📚 BookLens AI: A Semantic & Emotional Recommendation Engine

BookLens AI is an AI-powered book recommender system that uses LLM-based embeddings, semantic vector search, and emotion-aware filtering to help users discover books that match both topic and mood.

Built using the LangChain ecosystem, ChromaDB, and Google’s embedding-001 model, the app provides an intuitive UI via Gradio for real-time, personalized recommendations.

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🔍 Features

• 🧬 Semantic Search: Matches user queries with book descriptions using AI-powered embeddings.
• 📂 Category & Emotion Filters: Narrow down results by genre and emotional tone (Joy, Fear, Anger, etc.).
• 🧱 Google Generative AI Embeddings: Captures deep semantic meaning of queries and descriptions.
• 💾 Chroma Vector Database: Efficient similarity search powered by LangChain.
• 🎨 Interactive Gradio UI: Simple and intuitive user interface to explore recommendations visually.
• 🎭 Thumbnail & Metadata Display: Clean book previews with titles and summaries

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📊 How It Works

1. 📘 Books are embedded using Google’s embedding-001 via LangChain
2. 🗃️ ChromaDB stores these embeddings for fast semantic retrieval
3. 🧠 User inputs a query (e.g., “A cozy mystery involving cats”)
4. 🧮 Top-k similar descriptions are retrieved via vector similarity
5. 🎯 Emotion & category filters are applied for final ranking
6. 🖼️ Results displayed as book covers with title, author, and blurb

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🛠️ Requirements

• Python 3.11.0
• Required Python packages (listed in requirements.txt)
• Google API Key for accessing the Gemini AI services

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🚀 Installation

1. Clone the repository:

   git clone https://github.com/Vish501/Book-Recommender.git

   cd Book-Recommender

2. Install dependencies:

   It's recommended to use a virtual environment to manage dependencies. You can create and activate one using:

   conda create -p venv python=3.11.0 -y

   conda activate venv/

   Then, install the required packages: pip install -r requirements.txt

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💻 Setting Up the Google API Key

To use the Gemini API, you need to set up your GOOGLE_API_KEY:

1. Obtain your Google API Key from the Google Cloud Console.

2. Add the API key to your environment:

   • Locally (Linux/macOS): export GOOGLE_API_KEY="your-api-key-here"
   • Locally (Windows - Command Prompt): set GOOGLE_API_KEY="your-api-key-here"
   • Locally (Windows - PowerShell): $env:GOOGLE_API_KEY="your-api-key-here"

3. If you are using GitHub Codespaces, store the API key as a GitHub repository secret:

   • Go to your GitHub repository
   • Navigate to Settings > Secrets and variables > Actions
   • Click New repository secret
   • Set the name as GOOGLE_API_KEY and paste your API key as the value
   • Click Add secret

4. Using a .venv file: Add the following line to your .venv file in the root directory:

   GOOGLE_API_KEY=your_google_api_key_here

This will allow the project to authenticate and communicate with the Gemini API securely.

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📁 Setting Up the Working Directory

To ensure the app can locate and load the dataset correctly, you need to set the WORKING_DIRECTORY environment variable. This should point to the root directory of the project (where app.py and the data/ folder are located).

1. Determine the absolute path of your project directory.

2. Set the WORKING_DIRECTORY environment variable:

   • Locally (Linux/macOS): export WORKING_DIRECTORY="/absolute/path/to/Book-Recommender"
   • Locally (Windows - Command Prompt): set WORKING_DIRECTORY="C:\path\to\Book-Recommender"
   • Locally (Windows - PowerShell): $env:WORKING_DIRECTORY="C:\path\to\Book-Recommender"

3. Using GitHub Codespaces or GitHub Actions:

   • Store the working directory as a secret or variable:
     • Go to your GitHub repository
     • Navigate to Settings > Secrets and variables > Actions
     • Click New repository secret
     • Set the name as WORKING_DIRECTORY and the value as your project path
     • Click Add secret

4. Using a .venv file: Add the following line to your .venv file in the root directory:

   WORKING_DIRECTORY=/absolute/path/to/Book-Recommender

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💻 Usage

To run the application:​

python app.py

This will launch the application, which will provide you with a link in the terminal that needs to be pasted into your browser.

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📁 Project Structure

• app.py: Main application file for Gardio.
• data: CSV datasets used in the project.
• requirements.txt: List of required Python packages.
• setup.py: Setup configuration for the project.
• template.py: Contains template code to create the basic project structure.
• research/: Directory for research-related and data-cleaning files.
• src/: Source code directory containing modules and packages.
• .venv: Environment variables (not tracked in Git)

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📄 License

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

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🙋‍♂️ Author

Feel free to contribute to this project by submitting issues or pull requests. For any questions or suggestions, please contact Vish501.