With the growing integration of deep learning models into everyday applications, ensuring their decisions are interpretable and trustworthy is critical. Explainable AI techniques address this need by giving insights into the model behavior. Concept-based XAI methods, which link predictions to human-understandable attributes, show promise but are often post-hoc, resulting in explanations lacking a robust connection to the model's predictions. Attribute-based classifiers are an alternative that integrates explanatory elements into the model's design. However, existing methods often face challenges in seamlessly aligning attributes as explanations with input data and predictions. This paper introduces Deep-DAP, a novel architecture where classification outputs are directly influenced by attribute scores, providing intuitive and transparent explanations. We evaluate Deep-DAP, showcasing competitive performance in both classification and attribute prediction. Through sensitivity and similarity analyses, we assess its explainability, revealing that while there is room for improving predictive accuracy, Deep-DAP's capability to provide explanations alongside predictions establishes it as a valuable tool for Explainable AI.