start ai-moderation

ai-moderation/app/api/text_moderation_router.py

@@ -0,0 +1,24 @@
+from fastapi import APIRouter
+
+from app.models.dto.text_request import TextRequest
+from app.models.response.moderation_response import ModerationResponse
+from app.services.text_moderation_service import TextModerationService
+
+router = APIRouter(
+    prefix="/api/v1/moderation",
+    tags=["Moderation"]
+)
+
+service = TextModerationService()
+
+
+@router.post("/text", response_model=ModerationResponse)
+def moderate_text(request: TextRequest):
+
+  prediction = service.moderate(request.text)
+
+  return ModerationResponse(
+      approved=prediction.approved,
+      score=prediction.score,
+      reason=prediction.reason
+  )

ai-moderation/app/config/logging.py

@@ -0,0 +1,11 @@
+import logging
+
+logging.basicConfig(
+    level=logging.INFO,
+    format="%(asctime)s %(levelname)s %(message)s",
+    handlers=[
+        logging.StreamHandler()
+    ]
+)
+
+logger = logging.getLogger("ai-moderation")

ai-moderation/app/config/settings.py

@@ -0,0 +1,29 @@
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+
+class Settings(BaseSettings):
+  APP_NAME: str = "AI Moderation"
+
+  APP_VERSION: str = "1.0.0"
+
+  HOST: str = "0.0.0.0"
+
+  PORT: int = 8000
+
+  DEBUG: bool = True
+
+  # ===== AI =====
+
+  TEXT_MODEL: str = "textdetox/bert-multilingual-toxicity-classifier"
+
+
+  MODEL_CACHE_DIR: str = "./models"
+
+  DEVICE: str = "auto"
+
+  model_config = SettingsConfigDict(
+      env_file=".env",
+      extra="ignore"
+  )
+
+settings = Settings()

ai-moderation/app/dependencies.py

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+from app.ml.model_manager import model_manager
+
+def get_model_manager():
+    return model_manager

ai-moderation/app/lifespan.py

@@ -0,0 +1,13 @@
+from contextlib import asynccontextmanager
+from fastapi import FastAPI
+from app.ml.model_manager import model_manager
+from app.config.logging import logger
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+  logger.info("Loading AI models...")
+  model_manager.initialize()
+  logger.info("AI models loaded.")
+  yield
+  logger.info("Stopping AI Service...")

ai-moderation/app/main.py

@@ -0,0 +1,11 @@
+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
+
+app = FastAPI(
+    title=settings.APP_NAME,
+    version=settings.APP_VERSION,
+    lifespan=lifespan
+)
+app.include_router(moderation_router)

ai-moderation/app/ml/base_classifier.py

@@ -0,0 +1,7 @@
+from abc import ABC, abstractmethod
+
+
+class BaseClassifier(ABC):
+  @abstractmethod
+  def predict(self, value):
+    pass

ai-moderation/app/ml/model_manager.py

@@ -0,0 +1,56 @@
+from transformers import AutoTokenizer
+from transformers import AutoModelForSequenceClassification
+
+from app.config.settings import settings
+from app.config.logging import logger
+
+import torch
+
+
+class ModelManager:
+
+  def __init__(self):
+    self.device = None
+    self.text_model = None
+    self.text_tokenizer = None
+
+  def load_device(self):
+    if settings.DEVICE == "auto":
+      self.device = torch.device(
+          "cuda" if torch.cuda.is_available() else "cpu"
+      )
+    else:
+      self.device = torch.device(settings.DEVICE)
+
+  def load_tokenizer(self):
+    print("Loading tokenizer...")
+
+    self.text_tokenizer = AutoTokenizer.from_pretrained(
+        settings.TEXT_MODEL,
+        cache_dir=settings.MODEL_CACHE_DIR
+    )
+
+    print("Tokenizer loaded.")
+
+  def load_model(self):
+    print("Loading model...")
+
+    self.text_model = AutoModelForSequenceClassification.from_pretrained(
+        settings.TEXT_MODEL,
+        cache_dir=settings.MODEL_CACHE_DIR
+    )
+
+    self.text_model.to(self.device)
+    self.text_model.eval()
+    print(self.text_model.config.id2label)
+
+    print("Text model loaded.")
+
+  def initialize(self):
+    self.load_device()
+    print(f"Using device: {self.device}")
+    self.load_tokenizer()
+    self.load_model()
+
+
+model_manager = ModelManager()

ai-moderation/app/ml/prediction_result.py

@@ -0,0 +1,14 @@
+from dataclasses import dataclass, field
+
+@dataclass(slots=True)
+class PredictionResult:
+
+  label: str
+
+  score: float
+
+  approved: bool
+
+  raw_scores: list[float]
+
+  reason: str = field(default="")

ai-moderation/app/ml/text_classifier.py

@@ -0,0 +1,47 @@
+import torch
+
+from app.ml.model_manager import model_manager
+from app.config.logging import logger
+from app.ml.prediction_result import PredictionResult
+
+class TextClassifier:
+  def predict(self, text: str):
+    inputs = model_manager.text_tokenizer(
+        text,
+        return_tensors="pt",
+        truncation=True,
+        max_length=512,
+        padding=True
+    )
+
+    inputs = {
+      key: value.to(model_manager.device)
+      for key, value in inputs.items()
+    }
+
+    print(inputs)
+
+    with torch.no_grad():
+      outputs = model_manager.text_model(**inputs)
+
+    print(outputs)
+
+    probabilities = torch.softmax(outputs.logits, dim=1)
+    print("Labels:", model_manager.text_model.config.id2label)
+    print("Logits:", outputs.logits)
+    print("Probabilities:", probabilities)
+
+    print(probabilities)
+    raw_scores = probabilities[0].tolist()
+    score = raw_scores[1]
+
+    label = (
+      model_manager.text_model.config.id2label.get(1, "LABEL_1")
+    )
+
+    return PredictionResult(
+        label=label,
+        score=score,
+        approved=False,
+        raw_scores=raw_scores
+    )

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

@@ -0,0 +1,4 @@
+from pydantic import BaseModel, Field
+
+class TextRequest(BaseModel):
+    text: str = Field(..., description="The text to be moderated")

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

@@ -0,0 +1,6 @@
+from pydantic import BaseModel, Field
+
+class ModerationResponse(BaseModel):
+    approved: bool = Field(..., description="Indicates if the text is approved or not")
+    score: float = Field(..., description="The score indicating the level of toxicity or appropriateness of the text")
+    reason: str = Field(..., description="The reason for the moderation decision")

ai-moderation/app/services/text_moderation_service.py

@@ -0,0 +1,26 @@
+from app.ml.text_classifier import TextClassifier
+from app.ml.prediction_result import PredictionResult
+
+
+class TextModerationService:
+
+  TOXIC_THRESHOLD = 0.80
+
+  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.reason = (
+      "OK"
+      if prediction.approved
+      else "TOXIC"
+    )
+
+    return prediction

ai-moderation/app/test/test_text_classifier.py

@@ -0,0 +1,20 @@
+from app.ml.model_manager import model_manager
+from app.services.text_moderation_service import TextModerationService
+
+model_manager.initialize()
+
+service = TextModerationService()
+
+texts = [
+  "Привет",
+  "Спасибо",
+  "Ты идиот",
+  "Хорошего дня"
+]
+
+for text in texts:
+  result = service.moderate(text)
+
+  print("=" * 50)
+  print(text)
+  print(result)

ai-moderation/apptests/test_text_classifier.py

@@ -1 +0,0 @@
-