title	NextRead
emoji	📖
colorFrom	blue
colorTo	indigo
sdk	gradio
sdk_version	5.33.1
app_file	app.py
pinned	false

📚 NextRead — AI-Powered Semantic Book Recommender

“The right book at the right time can change a life. NextRead helps you find it.”

NextRead is a modern book recommendation app that uses the power of Large Language Models, semantic search, and emotion detection to connect you with books that match your mood, preferences, and curiosity.

🎥 Demo

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🚀 Overview

NextRead is an interactive web application built with Gradio and LangChain. It enables users to:

• Understand natural language book queries using LLMs.
• Retrieve relevant books through semantic search with MiniLM embeddings.
• Classify books as Fiction or Non-Fiction using zero-shot learning.
• Filter suggestions based on emotional tone like joy, sadness, or fear.
• Explore books via a responsive UI featuring animated, scrollable carousels.

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✨ Features

🔍 Smart Semantic Search

Search using everyday language. Forget keywords—NextRead uses embeddings and cosine similarity to retrieve books that match your intent, not just your words.

Try: “heartwarming travel memoir with humor” or “dark, fast-paced thriller”.

🏷️ Fiction vs. Non-Fiction Classification

Uses the bart-large-mnli model to instantly classify and filter books by type — no pre-labeling needed.

🎭 Emotion-Based Filtering

Detects the dominant emotions in each book description (joy, sadness, anger, etc.), helping you choose books that match or shift your mood.

🎨 Clean & Responsive Frontend

Built with Gradio Blocks, the UI is modular and mobile-friendly with interactive components like:

• Featured book carousel
• Hover effects for rich previews
• Clean layout optimized for engagement

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🛠 Tech Stack

Layer	Technology
Embeddings	all-MiniLM-L6-v2 (Sentence Transformers)
Vector DB	Chroma (via LangChain)
Classification	facebook/bart-large-mnli (Zero-shot)
Frontend	Gradio (Blocks API)
NLP Backend	Hugging Face Transformers, Datasets
Data Handling	Pandas, NumPy
Analysis & Plots	Plotly (used in notebooks)
Environment	Conda + requirements.txt

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📁 Folder Structure

NextRead/
├── app.py        # Main Gradio UI logic
├── data-exploration.ipynb     # Data cleaning and emotion tagging
├── vector-search.ipynb        # Embedding and similarity search
├── text-classification.ipynb  # Zero-shot category classification
├── sentiment-analysis.ipynb   # Emotion detection logic
├── books_with_emotions.csv    # Final dataset with emotions
├── requirements.txt           # Python dependencies
├── .env                       # API keys (excluded from version control)
└── README.md                  # Project documentation

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⚙️ Setup Instructions

Prerequisites

• Python 3.8 or above
• Optional: Hugging Face API token (for gated models)

1. Clone the Repository

git clone https://github.com/ArshhAnsari/NextRead.git
cd NextRead

2. Create Virtual Environment

conda create -n nextread python=3.10
conda activate nextread

3. Install Requirements

pip install -r requirements.txt

4. Add Hugging Face Token (Optional)

echo "HF_API_TOKEN=your_huggingface_token" > .env

5. Launch the App

python app.py

Then open http://localhost:7860 in your browser.

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🧭 Usage Guide

1. Type a Query

   Ex: “uplifting fiction with emotional depth”

2. Toggle Category

   • Choose between Fiction or Non-Fiction

3. Filter by Emotion

   • Pick from joy, sadness, fear, surprise, anger

4. Explore Results

   • Scroll through rich previews with summaries and metadata

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🙌 Acknowledgements

• 🤗 Hugging Face – Transformers & Datasets
• 🧠 LangChain – Simplified LLM orchestration
• 🧲 Chroma – High-speed vector store
• 🎛️ Gradio – No-fuss UI development

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🤝 Contributing

All contributions are welcome!

• Open issues for bugs or feature requests
• Fork → Create a Branch → Submit a PR
• Star ⭐ the repo if you find it useful!

Books meet AI. Welcome to the future of reading.

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