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https://github.com/sourodip20kar/mindfullibrary

MindfulLibrary is an LLM-driven book recommendation system that helps users discover books tailored to their interests, mood, and preferences. Built using Machine Learning, NLP, and Gradio, this project leverages semantic search techniques to provide personalized recommendations based on categories, themes, and tones.
https://github.com/sourodip20kar/mindfullibrary

chromadb gradio-python-llm huggingface-embeddings jupyter-notebook langchain llm python vector-database

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MindfulLibrary is an LLM-driven book recommendation system that helps users discover books tailored to their interests, mood, and preferences. Built using Machine Learning, NLP, and Gradio, this project leverages semantic search techniques to provide personalized recommendations based on categories, themes, and tones.

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# MindfulLibrary 📚 | Book Recommendation System
MindfulLibrary is an LLM-driven book recommendation system that helps users discover books tailored to their interests, mood, and preferences. Built using Machine Learning, NLP, and Gradio, this project leverages semantic search techniques to provide personalized recommendations based on categories, themes, and tones.

# Features
* Semantic Search – Finds books based on meaning, not just keywords.\
* Personalized Recommendations – Suggests books based on user input. \
* Gradio-Powered UI – Simple and interactive web interface. \
* Multi-Category & Mood-Based Filtering – Helps users find books matching their current mood.\
* Image & Description Display – Shows book covers and short descriptions.

# Tech Stack
Python (Pandas, NumPy),
Machine Learning (TF-IDF, NLP),
Gradio

# How It Works
* User enters a query, category, and tone.\
* The system retrieves relevant books using semantic search.\
* Recommended books are displayed with images and descriptions.

# 📌 Installation & Usage
1. Clone the repo:
```
git clone https://github.com/Sourodip20kar/MindfulLibrary.git
cd MindfulLibrary

```
2. Install dependencies:
```
pip install -r requirements.txt
```
3. Run the application:
```
python dashboard.py
```
4. Open ```http://127.0.0.1:7860``` in your browser.