import logging
from typing import List, Dict
from .base import BaseNER

class NEREngine(BaseNER):
    def __init__(self, model_name="urchade/gliner_mediumv2.1"):
        self.model_name = model_name
        self.model = None
        self._initialize_model()

    def _initialize_model(self):
        logging.info(f"Initializing NER model: {self.model_name}")
        try:
            from backup.model import GLiNER
            self.model = GLiNER.from_pretrained(self.model_name)
            logging.info(f"NER model '{self.model_name}' loaded successfully.")
        except Exception as e:
            logging.error(f"Failed to load NER model: {e}. NER extraction will be unavailable.")

    def extract_entities(self, text: str, labels: List[str] = None) -> Dict[str, List[str]]:
        if not text:
            logging.warning("NER: Received empty text for extraction.")
            return {}
        
        if not self.model:
            logging.error("NER: Model not initialized. Skipping extraction.")
            return {}
        
        if labels is None:
            labels = ["Name", "Designation", "Company", "Contact", "Address", "Email", "Link"]

        logging.info(f"NER: Extracting entities for {len(text)} characters of text.")
        try:
            entities = self.model.predict_entities(text, labels, threshold=0.3)
            structured_data = {label: [] for label in labels}
            for ent in entities:
                label = ent["label"]
                if label in structured_data:
                    structured_data[label].append(ent["text"])
            
            non_empty_tags = sum(1 for v in structured_data.values() if v)
            logging.info(f"NER: Extracted entities for {non_empty_tags} labels.")
            return structured_data
        except Exception as e:
            logging.error(f"NER: Extraction pipeline crashed: {e}")
            return {}

    def process(self, text: str) -> Dict[str, List[str]]:
        return self.extract_entities(text)