🏛️ Book Recommender

A semantic book recommender finding reads by meaning and emotion. This project was developed as part of the challenges for the MLH Data Week.

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📸 Preview

Interface	Results

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🧠 Logic

The system processes the 7k books Kaggle dataset through two primary layers:

1. Semantic Search: Uses LangChain and ChromaDB to interpret user intent beyond simple keyword matching.
2. Sentiment Analysis: Classifies descriptions into 7 emotional tones:
   • 😡 Anger | 🤢 Disgust | 😨 Fear | 😊 Joy | 😢 Sadness | 😲 Surprise | 😐 Neutral

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📂 Pipeline

• Data Cleaning: Standardizing raw Kaggle metadata.
• Sentiment Logic: Emotional tagging of book descriptions.
• Semantic Engine: Vector indexing for contextual retrieval.
• UI Layer: Interactive dashboard via Gradio.

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🛠️ Tech Stack

• AI: LangChain & HuggingFace
• Database: ChromaDB
• Data: Pandas & Numpy
• Tooling: Managed with uv

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🚀 Setup

git clone https://github.com/Tek233/Book-Recommender.git
cd book-recommender
uv sync
uv run app.py