BookSense AI 📚✨

Welcome to BookSense AI, an intelligent book recommendation system designed to help you discover your next great read based on your taste, mood, and current trends.

📖 Introduction

BookSense AI is a modern web application that bridges the gap between traditional book catalogs and personalized discovery. By combining collaborative filtering techniques with state-of-the-art Generative AI (Google Gemini), BookSense offers a unique "Mood-to-Book" experience alongside classic popularity-based recommendations.

❓ Problem Statement

In the digital age, readers suffer from decision paralysis. With millions of books available, choosing one can be overwhelming.

• Generic Suggestions: Traditional platforms often push "bestsellers" that don't match individual tastes.
• Lack of Emotional Context: Most algorithms ignore the reader's current emotional state.
• Static Search: Finding a book requires knowing exactly what you're looking for.

BookSense AI solves this by offering:

1. Mood-Based Discovery: Analyze your facial expression or describe your mood to get tailored suggestions.
2. Hybrid Recommendations: Blends statistical popularity with AI-powered semantic understanding.

🏗️ Architecture

The project follows a Hybrid Microservices-like Architecture:

1. Frontend (Client)

• Framework: React 19 (Vite)
• Role: Handles the user interface, routing, and direct interactions with AI services.
• AI Integration: The frontend communicates directly with the Google Gemini API for:
  • Mood Detection: Analyzing facial cues from the webcam.
  • Summarization: Generating engaging book summaries on the fly.
  • Personalization: generating recommendations based on reading history.

2. Backend (Server)

• Framework: Python Flask
• Role: API server for the local dataset.
• Core Engine:
  • Collaborative Filtering: Uses pre-trained Pickle (.pkl) models to find books similar to user inputs based on historical user ratings.
  • Dataset Search: Efficiently queries the processed dataset of 50 top books and thousands of others.

3. AI Layer

• Model: Google Gemini 2.5 Flash
• Function: Provides real-time, context-aware intelligence that goes beyond the static dataset.

🛠️ Technical Stack

Frontend

• React 19: Latest version for robust UI state management.
• Vite: Ultra-fast build tool and development server.
• TypeScript: Ensures type safety across the codebase.
• Google GenAI SDK: For seamless integration with Gemini models.

Backend

• Python 3.x: The core logic language.
• Flask: Lightweight WSGI web application framework.
• Pandas & NumPy: For efficient data manipulation.
• Scikit-learn: For implementing the recommendation algorithms (Cosine Similarity).

🚀 How to Start

Follow these steps to set up the project locally.

Prerequisites

• Node.js (v18+)
• Python (v3.8+)

Step 1: Clone the Repository

git clone <repository-url>
cd BookSense-V3.0

Step 2: Backend Setup

1. Navigate to the backend directory:

   cd backend

2. Install dependencies:

   pip install -r requirements.txt

3. Put Your GEMINI API Key

BookSense-V3.0/frontend/services/geminiService.ts

4. Start the Flask server:

   python app.py

   The server will start on http://127.0.0.1:5001

Step 3: Frontend Setup

1. Open a new terminal and navigate to the frontend directory:

   cd frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

4. Open your browser and visit the URL shown (usually http://localhost:5173).

🔮 Future Scope & Impact

Potential Impact

• Enhanced Reading Culture: Making discovery fun encouraging more people to read.
• Emotional Well-being: Helping users find books that validate or uplift their current mood.

Future Roadmap

• User Authentication: Save your reading history and favorites to the cloud.
• Social Features: Share your "Mood Shelves" with friends.
• Mobile App: A React Native version for on-the-go discovery.
• Expanded Dataset: Integrating real-time pricing and availability from major bookstores.

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