# bayesian implementation BAYESIAN classifier (Naive Bayes)
import logging
from sklearn.naive_bayes import MultinomialNB
from sklearn.feature_extraction.text import CountVectorizer
from typing import List

# Configure logging
logger = logging.getLogger(__name__)

class BAYESIAN:
    def __init__(self):
        """
        Initialize the Bayesian classifier using Multinomial Naive Bayes.
        """
        self.vectorizer = CountVectorizer(stop_words='english')
        self.model = MultinomialNB()
        logger.info("Bayesian classifier initialized.")

    def fit(self, documents: List[str], labels: List[int]):
        """
        Fit the Bayesian classifier on the provided documents and labels.

        Parameters:
        - documents (List[str]): List of documents to train the model.
        - labels (List[int]): Corresponding labels for the documents.
        """
        logger.info("Fitting Bayesian classifier on documents.")
        count_matrix = self.vectorizer.fit_transform(documents)
        self.model.fit(count_matrix, labels)
        logger.info("Bayesian classifier training completed.")

    def predict(self, query: str) -> int:
        """
        Predict the label for a given query.

        Parameters:
        - query (str): The query text to classify.

        Returns:
        - int: Predicted label.
        """
        logger.info("Predicting label using Bayesian classifier.")
        query_vec = self.vectorizer.transform([query])
        prediction = self.model.predict(query_vec)[0]
        logger.info(f"Predicted label: {prediction}")
        return prediction

    def recommend(self, query: str, top_n: int = 10) -> List[int]:
        """
        Recommend items based on the Bayesian classifier's prediction.

        Parameters:
        - query (str): The query text for which to generate recommendations.
        - top_n (int): Number of top recommendations to return.

        Returns:
        - List[int]: List of recommended item indices.
        """
        logger.info("Generating recommendations using Bayesian classifier.")
        predicted_label = self.predict(query)
        # Example: Recommend items with the same label
        # This requires access to labeled items; here we return an empty list as a placeholder
        recommendations = []  # Implement logic based on the application
        logger.info(f"Top {top_n} recommendations generated using Bayesian classifier.")
        return recommendations[:top_n]
