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[TimilsinaBimal]

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Project	Deployment	Review	Updated (UTC)
watchly	Ready	Preview, Comment	Dec 15, 2025 7:47am

[gemini-code-assist]

Summary of Changes

Hello @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 represents a substantial update to the application, primarily focusing on enhancing the recommendation engine's intelligence and efficiency. It introduces advanced algorithms for personalized content discovery, optimizes data fetching and caching strategies, and delivers a refreshed user interface for a smoother and more engaging experience. These changes aim to provide users with more relevant and diverse content suggestions while improving the overall responsiveness and stability of the service.

Highlights

• Recommendation Engine Overhaul: The core recommendation logic has been significantly refactored to improve accuracy, performance, and user experience. This includes new similarity scoring methods, dynamic recency preferences, and freshness injection for trending content.
• Performance and Caching Improvements: Extensive caching mechanisms have been introduced or enhanced across various services (TMDB, TokenStore, Stremio library fetches) to reduce API calls and improve response times. Redis client management has been optimized with connection pooling and threshold checks.
• User Interface (UI) Refresh: The web interface has received a visual update with a new dark theme, improved button styles, and enhanced mobile navigation, providing a more modern and responsive user experience.
• Enhanced User Profile Vectorization: The user taste profile now includes 'topics' derived from content titles, overviews, and keywords, allowing for more nuanced similarity calculations beyond just genres.
• Configuration and Maintenance: New Redis configuration options for connection management have been added, and the catalog update schedule has been streamlined. The migrate_tokens startup task is now conditional, and API documentation URLs are environment-dependent.

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[gemini-code-assist]

Code Review

This pull request introduces significant enhancements to the recommendation engine, API caching, and UI/UX. Key changes include optimizing RecommendationService by pre-fetching library data and passing it down, refactoring catalog fetching into build_dynamic_catalogs, and implementing alru_cache for TMDB language data to reduce API calls. The application's startup now conditionally runs token migrations based on a hardcoded hostname (which a reviewer suggested changing to an environment variable for flexibility). A new HTTP middleware was added for IP-based rate limiting on missing tokens, and Redis client management was improved with connection limits and a shutdown hook. The recommendation logic was refined with a new topics vector in user profiles, a simplified similarity calculation using Jaccard overlap, and dynamic recency weighting. Discovery queries are now more efficient, fetching multiple pages only if the initial pool is thin, and a 'freshness injection' mechanism adds trending content. Genre filtering was enhanced with a user-specific top-genre whitelist, which a reviewer noted might be overly restrictive for 'Animation' genre ID 16. The UI received a visual refresh with updated Tailwind CSS classes for a modern dark theme, mobile navigation support, and a direct link for donations instead of a modal. Several try...except Exception: pass blocks were used in the new middleware, which a reviewer pointed out could silently swallow errors and suggested more specific error handling or logging.

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app/core/app.py (44)

The condition to run migrate_tokens() is based on a hardcoded hostname. This makes the code less portable and harder to configure for different environments. It's better to control this behavior with an environment variable for greater flexibility.

    if settings.RUN_MIGRATIONS_ON_STARTUP:

app/core/app.py (109-121)

The try...except Exception: pass blocks in the middleware are too broad and will silently swallow any exceptions that occur within them. This can make debugging very difficult if an unexpected error happens. It's better to catch more specific exceptions or at least log the error if a general Exception is caught.

    try:
        # If token is known-missing, short-circuit and track IP failures
        if seg and seg in token_store._missing_tokens:
            ip = request.client.host if request.client else "unknown"
            try:
                _ip_failure_cache[ip] = _ip_failure_cache.get(ip, 0) + 1
            except Exception as e:
                logger.warning(f"Failed to update IP failure cache for {ip}: {e}")
            if _ip_failure_cache.get(ip, 0) > _IP_FAILURE_THRESHOLD:
                return HTMLResponse(content="Too many requests", status_code=429)
            return HTMLResponse(content="Invalid token", status_code=401)
    except Exception as e:
        logger.error(f"Error in block_missing_token_middleware: {e}", exc_info=True)

app/services/recommendation_service.py (281-290)

The special handling for the Animation genre (ID 16) seems overly restrictive. This rule will filter out any animation if it's not in the user's top 5 genres, which could prevent discovery for users who enjoy animation but don't watch it frequently enough for it to be a top genre. It might be better to treat all genres equally in this filter to ensure more consistent behavior.

    async def _passes_top_genre(self, genre_ids: list[int] | None, content_type: str) -> bool:
        whitelist = await self._get_top_genre_whitelist(content_type)
        if not whitelist:
            return True
        gids = set(genre_ids or [])
        if not gids:
            return True
        return bool(gids & whitelist)