Automated Image Fetcher

A Python and Flask-based web application for automated image extraction with advanced filtering and bulk download capabilities.

🚀 Live Demo

View Live Application

📋 Overview

This automated image fetcher streamlines the process of extracting images from web sources using advanced web scraping techniques. Built with Python and Flask, it offers intelligent filtering options and bulk download functionality, achieving a 95% satisfaction rating from over 100 user testers.

✨ Key Features

• Automated Image Extraction: Leverages BeautifulSoup and Requests for intelligent web scraping
• Advanced Filtering: Filter images by dimensions, file size, and format
• Multiple Format Support: Supports .jpg, .jpeg, .png, and .webp formats
• Bulk Download: Bundle multiple images into ZIP files for efficient transfer
• Intuitive UI: User-friendly interface for specifying quantity and format preferences
• Performance Optimized: Efficient processing and storage mechanisms
• High User Satisfaction: 95% satisfaction rating from extensive user testing

🛠️ Tech Stack

Backend

• Python - Core programming language for web scraping logic
• Flask - Lightweight web framework for API and routing
• BeautifulSoup - HTML/XML parsing for web scraping
• Requests - HTTP library for web requests

Frontend

• HTML - Structure and markup
• CSS - Styling and responsive design
• JavaScript - Interactive UI elements and client-side functionality

Additional Tools

• ZIP Compression - For bundling multiple images
• Image Processing - Dimension and format validation

🏗️ Architecture

• Web Scraping Engine: BeautifulSoup + Requests for intelligent image extraction
• Filtering System: Multi-parameter filtering (size, dimensions, format)
• Download Manager: ZIP compression for bulk downloads
• User Interface: Responsive web interface built with HTML/CSS/JS
• Backend API: Flask-based REST API for processing requests

🚀 Getting Started

Prerequisites

• Python 3.8+
• pip (Python package installer)

Installation

1. Clone the repository

git clone https://github.com/PrashantPKP/Automated-image-fetcher.git
cd Automated-image-fetcher

2. Create a virtual environment (recommended)

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install required dependencies

pip install -r requirements.txt

4. Set up environment variables (if needed)

cp .env.example .env
# Configure any necessary environment variables

5. Run the application

python app.py

6. Open your browser and navigate to:

http://localhost:5000

📱 Usage

1. Enter Source URL: Provide the website URL to extract images from
2. Set Filters: Specify desired dimensions, file size limits, and formats
3. Choose Quantity: Select the number of images to fetch
4. Select Formats: Choose from .jpg, .jpeg, .png, .webp options
5. Download: Get individual images or bulk download as ZIP file

🎯 Key Achievements

• ✅ 95% user satisfaction rating from 100+ testers
• ✅ Advanced filtering by dimensions, size, and format
• ✅ Efficient bulk download with ZIP compression
• ✅ Intuitive and responsive user interface
• ✅ Optimized performance for large-scale image processing
• ✅ Support for multiple popular image formats

📊 Performance Metrics

• User Satisfaction: 95% (100+ user testers)
• Supported Formats: 4 major image formats
• Processing Speed: Optimized for bulk operations
• UI Responsiveness: Cross-device compatibility

🔧 Technical Features

Web Scraping

• BeautifulSoup for HTML parsing
• Requests for efficient HTTP handling
• Error handling and retry mechanisms

Image Processing

• Dimension validation and filtering
• File size optimization
• Format conversion support

Download Management

• ZIP compression for bulk downloads
• Progress tracking for large operations
• Efficient memory usage

🌐 Live Application

Experience the application live at: https://aif.zapsas.life/

🤝 Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📝 License

This project is licensed under the MIT License - see the LICENSE file for details.

👨‍💻 Author

Prashant Parshuramkar

• GitHub: @PrashantPKP
• LinkedIn: Prashant Parshuramkar
• Live Demo: Automated Image Fetcher

🙏 Acknowledgments

• Thanks to the Python community for excellent libraries
• BeautifulSoup and Requests developers for robust web scraping tools
• All 100+ beta testers who provided valuable feedback

📞 Support

If you have any questions or run into issues, please open an issue on GitHub or contact me directly.

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⭐ If you found this project helpful, please give it a star on GitHub!

Try it live: https://aif.zapsas.life/