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How to pick the next UI action from a screenshot with One Jev

Ask a local model whether a task is done, what to click next and how far along it is.

Authorization notice. One Jev is dual-use. Only use it on software, sites and systems you own or have explicit permission to test.

Credentials. Screenshots can show passwords, tokens or account pages, so keep the server on localhost and do not send those captures to any other host.

Who it is forA developer building a browser or desktop automation agent who needs a step-by-step decision from each screen capture.

The problemScripted automation breaks when a page changes, and sending every screenshot to a hosted model is costly or not allowed.

The outcomeYour agent gets probabilities for done, one probability per next action and an expected progress level for each screenshot it sends to a local One Jev server.

What you need first

  • Python with pip
  • An NVIDIA GPU for the PyTorch backend, or llama.cpp installed for the GGUF backend
  • Docker, only if you use the Docker option
  • A Hugging Face token only for gated or private repos (set HF_TOKEN in docker/.env for Docker)

Steps

  1. Run pip install "qev[torch] @ git+https://github.com/OmniJev/OneJev.git"
  2. Run qev serve --model OmniJev/OneJev1.6-4B --port 8000 and leave it running
  3. In another terminal, save your screen as screenshot.png and run the Python example from the README with your task text in state
  4. Read r.answers["done"].noul for the probability of yes, r.answers["next"].probabilities for the action options and r.answers["progress"].score for the expected level

Prompt for your agent

Using the OneJev repo at https://github.com/OmniJev/OneJev, start the server with `qev serve --model OmniJev/OneJev1.6-4B --port 8000`, then send <screenshot path> with the task <task text> through the Python Client example. Return the done probability, the next-action probabilities and the progress score as JSON. Verify that the probabilities for the next-action question sum to about 1 before you act on them.

Caveat. The README gives the probabilities as the output, so your agent must still map the chosen action to real clicks and typing itself.

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