metadata

title: LSI
emoji: 📊
colorFrom: blue
colorTo: indigo
sdk: streamlit
sdk_version: 1.40.1
app_file: app.py
pinned: false

Car Search Engine Based on LSI (Latent Semantic Indexing)

Description

This interactive search engine uses the LSI (Latent Semantic Indexing) algorithm to search for cars based on keywords provided by the user. It is designed to explore a car dataset and display the most relevant results based on the search query.

The application presents results in card format, including details such as the car model, year, price, and a link for more information.

Features

Search for cars using keywords (e.g., "Peugeot Diesel Manual").
Displays relevant results using advanced natural language processing techniques.
Interactive user interface developed with Streamlit.
Results pagination with cards.

How to Use the Application

Enter your search query in the input field.
Use the slider to set the number of results to display per page.
Click on the Search button.
Navigate between result pages using the Previous Page and Next Page buttons.

Installation and Dependencies

To run this application locally, you will need Python and the following libraries:

Streamlit: For the user interface.
scikit-learn: For LSI algorithms and similarity calculations.
pandas: For dataset manipulation.

Installation Steps

Clone the repository:

git clone https://huggingface.co/spaces/sanaa-11/LSI
cd your-app-repository

2-Install the required dependencies:

 pip install -r requirements.txt

3 - Run the application:

```bash
streamlit run app.py

Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference