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import numpy as np |
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from sklearn.metrics.pairwise import cosine_similarity |
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from model import build_model |
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from utils import map_book_name_to_id |
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loaded_model = build_model( |
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num_users=len(ratings["user_id"].unique()), |
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num_books=len(ratings["book_id"].unique()), |
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) |
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loaded_model.load_weights("recommendation_model.h5") |
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def recommend_books_for_user(input_name, model, num_recommendations=10): |
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""" |
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Recommend books for a user based on input book name or author name. |
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Args: |
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input_name (str): The input book name or author name. |
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model: The trained recommendation model. |
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num_recommendations (int): The number of books to recommend. |
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Returns: |
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tuple: A tuple containing the recommended book names and their similarity scores. |
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""" |
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is_author = input_name.lower() in books["authors"].str.lower().values |
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input_name = "Harry Potter and the Sorcerer's Stone" |
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recommended_books, similarity_scores = recommend_books_for_user( |
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input_name, loaded_model |
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) |
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if recommended_books is not None: |
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print("Recommended Books:") |
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print("------------------") |
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for book, score in zip(recommended_books, similarity_scores): |
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print(f"{book:<60} {score:.4f}") |
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else: |
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print("No recommendations found.") |
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