Published Paper Details:

The increasing demand for health and fitness applications has driven the development of AI- powered systems for food recognition and calorie management. This project utilizes Convolutional Neural Networks (CNN) and Artificial Intelligence (AI) to create a robust food image detection and calorie calculation platform. The system allows users to upload images of food, which are processed using a CNN model trained to recognize a wide variety of food items. Once the food is identified, users can input additional details such as portion size, ingredients, drinks, or preparation method to enhance the accuracy of the calorie estimation. The core functionality of the system revolves around accurate food recognition through CNNs, which are well-suited for image classification tasks. The network is trained on a large dataset of labeled food images to classify the food items and estimate their nutritional content. Based on the identified food, the system calculates the total calorie count using a deep learning model integrated with a nutritional database. In addition to calorie calculation, the system provides personalized exercise recommendations based on the user's calorie intake. By leveraging AI algorithms, the platform suggests various types of exercises (e.g., running, cycling, yoga) and the duration needed to burn the calories consumed, ensuring that users can make informed decisions about their physical activity to maintain a healthy lifestyle. The system can access this information via a Google Gemini API or the pre-trained LLAMA model from Hugging Face for more complex, real-time conversational interactions. This platform not only provides accurate food detection and calorie tracking but also empowers users to make healthier choices by offering actionable insights for maintaining balance between calorie intake and expenditure. The integration of AI technologies ensures a seamless, interactive, and personalized experience for the end-users.