/RAG-Metric-Improvement

Primary LanguageJava

RAG-Metric-Improvement

Watch Demo

FoodiBot

RecipeRecommendation is an intelligent recommendation system designed to help users generate personalized recipe suggestions based on their dietary preferences and available ingredients. The system combines advanced machine learning algorithms and a rich ingredient database to provide accurate and diverse recipe recommendations that cater to different user needs.

Features

1. User Preference Analysis

  • Dietary Preferences: Users can set their dietary preferences, including options like vegetarian, gluten-free, low sugar, and more.
  • Allergen Filtering: The system allows users to specify allergens (such as nuts, dairy, etc.) to ensure the recommended recipes are safe to consume.

2. Ingredient Management

  • Ingredient Inventory: Users can manage their ingredient inventory by inputting the ingredients they currently have available.
  • Smart Matching: The system recommends recipes that best match the available ingredients, reducing food waste.

3. Personalized Recommendations

  • History Analysis: The system analyzes users' historical choices and ratings to provide better-suited recipe recommendations.
  • Diverse Options: Each recommendation session provides multiple options, ensuring users have a variety of choices.

4. Social Interaction

  • Sharing Functionality: Users can share their favorite recipes on social media platforms.
  • Comments and Ratings: Users can comment on and rate recipes, helping other users make better choices.

Technical Architecture

Frontend

  • User interface built with React.js, providing a smooth user experience.

Backend

  • Backend services powered by the Spring framework, handling user requests and data management.
  • Pinecone used as the vector database to support efficient similar recipe recommendations.

Database

  • Nutritionix API used to fetch a rich set of ingredients and nutritional information.

Deployment

  • The system is deployed on the AWS cloud platform, ensuring high availability and scalability.

Installation and Usage

Clone the Repository

git clone https://github.com/YJCatherine/RecipeRecommendation.git
cd RecipeRecommendation

Install Dependencies

  • Frontend:
cd frontend
npm install
  • Backend:
cd backend
mvn install

Start the Services

  • Frontend:
npm start
  • Backend:
bash
mvn spring-boot:run

Contribution Guidelines

We welcome contributions to RecipeRecommendation! If you have suggestions or find any issues, please submit an issue or a pull request.

License

This project is licensed under the MIT License. For more details, please refer to the LICENSE file.