Album sequencing has long been a critical part of the album production process. Recently, a data-driven approach was proposed by Ashley and Herrmann et al. (2024) for sequencing general collections of independent media by extracting the narrative essence of the items in the collection. While their approach implies an album sequencing technique, it is not widely accessible to a less technical audience, requiring advanced knowledge of machine learning techniques to use. To address this, we introduce a new user-friendly web-based tool that allows a less technical audience to upload music tracks, execute this technique in one click, and subsequently presents the result in a clean visualization to the user. To both increase the number of templates available to the user and address shortcomings of the work of Ashley and Herrmann et al., we also introduce a new direct transformer-based album sequencing method. We find that our more direct method outperforms a random baseline but does not reach the same performance as the narrative essence approach. Both methods are included in our web-based user interface, and this—alongside a full copy of our implementation—is publicly available at REDACTED