Image Size Reduction

This project is a backend system that allows users to upload a CSV file containing product details and image URLs. The system asynchronously processes the images, reducing their size by 50%, and provides a status update via an API.

Features

Upload CSV files with image URLs and product information.
Asynchronously compress images by reducing their quality by 50%.
Track the progress of image processing through a status API.
Generates compressed images in your local project.

Getting Started
Installation
Usage
APIs
- Upload API
- Status API
Project Structure
Technologies Used
License

Getting Started

These instructions will help you set up the project locally for development and testing purposes.

Prerequisites

Node.js (v14 or later)
MongoDB (or any other database supported by the project)

Installation

Clone the repository:

git clone https://github.com/Deep-afk/ImageSizeReduction.git

Navigate to the project directory:
```
cd ImageSizeReduction
```
Install the dependencies:
```
npm install
```
Set up the environment variables:
- Replace <your-mongo-database-url> with your actual MongoDB connection string.
Start the server:
```
npm start
```
The application will start on http://localhost:3000.

Usage

Upload a CSV file containing product names and image URLs.
The server will process the images asynchronously.
Use the status API to check the progress of image processing.

APIs

Upload API

Endpoint: /upload
Method: POST
Description: Accepts a CSV file with product details and image URLs, assigns a unique request ID, and begins image processing.

Request Example:

curl -X POST http://localhost:3000/upload \
  -F 'file=@/path/to/your/file.csv'

CSV Format:

Serial Number	Product Name	Input Image URLs
1	Product A	https://example.com/image1.jpg,https://example.com/image2.jpg
2	Product B	https://example.com/image3.jpg

Response Example:

{
  "requestId": "12345abcd"
}

Status API

Endpoint: /status/:requestId Method: GET Description: Checks the status of the image processing job using the request ID.

Request Example:

curl --location 'localhost:3000/status/e95d16b3-df4b-4660-adff-963bfe6175f1'

Response Example:

{
  "requestId": "12345abcd",
  "status": "Completed",
  "productData": [
    {
      "productName": "Product A",
      "inputImageUrls": [
        "https://example.com/image1.jpg",
        "https://example.com/image2.jpg"
      ],
      "outputImageUrls": [
        "/images/processed-image1.jpg",
        "/images/processed-image2.jpg"
      ]
    }
  ]
}

Project Structure

ImageSizeReduction/
├── public/
│   └── images/            # Processed images saved here
├── models/
│   └── jobmodel.js        # Job model for MongoDB
├── utils/
│   └── processimage.js     # Image processing logic
│   └── jobqueue.js         # Job queue for asynchronous processing
│   └── validate.js         # validate CSV file
├── index.js               # Main application entry point
└── package.json           # Project dependencies and scripts

Technologies Used

Node.js: Server-side JavaScript runtime.
Express: Web framework for creating APIs.
Multer: Middleware for handling file uploads.
Sharp: High-performance image processing.
MongoDB: NoSQL database for storing job and image data.
Axios: HTTP client for fetching images from URLs.
uuid: Unique ID generator

License

This project is licensed under the MIT License. See the file for more details.

Additional Steps:

Testing: Add examples and instructions on how to test the system locally (e.g., using Postman or cURL).
Contributions: Add a section for contributions if you plan to open the repository for collaboration.
Links: Add any relevant links, such as to the live deployment, project documentation, or APIs.

Feel free to let me know if you need any more adjustments!

Future Enhancements:

Compressing an image and providing its URL using Amazon S3.
Host the website and allow the user to control the level of compression.

Deep-afk/ImageSizeReduction