jVdZddlmZddlmZmZmZmZddlm Z ddl m Z ddl m Z  ddlZeegeeeffZGdd e Zy#e$rdZY"wxYw) zLLanguage adherence metric leveraging fastText-style language identification.) annotations)AnyCallableOptionalTuple)MetricComputationError) BaseMetric) ScoreResultNcZeZdZdZ d dfd ZddZddZxZS) LanguageAdherenceMetrica Check whether text is written in the expected language. The metric relies on a fastText language identification model (or a user-supplied detector callable) to predict the language of the evaluated text and compares it with ``expected_language``. It outputs ``1.0`` when the detected language matches and ``0.0`` otherwise, along with the detected label and confidence score in ``metadata``. References: - fastText language identification models https://fasttext.cc/docs/en/language-identification.html - Joulin et al., "Bag of Tricks for Efficient Text Classification" (EACL 2017) https://aclanthology.org/E17-2068/ Args: expected_language: Language code the text should conform to, e.g. ``"en"``. model_path: Path to a fastText language identification model. Required unless ``detector`` is provided. name: Display name for the metric result. Defaults to ``"language_adherence_metric"``. track: Whether to automatically track metric results. Defaults to ``True``. project_name: Optional tracking project name. Defaults to ``None``. detector: Optional callable accepting text and returning a ``(language, confidence)`` tuple. When provided, ``model_path`` is not needed. Example: >>> from opik.evaluation.metrics import LanguageAdherenceMetric >>> # Assuming `lid.176.ftz` is available locally for fastText >>> metric = LanguageAdherenceMetric(expected_language="en", model_path="lid.176.ftz") >>> result = metric.score("This response is written in English.") # doctest: +SKIP >>> result.value # doctest: +SKIP 1.0 ct||||||_|||_||||_d|_yt td| tdt j||_|j|_y)N)nametrack project_namezInstall fasttext via `pip install fasttext` and provide a fastText language model (e.g., lid.176.ftz) or supply a custom detector callable.z>model_path is required when using the fastText-based detector.) super__init___expected_language _model_path _detector_fn_fasttext_modelfasttext ImportError ValueError load_model_predict_with_fasttext)selfexpected_language model_pathrrrdetector __class__s /Users/manta/Documents/Projects/TheRoad-I1/backend/.venv/lib/python3.12/site-packages/opik/evaluation/metrics/heuristics/language_adherence.pyrz LanguageAdherenceMetric.__init__9s d%lK"3 %   (D #'D   S   P  (22:> 77c |}|js td|j|\}}||jk(rdnd}|||jd}|dk(rdnd|d|jd}t ||j || S) Nz+Text is empty for language adherence check.g?)detected_language confidencerzLanguage adheres to expectationzDetected language 'z' differs from expected '')valuerreasonmetadata)striprrrr r) routputignored_kwargs processedlanguager& adherencer*r)s r!scorezLanguageAdherenceMetric.scoreZs  ()VW W#00;*#t'>'>>CC "*$!%!8!8 C .&xj0I$JaJaIbbcd  $))FX  r"c|j td|jj|}|dr|ddnd}|jdd}|drt |ddnd}||fS)NzfastText model is not loaded. Ensure that LanguageAdherenceMetric was initialized with a valid model_path and fastText is installed.r __label__r$)rrpredictreplacefloat)rtext predictionlabelr/r&s r!rz.LanguageAdherenceMetric._predict_with_fasttextrs    '(W ))11$7 $.qM 1 a r==b10:1 U:a=+,3 ##r")Nlanguage_adherence_metricTNN)rstrr Optional[str]rr=rboolrr>rzOptional[DetectorFn]returnNone)r,r=r-rr@r )r9r=r@ztuple[str, float])__name__ __module__ __qualname____doc__rr1r __classcell__)r s@r!r r sn"N%)/&*)-88"8 8  8 $ 8'8 8B 0 $r"r )rE __future__rtypingrrrropik.exceptionsr#opik.evaluation.metrics.base_metricr $opik.evaluation.metrics.score_resultr rrr=r8 DetectorFnr r"r!rNsbR"112:< seU3:.. / g$jg$HsAAA