The goal of this project is to create a machine learning model capable of receiving text describing a clothing item and returning a ranked list of links to similar items from different websites. Your solution must be a function deployed on Google Cloud that accepts a text string and returns JSON responses with ranked suggestions.
Clothing Similarity Search This project aims to develop a machine learning model capable of receiving text descriptions of clothing items and returning a ranked list of links to similar items from different websites. The solution is implemented using Python and Docker, with deployment on Google Cloud.
Project Structure The project is organized into the following directories and files:
Data_Collection: Contains scripts for web scraping and data collection from various e-commerce websites. Data_Clean: Includes scripts for data preprocessing and cleaning. Model_Training: Contains the script for training the clothing similarity model. Dockerfile: Specifies the Docker image configuration for containerizing the application. similarity_check.py: Implements the similarity checking function. requirements.txt: Lists the required Python packages. Getting Started To run the project locally, follow these steps:
Clone the repository:
bash Copy code git clone Install the required packages:
Copy code pip install -r requirements.txt Collect and preprocess data:
Run the scripts in the Data_Collection directory to scrape clothing item descriptions and URLs from various e-commerce websites. Run the scripts in the Data_Clean directory to preprocess and clean the collected data. Train the clothing similarity model:
Run the script in the Model_Training directory to train the model using the preprocessed data. Build and run the Docker container:
sql Copy code docker build -t clothing-similarity-search . docker run -p 8080:8080 clothing-similarity-search Access the application locally: Open a web browser and go to http://localhost:8080 to interact with the clothing similarity search application.
Deploying on Google Cloud Due to technical issues with the Google Cloud Platform (GCP) billing system, the deployment on GCP could not be completed. However, the project can be run locally in a Docker container following the steps mentioned above.
Future Improvements Here are some potential areas for future improvements:
Implementing more advanced natural language processing techniques to enhance the similarity matching. Scaling the application to handle larger datasets and increased user traffic. Adding user authentication and personalized recommendations. Expanding the range of supported e-commerce websites for data collection. Contributors Shephin Philip License This project is licensed under the MIT License.