# lsa implementation (Latent Semantic Analysis)
import logging
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
from typing import List, Any, Dict

# Configure logging
logger = logging.getLogger(__name__)

class LSA:
    def __init__(self, n_components: int = 100):
        """
        Initialize the LSA model with the specified number of components.

        Parameters:
        - n_components (int): Number of latent components to extract.
        """
        self.vectorizer = TfidfVectorizer(stop_words='english', max_features=1000)
        self.lsa_model = TruncatedSVD(n_components=n_components, random_state=42)
        self.item_ids = []
        logger.info(f"LSA initialized with {n_components} components.")

    def fit(self, documents: List[str]):
        """
        Fit the LSA model on the provided documents.

        Parameters:
        - documents (List[str]): List of documents to train the model.
        """
        logger.info("Fitting LSA model on documents.")
        tfidf_matrix = self.vectorizer.fit_transform(documents)
        self.lsa_model.fit(tfidf_matrix)
        logger.info("LSA model training completed.")

    def transform(self, documents: List[str]) -> Any:
        """
        Transform documents into the LSA latent space.

        Parameters:
        - documents (List[str]): List of documents to transform.

        Returns:
        - Transformed document matrix in latent space.
        """
        logger.info("Transforming documents into LSA latent space.")
        tfidf_matrix = self.vectorizer.transform(documents)
        return self.lsa_model.transform(tfidf_matrix)

    def recommend(self, query: str, top_n: int = 10) -> List[int]:
        """
        Recommend items based on the similarity of the query to the documents.

        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 LSA.")
        query_vec = self.transform([query])
        doc_vecs = self.lsa_model.transform(self.vectorizer.transform(self.vectorizer.get_feature_names_out()))
        similarity_scores = (doc_vecs @ query_vec.T).flatten()
        top_indices = similarity_scores.argsort()[::-1][:top_n]
        logger.info(f"Top {top_n} recommendations generated using LSA.")
        return top_indices.tolist()

    def add_item(self, item_id: int, item_features: Dict[str, Any]):
        logger.info(f"Adding item {item_id} to LSA.")
        # Assuming item_features contains 'genres' as a list
        genres = ' '.join(item_features.get('genres', []))
        new_tfidf = self.vectorizer.transform([genres])  # Use transform, not fit
        new_vec = self.lsa_model.transform(new_tfidf)
        # You would need to handle incorporating the new_vec into the existing model
        # This is a placeholder for actual implementation
        logger.info(f"Item {item_id} added to LSA successfully.")

    def remove_item(self, item_id: int):
        logger.info(f"Removing item {item_id} from LSA.")
        # Placeholder for actual implementation
        logger.info(f"Item {item_id} removed from LSA successfully.")

    def update_item_features(self, item_id: int, new_features: Dict[str, Any]):
        logger.info(f"Updating features for item {item_id} in LSA.")
        # Placeholder for actual implementation
        logger.info(f"Item {item_id} features updated in LSA successfully.")

    def update_user_profile(self, user_id: int, item_id: int, feedback_score: float):
        logger.info(f"Updating user {user_id}'s profile based on feedback for item {item_id}.")
        # Placeholder for actual implementation
        logger.info(f"User {user_id}'s profile updated successfully.")
