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Recipe Recommendation System

This project implements a recipe recommendation system content based on user-provided ingredients. The system utilizes cosine similarity and TF-IDF (Term Frequency-Inverse Document Frequency) to suggest recipes similar to the user's input.

Overview

The recipe recommendation system allows users to input a list of ingredients, and it recommends recipes from a dataset that match or are similar to the provided ingredients. The recommendations include details such as recipe names, ingredients, nutritional information, images and videos.

Features

  • Content-Based Filtering: Recommendations are based on the similarity between user-input ingredients and recipes in the dataset.
  • Cosine Similarity: Calculates similarity scores using cosine similarity metric.
  • TF-IDF Vectorization: Vectorizes ingredients using TF-IDF for text representation.

Installation

  1. Clone the repository:

    git clone https://github.com/your-username/Seasonings.git
cd Seasonings

  2. Install the required Python packages:

    pip install -r requirements.txt

  3. Run the app:

    streamlit run Streamlit.py

Dependencies

  • pandas
  • scikit-learn
  • requests
  • PIL
  • fastapi

Medium Article

If you want to read about the entire project, please go to the following link: https://medium.com/@abhinavbammidi/from-text-to-tasty-how-nlp-transforms-ingredients-into-recipes-cbdbdef57d2d

License

This project is licensed under the MIT License - see the LICENSE file for details.

Replace the placeholders (your-username, http://localhost:8000, link-to-dataset, link-to-notebook, etc.) with the appropriate URLs, paths, and information relevant to your project.

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