!pip install vlmrun
from vlmrun.client import VLMRun
from vlmrun.client.types import PredictionResponse
client = VLMRun(api_key="<VLMRUN_API_KEY>")
response: PredictionResponse = client.predictions.get("<prediction_id>")
{
"usage": {
"elements_processed": 123,
"element_type": "image",
"credits_used": 123,
"steps": 123,
"message": "<string>"
},
"id": "<string>",
"created_at": "2023-11-07T05:31:56Z",
"completed_at": "2023-11-07T05:31:56Z",
"response": "<any>",
"status": "pending"
}
Get the predictions for a given prediction ID.
!pip install vlmrun
from vlmrun.client import VLMRun
from vlmrun.client.types import PredictionResponse
client = VLMRun(api_key="<VLMRUN_API_KEY>")
response: PredictionResponse = client.predictions.get("<prediction_id>")
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Base prediction response for all API responses.
The usage metrics for the request.
Number of elements processed.
The type of element processed (e.g. image, page, video, audio).
Available options:
image,
page,
video,
audio
Amount of total credits used.
Number of steps processed, in case of agentic execution.
The message from the credit usage job.
Unique identifier of the response.
Date and time when the request was created (in UTC timezone)
Date and time when the response was completed (in UTC timezone)
The response from the model.
status
enum<string>
default:pending
Available options:
pending,
enqueued,
running,
completed,
failed,
paused
