Developer-friendly & type-safe Python SDK specifically catered to leverage img-src API.
img-src API: Image processing and delivery API.
A serverless image processing and delivery API built on Cloudflare Workers with parameter-driven image transformation and on-demand transcoding.
- Image Upload: Store original images in R2 with SHA256-based deduplication
- On-Demand Transformation: Resize, crop, and convert images via URL parameters
- Format Conversion: WebP, AVIF, JPEG, PNG, JXL output formats
- Path Organization: Organize images into folders with multiple paths per image
- CDN Caching: Automatic edge caching for transformed images
Authenticate using API Keys with imgsrc_ prefix. Create your API key at https://img-src.io/settings
- Free Plan: 100 requests/minute
- Pro Plan: 500 requests/minute
Rate limit headers are included in all responses.
/docs/github-setup#step-by-step-guide).
Note
Python version upgrade policy
Once a Python version reaches its official end of life date, a 3-month grace period is provided for users to upgrade. Following this grace period, the minimum python version supported in the SDK will be updated.
The SDK can be installed with uv, pip, or poetry package managers.
uv is a fast Python package installer and resolver, designed as a drop-in replacement for pip and pip-tools. It's recommended for its speed and modern Python tooling capabilities.
uv add img-srcPIP is the default package installer for Python, enabling easy installation and management of packages from PyPI via the command line.
pip install img-srcPoetry is a modern tool that simplifies dependency management and package publishing by using a single pyproject.toml file to handle project metadata and dependencies.
poetry add img-srcYou can use this SDK in a Python shell with uv and the uvx command that comes with it like so:
uvx --from img-src pythonIt's also possible to write a standalone Python script without needing to set up a whole project like so:
#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.9"
# dependencies = [
# "img-src",
# ]
# ///
from img_src import Imgsrc
sdk = Imgsrc(
# SDK arguments
)
# Rest of script here...Once that is saved to a file, you can run it with uv run script.py where
script.py can be replaced with the actual file name.
Generally, the SDK will work well with most IDEs out of the box. However, when using PyCharm, you can enjoy much better integration with Pydantic by installing an additional plugin.
import os
from img_src import Imgsrc
# Create API key at https://img-src.io/settings
client = Imgsrc(bearer_auth=os.environ["IMGSRC_API_KEY"])
# Upload an image
with open("photo.jpg", "rb") as f:
uploaded = client.images.upload_image(file=f, target_path="photos/2024")
print(f"Uploaded: {uploaded.url}")
# Access with transformations via CDN
# https://img-src.io/i/{username}/photos/2024/photo.webp?w=800&h=600&fit=cover&q=85
# List images
images = client.images.list_images(limit=20)
print(f"Total: {images.total} images")This SDK supports the following security scheme globally:
| Name | Type | Scheme |
|---|---|---|
bearer_auth |
http | HTTP Bearer |
To authenticate with the API the bearer_auth parameter must be set when initializing the SDK client instance. For example:
from img_src import Imgsrc
with Imgsrc(
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
res = imgsrc.settings.get_settings()
assert res.settings_response is not None
# Handle response
print(res.settings_response)Available methods
- upload_image - Upload image
- list_images - List images
- search_images - Search images
- get_image - Get image metadata
- delete_image - Delete image
- create_signed_url - Create signed URL
- delete_image_path - Delete image path
- list_presets - List presets
- create_preset - Create preset
- get_preset - Get preset
- update_preset - Update preset
- delete_preset - Delete preset
- get_settings - Get user settings
- update_settings - Update user settings
- get_usage - Get usage statistics
Some of the endpoints in this SDK support pagination. To use pagination, you make your SDK calls as usual, but the
returned response object will have a Next method that can be called to pull down the next group of results. If the
return value of Next is None, then there are no more pages to be fetched.
Here's an example of one such pagination call:
from img_src import Imgsrc
with Imgsrc(
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
res = imgsrc.images.list_images(limit=50, offset=0, path="blog/2024")
while res is not None:
# Handle items
res = res.next()Certain SDK methods accept file objects as part of a request body or multi-part request. It is possible and typically recommended to upload files as a stream rather than reading the entire contents into memory. This avoids excessive memory consumption and potentially crashing with out-of-memory errors when working with very large files. The following example demonstrates how to attach a file stream to a request.
Tip
For endpoints that handle file uploads bytes arrays can also be used. However, using streams is recommended for large files.
from img_src import Imgsrc
with Imgsrc(
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
res = imgsrc.images.upload_image(request={
"target_path": "blog/2024",
})
assert res.upload_response is not None
# Handle response
print(res.upload_response)Some of the endpoints in this SDK support retries. If you use the SDK without any configuration, it will fall back to the default retry strategy provided by the API. However, the default retry strategy can be overridden on a per-operation basis, or across the entire SDK.
To change the default retry strategy for a single API call, simply provide a RetryConfig object to the call:
from img_src import Imgsrc
from img_src.utils import BackoffStrategy, RetryConfig
with Imgsrc(
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
res = imgsrc.settings.get_settings(,
RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False))
assert res.settings_response is not None
# Handle response
print(res.settings_response)If you'd like to override the default retry strategy for all operations that support retries, you can use the retry_config optional parameter when initializing the SDK:
from img_src import Imgsrc
from img_src.utils import BackoffStrategy, RetryConfig
with Imgsrc(
retry_config=RetryConfig("backoff", BackoffStrategy(1, 50, 1.1, 100), False),
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
res = imgsrc.settings.get_settings()
assert res.settings_response is not None
# Handle response
print(res.settings_response)ImgsrcError is the base class for all HTTP error responses. It has the following properties:
| Property | Type | Description |
|---|---|---|
err.message |
str |
Error message |
err.status_code |
int |
HTTP response status code eg 404 |
err.headers |
httpx.Headers |
HTTP response headers |
err.body |
str |
HTTP body. Can be empty string if no body is returned. |
err.raw_response |
httpx.Response |
Raw HTTP response |
err.data |
Optional. Some errors may contain structured data. See Error Classes. |
from img_src import Imgsrc, errors
with Imgsrc(
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
res = None
try:
res = imgsrc.settings.get_settings()
assert res.settings_response is not None
# Handle response
print(res.settings_response)
except errors.ImgsrcError as e:
# The base class for HTTP error responses
print(e.message)
print(e.status_code)
print(e.body)
print(e.headers)
print(e.raw_response)
# Depending on the method different errors may be thrown
if isinstance(e, errors.ErrorResponse):
print(e.data.error) # models.ErrorDetailPrimary errors:
ImgsrcError: The base class for HTTP error responses.ErrorResponse: Generic error.
Less common errors (5)
Network errors:
httpx.RequestError: Base class for request errors.httpx.ConnectError: HTTP client was unable to make a request to a server.httpx.TimeoutException: HTTP request timed out.
Inherit from ImgsrcError:
ResponseValidationError: Type mismatch between the response data and the expected Pydantic model. Provides access to the Pydantic validation error via thecauseattribute.
The default server can be overridden globally by passing a URL to the server_url: str optional parameter when initializing the SDK client instance. For example:
from img_src import Imgsrc
with Imgsrc(
server_url="https://api.img-src.io",
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
res = imgsrc.settings.get_settings()
assert res.settings_response is not None
# Handle response
print(res.settings_response)The Python SDK makes API calls using the httpx HTTP library. In order to provide a convenient way to configure timeouts, cookies, proxies, custom headers, and other low-level configuration, you can initialize the SDK client with your own HTTP client instance.
Depending on whether you are using the sync or async version of the SDK, you can pass an instance of HttpClient or AsyncHttpClient respectively, which are Protocol's ensuring that the client has the necessary methods to make API calls.
This allows you to wrap the client with your own custom logic, such as adding custom headers, logging, or error handling, or you can just pass an instance of httpx.Client or httpx.AsyncClient directly.
For example, you could specify a header for every request that this sdk makes as follows:
from img_src import Imgsrc
import httpx
http_client = httpx.Client(headers={"x-custom-header": "someValue"})
s = Imgsrc(client=http_client)or you could wrap the client with your own custom logic:
from img_src import Imgsrc
from img_src.httpclient import AsyncHttpClient
import httpx
class CustomClient(AsyncHttpClient):
client: AsyncHttpClient
def __init__(self, client: AsyncHttpClient):
self.client = client
async def send(
self,
request: httpx.Request,
*,
stream: bool = False,
auth: Union[
httpx._types.AuthTypes, httpx._client.UseClientDefault, None
] = httpx.USE_CLIENT_DEFAULT,
follow_redirects: Union[
bool, httpx._client.UseClientDefault
] = httpx.USE_CLIENT_DEFAULT,
) -> httpx.Response:
request.headers["Client-Level-Header"] = "added by client"
return await self.client.send(
request, stream=stream, auth=auth, follow_redirects=follow_redirects
)
def build_request(
self,
method: str,
url: httpx._types.URLTypes,
*,
content: Optional[httpx._types.RequestContent] = None,
data: Optional[httpx._types.RequestData] = None,
files: Optional[httpx._types.RequestFiles] = None,
json: Optional[Any] = None,
params: Optional[httpx._types.QueryParamTypes] = None,
headers: Optional[httpx._types.HeaderTypes] = None,
cookies: Optional[httpx._types.CookieTypes] = None,
timeout: Union[
httpx._types.TimeoutTypes, httpx._client.UseClientDefault
] = httpx.USE_CLIENT_DEFAULT,
extensions: Optional[httpx._types.RequestExtensions] = None,
) -> httpx.Request:
return self.client.build_request(
method,
url,
content=content,
data=data,
files=files,
json=json,
params=params,
headers=headers,
cookies=cookies,
timeout=timeout,
extensions=extensions,
)
s = Imgsrc(async_client=CustomClient(httpx.AsyncClient()))The Imgsrc class implements the context manager protocol and registers a finalizer function to close the underlying sync and async HTTPX clients it uses under the hood. This will close HTTP connections, release memory and free up other resources held by the SDK. In short-lived Python programs and notebooks that make a few SDK method calls, resource management may not be a concern. However, in longer-lived programs, it is beneficial to create a single SDK instance via a context manager and reuse it across the application.
from img_src import Imgsrc
def main():
with Imgsrc(
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
# Rest of application here...
# Or when using async:
async def amain():
async with Imgsrc(
bearer_auth="process.env["IMGSRC_API_KEY"]",
) as imgsrc:
# Rest of application here...You can setup your SDK to emit debug logs for SDK requests and responses.
You can pass your own logger class directly into your SDK.
from img_src import Imgsrc
import logging
logging.basicConfig(level=logging.DEBUG)
s = Imgsrc(debug_logger=logging.getLogger("img_src"))