---
title: Book Recommendation
sdk: streamlit
emoji: 📚
colorFrom: blue
colorTo: pink
sdk_version: 1.33.0
app_file: app.py
pinned: false
---

## **The Smart Book Search project for russian users 📚**

### **Task 📍:**
To develop a system for recommending books based on a user request. The user enters a promt with a description of the book he wants to receive - the model gives a set number of recommended books.

### **Steps of work 💪:**

**1. Data parsing.** We used an online bookstore https://www.biblio-globus.ru . The main problem was the parsing of different genres. We solved it by creating a dictionary with genres in the cycle, in which the key was the name of the genre, and the value was a link to a section of the catalog. As a result, we managed to collect about 5000 books, although initially several hundred more were planned, but not all book cards were allowed to be parsed. The final dataset has 6 columns: page url, image url, author, genre, title, annotation. 

**2. Creating the model.** It was necessary to implement two models.
   
• **First model:** SentenceTransformer(sentence-transformers/all-mpnet-base-v2) and FAISS libraries(IndexFlatIP) for efficient similarity search.

• **Second model:** cointegrated/rubert-tiny2 and Cosine similarity is used as a metric

**3. The app on streamlit via hugging face.**

### **How to run locally? 💻**
**1. To create a Python virtual environment for running the code, enter:**
   
>python3 -m venv my-env

**2. Activate the new environment:**
* Windows:

>my-env\Scripts\activate.bat

* MacOS and Linux:

>source my-env/bin/activate

**3. Install all dependencies from the requirements.txt file:**
>pip install -r requirements.txt