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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 |