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| import gradio as gr | |
| import pickle | |
| import numpy as np | |
| # Load the pre-trained data and models | |
| popular_df = pickle.load(open("popular.pkl", "rb")) | |
| pt = pickle.load(open("pt.pkl", "rb")) | |
| books = pickle.load(open("books.pkl", "rb")) | |
| similarity_score = pickle.load(open("similarity_scores.pkl", "rb")) | |
| pt_index_list = pickle.load(open("pt_index_list.pkl", "rb")) | |
| # Function to display the top 50 books | |
| def display_top_50_books(): | |
| book_names = popular_df["Book-Title"].values | |
| authors = popular_df["Book-Author"].values | |
| images = popular_df["Image-URL-L"].values | |
| votes = popular_df["num_ratings"].values | |
| ratings = popular_df["avg_rating"].round(2).values | |
| book_cards = '<div style="display: flex; flex-wrap: wrap; justify-content: space-between; margin: 10px 0;">' | |
| for i in range(len(book_names)): | |
| book_cards += f""" | |
| <div style="flex: 1 1 calc(25% - 100px); height: 570px; margin: 20px 10px; border-radius: 8px; overflow: hidden; border: 1px solid #ddd; box-sizing: border-box;"> | |
| <img src="{images[i]}" alt="Book Image" style="width: 100%; height: 70%;"> | |
| <div style="padding: 10px;"> | |
| <h3 style="font-size: 16px; margin: 5px 0; font-weight: bold;">{book_names[i]}</h3> | |
| <p style="font-size: 14px; margin: 5px 0; font-style: italic;">By: {authors[i]}</p> | |
| <p style="font-size: 14px; margin: 5px 0; font-weight: bold;">Votes: {votes[i]}</p> | |
| <p style="font-size: 14px; margin: 5px 0; font-weight: bold;">Rating: {ratings[i]}</p> | |
| </div> | |
| </div> | |
| """ | |
| book_cards += '</div>' | |
| return book_cards | |
| # Function to recommend books based on user input | |
| def recommend_books(user_input): | |
| index = np.where(pt.index == user_input)[0][0] | |
| distances = similarity_score[index] | |
| similar_items = list( | |
| sorted(enumerate(distances), reverse=True, key=lambda x: x[1])[1:5]) | |
| data = [] | |
| for i in similar_items: | |
| item = [] | |
| temp_df = books[books["Book-Title"] == pt.index[i[0]]] | |
| item.extend(list(temp_df.drop_duplicates( | |
| "Book-Title")["Book-Title"].values)) | |
| item.extend(list(temp_df.drop_duplicates( | |
| "Book-Title")["Book-Author"].values)) | |
| item.extend(list(temp_df.drop_duplicates( | |
| "Book-Title")["Image-URL-L"].values)) | |
| data.append(item) | |
| return data | |
| # Gradio interface for the top 50 books | |
| def top_50_books_interface(): | |
| books = display_top_50_books() | |
| return gr.update(value=books) | |
| # Gradio interface for recommending books | |
| def recommend_interface(book_title): | |
| recommendations = recommend_books(book_title) | |
| rec_cards = '<div style="display: flex;justify-content: space-between;padding: 20px;margin: 0 auto;max-width: 100%">' | |
| for rec in recommendations: | |
| rec_cards += f""" | |
| <div style="width:280px !important; height: 520px !important; margin: 0 10px;border: 1px solid #ddd;border-radius: 8px;overflow: hidden; "> | |
| <img src={ rec[2] } alt="Book 1" style="width: 100%; height: 370px; display: block"> | |
| <div style="padding: 15px; height: 130px;"> | |
| <div style="font-size: 18px;font-weight: bold;margin: 10px 0;">{ rec[0] }</div> | |
| <div style="font-size: 16px; font-style:italic;">By: { rec[1] }</div> | |
| </div> | |
| </div> | |
| """ | |
| rec_cards += "</div>" | |
| return gr.update(value=rec_cards) | |
| # Create Gradio components | |
| book_dropdown = gr.Dropdown( | |
| choices=pt_index_list, label="Choose a book") | |
| recommend_button = gr.Button("Recommend") | |
| top_50_button = gr.Button("Show Top 50 Books") | |
| top_50_output = gr.HTML() | |
| recommend_output = gr.HTML() | |
| # Create Gradio interface | |
| with gr.Blocks() as demo: | |
| with gr.Tab("Top 50 Books"): | |
| gr.Markdown("# Top 50 Books") | |
| top_50_button.render() | |
| top_50_button.click(fn=top_50_books_interface, outputs=top_50_output) | |
| top_50_output.render() | |
| with gr.Tab("Recommend Books"): | |
| gr.Markdown("# Recommend Books") | |
| book_dropdown.render() | |
| recommend_button.render() | |
| recommend_button.click(fn=recommend_interface, | |
| inputs=book_dropdown, outputs=recommend_output) | |
| recommend_output.render() | |
| # Launch Gradio app | |
| if __name__ == '__main__': | |
| demo.launch() | |