Janmenjoy Nayak

Accurately monitoring diet remains a challenge, especially for home-cooked or restaurant meals that do not carry nutritional labels and are difficult to record using conventional calorie-counting apps. Most existing tools rely on manual entry or barcode scanning, which are time-consuming and often inaccurate. To address these limitations, this study introduces an AI-powered calorie tracking system that combines image captioning (BLIP model) with large language models (Groq LLM via LangChain) to automatically recognize food items, estimate their nutritional values, and generate personalized feedback. Users can simply upload a photo of a meal or enter food names, and the system provides calorie and nutrient breakdowns, daily summaries, and fitness suggestions when caloric excess is detected. Reports are automatically compiled into a downloadable PDF for long-term monitoring. The system demonstrated strong performance, achieving a precision of 92.4%, a recall of 91.1%, and an F1-score of 91.7% in food recognition, with an AUC of 0.95 for nutritional estimation. Case studies confirmed its ability to handle multi-item meals, correctly identify key nutrients, and provide contextual health advice. To ensure transparency and trust, the framework incorporates Explainable AI (XAI) techniques such as Grad-CAM, SHAP, LIME, and EDGE, which help users and healthcare professionals understand how predictions are generated. By reducing manual effort, adapting to food variability, and offering real-time, interpretable insights, this system provides a practical, reliable, and user-friendly solution for personal health management, chronic disease care, and commercial fitness applications.