📚 Streamlit Books Catalog

An interactive Streamlit web application for exploring and analyzing book data. Browse thousands of books with dynamic filtering, visualize market trends, and discover insights through interactive charts and analytics.

Live Demo: Deploy on Streamlit Cloud

✨ Features

📊 Data Visualization

• Price Distribution Histogram - Analyze price ranges across the catalog
• Rating Breakdown Chart - See the distribution of book ratings
• Books Per Category Analysis - Identify popular categories
• Average Price by Category - Compare pricing across categories
• Price vs Rating Scatter Plot - Explore the relationship between price and customer ratings
• Stock Availability Donut Chart - Track inventory status

🔍 Advanced Filtering

• Filter by category (multi-select)
• Set price range with slider control
• Filter by minimum rating (1-5 stars)
• Stock availability toggle
• Keyword search in titles and descriptions

📋 Book Management

• Sortable book table with customizable ordering
• Book detail cards with comprehensive information
• Real-time search and filter functionality
• Display of 5 summary metrics (books shown, avg price, avg rating, categories, in stock)

🎨 User Interface

• Clean, modern design with professional styling
• Responsive layout that works on all devices
• Smooth animations and hover effects
• Light color scheme for comfortable viewing

🛠️ Tech Stack

Component	Technology
Framework	Streamlit
Data Processing	Pandas
Visualization	Plotly
Language	Python 3.13+

📦 Installation

Prerequisites

• Python 3.10 or higher
• Git
• pip package manager

Step 1: Clone the Repository

git clone https://github.com/DeepighaJ/streamlit-books-catalog.git
cd streamlit-books-catalog

Step 2: Create Virtual Environment

# Windows
python -m venv .venv
.venv\Scripts\activate

# macOS/Linux
python3 -m venv .venv
source .venv/bin/activate

Step 3: Install Dependencies

pip install -r requirements.txt

Step 4: Prepare the Data

python scrape_all_books.py

This will create/update all_books.csv with book data from books.toscrape.com.

Step 5: Run the Application

streamlit run src/app.py

The app will open in your default browser at: http://localhost:8501

📁 Project Structure

streamlit-books-catalog/
├── src/
│   ├── app.py                    # Main application entry point
│   ├── config.py                 # Configuration & constants
│   ├── styles.py                 # CSS styling
│   ├── data/
│   │   └── loader.py             # Data loading & preprocessing
│   └── components/
│       ├── __init__.py
│       ├── sidebar.py            # Filter sidebar component
│       ├── metrics.py            # Summary metrics component
│       ├── charts.py             # Chart visualization components
│       ├── table.py              # Book table component
│       └── detail.py             # Book detail card component
├── all_books.csv                 # Book dataset (~11,000 books)
├── scrape_all_books.py           # BeautifulSoup data scraper
├── requirements.txt              # Python dependencies
├── .gitignore                    # Git ignore rules
└── README.md                     # This file

🚀 Deploy on Streamlit Cloud

Option 1: Automatic Deployment (Recommended)

1. Push code to GitHub (already done ✅)
2. Visit Streamlit Cloud
3. Click "New app"
4. Connect your GitHub account
5. Select repository: streamlit-books-catalog
6. Set main file: src/app.py
7. Click Deploy

Your app will be live at: https://app-books-catalog-djdeploy.streamlit.app/

Option 2: Manual Steps

# Ensure all changes are pushed to GitHub
git add .
git commit -m "Deploy ready"
git push origin main

Deployment Settings

• Python version: 3.10+
• Main file: src/app.py
• Requirements file: requirements.txt

💡 Usage Guide

Filtering Books

1. Use the sidebar on the left to apply filters
2. Select multiple categories or adjust price/rating ranges
3. Use the search box to find books by title or description
4. Toggle "In stock only" to see available books

Viewing Analytics

• Hover over charts to see detailed information
• Click on chart elements to interact with them
• The table updates automatically based on filters

Exploring Book Details

1. Go to the "Book detail" section
2. Type in the search box to filter books
3. Select a book from the dropdown
4. View comprehensive book information including:
   • Title, category, and price
   • Rating (in stars)
   • Stock availability and quantity
   • UPC code
   • Direct link to book
   • Full description

📊 Data Source

The application uses book data scraped from books.toscrape.com, a practice website for web scraping enthusiasts.

Dataset Details

• Total Books: ~11,000+
• Categories: 50+ book categories
• Data Points: Title, price, rating, availability, stock, category, UPC, description, URL
• Update Frequency: Run python scrape_all_books.py to refresh

Refresh Data

To update the book catalog with the latest data:

python scrape_all_books.py

🔧 Development

Project Architecture

The project follows a modular, component-based architecture:

• Separation of Concerns: Each feature is isolated in its own module
• Reusability: Components can be easily reused or extended
• Maintainability: Easy to locate and modify specific features
• Scalability: Simple to add new components or visualizations

Adding New Features

1. Create a new file in src/components/
2. Import in src/app.py
3. Call the render function in the main flow

Example:

# src/components/my_feature.py
def render_my_feature(filtered: pd.DataFrame):
    st.markdown("### My Feature")
    # Your code here

📈 Performance Tips

• The app caches data loading with @st.cache_data decorator
• Charts are optimized with Plotly for smooth interactions
• Filtering is performed in-memory for fast response times

🐛 Troubleshooting

Issue: "all_books.csv not found"

Solution: Run python scrape_all_books.py to generate the dataset

Issue: Port 8501 already in use

Solution: Run on a different port:

streamlit run src/app.py --server.port 8502

Issue: Slow performance on large filters

Solution: Streamlit cloud instances are lightweight. For better performance, consider Streamlit's Streamlit Community Cloud Pro

📝 Requirements

pandas>=2.0.3
plotly>=5.18.0
streamlit>=1.36.0

For latest versions, see requirements.txt

🤝 Contributing

Contributions are welcome! Please feel free to:

• 🐛 Report bugs by opening an issue
• 💡 Suggest features or improvements
• 🔧 Submit pull requests for enhancements
• 📝 Improve documentation

Development Workflow

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit changes (git commit -m 'Add amazing feature')
4. Push to branch (git push origin feature/amazing-feature)
5. Open a Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

You are free to:

• Use this project for personal or commercial purposes
• Modify and distribute the code
• Use it as a learning resource

🎓 Learning Resources

This project demonstrates:

• Streamlit: Building interactive web apps with Python
• Pandas: Data manipulation and analysis
• Plotly: Creating interactive visualizations
• Web Scraping: Using BeautifulSoup for data collection
• Git/GitHub: Version control and collaboration
• Modular Design: Component-based architecture

📞 Support

If you encounter any issues:

1. Check the Troubleshooting section
2. Review the Streamlit Documentation
3. Search existing GitHub Issues
4. Create a new issue with detailed description

🎯 Roadmap

Potential future enhancements:

• User reviews and ratings
• Wishlist/favorites functionality
• Export filtered results to CSV/PDF
• Price tracking and alerts
• Book recommendations engine
• Multi-language support
• Dark mode theme toggle

🙏 Acknowledgments

• Streamlit for the amazing framework
• books.toscrape.com for the practice dataset
• Plotly for interactive visualizations
• All contributors and users of this project

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Made with ❤️ using Streamlit

⭐ If you find this project helpful, please consider giving it a star on GitHub!

Author: Deepigha Japamony

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