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

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=min(k, len(recommended_books)), replace=False).tolist()

__main__.ContentBasedRecommender = ContentBasedRecommender

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]]:
        # Extract the 'inputs' from the data
        inputs = data.get('inputs', {})
        
        # If inputs is a string (for single user_id input), convert it to a dict
        if isinstance(inputs, str):
            inputs = {'user_id': inputs}
        
        user_id = inputs.get('user_id')
        k = inputs.get('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}]
        except Exception as e:
            return [{"error": str(e)}]

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