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Reorganize hf detector
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‎.gitignore‎

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.coverage
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# .vscode
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.vscode/
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.vscode/
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dist/

‎detectors/huggingface/LICENSE‎

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‎detectors/huggingface/__init__.py‎

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‎detectors/huggingface/pyproject.toml‎

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[project]
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name = "guardrails-detector-huggingface"
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version = "0.1.0"
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description = "Huggingface detector implementation for guardrails"
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readme = "README.md"
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requires-python = ">=3.9"
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license = { text = "Apache-2.0" }
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authors = [
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{name = "Mac Misiura", email = "[email protected]"},
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{name = "Rob Geada", email = "[email protected]"},
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{name = "Rui Vieira", email = "[email protected]"},
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{name = "Sai Chandra", email = "[email protected]"},
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{name = "TrustyAI team"}
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]
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classifiers = [
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"Programming Language :: Python :: 3",
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"Programming Language :: Python :: 3.9",
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"Programming Language :: Python :: 3.10",
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"Programming Language :: Python :: 3.11",
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"License :: OSI Approved :: Apache Software License",
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"Development Status :: 3 - Alpha",
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"Intended Audience :: Developers",
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"Intended Audience :: Science/Research",
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"Topic :: Scientific/Engineering :: Artificial Intelligence",
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"Topic :: Software Development :: Libraries :: Python Modules",
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]
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keywords = ["AI", "LLM", "Guardrails", "Detectors", "Responsible AI", "TrustyAI", "Huggingface", "Transformers"]
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dependencies = [
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"guardrails-detectors-common>=0.1.1",
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"transformers>=4.50.0",
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"torch>=1.13.0",
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]
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[project.optional-dependencies]
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dev = [
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"build",
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"pytest>=8.3.2",
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"pre-commit>=3.8.0",
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]
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[project.urls]
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Homepage = "https://github.com/trustyai-explainability/guardrails-detectors"
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Repository = "https://github.com/trustyai-explainability/guardrails-detectors.git"
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Issues = "https://github.com/trustyai-explainability/guardrails-detectors/issues"
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[build-system]
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requires = ["hatchling"]
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build-backend = "hatchling.build"
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[tool.hatch.build.targets.wheel]
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packages = ["src/guardrails_detector_huggingface"]
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[tool.hatch.build.targets.sdist]
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include = [
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"src/",
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"pyproject.toml",
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"README.md",
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"LICENSE"
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]

‎detectors/huggingface/src/guardrails_detectors_huggingface/__init__.py‎

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"""
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Huggingface Guardrails Detector
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"""
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from .detector import Detector
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from .app import app
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from .scheme import (
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ContentAnalysisHttpRequest,
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ContentAnalysisResponse,
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ContentsAnalysisResponse,
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Error,
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)
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__version__ = "0.1.0"
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__all__ = [
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"Detector",
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"app",
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"ContentAnalysisHttpRequest",
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"ContentAnalysisResponse",
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"ContentsAnalysisResponse",
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"Error",
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]

detectors/huggingface/app.py renamed to detectors/huggingface/src/guardrails_detectors_huggingface/app.py

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detectors/huggingface/detector.py renamed to detectors/huggingface/src/guardrails_detectors_huggingface/detector.py

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‎detectors/huggingface/src/guardrails_detectors_huggingface/scheme.py‎

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from enum import Enum
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from typing import List, Optional
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from pydantic import BaseModel, Field, RootModel
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class Evidence(BaseModel):
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source: str = Field(
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title="Source",
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example="https://en.wikipedia.org/wiki/IBM",
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description="Source of the evidence, it can be url of the evidence etc",
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)
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class EvidenceType(str, Enum):
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url = "url"
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title = "title"
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class EvidenceObj(BaseModel):
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type: EvidenceType = Field(
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title="EvidenceType",
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example="url",
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description="Type field signifying the type of evidence provided. Example url, title etc",
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)
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evidence: Evidence = Field(
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description="Evidence object, currently only containing source, but in future can contain other optional arguments like id, etc",
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)
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class ContentAnalysisHttpRequest(BaseModel):
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contents: List[str] = Field(
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min_length=1,
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title="Contents",
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description="Field allowing users to provide list of texts for analysis. Note, results of this endpoint will contain analysis / detection of each of the provided text in the order they are present in the contents object.",
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example=[
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"Martians are like crocodiles; the more you give them meat, the more they want"
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],
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)
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class ContentAnalysisResponse(BaseModel):
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start: int = Field(example=14)
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end: int = Field(example=26)
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detection: str = Field(example="huggingface_model")
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detection_type: str = Field(example="Sequence Classification")
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score: float = Field(example=0.8)
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sequence_classification: Optional[str] = Field(example="Sequence Classification")
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sequence_probability: Optional[float] = Field(example=0.8)
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token_classifications: Optional[List[str]] = Field(example="[0, 1, 0")
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token_probabilities: Optional[List[float]] = Field(example="[0.5, 0.8, 0.0]")
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text: str = Field(example="A problematic word is fiddlesticks")
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evidences: Optional[List[EvidenceObj]] = Field(
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description="Optional field providing evidences for the provided detection",
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default=[],
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)
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class ContentsAnalysisResponse(RootModel):
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root: List[List[ContentAnalysisResponse]] = Field(
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title="Response Text Content Analysis Unary Handler Api V1 Text Content Post"
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)
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class Error(BaseModel):
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code: int
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message: str

llm_judge/requirements-dev.txt renamed to detectors/llm_judge/requirements-dev.txt

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‎detectors/llm_judge/requirements.txt‎

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vllm-judge[jinja2]==0.1.8
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vllm-judge[jinja2]==0.1.8
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guardrails-detectors-common>=0.1.1

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