from typing import Dict, List, Any
import pickle
import numpy as np
import pandas as pd
import os

class ContentBasedRecommender:
    def __init__(self, train_data):
        self.train_data = train_data

    def predict(self, user_id, k=10):
        user_books = set(self.train_data[self.train_data['user_id'] == user_id]['book_id'])
        similar_books = set().union(*(self.train_data[self.train_data['book_id'] == book_id]['similar_books'].iloc[0] for book_id in user_books))
        recommended_books = list(similar_books - user_books)

        return np.random.choice(recommended_books, size=k, replace=False) if len(recommended_books) >= k else recommended_books

    def evaluate(self, test_data, k=10):
        user_ids = test_data['user_id'].unique()
        hit_rate, ndcg_scores = [], []

        for user_id in user_ids[:100]:
            true_books = set(test_data[test_data['user_id'] == user_id]['book_id'])
            pred_books = set(self.predict(user_id, k))

            hits = len(true_books & pred_books)
            hit_rate.append(hits / min(k, len(true_books)))

            dcg = sum(1 / math.log2(rank + 2) for rank, book in enumerate(pred_books) if book in true_books)
            idcg = sum(1 / math.log2(i + 2) for i in range(min(k, len(true_books))))
            ndcg = dcg / idcg if idcg > 0 else 0
            ndcg_scores.append(ndcg)

        return np.mean(hit_rate), np.mean(ndcg_scores)

class EndpointHandler:
    def __init__(self, path=""):
        model_path = os.path.join(path, "model.pkl")
        with open(model_path, 'rb') as f:
            self.model = pickle.load(f)

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        """
        data args:
            user_id (:obj: `str` or `int`)
            k (:obj: `int`, optional)
        Return:
            A :obj:`list` of :obj:`dict`: will be serialized and returned
        """
        user_id = data.pop("user_id", None)
        k = data.pop("k", 10)  # Default to 10 if not provided

        if user_id is None:
            return [{"error": "user_id is required"}]

        try:
            recommended_books = self.model.predict(user_id, k=k)
            return [{"recommended_books": recommended_books.tolist()}]
        except Exception as e:
            return [{"error": str(e)}]

def load_model(model_path):
    handler = EndpointHandler(model_path)
    return handler