pexels-sdk 1.1.1

A production-ready Python wrapper for the Pexels REST API featuring robust Pydantic input validation.

Pexels API Python Wrapper

Disclaimer

This project is an independent, community-maintained Python wrapper for the Pexels API. It is not affiliated with, endorsed by, or maintained by Pexels.

An elegant, developer-friendly Python client library for interacting seamlessly with the Pexels REST API.

Unlike naive wrappers, this package uses Pydantic v2 to perform robust client-side validation on your parameters before hitting the network—saving you latency, bandwidth, and unhandled remote API errors.

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Features

• Pydantic-powered Validation: Caught invalid page bounds, dimensions, orientations, or hexadecimal colors locally before triggering HTTP requests.
• Clean Interface: Consistent method signatures exposing Photo, Video, and Curated/Featured Collection endpoints.
• Fail-Safe Response Analysis: Safe management of common HTTP statuses (400, 401, 404) with programmatic reporting.
• Transparent Rate Limiting: Automatic tracking and console reporting of monthly quotas (X-Ratelimit-*) parsed exclusively from successful hits.

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Installation

Install the package via pip:

pip install pexels-sdk

Quick Start

1. Initializing the Client

Obtain an API key from the Pexels API Documentation portal.

from pexels import Client

# Note: Pexels uses raw keys. The client automatically manages 
# the header authorization for you without a "Bearer" prefix.
client = Client(api_key="YOUR_PEXELS_API_KEY")

Photo Endpoints

Search Photos

Search for photos using a required query and optional filters such as orientation, size, color, locale, page, and per_page.

search_results = client.search_photos(
query="nature",
orientation="landscape",
size="large",
page=1,
per_page=10
)

for photo in search_results["photos"]:
    print(photo["photographer"])
    print(photo["src"]["original"])

Curated Photos

Retrieve curated photos from the Pexels curated feed.

curated = client.curated_photos(page=1, per_page=10)

for photo in curated["photos"]:
    print(photo["photographer"])

Get a Single Photo

Fetch a specific photo by its unique ID.

photo = client.get_a_photo(photo_id=2014422)
print(photo)

Video Endpoints

Search Videos

Search for videos using keyword-based filtering and supported video parameters.

videos = client.search_for_videos(
query="mountains",
orientation="landscape",
size="large",
page=1,
per_page=10
)

for video in videos["videos"]:
    print(video["user"]["name"])

Popular Videos

Retrieve popular videos with optional dimension and duration filters.

popular = client.popular_videos(
min_width=1280,
min_height=720,
min_duration=5,
max_duration=30,
page=1,
per_page=10
)

for video in popular["videos"]:
    print(video["id"])

Get a Single Video

Fetch a specific video by its unique ID.

video = client.get_a_video(video_id=123456)
print(video)

Collection Endpoints Featured Collections

Get a list of featured collections.

featured = client.featured_collections(page=1, per_page=10)

for collection in featured["collections"]:
    print(collection["title"])

My Collections

Retrieve the authenticated user’s collections.

collections = client.my_collections(page=1, per_page=10)

for collection in collections["collections"]:
    print(collection["title"])

Collection Media

Get the media items inside a specific collection.

media = client.collection_media(
collection_id=12345,
collection_type="photos",
sort="asc",
page=1,
per_page=10
)

print(media)

Validation

This package uses Pydantic v2 to validate input parameters before sending any HTTP request.

Invalid values are rejected locally, which helps reduce unnecessary network traffic and prevents avoidable API errors.

Examples of validation-covered parameters include:

page bounds
image dimensions
orientations
colors in hexadecimal format
video duration filters

from pydantic import ValidationError

try:
    client.search_photos(query="city", page=-1)
except ValidationError as e:
    print(f"Validation error: {e}")

Error Handling

The client includes a ResponseAnalyzer layer that inspects API responses and handles common HTTP statuses such as:

400 Bad Request
401 Unauthorized
404 Not Found

This gives you a cleaner and more predictable error-handling flow.

from pexels.exceptions import PexelsAPIError

try:
    client.search_photos(query="ocean")
except PexelsAPIError as e:
    print(f"API Error ({e.status_code}): {e.message}")

Rate Limiting

The client also tracks rate-limit information returned by Pexels headers such as:

X-Ratelimit-Limit
X-Ratelimit-Remaining
X-Ratelimit-Reset

This makes quota usage more transparent during development and production use.

client.search_photos(query="architecture")
print(client.rate_limit_remaining)

Project Highlights

Pydantic-powered validation before API calls
Clean endpoint coverage for photos, videos, and collections
Safe response analysis for common HTTP failures
Transparent quota awareness via rate-limit headers
Simple authorization flow using raw Pexels API keys

Contributing

Contributions are welcome and appreciated.

Fork the Project
Create your Feature Branch:

git checkout -b feature/AmazingFeature

Commit your Changes:

git commit -m "Add some AmazingFeature"

Push to the Branch:

git push origin feature/AmazingFeature

License

Distributed under the MIT License. See the LICENSE file for more information.