Book Recommendation System using Python

Book Recommendation System

Looking for a smart, AI-powered book recommendation engine for your application or portfolio? Presenting a professionally developed Book Recommendation System using modern machine learning tools and APIs. This project is ideal for developers, startups, and students seeking a ready-to-integrate solution for book suggestions based on user preferences.

🔧 Project Summary

💡 Project Description

This intelligent system recommends books similar to the one selected by a user. Built on top of a precomputed similarity matrix and book metadata, it dynamically fetches book cover images using the Open Library API. The model files are optimized for quick loading from Google Drive using gdown.

Users get a seamless experience with an interactive frontend built using Streamlit – perfect for instant deployments and demonstrations.

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🚀 Available Features

• 🔍 Search and select a favorite book from the list
• 📚 Get up to 20 AI-recommended similar books
• 🖼️ View dynamically fetched book cover images using the Open Library API
• ⚡ Fast performance with precomputed .pkl model files
• ☁️ Google Drive integration for remote file loading
• 🔐 Secure secret management via .streamlit/secrets.toml

🛠️ Tech Stack

• Language: Python
• Framework: Streamlit
• Libraries: pandas, pickle, gdown
• External API: Open Library API
• Cloud Storage: Google Drive for .pkl model files
• Database: ❌ No database used (Data is handled using .pkl files)