EyePop.ai Python SDK

Python SDK for EyePop.ai's inference and data APIs.

pip install eyepop

Requires Python 3.12+.

Quickstart

from eyepop import EyePopSdk

with EyePopSdk.sync_worker() as endpoint:
    result = endpoint.upload('photo.jpg').predict()
    print(result)

Set EYEPOP_API_KEY in your environment (get one at dashboard.eyepop.ai), or pass api_key=... to sync_worker():

endpoint = EyePopSdk.sync_worker(api_key='my-api-key', pop_id='my-pop-id')

Configuration

Credentials are read from environment variables. Set one auth method:

Variable	Description
`EYEPOP_API_KEY`	API key from your dashboard.
`EYEPOP_ACCESS_TOKEN`	Pre-issued OAuth access token.

Optional:

Variable	Description
`EYEPOP_POP_ID`	Named pop ID. Defaults to `transient`.
`EYEPOP_ACCOUNT_ID`	Required for some Data API calls.

Usage

Single image

from eyepop import EyePopSdk

with EyePopSdk.sync_worker() as endpoint:
    result = endpoint.upload('photo.jpg').predict()
    print(result)

upload() queues the file; predict() blocks until the result is ready. For videos or multi-frame containers, call predict() in a loop until it returns None.

Binary streams

with EyePopSdk.sync_worker() as endpoint:
    with open('photo.jpg', 'rb') as file:
        result = endpoint.upload_stream(file, 'image/jpeg').predict()

URLs (HTTP, RTSP, RTMP)

with EyePopSdk.sync_worker() as endpoint:
    result = endpoint.load_from('https://example.com/image.jpg').predict()

Videos

with EyePopSdk.sync_worker() as endpoint:
    job = endpoint.load_from('https://example.com/video.mp4')
    while result := job.predict():
        print(result)

Cancel a job mid-stream with job.cancel().

Image groups (multiple images, one result)

Send several images as a single source that the pop processes together as one inference unit — for example a multi-image VLM prompt. The group yields one prediction for the whole set, unlike Batching below, where each image is an independent inference.

with EyePopSdk.sync_worker() as endpoint:
    # local files
    result = endpoint.upload_group(['a.jpg', 'b.jpg', 'c.jpg']).predict()

    # in-memory streams (optional parallel content types)
    with open('a.jpg', 'rb') as a, open('b.jpg', 'rb') as b:
        result = endpoint.upload_stream_group([a, b]).predict()

    # remote URLs (the server fetches each)
    result = endpoint.load_from_group([
        'https://example.com/a.jpg',
        'https://example.com/b.jpg',
    ]).predict()

Image order is preserved end-to-end. A group may contain up to 16 images (enforced server-side). The pop's ability must be multi-image-capable; a single-image ability handed a group returns an error.

Batching

Queue multiple uploads, then collect results:

file_paths = ['photo1.jpg', 'photo2.jpg']

with EyePopSdk.sync_worker() as endpoint:
    jobs = [endpoint.upload(p) for p in file_paths]
    for job in jobs:
        print(job.predict())

Async with callbacks

import asyncio
from eyepop import EyePopSdk, Job

async def main(paths):
    async def on_ready(job: Job):
        print(await job.predict())

    async with EyePopSdk.async_worker() as endpoint:
        for p in paths:
            await endpoint.upload(p, on_ready=on_ready)

asyncio.run(main(['photo1.jpg', 'photo2.jpg']))

Visualize results

from PIL import Image
import matplotlib.pyplot as plt
from eyepop import EyePopSdk

with EyePopSdk.sync_worker() as endpoint:
    result = endpoint.upload('photo.jpg').predict()

with Image.open('photo.jpg') as image:
    plt.imshow(image)
EyePopSdk.plot(plt.gca()).prediction(result)
plt.show()

Composable Pops

Build multi-stage inference pipelines by chaining models. Configure at runtime with endpoint.set_pop(pop).

Components

Component	Purpose
`InferenceComponent`	Run a model. Supports chunked video via `videoChunkLengthSeconds` / `videoChunkOverlap`.
`TrackingComponent`	Track detected objects across frames.
`ContourFinderComponent`	Extract contours from segmentation masks.
`ComponentFinderComponent`	Extract connected components from masks.
`ForwardComponent`	Route outputs between stages.

Forwarding

CropForward — pass each detection crop to sub-components.
FullForward — pass the full image to sub-components.

Both accept includeClasses to filter forwarded detections.

Example: Vehicle → License Plate → OCR

from eyepop.worker.worker_types import (
    Pop, InferenceComponent, TrackingComponent, CropForward, MotionModel,
)

pop = Pop(components=[
    InferenceComponent(
        ability='eyepop.vehicle:latest',
        categoryName='vehicles',
        confidenceThreshold=0.8,
        forward=CropForward(targets=[
            TrackingComponent(
                maxAgeSeconds=5.0,
                motionModel=MotionModel.CONSTANT_VELOCITY,
                agnostic=True,
            ),
            InferenceComponent(
                ability='eyepop.vehicle.license-plate:latest',
                topK=1,
                forward=CropForward(targets=[
                    InferenceComponent(
                        ability='eyepop.text.recognize.landscape:latest',
                        categoryName='license-plate',
                    ),
                ]),
            ),
        ]),
    ),
])

Example: VLM open-vocabulary detection

from eyepop.worker.worker_types import Pop, InferenceComponent, CropForward

pop = Pop(components=[
    InferenceComponent(
        ability='eyepop.localize-objects:latest',
        params={'prompts': [{'prompt': 'person'}]},
        forward=CropForward(targets=[
            InferenceComponent(
                ability='eyepop.image-contents:latest',
                params={'prompts': [{'prompt': 'hair color?'}]},
            ),
        ]),
    ),
])

Data Endpoint

Dataset management, VLM inference, and evaluation workflows.

import asyncio
from eyepop import EyePopSdk

async def main():
    async with EyePopSdk.dataEndpoint(is_async=True) as endpoint:
        datasets = await endpoint.list_datasets()
        print(datasets)

asyncio.run(main())

VLM inference on a single asset

from eyepop.data.data_types import InferRequest, TranscodeMode

async with EyePopSdk.dataEndpoint(is_async=True) as endpoint:
    job = await endpoint.infer_asset(
        asset_uuid='your-asset-uuid',
        infer_request=InferRequest(text_prompt='Describe this image.'),
        transcode_mode=TranscodeMode.image_cover_1024,
    )
    while result := await job.predict():
        print(result)

Batch dataset evaluation

from eyepop.data.data_types import EvaluateRequest, InferRequest

request = EvaluateRequest(
    dataset_uuid='your-dataset-uuid',
    infer=InferRequest(text_prompt='How many people are in this image?'),
)

async with EyePopSdk.dataEndpoint(is_async=True, job_queue_length=4) as endpoint:
    job = await endpoint.evaluate_dataset(evaluate_request=request)
    response = await job.response
    print(response.model_dump_json(indent=2))

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

eyepop-3.18.2.tar.gz (1.1 MB view details)

Uploaded Aug 11, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

eyepop-3.18.2-py3-none-any.whl (96.4 kB view details)

Uploaded Aug 11, 2026 Python 3

File details

Details for the file eyepop-3.18.2.tar.gz.

File metadata

Download URL: eyepop-3.18.2.tar.gz
Upload date: Aug 11, 2026
Size: 1.1 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for eyepop-3.18.2.tar.gz
Algorithm	Hash digest
SHA256	`507534feed161ac74f51f7697a603f032a329c0702d3aea05c47e0a8a28a23bc`
MD5	`1d43ea1bcdd8c3acc16ea6fffc839fa2`
BLAKE2b-256	`651fdffbdd03e8b38e5cc23561da6be574f87785c84c22de831f3bbeaa39d59b`

See more details on using hashes here.

File details

Details for the file eyepop-3.18.2-py3-none-any.whl.

File metadata

Download URL: eyepop-3.18.2-py3-none-any.whl
Upload date: Aug 11, 2026
Size: 96.4 kB
Tags: Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/6.1.0 CPython/3.13.14

File hashes

Hashes for eyepop-3.18.2-py3-none-any.whl
Algorithm	Hash digest
SHA256	`26a06223e4a579800149326f29a969cdaaf6bdad8730580f1fe4d7a231cca795`
MD5	`461b6b0202132844f98680bcf7ce45d5`
BLAKE2b-256	`f7e907bf2e9b4b99b8d0449b9459949d771717b247b1f3b9ecf2d712737a27fb`