Abstract: The Food Calories Estimation System provides an efficient and user-friendly approach to tracking daily calorie intake using food image recognition and manual weight input. The system integrates a Flask-based web application with the Gemini API to identify food items from uploaded images and estimate their caloric value per 100g. Users can register, log in, set target calorie goals, and track their remaining calorie intake dynamically. The system calculates Total Daily Energy Expenditure (TDEE) based on user inputs such as weight, height, and activity level. The real-time calorie tracking module updates the consumed and remaining calories after each meal entry. The web application features a responsive frontend using HTML, CSS, and JavaScript, ensuring an interactive user experience. This solution provides a practical tool for individuals to monitor and manage their dietary intake effectively, promoting healthier eating habits.
Keywords: Food calorie estimation, Flask web application, real-time calorie tracking, food image recognition, nutrition monitoring, AI-based food identification.
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