File size: 1,514 Bytes
8b2024e 8bfa208 8b2024e 8bfa208 a43b193 ae32382 f6796fc 8bfa208 ae32382 8bfa208 f6796fc 8bfa208 2fe00c8 8bfa208 2fe00c8 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 |
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() |