Here is the formatted README.md in proper Markdown format.
Addressing the Tip-of-the-Tongue Phenomenon
The Book Finder project is an intelligent book search system designed to help users locate books even when they only remember vague or partial details. This project leverages Natural Language Processing (NLP) techniques, specifically BM25 for keyword-based search and Sentence-BERT (SBERT) for semantic search, to provide accurate and relevant search results.
The system features a modern, interactive web interface built with Flask, making it accessible and user-friendly. Whether users recall a book's theme, character names, or plot points, the Book Finder efficiently bridges the gap between incomplete memory and book discovery.
- 🔍 Keyword-Based Search: BM25 for tokenized keyword matching.
- 🤖 Semantic Search: SBERT for context-aware similarity-based searching.
- 🎨 Modern User Interface: Responsive and interactive Flask-based UI.
- 📊 Dynamic Results: Display book titles, authors, and ratings dynamically.
- 🚀 Scalable: Can be extended to other domains such as movies and music.
| Component | Technology/Library |
|---|---|
| Programming Language | Python |
| Web Framework | Flask |
| NLP Models | BM25, SBERT (all-MiniLM-L6-v2) |
| Libraries | Pandas, NumPy, Torch, Rank-BM25, Sentence-Transformers |
| Frontend | HTML, CSS |
| Deployment | Local Server / Cloud Platforms |
Follow the steps below to set up and run the Book Finder project on your local machine.
Ensure you have the following installed on your system:
- Python (version 3.8 or above)
- pip (Python package manager)
git clone https://github.com/your-username/book-finder.git
cd book-finderRun the following command to install the required libraries:
pip install -r requirements.txtEnsure you have the following files and folder structure:
project_folder/
│
├── app.py # Flask application
├── books_data.pkl # Preprocessed book dataset with SBERT embeddings
├── bm25_model.pkl # BM25 precomputed model
├── templates/ # HTML templates
│ └── index.html # Frontend interface
├── static/ # Static files (CSS, images)
│ └── style.css # Custom CSS for styling
├── requirements.txt # Project dependencies
└── README.md # Project documentation
Start the Flask application by running:
python app.pyThe app will run on your local server. Open your browser and go to:
http://127.0.0.1:5000/
-
Input Your Query:
- Enter keywords, themes, author names, or plot details into the search box.
-
Select a Search Method:
- By default, the app uses BM25 for keyword-based search.
- You can explore semantic search powered by SBERT.
-
View Results:
- The most relevant book titles, authors, and ratings will be displayed dynamically.
Home
The following features can be added to improve the system:
- 📸 Book Covers: Display images of book covers.
- 🔖 Save Searches: Allow users to save search history.
- 🎬 Other Domains: Extend support for movie and music searches.
- ☁️ Cloud Deployment: Deploy the application to cloud platforms for wider accessibility.
Contributions are welcome! Follow these steps to contribute:
- Fork the repository.
- Create a new branch:
git checkout -b feature/your-feature-name
- Make your changes and commit them:
git commit -m "Add your feature description" - Push to the branch:
git push origin feature/your-feature-name
- Submit a pull request.
For any questions or suggestions, feel free to reach out:
- Name: Talha Satti and Muhammad Huzaifa
- Email: [email protected] / [email protected]
- LinkedIn: Talha Satti Muhammad Huzaifa
This project is licensed under the MIT License.
- Inspired by the need to address the Tip-of-the-Tongue (TOT) phenomenon.
- Datasets sourced from publicly available platforms like Goodreads.
- Powered by Sentence-BERT and BM25 for efficient search.