services/label_model_manager.py

"""Model manager for keypoint–argument matching model"""

import os
import torch
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import logging

logger = logging.getLogger(__name__)


class KpaModelManager:
    """Manages loading and inference for keypoint matching model"""

    def __init__(self):
        self.model = None
        self.tokenizer = None
        self.device = None
        self.model_loaded = False
        self.max_length = 256
        self.model_id = None

    def load_model(self, model_id: str, api_key: str = None):
        """Load complete model and tokenizer directly from Hugging Face"""
        if self.model_loaded:
            logger.info("KPA model already loaded")
            return

        try:
            # Debug: Vérifier les paramètres d'entrée
            logger.info(f"=== DEBUG KPA MODEL LOADING ===")
            logger.info(f"model_id reçu: {model_id}")
            logger.info(f"model_id type: {type(model_id)}")
            logger.info(f"api_key présent: {api_key is not None}")
            
            if model_id is None:
                raise ValueError("model_id cannot be None - check your .env file")
                
            logger.info(f"Loading KPA model from Hugging Face: {model_id}")
            
            # Determine device
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            logger.info(f"Using device: {self.device}")
            
            # Store model ID
            self.model_id = model_id
            
            # Prepare token for authentication if API key is provided
            token = api_key if api_key else None
            
            # Load tokenizer and model directly from Hugging Face
            logger.info("Step 1: Loading tokenizer...")
            self.tokenizer = AutoTokenizer.from_pretrained(
                model_id,
                token=token,
                trust_remote_code=True
            )
            logger.info("✓ Tokenizer loaded successfully")
            
            logger.info("Step 2: Loading model...")
            self.model = AutoModelForSequenceClassification.from_pretrained(
                model_id,
                token=token,
                trust_remote_code=True
            )
            logger.info("✓ Model architecture loaded")
            
            self.model.to(self.device)
            self.model.eval()
            logger.info("✓ Model moved to device and set to eval mode")

            self.model_loaded = True
            logger.info("✓ KPA model loaded successfully from Hugging Face!")
            logger.info(f"=== KPA MODEL LOADING COMPLETE ===")

        except Exception as e:
            logger.error(f"❌ Error loading KPA model: {str(e)}")
            logger.error(f"❌ Model ID was: {model_id}")
            logger.error(f"❌ API Key present: {api_key is not None}")
            raise RuntimeError(f"Failed to load KPA model: {str(e)}")

    def predict(self, argument: str, key_point: str) -> dict:
        """Run a prediction for (argument, key_point)"""
        if not self.model_loaded:
            raise RuntimeError("KPA model not loaded")

        try:
            # Tokenize input
            encoding = self.tokenizer(
                argument,
                key_point,
                truncation=True,
                padding="max_length",
                max_length=self.max_length,
                return_tensors="pt"
            ).to(self.device)

            # Forward pass
            with torch.no_grad():
                outputs = self.model(**encoding)
                logits = outputs.logits
                probabilities = torch.softmax(logits, dim=-1)

                predicted_class = torch.argmax(probabilities, dim=-1).item()
                confidence = probabilities[0][predicted_class].item()

            return {
                "prediction": predicted_class,
                "confidence": confidence,
                "label": "apparie" if predicted_class == 1 else "non_apparie",
                "probabilities": {
                    "non_apparie": probabilities[0][0].item(),
                    "apparie": probabilities[0][1].item(),
                },
            }

        except Exception as e:
            logger.error(f"Error during prediction: {str(e)}")
            raise RuntimeError(f"KPA prediction failed: {str(e)}")
        
    def get_model_info(self):
        """Get model information"""
        if not self.model_loaded:
            return {"loaded": False}
            
        return {
            "model_name": self.model_id,
            "device": str(self.device),
            "max_length": self.max_length,
            "num_labels": 2,
            "loaded": self.model_loaded
        }


# Initialize singleton instance
kpa_model_manager = KpaModelManager()