BiblioFeels

Bibliofeels - made for the bibliophiles by the bibliophiles!

We believe books are for everyone and for every emotion! Looking for something deep and reflective? We have a book for you! Want somehting that can help you escape reality? We have a book for you! Or maybe something spooky? Guess what, We have a book for you!

Inspiration

Necessity is the mother of invention

Ever felt that none of the books in the library seem to be matching the type of book you want to read in that mood? Fear no more! Bibliofeels brings you a two-tap solution to find the perfect book for you.

What it does

BiblioFeels aims to recommend the best book to the best person(YES YOU!), along with relevant details and all you need to do is click on a button and let us know how you want to feel. It is that simple!

How we built it

We wanted the user's experience with the app to be as smooth as possible, so we started with an aesthetic, minimalistic design for the front end.

We used the CMU Book summary dataset(consisting of more than 16000 books), applied emotion analysis to the summary of all of these books after performing relevant data cleaning operations and added the emotions column to the dataset. Cover images of books were scraped and were also saved in the dataset as base64 strings.

After populating the database we built a React app to take input from the user of pass it on the backend app built using flask. The flask app queries the database that we produced above and returns the best match to the user along with relevant details to the user such as book title, author, genre, summary, and a link to the wiki among other things.

Challenges we ran into

We worked on the following challenges:

The biggest challenge for this project was planning around the team's time zones. But we realized that when we decided on times well in advance, that problem seemed to subside.
planning the workflow
learning new technologies
On the NLP end, a major challenge was the unavailability of fine-grained emotion analysis models at the document/paragraph levels.

Accomplishments that we're proud of

the whole team pulled their weight and pulled off a project we are all proud of
learning the new technologies needed for the project.
compiling a database of about 16000 books to find the perfect book to match one's mood
making new friends across the world

What we learned

working with an international team, with different time zones
new technologies for the project like figma, CSS, flask, etc. -working with a large database

What's next for bibliofeels

Features we think could make bibliofeels even better:

adding a bot to analyze the user's mood
adding the feature to look for more books to match a particular mood, e.g. a list of 10 books
adding the feature of 'books like this' cluster that takes a book title as input
deploying the app
take multiple emotions from the user to match in the database

Setup Instructions

Clone this repo

git clone https://github.com/ShezaMunir/bibliofeels.git

Navigate to the project's root and start a virtual environment

cd bibliofeels

Create a virtual environment with python3

python3 -m virtualenv venv

Activate the virtual environment with the following command:
- Linux
```
source venv/bin/activate
```
- Windows
```
venv/Scripts/activate
```
Install all dependencies

pip install -r requirements.txt

Generate the database

python3 data/generate_db.py

To run the server

Windows

set FLASK_APP=run.py:app
flask run

Linux

export FLASK_APP=run.py:app
flask run

Recommendations can be found at the "/recommend" route

Example: http://localhost:5000/recommend?emotions=joy,analytical