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AI-Powered Visual Search for E-commerce

In this blog, we’In the world of e-commerce, searching for products using images instead of keywords is the next big thing.

Updated on: May 02, 2025
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Aiforbusiness Ecommerce AI

Transforming Product Search with AI & Computer Vision

In the world of e-commerce, searching for products using images instead of keywords is the next big thing. Imagine snapping a photo of a pair of shoes you love and instantly finding similar ones in an online store. This is exactly what E-commerce Visual Search AI does — making product discovery easier, smarter, and more intuitive.

In this blog, we’ll explore how we built this AI-powered visual search engine and how it enhances the online shopping experience.

What is E-commerce Visual Search AI?

E-commerce Visual Search AI is an AI-powered image search system that allows users to:
Upload an image of a product they like
Find visually similar products from an existing product database
Get instant results using deep learning & FAISS-based indexing

This project combines computer vision, deep learning, and efficient search algorithms to deliver a seamless shopping experience.


🔗 Live GitHub Repository: https://github.com/tecmantras/ecommerce-visual-search-ai

Why Visual Search?

Traditional text-based search has limitations:
❌ Users might not know the exact product name
❌ Descriptions vary across brands
❌ Keywords may not match the actual product

With visual search, shoppers can:
✔ Find products instantly by uploading a photo
✔ Get highly relevant matches based on appearance
✔ Enjoy a frictionless shopping experience


How It Works

Our AI-powered search system follows this simple 3-step process:

Feature Extraction (Deep Learning)

  • Uses ResNet-50, a powerful deep learning model, to extract image features

  • Converts an image into a feature vector (a set of numerical values representing its content

FAISS Indexing (Efficient Search)

  • Stores extracted features in FAISS (Facebook AI Similarity Search

  • Enables fast and scalable image similarity search

Image Search API

  • When a user uploads an image, the API

  • Extracts its feature vector

  • Searches the FAISS index for similar vectors
  • Returns the closest matching products

Tech Stack & Implementation

We built this system using state-of-the-art technologies:

  • Backend: Python (Flask API)

  • Frontend: HTML, Bootstrap, jQuery

  • Deep Learning: ResNet-50 (PyTorch)

  • Search Engine: FAISS (Facebook AI Similarity Search)

  • Database: MySQL

  • Cloud Storage: AWS S3 (for product images)


Step-by-Step Guide to Running This Project

Step 1: Clone the Repository

git clonehttps://github.com/tecmantras/ecommerce-visual-search-ai.git

cd ecommerce-visual-search-ai


Step 2: Install & Set Up Conda Environment

To install Conda on your Ubuntu system, you can use the Miniforge installer, which comes pre-configured with Conda and the conda-forge channel.

  • Download the Miniforge Installer: For a 64-bit x86 system (most common), use the following command
    • Wget https://github.com/conda-forge/miniforge/releases/download/24.11.3-2/Miniforge3-24.11.3-2-Linux-x86_64.sh
  • Verify the Installer’s Integrity (Optional but Recommended
    • sha256sum Miniforge3-24.11.3-2-Linux-x86_64.sh
  • Run the Installer:
    • chmod +x Miniforge3-24.11.3-2-Linux-x86_64.sh
    • ./Miniforge3-24.11.3-2-Linux-x86_64.sh

  • Initialize Conda:
    • ~/miniforge3/bin/conda init
  • Verify the Installation
    • conda --version
  • Create and activate a Conda environment for this project
    • conda create --name faiss_ecom python=3.11.11 -y

    • conda activate faiss_ecom

Step 3: Install Required Dependencies

Run the following command to install FAISS and other required libraries:

conda install -c conda-forge faiss-cpu -y

pip install --upgrade pip

pip install flask flask-cors numpy pymysql boto3 torch torchvision pillow


Step 4: Verify FAISS Installation

python -c "import faiss; print(f'✅ FAISS Installed: Version {faiss.__version__}')"



Step 5: MySQL Database Setup

Create a MySQL database and table for storing product information:

CREATE DATABASE image_search_db;


CREATE TABLE product (

   id INT AUTO_INCREMENT PRIMARY KEY,

   name VARCHAR(255),

   image VARCHAR(255) -- Image filename stored in S3

);



Step 6: Configure Project Settings

Create a config.py file in the project root and add the following details:

# MySQL Configuration

MYSQL_HOST = "your-mysql-host"

MYSQL_PORT = 3306

MYSQL_USER = "your-username"

MYSQL_PASSWORD = "your-password"

MYSQL_DB = "image_search_db"


# AWS S3 Configuration

S3_BUCKET = "your-s3-bucket"

S3_REGION = "your-region"

S3_ACCESS_KEY = "your-access-key"

S3_SECRET_KEY = "your-secret-key"

S3_FOLDER = "ecom-image-ai"

Replace your-mysql-host, your-username, and AWS credentials with actual values.


Step 7: Index Product Images

Run the following script to fetch images from AWS S3, extract features, and index them in FAISS:

python index_images.py

✔ This will create product_index.faiss and product_ids.npy.


Step 8: Start the Flask API

Run the following command to start the API server:

python image_search.py

The API will be available at: http://127.0.0.1:5000


Step 9: How to Use the Image Search API

Upload an Image & Get Matching Products

curl -X POST -F "image=@query.jpg"http://127.0.0.1:5000/search



Conclusion

The future of e-commerce is visual, and AI-powered search is leading the way. E-commerce Visual Search AI bridges the gap between inspiration and purchase, enabling users to find products effortlessly using just an image. Whether you’re an e-commerce business or a developer, implementing AI-driven search can help you:

✔ Provide a seamless user experience
✔ Boost customer satisfaction and retention
✔ Stay ahead in the competitive online marketplace

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