Digikala is a prominent Iranian e-commerce platform offering a wide range of products, from digital devices and mobiles to laptops, books, and clothing. In this project, my focus was on creating a specialized clothing search engine based on exact color preferences and clothing types. The project primarily centers on men's t-shirts and shirts to showcase the functionality of the system.

Project Github Repository

Demo:

https://meysamraz-cloth-color-search-digikala-project.streamlit.app/

Demo Heroku version:

Update: Heroku may discontinue free hosting after November 28, 2022. If this link does not work, please use the link above.

https://clothing-search-by-color.herokuapp.com/

Project Preview:

1. Data Collection:

• Data collection was achieved by extracting data from a hidden API provided by Digikala, a prominent Iranian e-commerce platform. This method was chosen for simplicity and effectiveness.

2. Data Preprocessing:

• Preprocessing involved several key steps:

  • Dropping null values to ensure data quality.

  • Creating a "Type of Cloth" column to categorize clothing items.

  • Creating a "Color" column to represent the color of each item.

3. Image Color Detection:

• For image color detection, the K-Means clustering algorithm was employed. A threshold value and specific conditions were applied to accurately identify colors on various clothing items.

4. Front-End Development:

• The front-end of the project was built using Streamlit, a Python library for creating web applications. Streamlit offers a straightforward approach to web development entirely in Python. However, some limitations were encountered when customizing certain widgets, particularly in the color selection section.

5. Deployment:

• The project was made accessible online through Heroku, a cloud platform as a service. Heroku provided a flexible hosting solution for deploying the application.

Installation and Execution:

• To run the project, follow these steps:

  • Install the necessary requirements using pip install -r requirements.txt.

  • Execute the project with streamlit run main.py.

Libraries and Frameworks Used:

• Streamlit

• Flask

• Pandas

• scikit-learn

• Heroku

Future Updates:

• In future iterations of this project, I plan to implement the following enhancements:

  • Expand the clothing categories to provide a more comprehensive search experience.

  • Improve the accuracy and granularity of color detection .

  • Enhance the user interface and interactivity to address Streamlit's limitations.

  • Explore advanced image analysis techniques to extract more detailed clothing attributes.