inference.py

import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

from model import build_model
from utils import map_book_name_to_id

# Load the saved model for inference
loaded_model = build_model(
    num_users=len(ratings["user_id"].unique()),
    num_books=len(ratings["book_id"].unique()),
)
loaded_model.load_weights("recommendation_model.h5")


# Function to recommend books for a user based on input book name or author name
def recommend_books_for_user(input_name, model, num_recommendations=10):
    """
    Recommend books for a user based on input book name or author name.

    Args:
        input_name (str): The input book name or author name.
        model: The trained recommendation model.
        num_recommendations (int): The number of books to recommend.

    Returns:
        tuple: A tuple containing the recommended book names and their similarity scores.
    """
    # Check if input_name is a book name or author name
    is_author = input_name.lower() in books["authors"].str.lower().values

    # Rest of the code...


# Recommend books for a user based on input name along with similarity score.

input_name = "Harry Potter and the Sorcerer's Stone"
recommended_books, similarity_scores = recommend_books_for_user(
    input_name, loaded_model
)

if recommended_books is not None:
    print("Recommended Books:")
    print("------------------")
    for book, score in zip(recommended_books, similarity_scores):
        print(f"{book:<60} {score:.4f}")
else:
    print("No recommendations found.")