| import gradio as gr | |
| import numpy as np | |
| import pandas as pd | |
| import pickle | |
| from sklearn.metrics.pairwise import cosine_similarity | |
| pt = pd.read_pickle('pt.pkl') | |
| user_similarity_scores = cosine_similarity(pt.T) | |
| books = pd.read_pickle("books.pkl") | |
| def recommend_books_for_user(user_id): | |
| user_index = pt.columns.get_loc(user_id) | |
| similar_users = sorted(list(enumerate(user_similarity_scores[user_index])), key=lambda x: x[1], reverse=True)[1:5] | |
| recommended_books = [] | |
| for similar_user_index, similarity_score in similar_users: | |
| user_ratings = pt.iloc[:, user_index] | |
| similar_user_ratings = pt.iloc[:, similar_user_index] | |
| unrated_books = similar_user_ratings[(user_ratings == 0) & (similar_user_ratings > 0)] | |
| recommended_books.extend(unrated_books.index) | |
| recommended_books_set = set(recommended_books) | |
| ans = [(book_title, image_url) for book_title, image_url in zip(books["Book-Title"], books["Image-URL-M"]) if book_title in recommended_books_set] | |
| return ans | |
| def recommend_books_gradio(user_id): | |
| """Recommends books for a user based on collaborative filtering""" | |
| recommended_books = recommend_books_for_user(int(user_id)) | |
| return [[book] for book in recommended_books] | |
| interface = gr.Interface(fn=recommend_books_gradio, | |
| inputs=gr.Textbox(label="Enter User ID"), | |
| outputs=gr.List(label="Recommended Books"), | |
| title="Book Recommender System") | |
| interface.launch() |