The exponential growth of digital reading has in- troduced vast volumes of textual content available in PDF format. Conventional PDF readers primarily serve as passive tools for viewing and annotating documents, providing no an- alytical insight into the emotional or semantic context of the text. This paper presents the design phase of a Smart PDF Reader integrated with Natural Language Processing (NLP) techniques, enabling real-time sentiment analysis and text sum- marization of book content. The system employs both lexicon- based and deep learning models to classify emotional polarity and generate concise summaries. The hybrid architecture leverages VADER for rule-based analysis and transformer-based models such as BERT and RoBERTa for contextual understanding. Extractive and abstractive summarization using TextRank and T5 models provide complementary summarization perspectives. The system further integrates visualization modules that depict sentiment trends across chapters, offering an emotion-aware reading experience. The proposed framework contributes toward the design of intelligent reading systems that combine linguistic comprehension, emotion detection, and interactive visualization.