As a Python developer, I often work with screenshots, product images, blog images, and photographs that need to be uploaded to websites or shared online. Large image files can slow down websites, consume storage, and make file sharing inconvenient.
To solve this problem, I built an Image Compressor using Python and Streamlit.
This tool allows users to upload multiple images, choose a compression quality and compression mode, compress up to 50 images in one operation, and download all compressed images in a single ZIP file.
The tool supports JPG, JPEG, PNG, and WEBP formats and includes important validations for file size, image format, corrupted files, dimensions, and duplicate filenames.
Image Compressor using Python
I developed this Image Compressor using Python and Streamlit to provide a simple interface for compressing images without manually processing each image one by one.
The application allows users to:
- Upload multiple images simultaneously
- Compress up to 50 images in one operation
- Select a compression quality
- Choose a compression mode
- Validate images before processing
- Handle invalid or corrupted files
- Preserve PNG transparency where supported
- Handle image orientation correctly
- Display overall compression statistics
- Download all compressed images as a ZIP file
- Clear selected images and start again
This makes the tool useful for developers, bloggers, designers, content creators, and anyone who regularly works with large image files.
If you are interested in working with Python graphical interfaces, you can also explore our guide on Python GUI Programming. For working with images in Python applications, you may also find How to Save Images to a File in Python useful.
Libraries Used in the Image Compressor
The Image Compressor uses a few important Python libraries.
import streamlit as st
import zipfile
from io import BytesIO
from PIL import ImageStreamlit
Streamlit is used to create the complete user interface.
It handles:
- Image uploads
- Compression settings
- Buttons
- Progress indicators
- Error messages
- Compression statistics
- ZIP downloads
Streamlit is particularly useful when you want to turn a Python script into an interactive tool without building a complete frontend from scratch.
Pillow (PIL)
The Pillow library is responsible for image processing.
It is used to:
- Open uploaded images
- Detect the actual image format
- Check image dimensions
- Verify image integrity
- Convert image modes when required
- Compress images
- Preserve transparency for supported PNG images
Pillow acts as the main image-processing engine of the application.
BytesIO
BytesIO allows the application to process images directly in memory.
Instead of saving every uploaded and compressed image temporarily to the computer, the tool works with image data in memory.
This approach is useful when generating downloadable files dynamically.
zipfile
The built-in zipfile module creates a ZIP archive containing all successfully compressed images.
This is particularly useful when users upload many files. Instead of downloading images individually, they can download everything at once.
You can also explore How to Split a File into Multiple Files in Python when working with large files and file-processing applications.
User Controls in the Image Compressor
The application includes simple controls so users can compress images without needing technical knowledge.
Upload Images
Users can upload one or multiple images.
The supported formats are:
- JPG
- JPEG
- PNG
- WEBP
The tool supports a maximum of 50 images in one batch. This limit helps maintain stable performance and prevents excessive memory usage.
The application can also display the number of selected images so users know how many files are ready for processing.
For other file-related Python operations, you can check How to Open a File in Python and How to Check if a File Exists in Python.
Compression Quality
The Compression Quality setting allows users to control the balance between image quality and file size.
For example:
Higher Quality
↓
Better Image Appearance
↓
Larger File SizeWhereas:
Lower Quality
↓
Smaller File Size
↓
More CompressionA user might choose:
- 90–100 for better visual quality
- 60–80 for balanced results
- 20–50 when reducing file size is the primary goal
The actual output size depends on several factors, including:
- Original image size
- Image dimensions
- Image format
- Image content
- Compression mode
If you need to calculate or display uploaded file sizes in your own Python projects, see How to Get File Size in MB Using Python.
Compression Mode
The tool provides three compression modes.
High Quality
This mode focuses on preserving image quality.
It is useful for:
- Professional photographs
- Product images
- Portfolio images
- Images where visual quality is important
The output file may be larger than other compression modes.
Balanced
Balanced mode provides a compromise between image quality and file size.
It is useful for:
- Website images
- Blog images
- General-purpose images
- Documentation screenshots
For most users, this is a practical default option.
Maximum Compression
This mode focuses on reducing the image file size as much as possible.
It is useful for:
- Large batches of images
- Storage optimization
- Images used on lightweight web pages
- Situations where smaller files are more important than perfect visual quality
The output may have more visible compression artifacts depending on the image.
Batch Image Compression using Python
One of the main features of this application is batch image compression.
Instead of uploading and compressing one image at a time, users can select multiple files and process them together.
For example:
Image 1
Image 2
Image 3
Image 4
...
Image 50
The tool processes all valid images in the selected batch. During processing, the application displays progress information so users know that compression is running.
For example:
Compressing 12 of 50This is particularly useful when users upload larger batches.
Python lists are useful for managing multiple uploaded files. You can also read How to Find the Length of a List in Python to understand how to work with collections of files.
Validations Used in the Image Compressor
The application includes several validations to prevent errors and improve reliability.
Maximum Number of Images
The tool accepts up to:
50 imagesIf a user selects more than the allowed number, the application prevents processing and displays an appropriate message.
This helps control memory usage and keeps the tool responsive.
File Size Validation
Each uploaded image is checked against the configured maximum file size.
For example:
Maximum 50 MB per fileIf an image exceeds the limit, it is marked as invalid. This prevents extremely large files from affecting application performance.
Actual Image Format Validation
The file extension alone is not trusted.
For example, someone could rename:
document.pdfto:
image.jpgThe tool checks the actual image data using Pillow.
Only supported formats are accepted:
JPEG
PNG
WEBPThis provides more reliable validation than checking only the filename extension.
Corrupted Image Validation
An uploaded file may have a valid extension but still contain damaged or incomplete image data.
The application verifies the image before compression. If the image is corrupted, processing is skipped, and the user receives a clear message.
For example:
❌ abc.png — The file is not a valid image.
Using proper exception handling is important when processing uploaded files because a single invalid file should not necessarily crash the entire application.
Image Dimension Validation
Very large image dimensions can consume significant memory during processing. The tool checks the width and height of each uploaded image.
For example:
Maximum Width: 20000 pixels
Maximum Height: 20000 pixelsImages exceeding the configured dimensions can be rejected before compression.
Duplicate Filename Handling
Duplicate filenames can create problems when generating a ZIP file.
For example:
photo.jpg
photo.jpgThe application handles duplicate filenames to avoid files accidentally overwriting each other inside the ZIP archive. Unique filenames can be generated when required.
If you work with filenames in other Python projects, How to Get a File Name Without Extension in Python is also helpful.
Handling Invalid Files
A user may upload a combination of valid and invalid files.
For example:
image1.jpg ✅ Valid
image2.png ✅ Valid
file.gif ❌ Unsupported
broken.jpg ❌ CorruptedThe tool handles this situation intelligently. Valid images continue to be processed. I