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In the digital era, where lifestyle-related health issues are becoming increasingly prevalent, diet management and calorie monitoring have emerged as essential components of personal wellbeing. Individuals today are often overwhelmed with dietary choices and fitness recommendations, making it difficult to adopt and sustain a balanced lifestyle. While numerous commercial fitness and diet applications such as HealthifyMe [3], MyFitnessPal [4], and JEFIT [5] exist, they are frequently characterized by certain drawbacks: heavy reliance on artificial intelligence and cloud-based services, mandatory internet connectivity, and costly subscription models that limit access to premium features. Furthermore, these platforms often overwhelm basic users with complex dashboards and features that extend beyond the fundamental goal of tracking calories and nutrients. This research introduces NutriTrack, a Python [8]-based calorie and diet tracking application that is designed to address the limitations of existing fitness solutions. Unlike conventional AIdriven applications, NutriTrack adopts a rule-based, offline-first approach. Built using Python [8] as the core programming language, the application integrates Tkinter for an intuitive graphical user interface (GUI) and SQLite [9] for secure and lightweight database management. Through this architecture, users are empowered to log meals manually, calculate calorie and nutrient intake, monitor their Body Mass Index (BMI), and track progress against daily health goals. NutriTrack also generates easy-to-read summaries and health reports, enabling users to make informed dietary decisions without the need for constant internet connectivity or costly subscriptions. A distinguishing feature of NutriTrack lies in its emphasis on simplicity and accessibility. By employing a static food database, the system ensures reliable nutrient calculations and avoids the inaccuracies that sometimes arise from dynamic AI predictions. The use of local storage through SQLite [9] allows for secure handling of diet histories, meal logs, and user accounts, while ensuring data privacy. Unlike many mainstream applications, NutriTrack gives users complete control over their dietary records, fostering transparency and trust. The scope of the project includes features such as BMI computation, goal-based summaries, and offline log export. The user interface has been deliberately designed to prioritize ease of use, particularly for individuals with limited technical expertise. Additionally, the modular design of the system allows for future enhancements, such as integration with mobile devices, wearable fitness trackers, and expanded food datasets. This adaptability ensures that NutriTrack is not only relevant today but also scalable for future applications in personal health monitoring. The outcomes of this project indicate that NutriTrack successfully bridges the gap between complex AI-driven fitness applications and basic manual logging systems. It delivers a lightweight, offline, and user-friendly platform that can be utilized by diverse groups, including students, working professionals, and health-conscious individuals. Initial testing highlights the system's efficiency in providing accurate calorie and nutrient tracking, while maintaining a low learning curve for new users. In conclusion, NutriTrack demonstrates that calorie and diet tracking can be both accessible and effective without reliance on advanced artificial intelligence or costly infrastructure. Its offlinefirst, Python [8]-based framework emphasizes usability, transparency, and extendibility. By laying the foundation for mobile and wearable integration, NutriTrack positions itself as a promising solution for modern dietary management, offering a balance of functionality and simplicity that directly addresses the needs of everyday users. [ABSTRACT FROM AUTHOR] |