added image ai-moderation

ai-moderation/app/config/image_policy.py

@@ -0,0 +1,10 @@
+# app/config/image_policy.py
+FORBIDDEN_IMAGE_LABELS = {
+  s.lower() for s in {
+    "a photo containing marijuana",
+    "a photo containing drugs",
+    "a photo containing weapons",
+    "a pornographic photo",
+    "a photo containing violence",
+  }
+}

ai-moderation/app/config/settings.py

@@ -12,11 +12,18 @@ class Settings(BaseSettings):
 
   DEBUG: bool = True
 
+  TEXT_TOXIC_THRESHOLD: float = 0.90
+
+
   # ===== AI =====
 
   TEXT_MODEL: str = "textdetox/bert-multilingual-toxicity-classifier"
 
-
+  IMAGE_MODEL: str = "Falconsai/nsfw_image_detection"
+  NSFW_THRESHOLD: float = 0.85
+  IMAGE_CLIP_MODEL: str = "openai/clip-vit-base-patch32"
+  CLIP_THRESHOLD: float = 0.75
+  HF_TOKEN: str = "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"
   MODEL_CACHE_DIR: str = "./models"
 
   DEVICE: str = "auto"

ai-moderation/app/container.py

@@ -0,0 +1,17 @@
+from app.moderation.profanity.dictionary import ProfanityDictionary
+from app.moderation.profanity.lemmatizer import Lemmatizer
+from app.moderation.profanity.detector import ProfanityDetector
+
+
+dictionary = ProfanityDictionary(
+    "app/resources/profanity_words.txt"
+)
+
+
+lemmatizer = Lemmatizer()
+
+
+profanity_detector = ProfanityDetector(
+    dictionary,
+    lemmatizer
+)

ai-moderation/app/core/dependencies.py

@@ -0,0 +1,65 @@
+from functools import lru_cache
+
+from app.ml.image.image_classifier import ImageClassifier
+from app.ml.text_classifier import TextClassifier
+from app.moderation.image.validator import ImageValidator
+from app.moderation.profanity.detector import ProfanityDetector
+from app.moderation.profanity.dictionary import ProfanityDictionary
+from app.moderation.profanity.lemmatizer import Lemmatizer
+from app.services.image_moderation_service import ImageModerationService
+from app.services.text_moderation_service import TextModerationService
+from app.ml.image.clip_classifier import ClipClassifier
+
+
+_clip_classifier = None
+
+@lru_cache
+def get_clip_classifier():
+
+  global _clip_classifier
+
+  if _clip_classifier is None:
+    _clip_classifier = ClipClassifier()
+
+  return _clip_classifier
+
+@lru_cache
+def get_text_classifier() -> TextClassifier:
+  return TextClassifier()
+
+
+@lru_cache
+def get_profanity_detector() -> ProfanityDetector:
+  return ProfanityDetector(
+      dictionary=ProfanityDictionary(
+          file_path="app/resources/profanity_words.txt"
+      ),
+      lemmatizer=Lemmatizer()
+  )
+
+
+@lru_cache
+def get_text_moderation_service() -> TextModerationService:
+  return TextModerationService(
+      classifier=get_text_classifier(),
+      profanity_detector=get_profanity_detector()
+  )
+
+@lru_cache
+def get_image_validator():
+
+  return ImageValidator()
+
+
+def get_image_classifier():
+  return ImageClassifier()
+
+
+@lru_cache
+def get_image_moderation_service():
+
+  return ImageModerationService(
+      classifier=get_image_classifier(),
+      validator=get_image_validator(),
+      clip_classifier=get_clip_classifier()
+  )

ai-moderation/app/dependencies.py

@@ -1,4 +1,32 @@
-from app.ml.model_manager import model_manager
+from functools import lru_cache
 
-def get_model_manager():
-    return model_manager
+from app.ml.text_classifier import TextClassifier
+from app.services.text_moderation_service import TextModerationService
+from app.ml.image.image_classifier import ImageClassifier
+from app.services.image_moderation_service import ImageModerationService
+from app.container import profanity_detector
+
+@lru_cache
+def get_text_classifier() -> TextClassifier:
+  return TextClassifier()
+
+@lru_cache
+def get_text_moderation_service() -> TextModerationService:
+  return TextModerationService(
+      classifier=get_text_classifier(),
+      profanity_detector=profanity_detector
+  )
+
+@lru_cache
+def get_image_classifier():
+
+  return ImageClassifier()
+
+
+
+@lru_cache
+def get_image_moderation_service():
+
+  return ImageModerationService(
+      classifier=get_image_classifier()
+  )

ai-moderation/app/exceptions/handlers.py

@@ -0,0 +1,19 @@
+from fastapi import Request
+from fastapi.responses import JSONResponse
+
+from app.exceptions.invalid_text_exception import InvalidTextException
+from app.models.response.error_response import ErrorResponse
+
+async def invalid_text_exception_handler(
+    request: Request,
+    exc: InvalidTextException
+):
+  response = ErrorResponse(
+      code="INVALID_TEXT",
+      message=exc.message
+  )
+
+  return JSONResponse(
+      status_code=400,
+      content=response.model_dump()
+  )

ai-moderation/app/exceptions/invalid_image_exception.py

@@ -0,0 +1,2 @@
+class InvalidImageException(Exception):
+  pass

ai-moderation/app/exceptions/invalid_text_exception.py

@@ -0,0 +1,6 @@
+from app.exceptions.moderation_exception import ModerationException
+
+
+class InvalidTextException(ModerationException):
+
+  pass

ai-moderation/app/exceptions/moderation_exception.py

@@ -0,0 +1,4 @@
+class ModerationException(Exception):
+
+  def __init__(self, message: str):
+    self.message = message

ai-moderation/app/lifespan.py

@@ -1,11 +1,15 @@
+import os
 from contextlib import asynccontextmanager
 from fastapi import FastAPI
 from app.ml.model_manager import model_manager
 from app.config.logging import logger
-
+from app.config.settings import settings
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
+  if settings.HF_TOKEN:
+    os.environ["HF_TOKEN"] = settings.HF_TOKEN
+
   logger.info("Loading AI models...")
   model_manager.initialize()
   logger.info("AI models loaded.")

ai-moderation/app/main.py

@@ -1,11 +1,44 @@
 from fastapi import FastAPI
+
 from app.config.settings import settings
 from app.lifespan import lifespan
-from app.api.text_moderation_router import router as moderation_router
+
+from app.routers.text_moderation_router import (
+  router as moderation_router
+)
+
+from app.routers.image_moderation_router import (
+  router as image_router
+)
+
+
+from app.exceptions.handlers import (
+  invalid_text_exception_handler
+)
+
+from app.exceptions.invalid_text_exception import (
+  InvalidTextException
+)
+
 
 app = FastAPI(
     title=settings.APP_NAME,
     version=settings.APP_VERSION,
     lifespan=lifespan
 )
-app.include_router(moderation_router)
+
+
+app.include_router(
+    moderation_router
+)
+
+
+app.include_router(
+    image_router
+)
+
+
+app.add_exception_handler(
+    InvalidTextException,
+    invalid_text_exception_handler
+)

ai-moderation/app/ml/image/clip_classifier.py

@@ -0,0 +1,126 @@
+import torch
+
+from PIL import Image
+from transformers import (
+  CLIPProcessor,
+  CLIPModel
+)
+
+from app.ml.image.clip_prediction_result import ClipPredictionResult
+from app.config.settings import settings
+
+
+class ClipClassifier:
+
+  LABELS = [
+    "a normal photo",
+    "a photo containing marijuana",
+    "a photo containing drugs",
+    "a photo containing weapons",
+    "a pornographic photo",
+    "a photo containing violence"
+  ]
+
+
+  def __init__(self):
+
+    self.device = (
+      "cuda"
+      if torch.cuda.is_available()
+      else "cpu"
+    )
+
+    print(
+        f"Loading CLIP on {self.device}"
+    )
+
+    self.model = CLIPModel.from_pretrained(
+        settings.IMAGE_CLIP_MODEL
+    )
+
+    self.processor = CLIPProcessor.from_pretrained(
+        settings.IMAGE_CLIP_MODEL
+    )
+
+    self.model.to(self.device)
+
+    self.model.eval()
+
+
+  def predict(
+      self,
+      image: Image.Image
+  ) -> ClipPredictionResult:
+
+
+    inputs = self.processor(
+        text=self.LABELS,
+        images=image,
+        return_tensors="pt",
+        padding=True
+    )
+
+
+    inputs = {
+      k: v.to(self.device)
+      for k, v in inputs.items()
+    }
+
+
+    with torch.no_grad():
+
+      outputs = self.model(**inputs)
+
+      logits = (
+        outputs
+        .logits_per_image
+      )
+
+      probs = (
+        logits.softmax(dim=1)[0]
+      )
+
+
+    scores = {}
+
+    detected_labels = []
+
+
+    THRESHOLD = 0.75
+
+
+    for index, probability in enumerate(probs):
+
+      label = self.LABELS[index]
+
+      score = float(probability)
+
+
+      scores[label] = score
+
+
+      if (
+          label != "a normal photo"
+          and score >= THRESHOLD
+      ):
+
+        detected_labels.append(label)
+
+
+
+    max_index = torch.argmax(probs).item()
+
+
+    return ClipPredictionResult(
+
+        label=self.LABELS[max_index],
+
+        score=float(
+            probs[max_index]
+        ),
+
+        detected_labels=detected_labels,
+
+        scores=scores
+
+    )

ai-moderation/app/ml/image/clip_prediction_result.py

@@ -0,0 +1,13 @@
+from dataclasses import dataclass
+
+
+@dataclass(slots=True)
+class ClipPredictionResult:
+
+  label: str
+
+  score: float
+
+  detected_labels: list[str]
+
+  scores: dict[str, float]

ai-moderation/app/ml/image/image_classifier.py

@@ -0,0 +1,83 @@
+from PIL import Image
+import torch
+
+from app.ml.model_manager import model_manager
+from app.ml.image.image_prediction_result import ImagePredictionResult
+
+
+class ImageClassifier:
+
+
+  def predict(
+      self,
+      image: Image.Image
+  ) -> ImagePredictionResult:
+
+
+    inputs = (
+      model_manager.image_processor(
+          image,
+          return_tensors="pt"
+      )
+    )
+
+
+    inputs = {
+      key: value.to(model_manager.device)
+      for key, value in inputs.items()
+    }
+
+
+    with torch.no_grad():
+
+      outputs = (
+        model_manager.image_model(**inputs)
+      )
+
+
+    probabilities = torch.softmax(
+        outputs.logits,
+        dim=1
+    )
+
+
+    raw_scores = probabilities[0].tolist()
+
+
+    predicted_index = (
+      torch.argmax(probabilities, dim=1)
+      .item()
+    )
+
+
+    labels = (
+      model_manager.image_model
+      .config
+      .id2label
+    )
+
+
+    label = labels[predicted_index]
+
+
+    score = raw_scores[predicted_index]
+
+
+    print("====================")
+    print("IMAGE RESULT")
+    print("Labels:", labels)
+    print("Label:", label)
+    print("Score:", score)
+    print("Scores:", raw_scores)
+
+
+    return ImagePredictionResult(
+
+        label=label,
+
+        score=score,
+
+        approved=False,
+
+        raw_scores=raw_scores
+    )

ai-moderation/app/ml/image/image_prediction.py

@@ -0,0 +1,14 @@
+from dataclasses import dataclass
+from dataclasses import field
+
+
+@dataclass(slots=True)
+class ImagePredictionResult:
+
+  approved: bool
+
+  score: float
+
+  reason: str
+
+  detected_labels: list[str] = field(default_factory=list)

ai-moderation/app/ml/image/image_prediction_result.py

@@ -0,0 +1,19 @@
+from dataclasses import dataclass, field
+
+
+@dataclass(slots=True)
+class ImagePredictionResult:
+
+  label: str
+
+  score: float
+
+  approved: bool
+
+  raw_scores: list[float] = field(default_factory=list)
+
+  reason: str = ""
+
+  detected_labels: list[str] = field(
+      default_factory=list
+  )

ai-moderation/app/ml/model_manager.py

@@ -3,6 +3,8 @@ from transformers import AutoModelForSequenceClassification
 
 from app.config.settings import settings
 from app.config.logging import logger
+from transformers import AutoImageProcessor
+from transformers import AutoModelForImageClassification
 
 import torch
 
@@ -13,6 +15,8 @@ class ModelManager:
     self.device = None
     self.text_model = None
     self.text_tokenizer = None
+    self.image_model = None
+    self.image_processor = None
 
   def load_device(self):
     if settings.DEVICE == "auto":
@@ -51,6 +55,35 @@ class ModelManager:
     print(f"Using device: {self.device}")
     self.load_tokenizer()
     self.load_model()
+    self.load_image_model()
+
+  def load_image_model(self):
+
+    print("Loading image model...")
+
+
+    self.image_processor = (
+      AutoImageProcessor.from_pretrained(
+          settings.IMAGE_MODEL,
+          cache_dir=settings.MODEL_CACHE_DIR
+      )
+    )
+
+
+    self.image_model = (
+      AutoModelForImageClassification.from_pretrained(
+          settings.IMAGE_MODEL,
+          cache_dir=settings.MODEL_CACHE_DIR
+      )
+    )
+
+
+    self.image_model.to(self.device)
+
+    self.image_model.eval()
+
+
+    print("Image model loaded.")
 
 
 model_manager = ModelManager()

ai-moderation/app/ml/prediction_result.py

@@ -11,4 +11,6 @@ class PredictionResult:
 
   raw_scores: list[float]
 
-  reason: str = field(default="")
+  reason: str = ""
+
+  detected_words: list[str] = field(default_factory=list)

ai-moderation/app/models/dto/text_request.py

@@ -1,4 +1,10 @@
-from pydantic import BaseModel, Field
+from pydantic import BaseModel
+from pydantic import Field
+
 
 class TextRequest(BaseModel):
-    text: str = Field(..., description="The text to be moderated")
+
+    text: str = Field(
+        min_length=1,
+        max_length=5000
+    )

ai-moderation/app/models/response/error_response.py

@@ -0,0 +1,6 @@
+from pydantic import BaseModel
+
+
+class ErrorResponse(BaseModel):
+  code: str
+  message: str

ai-moderation/app/models/response/image_moderation_response.py

@@ -0,0 +1,16 @@
+from typing import List
+
+from pydantic import BaseModel
+
+
+class ImageModerationResponse(BaseModel):
+
+  approved: bool
+
+  score: float
+
+  reason: str
+
+  label: str
+
+  detected_labels: List[str]

ai-moderation/app/moderation/image/validator.py

@@ -0,0 +1,39 @@
+from PIL import Image
+
+from app.exceptions.invalid_image_exception import InvalidImageException
+
+
+class ImageValidator:
+
+  MIN_WIDTH = 50
+  MIN_HEIGHT = 50
+
+  MAX_WIDTH = 10000
+  MAX_HEIGHT = 10000
+
+  def validate(
+      self,
+      image: Image.Image
+  ) -> None:
+
+    width, height = image.size
+
+    if width < self.MIN_WIDTH:
+      raise InvalidImageException(
+          f"Image width is too small: {width}"
+      )
+
+    if height < self.MIN_HEIGHT:
+      raise InvalidImageException(
+          f"Image height is too small: {height}"
+      )
+
+    if width > self.MAX_WIDTH:
+      raise InvalidImageException(
+          f"Image width is too large: {width}"
+      )
+
+    if height > self.MAX_HEIGHT:
+      raise InvalidImageException(
+          f"Image height is too large: {height}"
+      )

ai-moderation/app/moderation/profanity/detector.py

@@ -0,0 +1,51 @@
+import re
+
+from app.moderation.profanity.dictionary import ProfanityDictionary
+from app.moderation.profanity.lemmatizer import Lemmatizer
+
+
+
+class ProfanityDetector:
+
+
+  def __init__(
+      self,
+      dictionary: ProfanityDictionary,
+      lemmatizer: Lemmatizer
+  ):
+
+    self.dictionary = dictionary
+    self.lemmatizer = lemmatizer
+
+
+
+  def detect(
+      self,
+      text: str
+  ) -> list[str]:
+
+
+    words = re.findall(
+        r"[а-яА-ЯёЁ]+",
+        text.lower()
+    )
+
+
+    result = []
+
+
+    for word in words:
+
+      lemma = self.lemmatizer.normalize(word)
+
+
+      for bad_word in self.dictionary.words:
+
+        if lemma.startswith(bad_word):
+
+          result.append(word)
+
+          break
+
+
+    return result

ai-moderation/app/moderation/profanity/dictionary.py

@@ -0,0 +1,49 @@
+from pathlib import Path
+
+
+class ProfanityDictionary:
+
+
+  def __init__(
+      self,
+      file_path: str
+  ):
+
+    self.words = self.load(file_path)
+
+
+
+  def load(
+      self,
+      file_path: str
+  ) -> set[str]:
+
+    path = Path(file_path)
+
+
+    if not path.exists():
+      raise FileNotFoundError(
+          f"Profanity dictionary not found: {file_path}"
+      )
+
+
+    with open(
+        path,
+        "r",
+        encoding="utf-8"
+    ) as file:
+
+      return {
+        line.strip().lower().replace("\ufeff", "")
+        for line in file
+        if line.strip()
+      }
+
+
+
+  def contains(
+      self,
+      word: str
+  ) -> bool:
+
+    return word in self.words

ai-moderation/app/moderation/profanity/lemmatizer.py

@@ -0,0 +1,20 @@
+import pymorphy3
+
+
+class Lemmatizer:
+
+
+  def __init__(self):
+
+    self.morph = pymorphy3.MorphAnalyzer()
+
+
+
+  def normalize(
+      self,
+      word: str
+  ) -> str:
+
+    result = self.morph.parse(word)
+
+    return result[0].normal_form

ai-moderation/app/routers/image_moderation_router.py

@@ -0,0 +1,66 @@
+from fastapi import APIRouter, UploadFile, File, Depends, HTTPException
+
+from PIL import Image, UnidentifiedImageError
+
+from app.services.image_moderation_service import ImageModerationService
+from app.core.dependencies import get_image_moderation_service
+from app.models.response.image_moderation_response import ImageModerationResponse
+
+
+router = APIRouter(
+    prefix="/api/v1/moderation",
+    tags=["Moderation"]
+)
+
+MAX_SIZE = 5 * 1024 * 1024  # например, 5 МБ
+
+ALLOWED_CONTENT_TYPES = (
+  "image/jpeg",
+  "image/png",
+  "image/webp",
+)
+
+
+@router.post("/image", response_model=ImageModerationResponse)
+def moderate_image(
+    file: UploadFile = File(...),
+    service: ImageModerationService = Depends(get_image_moderation_service)
+):
+
+  # 1. Проверка content-type — самая дешёвая, делаем её первой
+  if file.content_type not in ALLOWED_CONTENT_TYPES:
+    raise HTTPException(
+        status_code=400,
+        detail="Unsupported image format"
+    )
+
+  # 2. Проверка размера — читаем файл целиком
+  contents = file.file.read()
+
+  if len(contents) > MAX_SIZE:
+    raise HTTPException(
+        status_code=413,
+        detail="Image too large"
+    )
+
+  file.file.seek(0)  # обязательно вернуть указатель в начало!
+
+  # 3. Только теперь пытаемся открыть изображение
+  try:
+    image = Image.open(file.file)
+    image = image.convert("RGB")
+  except UnidentifiedImageError:
+    raise HTTPException(
+        status_code=400,
+        detail="Invalid image"
+    )
+
+  prediction = service.moderate(image)
+
+  return ImageModerationResponse(
+      approved=prediction.approved,
+      score=prediction.score,
+      reason=prediction.reason,
+      label=prediction.label,
+      detected_labels=prediction.detected_labels
+  )

ai-moderation/app/routers/text_moderation_router.py

@@ -1,24 +1,37 @@
 from fastapi import APIRouter
+from typing import Annotated
+from fastapi import Depends
 
 from app.models.dto.text_request import TextRequest
 from app.models.response.moderation_response import ModerationResponse
 from app.services.text_moderation_service import TextModerationService
 
+
+from app.core.dependencies import (
+  get_text_moderation_service
+)
+
 router = APIRouter(
     prefix="/api/v1/moderation",
     tags=["Moderation"]
 )
 
-service = TextModerationService()
-
-
-@router.post("/text", response_model=ModerationResponse)
-def moderate_text(request: TextRequest):
 
+@router.post(
+    "/text",
+    response_model=ModerationResponse
+)
+def moderate_text(
+    request: TextRequest,
+    service: Annotated[
+      TextModerationService,
+      Depends(get_text_moderation_service)
+    ]
+):
   prediction = service.moderate(request.text)
 
   return ModerationResponse(
       approved=prediction.approved,
       score=prediction.score,
       reason=prediction.reason
-  )
+  )

ai-moderation/app/services/image_moderation_service.py

@@ -0,0 +1,115 @@
+from PIL import Image
+
+from app.ml.image.image_classifier import ImageClassifier
+from app.ml.image.image_prediction_result import ImagePredictionResult
+from app.ml.image.clip_classifier import ClipClassifier
+from app.moderation.image.validator import ImageValidator
+from app.config.settings import settings
+from app.config.image_policy import FORBIDDEN_IMAGE_LABELS
+
+
+class ImageModerationService:
+
+  def __init__(
+      self,
+      classifier: ImageClassifier,
+      validator: ImageValidator,
+      clip_classifier: ClipClassifier
+  ):
+
+    self.classifier = classifier
+    self.clip_classifier = clip_classifier
+    self.validator = validator
+
+
+
+  def moderate(
+      self,
+      image: Image.Image
+  ) -> ImagePredictionResult:
+
+
+    self.validator.validate(image)
+
+
+    # =========================
+    # 1. NSFW MODEL
+    # =========================
+
+    nsfw_prediction = self.classifier.predict(image)
+
+
+    if (
+        nsfw_prediction.label.lower() == "nsfw"
+        and nsfw_prediction.score >= settings.NSFW_THRESHOLD
+    ):
+
+      nsfw_prediction.approved = False
+
+      nsfw_prediction.reason = "NSFW"
+
+      nsfw_prediction.detected_labels = [
+        nsfw_prediction.label
+      ]
+
+      return nsfw_prediction
+
+
+
+    # =========================
+    # 2. CLIP MODEL
+    # =========================
+
+    clip_prediction = (
+      self.clip_classifier.predict(image)
+    )
+
+
+    detected_forbidden = [
+
+      label
+
+      for label in clip_prediction.detected_labels
+
+      if label.lower()
+         in FORBIDDEN_IMAGE_LABELS
+
+    ]
+
+
+    if detected_forbidden:
+
+
+      return ImagePredictionResult(
+
+          label=clip_prediction.label,
+
+          score=clip_prediction.score,
+
+          approved=False,
+
+          reason="FORBIDDEN_CONTENT",
+
+          detected_labels=detected_forbidden
+
+      )
+
+
+
+    # =========================
+    # 3. NORMAL IMAGE
+    # =========================
+
+    return ImagePredictionResult(
+
+        label="normal",
+
+        score=1.0,
+
+        approved=True,
+
+        reason="OK",
+
+        detected_labels=[]
+
+    )

ai-moderation/app/services/text_moderation_service.py

@@ -1,26 +1,76 @@
+from app.exceptions.invalid_text_exception import InvalidTextException
+from app.moderation.profanity.detector import ProfanityDetector
 from app.ml.text_classifier import TextClassifier
 from app.ml.prediction_result import PredictionResult
+from app.config.settings import settings
 
 
 class TextModerationService:
 
-  TOXIC_THRESHOLD = 0.80
+  def __init__(
+      self,
+      classifier: TextClassifier,
+      profanity_detector: ProfanityDetector
+  ):
+
+    self.classifier = classifier
+    self.profanity_detector = profanity_detector
+
+
+  def moderate(
+      self,
+      text: str
+  ) -> PredictionResult:
+
+
+    if text is None or not text.strip():
+
+      raise InvalidTextException(
+          "Text is empty"
+      )
+
+
+    detected_words = (
+      self.profanity_detector.detect(text)
+    )
+
+
+    if detected_words:
+
+
+      return PredictionResult(
+
+          label="PROFANITY",
+
+          score=1.0,
+
+          approved=False,
+
+          raw_scores=[],
+
+          reason="PROFANITY",
+
+          detected_words=detected_words
+
+      )
 
-  def __init__(self):
-    self.classifier = TextClassifier()
 
-  def moderate(self, text: str) -> PredictionResult:
 
     prediction = self.classifier.predict(text)
 
+
+
     prediction.approved = (
-        prediction.score < self.TOXIC_THRESHOLD
+        prediction.score <
+        settings.TEXT_TOXIC_THRESHOLD
     )
 
+
     prediction.reason = (
       "OK"
       if prediction.approved
       else "TOXIC"
     )
 
+
     return prediction

ai-moderation/app/test/test_text_classifier.py

@@ -1,20 +1,20 @@
-from app.ml.model_manager import model_manager
-from app.services.text_moderation_service import TextModerationService
+from app.container import profanity_detector
 
-model_manager.initialize()
 
-service = TextModerationService()
-
-texts = [
-  "Привет",
-  "Спасибо",
+tests = [
+  "ЗАлУпа",
   "Ты идиот",
+  "Это идиоты",
   "Хорошего дня"
 ]
 
-for text in texts:
-  result = service.moderate(text)
 
-  print("=" * 50)
-  print(text)
-  print(result)
+for text in tests:
+
+  result = profanity_detector.detect(text)
+
+  print(
+      text,
+      "=>",
+      result
+  )