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Merge pull request #7 from Tobkubos/backend-setup
Browse files- backend/app/api/routes.py +30 -1
- backend/app/config_manager.py +44 -0
- backend/app/core/config.py +2 -1
- backend/app/models/schemas.py +15 -3
- backend/app/services/text_analyzer.py +48 -17
- backend/app/utils/exceptions.py +6 -0
- guildConfigs.json +0 -9
- index.js +31 -7
backend/app/api/routes.py
CHANGED
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@@ -5,6 +5,7 @@ from app.models.schemas import (
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AnalysisRequest,
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AnalysisResponse,
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ErrorResponse,
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HealthResponse,
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TextAnalysisRequest,
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ImageAnalysisRequest,
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@@ -13,8 +14,9 @@ from app.services.download import download_file
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from app.services.text_analyzer import analyze_text
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from app.services.image_analyzer import analyze_image
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from app.core.config import get_settings
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-
from app.utils.exceptions import DeepfakeDetectionError
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from app.core.limiter import limiter
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logger = logging.getLogger(__name__)
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@@ -56,8 +58,35 @@ async def health_check() -> HealthResponse:
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version=settings.APP_VERSION,
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available_models=settings.AVAILABLE_MODELS,
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supported_types=list(settings.AVAILABLE_MODELS.keys()),
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)
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@router.post(
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"/analyze",
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response_model=AnalysisResponse,
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AnalysisRequest,
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AnalysisResponse,
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ErrorResponse,
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GuildConfigSchema,
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HealthResponse,
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TextAnalysisRequest,
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ImageAnalysisRequest,
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from app.services.text_analyzer import analyze_text
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from app.services.image_analyzer import analyze_image
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from app.core.config import get_settings
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from app.utils.exceptions import DeepfakeDetectionError, SetupRequiredError
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from app.core.limiter import limiter
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from app.config_manager import save_guild_config
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logger = logging.getLogger(__name__)
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version=settings.APP_VERSION,
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available_models=settings.AVAILABLE_MODELS,
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supported_types=list(settings.AVAILABLE_MODELS.keys()),
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models_status=models_status,
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)
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# Endpoint do zapisywania konfiguracji (wywoływany przez bota)
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@router.post("/guilds/{guild_id}/setup", tags=["Setup"])
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async def save_discord_guild_setup(guild_id: str, payload: GuildConfigSchema):
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# Walidacja modeli z pliku ustawień
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settings = get_settings()
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allowed_text_models = settings.AVAILABLE_MODELS.get("text", [])
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# Walidujemy tylko wtedy, gdy model nie jest ustawiony na "none"
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if payload.active_text_model and payload.active_text_model.lower() != "none":
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if payload.active_text_model not in allowed_text_models:
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raise HTTPException(
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status_code=400,
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detail=f"Model '{payload.active_text_model}' nie jest dozwolony. Wybierz z: {allowed_text_models}"
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)
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# Zapis konfiguracji przez config_manager
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config_dict = payload.dict()
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save_guild_config(guild_id, config_dict)
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logger.info(f"Zapisano nową konfigurację dla serwera Discord {guild_id}")
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return {
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"status": "success",
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"message": f"Konfiguracja dla serwera {guild_id} została zapisana.",
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"config": config_dict
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}
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@router.post(
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"/analyze",
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response_model=AnalysisResponse,
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backend/app/config_manager.py
ADDED
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@@ -0,0 +1,44 @@
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import json
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import os
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import logging
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from typing import Dict, Any, Optional
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logger = logging.getLogger(__name__)
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CONFIG_FILE = "guild_configs.json"
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def _load_all_configs() -> Dict[str, Dict[str, Any]]:
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"""Odczytuje konfiguracje wszystkich serwerów z pliku."""
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if os.path.exists(CONFIG_FILE):
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try:
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with open(CONFIG_FILE, "r", encoding="utf-8") as f:
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return json.load(f)
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except Exception as e:
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logger.error(f"Błąd podczas odczytu pliku konfiguracji: {e}")
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return {}
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def _save_all_configs(configs: Dict[str, Dict[str, Any]]):
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"""Zapisuje konfiguracje na dysk."""
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try:
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with open(CONFIG_FILE, "w", encoding="utf-8") as f:
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json.dump(configs, f, indent=4, ensure_ascii=False)
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except Exception as e:
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logger.error(f"Błąd podczas zapisu pliku konfiguracji: {e}")
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def save_guild_config(guild_id: str, config_data: Dict[str, Any]):
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"""Zapisuje kompletną konfigurację dla danego serwera Discord."""
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configs = _load_all_configs()
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configs[guild_id] = config_data
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_save_all_configs(configs)
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def get_active_text_model(guild_id: str) -> Optional[str]:
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"""
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Zwraca wybrany model dla serwera.
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Zwraca None, jeśli konfiguracja nie istnieje lub model jest ustawiony na 'none'.
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"""
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configs = _load_all_configs()
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guild_config = configs.get(guild_id, {})
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model = guild_config.get("active_text_model", "none")
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if not model or model.lower() == "none":
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return None
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return model
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backend/app/core/config.py
CHANGED
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@@ -29,7 +29,8 @@ class Settings:
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LOG_FILE: Optional[str] = os.getenv("LOG_FILE", None)
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AVAILABLE_MODELS = {
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"text": ["yaya36095/xlm-roberta-text-detector"
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"image": ["capcheck/ai-image-detection"],
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}
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LOG_FILE: Optional[str] = os.getenv("LOG_FILE", None)
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AVAILABLE_MODELS = {
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"text": ["yaya36095/xlm-roberta-text-detector",
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"almanach/xlmr-chatgptdetect-noisy"],
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"image": ["capcheck/ai-image-detection"],
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}
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backend/app/models/schemas.py
CHANGED
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@@ -1,5 +1,5 @@
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from pydantic import BaseModel, HttpUrl, Field
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from typing import Union, Literal, Optional
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class TextAnalysisRequest(BaseModel):
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status: str = Field(..., description="Service status")
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service: str = Field(..., description="Service name")
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version: str = Field(..., description="Service version")
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available_models:
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from pydantic import BaseModel, HttpUrl, Field
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from typing import Union, Literal, Optional, Dict, List
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class TextAnalysisRequest(BaseModel):
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status: str = Field(..., description="Service status")
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service: str = Field(..., description="Service name")
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version: str = Field(..., description="Service version")
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available_models: Dict[str, List[str]] = Field(
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..., description="Lista dostępnych modeli pogrupowana według typów"
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)
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supported_types: List[str] = Field(
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..., description="Obsługiwane typy danych"
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)
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models_status: Dict[str, str] = Field(
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..., description="Status gotowości handlerów dla poszczególnych typów"
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)
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class GuildConfigSchema(BaseModel):
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active_text_model: Optional[str] = "none"
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# Tutaj możesz dodać inne parametry, które bot zbiera w sesji setup (np. log_channel_id)
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log_channel_id: Optional[str] = None
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backend/app/services/text_analyzer.py
CHANGED
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@@ -1,45 +1,76 @@
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import logging
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import time
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from typing import Dict, Any
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from transformers import pipeline
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logger = logging.getLogger(__name__)
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_text_classifier = None
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def _load_model():
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global _text_classifier
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return _text_classifier
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async def analyze_text(text: str) -> Dict[str, Any]:
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start_time = time.time()
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result = classifier(text)
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label = result[0]["label"]
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score = result[0]["score"]
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is_deepfake = label.lower()
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confidence = score
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analysis_time = time.time() - start_time
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response = {
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"is_deepfake": is_deepfake,
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"confidence": round(confidence, 3),
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"analysis_time": round(analysis_time, 3),
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}
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logger.info(f"
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return response
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import logging
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import time
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import gc
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from typing import Dict, Any
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from app.config_manager import get_active_text_model
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from app.utils.exceptions import SetupRequiredError
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from transformers import pipeline
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# Importujesz helpery z Kroku 2:
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# from config_manager import get_active_text_model
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logger = logging.getLogger(__name__)
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# Przechowujemy nazwę aktualnie załadowanego modelu oraz sam obiekt klasyfikatora
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_loaded_model_name = None
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_text_classifier = None
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def _load_model(target_model_name: str):
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global _text_classifier, _loaded_model_name
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# Jeśli model w pamięci jest tym, którego potrzebujemy, po prostu go zwracamy
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if _text_classifier is not None and _loaded_model_name == target_model_name:
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return _text_classifier
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logger.info(f"Wymagana zmiana modelu. Obecny w RAM: {_loaded_model_name}, Nowy: {target_model_name}")
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# Zwalnianie pamięci po poprzednim modelu
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_text_classifier = None
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gc.collect()
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logger.info(f"Ładowanie modelu text detector: {target_model_name}...")
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_text_classifier = pipeline(
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"text-classification",
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model=target_model_name,
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device=-1 # -1 oznacza CPU, jeśli masz GPU ustaw np. 0
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)
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_loaded_model_name = target_model_name
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logger.info(f"Model {target_model_name} został pomyślnie załadowany.")
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return _text_classifier
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async def analyze_text(text: str, guild_id: str) -> Dict[str, Any]:
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start_time = time.time()
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# Pobranie aktywnego modelu dla danej gildii
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active_model = get_active_text_model(guild_id)
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# BLOKADA: Jeżeli model to 'none' lub brak konfiguracji, natychmiast wyrzucamy błąd
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if not active_model:
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logger.warning(f"Zablokowano zapytanie! Serwer {guild_id} nie ma skonfigurowanego modelu.")
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raise SetupRequiredError(
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f"Serwer o ID '{guild_id}' nie został jeszcze skonfigurowany. "
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"Użyj komendy setup na Discordzie przed wykonaniem analizy."
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)
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logger.info(f"Rozpoczęcie analizy tekstu dla serwera {guild_id} przy użyciu modelu: {active_model}")
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classifier = _load_model(active_model)
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result = classifier(text)
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label = result[0]["label"]
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score = result[0]["score"]
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is_deepfake = label.lower() in ["fake", "ai", "chatgpt", "label_1", "machine-generated"]
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confidence = score
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analysis_time = time.time() - start_time
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response = {
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"is_deepfake": is_deepfake,
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"confidence": round(confidence, 3),
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"analysis_time": round(analysis_time, 3),
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"used_model": active_model,
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}
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logger.info(f"Analiza zakończona sukcesem dla serwera {guild_id}.")
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return response
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backend/app/utils/exceptions.py
CHANGED
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@@ -51,3 +51,9 @@ class UnsupportedModelError(DeepfakeDetectionError):
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def __init__(self, model_name: str):
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message = f"Detector model '{model_name}' is not supported"
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super().__init__(message, 400)
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def __init__(self, model_name: str):
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message = f"Detector model '{model_name}' is not supported"
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super().__init__(message, 400)
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class SetupRequiredError(Exception):
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"""Wyjątek zgłaszany, gdy bot nie został jeszcze skonfigurowany na danym serwerze."""
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def __init__(self, message: str = "Setup required"):
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super().__init__(message, 500)
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guildConfigs.json
DELETED
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@@ -1,9 +0,0 @@
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{
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"1515307986963267595": {
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"logChannelId": "1515373138937123007",
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"models": {
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"text": "yaya36095/xlm-roberta-text-detector",
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"image": "capcheck/ai-image-detection"
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}
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}
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}
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index.js
CHANGED
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@@ -425,13 +425,37 @@ client.on(Events.InteractionCreate, async (interaction) => {
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if (interaction.customId === "setup_save") {
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const tempSession = activeSetupSessions.get(guildId);
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if (tempSession) {
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-
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| 432 |
-
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| 433 |
-
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| 434 |
-
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|
| 435 |
}
|
| 436 |
}
|
| 437 |
|
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|
| 425 |
if (interaction.customId === "setup_save") {
|
| 426 |
const tempSession = activeSetupSessions.get(guildId);
|
| 427 |
if (tempSession) {
|
| 428 |
+
try {
|
| 429 |
+
const response = await fetch(`http://backend-api-url/guilds/${guildId}/setup`, {
|
| 430 |
+
method: "POST",
|
| 431 |
+
headers: {
|
| 432 |
+
"Content-Type": "application/json"
|
| 433 |
+
},
|
| 434 |
+
body: JSON.stringify({
|
| 435 |
+
active_text_model: tempSession.config.active_text_model || "none",
|
| 436 |
+
log_channel_id: tempSession.config.log_channel_id || null
|
| 437 |
+
})
|
| 438 |
+
});
|
| 439 |
+
|
| 440 |
+
if (!response.ok) {
|
| 441 |
+
const errData = await response.json();
|
| 442 |
+
throw new Error(errData.detail || "Błąd zapisu na backendzie");
|
| 443 |
+
}
|
| 444 |
+
|
| 445 |
+
activeSetupSessions.delete(guildId);
|
| 446 |
+
await interaction.update({
|
| 447 |
+
content: "✅ **Ustawienia zostały pomyślnie zapisane na backendzie!**",
|
| 448 |
+
embeds: [],
|
| 449 |
+
components: []
|
| 450 |
+
});
|
| 451 |
+
} catch (error) {
|
| 452 |
+
console.error("[SETUP ERROR]", error);
|
| 453 |
+
await interaction.update({
|
| 454 |
+
content: `❌ **Wystąpił błąd podczas zapisywania konfiguracji:** ${error.message}`,
|
| 455 |
+
embeds: [],
|
| 456 |
+
components: []
|
| 457 |
+
});
|
| 458 |
+
}
|
| 459 |
}
|
| 460 |
}
|
| 461 |
|