Ecommerce Product Recommendation Application using OpenAI LLM

• Application Demo

The "Ecommerce Product Recommendation using OpenAI LLM" project aims to develop an ecommerce product recommendation system that returns a list of recommended products based on users preferences.

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File Explanation

This repository consists of several files :

    ├── backend/
    │   ├── app.py
    │   ├── bq-results-20240205-004748-1707094090486.csv
    │   ├── chatbot.py
    │   ├── dockerfile
    │   ├── requirements.txt
    ├── frontend/
    │   ├── app.py
    │   ├── dockerfile
    │   ├── requirements.txt
    ├── .gitignore
    └── README.md

• backend/ app.py: This file contains the backend code for the application. It responsible for handling server-side logic, API endpoints, or any other backend functionality.

• backend/ bq-results-20240205-004748-1707094090486.csv: This is the CSV file used as the dataset in this project. Dataset obtained from Google Cloud Platform - BigQuery database : thelook_ecommerce table: order_items, inventory_items, users.

• backend/ dockerfile: Dockerfile is used to build a Docker image for backend application. It includes instructions on how to set up the environment and dependencies needed for backend.

• backend/ chatbot.py: This file contains the code used to create the Langchain framework and LLM (OpenAI), which is used to create the recommendation system.

• backend/ requirements.txt: This file lists the Python dependencies required for backend application. These dependencies can be installed using a package manager like pip.

• frontend/ app.py: This file is the main script for the frontend of the application and is developed using the Streamlit framework. It contain sections for user input, and the integration of backend functionality through API calls.

• frontend/ Dockerfile: Similar to the backend dockerfile, this file is used to build a Docker image for frontend application. It includes instructions on setting up the environment and installing dependencies.

• frontend/ requirements.txt: This file lists the Python dependencies required for frontend application. These dependencies can be installed using a package manager like pip.

• README.md: This is a Markdown file that typically contains documentation for the project. It include information on how to set up and run your application, dependencies, and any other relevant details.

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Application

How to use

In this application, there are two methods used as a product recommendation system: manually and chatbot. Users can choose one of the methods.

Manual

For this method, users can input their product preferences in the fields provided.

streamlit-app-2024-02-05-20-02-45.mp4

ChatBot

For this method, users can chat with an assistant who helps users find products according to their preferences.

streamlit-app-2024-02-05-20-02-31.mp4