Flipkart Grid 6.0 - Smart Vision Technology Quality Control

This project implements a smart vision technology solution for quality control in e-commerce product management. It utilizes advanced image processing and artificial intelligence (AI) to compare product information extracted from images against user-provided data, ensuring that the products listed on e-commerce platforms meet quality standards.

Project Structure

.
├── static
│   ├── css
│   │   └── style.css
│   ├── img
│   └── upload
├── templates
│   └── bill.html
├── app.py
├── camera.py
├── compare_info.py
└── README.md

Files Description

• app.py: Flask application for web interface.
• camera.py: Handles image capture functionality.
• compare_info.py: Main script for comparing extracted information with user data.
• static/css/style.css: CSS styles for the web interface.
• static/upload/: Directory for storing product images captured from the camera.py.
• templates/bill.html: HTML template for displaying results.

Features

• Image capture and processing
• Text extraction from images using AI
• Comparison of extracted data with user-provided information
• MongoDB integration for data storage
• Web interface for displaying results

Extracted Information

The vision model extracts the following information from product images:

• Product Name
• Packaging Material
• Brand Name
• Pack Size
• Expiry Date
• Expiry Date Status (valid/expired)
• Count Confirmation
• Maximum Retail Price (MRP)
• Shelf Life Prediction (if applicable)

Technologies Used

1. Python: The primary programming language used for scripting and implementation.
2. OpenCV: A computer vision library used for image processing and capturing images.
3. Groq: A machine learning model provider used for product information extraction and comparison. In this project, the following models are employed:
   • Llama-3.2-11b-vision-preview: Used for extracting product information from images.
   • Gemma-7b-it: Used for comparing product information based on extracted and user-provided details.
4. MongoDB: A NoSQL database used for storing extracted data, user information, and comparison results.
5. Langchain: A framework that facilitates the integration of various components, allowing for easier interaction with the Groq API.
6. Flask: A lightweight web framework used for creating the user interface and handling web requests.

Setup and Installation

1. Clone the repository
2. Install required dependencies: pip install -r requirements.txt
3. Set up MongoDB and update the connection string in compare_info.py
4. Obtain a Groq API key and update it in compare_info.py

Usage

1. Run app.py to start the Flask server
2. Upload product images through the web interface
3. Enter product information for comparison
4. View results of the quality control check

Note

Ensure that the Groq API key and MongoDB connection details are kept secure and not shared publicly.