README.md

metadata

title: Recipe Recomendation System
sdk: streamlit
emoji: 🌖
colorFrom: red
colorTo: yellow
pinned: true
short_description: Your go to recommender system
sdk_version: 1.44.1

🍳 Recipe Personalization System

A smart recipe recommendation system that personalizes recipe suggestions based on user preferences, dietary restrictions, and fitness goals.

🌟 Features

• Personalized Recommendations: Get recipe suggestions based on your:

  • Dietary preferences and restrictions
  • Fitness goals (weight loss, muscle gain, maintenance)
  • Cooking time preferences
  • Calorie targets
  • Meal history and ratings

• Nutritional Tracking:

  • Detailed macronutrient breakdown
  • Calorie tracking
  • Sugar content monitoring (especially for diabetic users)
  • Portion size recommendations

🚀 Getting Started

Using the Web Interface

Visit our Hugging Face Space to try the system:

1. Enter your preferences or requirements in the text box
2. Adjust the number of recipes you want to see
3. Click "Get Recommendations"
4. View your personalized recipe suggestions

Local Development

1. Clone the repository:

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

2. Create and activate a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Run the application:

python app.py

🛠️ Project Structure

recipe-personalization/
├── app.py              # Gradio interface for Hugging Face
├── requirements.txt    # Python dependencies
├── data/              # Recipe dataset
└── notebooks/         # Jupyter notebooks for analysis

🤝 Contributing

We welcome contributions! Please feel free to submit a Pull Request.

Model Details

• Model type: Sentence Transformer
• Base model: all-mpnet-base-v2
• Source: Hugging Face Hub
• Input: Text (recipe titles, descriptions, or ingredients)
• Output: Vector embeddings for similarity search and recommendations

Notes

• The model will be downloaded automatically when you first use it
• Loading the model requires sufficient RAM
• For better performance, consider using GPU if available

🌐 Deployment Options

Option 1: Streamlit (Local/Cloud)

Follow the existing Streamlit instructions above.

Option 2: Hugging Face Spaces

You can also deploy this application on Hugging Face Spaces:

1. Visit our Hugging Face Space
2. Enter your preferences or requirements in the text box
3. Adjust the number of recipes you want to see
4. Click "Get Recommendations"
5. View your personalized recipe suggestions

To deploy your own Hugging Face Space:

1. Create a Hugging Face account
2. Go to https://huggingface.co/spaces
3. Click "Create new Space"
4. Choose "Gradio" as the SDK
5. Connect your GitHub repository
6. The Space will automatically deploy your app

Benefits of Hugging Face Spaces:

• Free hosting
• Automatic deployment from GitHub
• Built-in model hosting
• Easy sharing and collaboration
• No server management needed

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Made with ❤️ for better cooking experiences