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Eco-Friendly Fabric Recommendation System

The Eco-Friendly Fabric Recommendation System helps users discover sustainable and eco-friendly fabric alternatives. By selecting a fabric, the system suggests similar options using advanced similarity search algorithms powered by FAISS (Facebook AI Similarity Search) and TF-IDF. The application features an interactive UI built with Streamlit.

This project is part of the Eco-Friendly Tribe initiative, which aims to promote sustainable fabric choices.

Eco-Friendly Fabric Recommendation System

Features

  • Interactive Fabric Selection: Users can select a fabric to find similar eco-friendly alternatives.
  • Text Vectorization: Leverages TF-IDF to analyze fabric titles and descriptions.
  • Categorical Encoding: Encodes categorical labels using one-hot encoding for enhanced similarity search.
  • FAISS Integration: Employs FAISS for fast and accurate similarity search.
  • Streamlit Frontend: Provides a simple and user-friendly interface.

Dataset

The system uses the FabricFrontiers dataset. This dataset contains the following fields:

  • idx: Unique identifier for each fabric.
  • title: Name of the fabric.
  • description: Description of the fabric, including eco-friendly attributes.
  • source: Source or origin of the fabric.
  • label: Categorical label representing the fabric's type or category.

Download the dataset here.

Installation

  1. Clone the Repository:

    git clone https://github.com/Hacxmr/my_eco_tribe.git
cd my_eco_tribe

  2. Install Dependencies: Ensure you have Python 3.8+ installed. Then, install the required packages:

    pip install streamlit pandas faiss-cpu scikit-learn numpy

  3. Download the Dataset: Place the fabric_frontiers.csv file in the root directory of the project, or update the dataset URL in the code if needed.

Usage

  1. Run the Streamlit Application:

    streamlit run streamlit_app.py

  2. Interact with the Application:

    • Select a fabric from the dropdown menu.
    • Click the "Recommend" button to view similar fabric suggestions.

How It Works

  1. Data Preprocessing:

    • Combines title and description fields for text analysis.
    • Transforms text data into numerical vectors using TF-IDF.
    • Encodes categorical labels using one-hot encoding.

  2. Similarity Search:

    • Creates a FAISS index for efficient similarity search.
    • Searches for fabrics most similar to the selected fabric based on vectorized features.

  3. Interactive Recommendations:

    • Displays the top 5 fabric recommendations along with their descriptions and sources.

Example Output

Selected Fabric: Organic Cotton

Recommended Fabrics:

  1. Hemp Fabric (Source: Sustainable Textiles)
    • Description: Durable and eco-friendly fabric made from hemp fibers.
  2. Recycled Polyester (Source: EcoThreads)
    • Description: Sustainable polyester made from recycled plastic bottles.
  3. Bamboo Rayon (Source: Nature's Fabrics)
    • Description: Soft and eco-conscious fabric derived from bamboo pulp.

Contributions

Contributions are welcome! If you want to contribute:

  1. Fork the repository.
  2. Make your changes.
  3. Submit a pull request.

Contact

For any queries or suggestions, feel free to reach out: