ahmedrachid / greenplum-skin-cancer-search-engine

Skin Cancer Images - Semantic Search

This repository provides instructions and code for setting up a Skin Cancer Image Semantic Search engine leveraging VMware Greenplum as Vector Database.

Getting Started

Follow these steps to set up the Skin Image Search System on your local machine.

Prerequisites

• Greenplum Database
• pgvector extension
• Jupyter Notebook
• Python dependencies (requirements.txt)

Step 1: Create Database Tables and Python Setup

1. Run the script.sql file to create tables for storing images, metadata and embeddings in your Greenplum database:

   $ psql -U your_username -d your_database -a -f script.sql

2. Install the required Python packages listed in requirements.txt.

    $ pip install -r requirements.txt

Step 2: Generate Embeddings

1. Use the Skin_Cancer_Image_Semantic_Search.ipynb Notebook to download the dataset and generate embeddings into Greenplum.

Step 3: Run and Access the Web App

1. Run the Streamlit App using:

    $ streamlit run app.py

2. Access the web application by opening a web browser and navigating to:

   http://localhost:8501