pyrit.score.HumanInTheLoopScorerGradio

class HumanInTheLoopScorerGradio(*, open_browser: bool = False, validator: ~pyrit.score.scorer_prompt_validator.ScorerPromptValidator | None = None, score_aggregator: ~typing.Callable[[~typing.Iterable[~pyrit.models.score.Score]], ~pyrit.score.score_aggregator_result.ScoreAggregatorResult] = <function _create_aggregator.<locals>.aggregator>)

Bases: TrueFalseScorer

Create scores from manual human input using Gradio and adds them to the database.

In the future this will not be a TrueFalseScorer. However, it is all that is supported currently.

__init__(*, open_browser: bool = False, validator: ~pyrit.score.scorer_prompt_validator.ScorerPromptValidator | None = None, score_aggregator: ~typing.Callable[[~typing.Iterable[~pyrit.models.score.Score]], ~pyrit.score.score_aggregator_result.ScoreAggregatorResult] = <function _create_aggregator.<locals>.aggregator>) → None

Initialize the HumanInTheLoopScorerGradio.

Parameters:

• open_browser (bool) – If True, the scorer will open the Gradio interface in a browser instead of opening it in PyWebview. Defaults to False.

• validator (Optional[ScorerPromptValidator]) – Custom validator. Defaults to None.

• score_aggregator (TrueFalseAggregatorFunc) – Aggregator for combining scores. Defaults to TrueFalseScoreAggregator.OR.

Methods

__init__(*[, open_browser, validator, ...])	Initialize the HumanInTheLoopScorerGradio.
evaluate_async([file_mapping, ...])	Evaluate this scorer against human-labeled datasets.
get_identifier()	Get an identifier dictionary for the scorer for database storage.
get_scorer_metrics()	Get evaluation metrics for this scorer from the configured evaluation result file.
retrieve_score(request_prompt, *[, objective])	Retrieve a score from the human evaluator through the RPC server.
scale_value_float(value, min_value, max_value)	Scales a value from 0 to 1 based on the given min and max values.
score_async(message, *[, objective, ...])	Score the message, add the results to the database, and return a list of Score objects.
score_image_async(image_path, *[, objective])	Score the given image using the chat target.
score_image_batch_async(*, image_paths[, ...])	Score a batch of images asynchronously.
score_prompts_batch_async(*, messages[, ...])	Score multiple prompts in batches using the provided objectives.
score_response_async(*, response[, ...])	Score a response using an objective scorer and optional auxiliary scorers.
score_response_multiple_scorers_async(*, ...)	Score a response using multiple scorers in parallel.
score_text_async(text, *[, objective])	Scores the given text based on the task using the chat target.
validate_return_scores(scores)	Validate the scores returned by the scorer.

Attributes

evaluation_file_mapping
scorer_identifier	Get the scorer identifier.
scorer_type	Get the scorer type based on class hierarchy.

retrieve_score(request_prompt: MessagePiece, *, objective: str | None = None) → list[Score]

Retrieve a score from the human evaluator through the RPC server.

Parameters:

• request_prompt (MessagePiece) – The message piece to be scored.

• objective (Optional[str]) – The objective to evaluate against. Defaults to None.

Returns:

A list containing a single Score object from the human evaluator.

Return type:

list[Score]