handler.py

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

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

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

    def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
        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