Architecture Diagram For Ai Based Smart Meal Planner

Our AI Nutrition Advisor is more than just another meal planning tool. It's a conversational platform that Generates personalized meal plans based on health goals and dietary restrictions

ensure that the generated meal plans possess the available ingredients. 6. DESIGN The overall architecture of the web-based diet planner consists of a UI to input users' health details, food preferences, and image uploading of the available ingredients. The backend will include the Random Forest model and the CNN-based image processing model.

d Adaptive Meal Planning The meal planning module dynamically adjusts recommendations based on the user's progress and evolving needs, ensuring that the suggested meals remain relevant and effective over time. e Engaging User Experience The user-friendly mobile interface and integration of advanced technologies,

Use Case Diagram AI-Powered Personalized Nutrition Planner Example AI-Powered Personalized Nutrition Planner Use Case Diagram. The AI-Powered Personalized Nutrition Planner is designed to help users receive custom meal plans based on their personal data and goals. It uses AI to analyze inputs like age, weight, allergies, and preferences.

The AI Meal Planner LangGraph Architecture To ensure scalability and modularity , I structured the AI Meal Planner using the following LangGraph-based agent workflow Graph visulization via

Dietary intake is considered one of the major research issues in the field of nutrition and health care. However, existing tools are time consuming and require skilled people to interview patients and collect useful data. In this paper, we start by determining the nutritional needs of patients and then we propose a method for personalizing meals using artificial intelligence AI methods. The

Learn how to build an event-driven architecture that coordinates a team of four family-meal-planning agents, and leverages LangChain, Anthropic's Claude, Kafka, and Flink. Dinnertime with picky toddlers is chaos, so I built an AI-powered meal planner using event-driven multi-agent systems. With Kafka, Flink, and LangChain, agents handle meal

Create an image. Second, create an user interface. Appian has a great UI design with drag-n-drop capabilities. This allows me to parse the json data returned by the ChatGPT into a grid.

Using a state-of-the-art knowledge-based recommendation system as a reference, this work assesses the meal plans generated by two LLM models in terms of energy intake, nutrient accuracy and meal

Custom Meal Plans Generate personalized meal plans for 2-4 meals per day based on user data such as age, weight, height, and activity level. Allergy and Dietary Filtering Excludes meals based on allergies e.g., nuts, lactose, gluten and dietary choices e.g., vegan. Food Recommendations Suggests alternative food items using K-Nearest Neighbors KNN.