This is a simple Flask application that provides recipe recommendations based on dietary preferences. Users can select their dietary preference (e.g., vegetarian, meat, fish) and specify the number of recipe recommendations they want to receive. The application utilizes machine learning techniques to generate recommendations using TF-IDF vectorization and cosine similarity.
- Dietary Preference Selection: Users can choose their dietary preference from options like vegetarian, meat, or fish.
- Customizable Recommendations: Users can specify the number of recipe recommendations they want to receive.
- Recipe Information: Users can view detailed information about individual recipes, including ingredients and cooking instructions.
- Flask: Python web framework used for developing the backend of the application.
- scikit-learn: Python library used for machine learning tasks, specifically for TF-IDF vectorization and recommendation generation.
- HTML/CSS/JavaScript: Frontend technologies used for designing and implementing the user interface.
- JSON: Data format used for storing recipe data.
- Docker: Containerization tool used for packaging the application and its dependencies into a container for easy deployment.
- Install Python and Flask on your system.
- Clone the repository and navigate to the project directory.
- Run
pip install -r requirements.txtto install the required dependencies. - Run
python app.pyto start the Flask server. - Access the application in your web browser at
http://localhost:5000.
- Visit the homepage of the application.
- Select your dietary preference from the dropdown menu.
- Specify the number of recipe recommendations you want to receive.
- Click the "Get Recommendation" button to view the recommended recipes.
- Explore individual recipes by clicking on their titles.
Contributions are welcome! Feel free to submit bug reports, feature requests, or pull requests on GitHub.