Welcome to ShopSavvy! This innovative platform leverages advanced search technologies and natural language processing to enhance the shopping experience, providing users with powerful tools to find and compare products across various eCommerce platforms.
The ShopSavvy project includes several key components designed for robust data handling, efficient search functionality, and user interaction:
- Dockerfile: Defines the environment and commands to containerize the application.
- deploy.yaml: Kubernetes deployment configuration for managing services and volumes.
- es.py: Integrates Elasticsearch and OpenAI for advanced search and response capabilities.
- indexMapping.py: Configuration for Elasticsearch index properties.
- indexing.py: Handles the processing and indexing of product data into Elasticsearch.
- main.py: The Flask application that serves the web interface and handles all backend operations.
- Advanced Product Search: Utilize Elasticsearch with custom indexing for optimized search results.
- Machine Learning Integration: Use OpenAI for generating search query embeddings and responses.
- Interactive Web Interface: A Flask-based web interface for easy access and interaction.
- Containerization and Orchestration: Use Docker and Kubernetes for deployment and scaling.
- Docker
- Kubernetes (Optional, for deployment using
deploy.yaml) - Python 3.x
- Elasticsearch cluster credentials
-
Clone the repository:
git clone https://github.com/smaliaquib/ShopSavvy.git cd ShopSavvy -
Build the Docker container:
docker build -t shopsavvy-app . -
Run the container:
docker run -p 5000:5000 shopsavvy-app
To deploy the application using Kubernetes:
- Apply the YAML configuration:
kubectl apply -f deploy.yaml
Navigate to http://localhost:5000 to access the ShopSavvy web interface. Utilize the search functionality to explore product comparisons and details.
Interested in contributing? Great! You can contribute by:
- Submitting bugs and feature requests.
- Reviewing code changes.
- Enhancing the documentation.
Check out CONTRIBUTING.md for more information on how to contribute.
This project is licensed under the MIT License - see the LICENSE file for details.
For major changes, please open an issue first to discuss what you would like to change. For direct communication, you can reach out via email issue.
- Make sure to adjust the paths and credentials as per your setup.
- For production deployment, ensure environment variables and sensitive credentials are managed securely (e.g., using Kubernetes secrets or environment-specific config files).