Amazon User Recommender

Overview

The Amazon User Recommender App is designed to train a content based and a collaborative filter recommender system to give the top 10 product predictions for a specified user. The application is docerized and ran on Amazon Elastic Container Service and EC2, with all artificats stored in an S3 bucket.

Application Components

The application consists of the following components:

• The streamlit application allows for user to select a recommender system and input a user id.
• Based on the input and selected model, a prediction is made and displayed to the application

Setup Instructions

To set up this project, follow these steps:

• Clone the Repository: Clone the Clouds Data Pipeline repository to your local machine.

 git clone https://github.com/DarwinYip2022/Cloud_Engineering.git

• Install Requirements: Install the required Python packages using pip.

pip install -r requirements.txt

• Configure Environment Variables: Create a .env file to securley configure AWS S3 credentials

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION=

• Run the Python Application for Model Training:

 python3 pipeline.py --config config/default.yaml

• Run the Streamlit Application:

streamlit run app.py

Build the Application Docker image

docker build -t amazon-app . 

Run the entire model pipeline in a docker container

docker run -p 8501:8501 --env-file .env amazon-app