Smart Library

Demo

Team 1 Task (Librarians)

Description:

• The user uploads a PDF file and fills in the information.
• The file is sent to the backend server to be processed.
• The backend server using tysseract and HuggingFace models does the following:
  • Extracts the text from the PDF file.
  • Summarizes the text.
  • Predicts the topics of the text.
  • Predicts the sentiment of the text.
• The results are sent back to the user and displayed on the screen.
• The user can then edit the information if needed. After that, the user can confirm and the information will be added to the MongoDB database.

Team 2 Task (Users)

Description:

• The user enters a query in the search bar.
• The query is sent to the backend server to be processed.
• The backend server using HuggingFace models does the following:
  • Embeds the summaries of the books in the database.
  • Embeds the query.
  • Finds the most similar summary to the query.
  • Returns the related document.
• The results are sent back to the user and displayed on the screen.
• Additionally, if an OpenAI API key is provided, the query, summary, topic, and sentiment are sent to the OpenAI API to generate a response as a helpful librarian. The response is then displayed on the screen.

Getting Started

Clone the repository

git clone https://github.com/MeshalAlamr/smart-library.git

Move to project directory

cd smart-library

Note:

If you want to use the Librarian's Answer feature, you need to provide your OpenAI API key in the .env file. You can get one from here.

Modify the .env file to include your API key

OPENAI_API_KEY='Your Key Here'

This project supports both running on Docker or locally.

Using Docker:

Docker

docker compose up

Installing locally:

Local

Start by creating a Conda environmnet or whichever environment manager you prefer.

Install requirements

pip install -r requirements.txt

Backend Server

1. From the project directory, navigate to the backend directory

   cd services

2. Download the models

   python download_models.py
   

3. Start the backend server

   python app.py
   

Web Server

Note: The backend server must be running before starting the web server.

1. Start another terminal, from the project directory, navigate to the server directory

   cd server

2. Start the server

   python run.py
   

After running the server, open the browser and go to http://127.0.0.1:3000/ to start.