connection pooling. It's better to use FastAPI's dependency injection system to manage the lifecycle of services like this.
For example, you could create a dependency provider for TMDBService and inject it into your route function.
app/models/token.py (1-11)
This file appears to be unused. The models UserSettings and Credentials are defined and imported from app/core/settings.py throughout the application. To improve maintainability and reduce clutter, consider removing this redundant file.
app/services/recommendation_service.py (178-181)
The logic now requires an imdb_id to create a stremio_id and skips content that doesn't have one. This is a significant change that could reduce the number of available recommendations, especially for newer or international content that might only have a TMDB ID.
If this is the intended behavior, consider adding a comment to explain the reasoning. Otherwise, you might want to revert to a fallback mechanism, such as stremio_id = imdb_id if imdb_id else f"tmdb:{details.get('id')}".
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