Python SDK

Powerful Server-Side File Management with Python

Build robust file processing pipelines, automated workflows, and scalable backend services with our comprehensive Python SDK for popular web frameworks and applications.

pip install filestack

View Python Docs

Basic Python Upload

# Simple file upload and transformation
from filestack import Client

client = Client('YOUR_API_KEY')

# Upload a file
filelink = client.upload('/path/to/image.jpg')
print(f'Uploaded: {filelink.url}')

# Apply transformations
transformed = client.transform(
    filelink.handle,
    {'resize': {'width': 300, 'height': 300}}
)
print(f'Transformed: {transformed.url}')

Built for Python Developers

Our Python SDK integrates seamlessly with your existing Python stack, providing Pythonic APIs and comprehensive framework support.

Pythonic API Design

Clean, intuitive APIs that follow Python conventions and PEP standards for a familiar development experience.

Framework Integration

Native support for popular Python web frameworks with comprehensive middleware and utilities for seamless integration.

Async/Await Support

Full asyncio support for modern Python applications with async upload, transformation, and processing capabilities.

Batch Processing

Efficiently process thousands of files with batch upload, transformation, and delivery operations optimized for high-volume workflows.

Type Hints & Validation

Complete type annotations, Pydantic model support, and runtime validation for safer, more maintainable code.

Webhook Integration

Built-in webhook handling with signature verification, automatic retries, and framework-specific middleware.

Trusted by innovative companies worldwide

Perfect for Server-Side Workflows

From simple file uploads to complex media processing pipelines, our Python SDK handles your most demanding server-side file management needs.

🚀 API Development
Build REST APIs with popular Python frameworks that handle file uploads, transformations, and delivery.

⚙️ Background Processing
Create Celery tasks, RQ jobs, or asyncio workers for processing large volumes of files asynchronously.

🔄 Data Pipelines
Integrate with data processing libraries and workflow orchestration tools for comprehensive file transformation workflows.

🤖 ML/AI Workflows
Process training data, transform images for computer vision, or handle model artifacts with ease.

Django Integration Example

# Django integration example
from django.http import JsonResponse
from django.views.decorators.csrf import csrf_exempt
from filestack import Client
import json

client = Client('YOUR_API_KEY')

@csrf_exempt
def upload_and_transform(request):
    if request.method == 'POST':
        uploaded_file = request.FILES['file']
        
        # Upload to Filestack
        filelink = client.upload(uploaded_file)
        
        # Apply transformations
        transforms = {
            'resize': {'width': 800, 'height': 600},
            'quality': 85,
            'format': 'webp'
        }
        
        transformed = client.transform(
            filelink.handle, 
            transforms
        )
        
        return JsonResponse({
            'original': filelink.url,
            'transformed': transformed.url,
            'handle': filelink.handle
        })

# FastAPI async example
from fastapi import FastAPI, UploadFile
from filestack.async_client import AsyncClient

app = FastAPI()
async_client = AsyncClient('YOUR_API_KEY')

@app.post("/upload")
async def upload(file: UploadFile):
    result = await async_client.upload(file.file)
    return {"url": result.url, "handle": result.handle}

Quick Installation & Setup

Get started with our Python SDK using pip or your preferred package manager. Compatible with Python 3.7+ and all major frameworks.

Install Package

pip install filestack-python

poetry add filestack-python

conda install -c conda-forge filestack-python

Basic Usage

Import and initialize the client:

from filestack import Client
client = Client('YOUR_API_KEY')
filelink = client.upload('/path/to/file.jpg')

Complete Python Implementation

# Complete working example
import os
from filestack import Client

# Initialize client
client = Client(os.getenv('FILESTACK_API_KEY'))

# Upload a file
def upload(file_path):
    try:
        filelink = client.upload(file_path)
        print(f'Upload successful: {filelink.url}')
        return filelink
    except Exception as e:
        print(f'Upload failed: {e}')
        return None

# Transform an image
def create_thumbnail(handle):
    transforms = {
        'resize': {
            'width': 200,
            'height': 200,
            'fit': 'crop'
        },
        'quality': 80,
        'format': 'webp'
    }
    
    transformed = client.transform(handle, transforms)
    return transformed.url

# Batch processing
def process_images(image_paths):
    results = []
    for path in image_paths:
        filelink = upload(path)
        if filelink:
            thumbnail_url = create_thumbnail(filelink.handle)
            results.append({
                'original': filelink.url,
                'thumbnail': thumbnail_url,
                'filename': os.basename(path)
            })
    return results

# Example usage
if __name__ == '__main__':
    images = ['photo1.jpg', 'photo2.png', 'photo3.gif']
    processed = process_images(images)
    print(f'Processed {len(processed)} images')

Start Building with Python

Join thousands of Python developers who trust Filestack for their server-side file management needs. From startups to enterprise, our Python SDK scales with your applications.