Recipe Advisor

Used web scraping to fetch 4842 recipes from SanjeevKapoor.com. Implemented word2vec and TF-IDF for ingredient parsing and similarity measurement. Provides personalized recipe recommendations based on user-input ingredients using cosine similarity.

Table of Contents

• Introduction
• Images
• Features
• Getting Started
  • Installation
  • Running the App
• Usage
• Technologies Used
• Contributing
• License

Introduction

Recipe Advisor is a web application that allows users to discover delicious recipes based on the ingredients they have. By leveraging web scraping techniques and advanced NLP models, the app provides personalized recipe recommendations.

Images

Home Page

Recipe Collection

Recipe Details

Test Model

Recommendations

Features

• Browse a collection of 4000+ recipes from SanjeevKapoor.com.
• Get personalized recipe recommendations based on user-input ingredients.
• Detailed recipe view with ingredients, method, and other details.
• Pagination for easy navigation through recipes.

Getting Started

Installation

1. Clone the repository:

   git clone https://github.com/Transcendental-Programmer/Recipe-Advisor-App.git
   cd Recipe-Advisor-App

2. Create a virtual environment:

   python -m venv venv

3. Activate the virtual environment:

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install the required packages:

   pip install -r requirements.txt

Running the App

1. Make sure you are in the project directory and the virtual environment is activated.
2. Run the Flask application:

   python app.py

3. Open your web browser and go to http://127.0.0.1:5000 to access the app.

Usage

• Home Page: Provides an introduction and navigation to explore recipes or test the model for personalized recommendations.
• Explore Recipes: Browse through the entire collection of recipes.
• Recipe Details: View detailed information about a specific recipe.
• Test Model: Enter ingredients to get personalized recipe recommendations.

Technologies Used

• Web Scraping: BeautifulSoup, Requests
• NLP Models: word2vec, TF-IDF
• Frameworks: Flask, Pandas
• Frontend: HTML, CSS, JavaScript
• Data Storage: CSV files

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch

3. Make your changes and commit them:

   git commit -m "Add new feature"

4. Push to the branch:

   git push origin feature-branch

5. Create a pull request.

License

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