Diet-Meal Recommendation System

Project Overview

The Diet-Meal Recommendation System is designed to recommend personalized meal plans based on user preferences, health conditions, fitness goals, and dietary restrictions. This system utilizes a Large Language Model (LLM) to process user inputs and suggest optimized meal plans. Features

Personalized Recommendations: Suggest meals based on user preferences, such as vegetarian, vegan, gluten-free, etc.
Health & Fitness Goals: Recommend meals according to weight loss, muscle gain, or maintenance goals.
Dietary Restrictions: Suggest meals based on allergies (e.g., dairy-free, nut-free, etc.).
User Input Processing: Collect user data through forms or a conversational interface (e.g., chatbot).
Meal Plan Generation: Generate daily, weekly, or monthly meal plans for the user.

Technologies Used

Python: Programming language for backend development.
Hugging Face Transformers: For LLM models.
Flask/Django: For creating the web-based API or application.
OpenAI GPT: For generating meal suggestions.
Pandas, NumPy: For data manipulation and analysis.
SQL/NoSQL Database: To store meal database and user data.
NLP: To process and understand user preferences and goals.

System Architecture

User Interface:
    A web-based front-end or chatbot where users can input their meal preferences, dietary restrictions, and goals.
    Interacts with the backend via REST API or direct chatbot interaction.

Backend:
    The backend processes the user's input, such as dietary preferences and goals.
    The LLM model is used to interpret and generate meal recommendations.

Database:
    Stores a large set of recipes with nutritional information.
    Keeps track of user profiles, preferences, and dietary goals.

Recommendation Engine:
    Based on user data, a recommendation engine powered by the LLM generates personalized meal plans.
    LLM-based NLP models like GPT-3 or GPT-4 are used to understand complex user inputs and suggest optimized meal plans.

How the System Works

User Data Collection:
    The user provides input regarding their age, gender, fitness goals (e.g., weight loss, muscle gain), allergies, and dietary preferences (e.g., vegetarian, vegan, etc.).

NLP Processing:
    The user inputs are processed by a Large Language Model (LLM) (e.g., GPT) to extract relevant dietary information, preferences, and goals.
    The model generates meal recommendations based on this input.

Meal Plan Generation:
    Based on the processed data, the system pulls relevant meal information from the database.
    The meal plan is then optimized based on nutritional value, calorie requirements, and food preferences.

Meal Plan Delivery:
    The user receives a daily, weekly, or monthly meal plan.
    Each meal plan includes recipes, ingredients, nutritional breakdown, and preparation instructions.